arXiv Papers

Published: 2026-04-22 15:37:55

Authors: Mikhail Smagin, Iuliia Timankova, Pavel Pankin, Yong Li, Mihail Petrov

Categories: physics.class-ph, physics.app-ph

Abstract:
We study unidirectional transverse scattering in a two-dimensional acoustic dimer composed of two isotropic subwavelength scatterers. Using a coupled multipole model, we show that inter-particle coupling enables effective monopole-dipole interference and supports a transverse Kerker effect under plane wave excitation. In contrast to a single non-absorbing isotropic particle, where Kerker-type cancellation is only approached in the weak-scattering limit, the dimer can combine pronounced directionality with strong overall scattering. This regime is promising for compact acoustic beam steering and directional wave routing.

            {
  "tldr": "This paper explores unidirectional transverse scattering in a two-dimensional acoustic dimer, demonstrating that interparticle coupling enables effective monopole-dipole interference and a transverse Kerker effect, which is promising for applications like beam steering.",
  "whats_new": [
    "• Introduces a unidirectional transverse Kerker effect in acoustic dimers.",
    "• Demonstrates effective monopole-dipole interference in a two-dimensional setup.",
    "• Shows that a dimer can achieve strong scattering while maintaining directionality.",
    "• Validates findings with numerical simulations using labyrinthine meta-atoms."
  ],
  "method": "The study employs a coupled multipole model to analyze scattering in a dimer of two isotropic subwavelength scatterers. The induced monopole and dipole moments are calculated through a self-consistent solution of the coupled multiple-scattering problem.",
  "results": [
    "• The dimer exhibits a transverse Kerker effect, achieving unidirectional scattering.",
    "• Strong resonant scattering is observed at specific monopolar Mie angles.",
    "• Numerical simulations confirm the analytical predictions with high accuracy.",
    "• The effective dipole moment along the dimer axis enables pronounced directionality."
  ],
  "limitations": [
    "• The study focuses on idealized isotropic scatterers, which may not fully represent real-world materials.",
    "• The effects of higher-order multipoles are not extensively explored.",
    "• The practical implementation of the proposed structures needs further investigation."
  ],
  "why_useful": "This research provides insights into controlling sound with subwavelength structures, potentially leading to new acoustic devices for beam steering and directional routing. The findings could have significant implications in fields like acoustics engineering and wave manipulation.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-04-22 15:33:45

Authors: Andor Vári-Kakas, Ji Won Park, Natasa Tagasovska

Categories: cs.LG

Abstract:
Aligning large language models (LLMs) to desirable human values requires balancing multiple, potentially conflicting objectives such as helpfulness, truthfulness, and harmlessness, which presents a multi-objective optimisation challenge. Most alignment pipelines rely on a fixed scalarisation of these objectives, which can introduce procedural unfairness by systematically under-weighting harder-to-optimise or minority objectives. To promote more equitable trade-offs, we introduce MGDA-Decoupled, a geometry-based multi-objective optimisation algorithm that finds a shared descent direction while explicitly accounting for each objective's convergence dynamics. In contrast to prior methods that depend on reinforcement learning (e.g., GAPO) or explicit reward models (e.g., MODPO), our approach operates entirely within the lightweight Direct Preference Optimisation (DPO) paradigm. Experiments on the UltraFeedback dataset show that geometry-aware methods -- and MGDA-Decoupled in particular -- achieve the highest win rates against golden responses, both overall and per objective.

            {
  "tldr": "The paper presents MGDA-DECOUPLED, a geometry-aware algorithm for aligning large language models (LLMs) with multiple conflicting objectives, improving fairness in optimization without the overhead of reinforcement learning.",
  "whats_new": [
    "• Introduces MGDA-DECOUPLED, a novel multi-objective optimization algorithm.",
    "• Addresses fairness in LLM alignment by managing gradient conflicts.",
    "• Achieves the highest win rates against golden responses in experiments.",
    "• Operates within the Direct Preference Optimization (DPO) framework."
  ],
  "method": "MGDA-DECOUPLED computes mixing coefficients from loss-normalized per-objective gradients and applies them to raw gradients. This decoupling allows for better management of conflicting objectives during optimization.",
  "results": [
    "• MGDA-DECOUPLED achieved the highest net win rate of +2.3% over the reference model.",
    "• Outperformed other algorithms in three out of four objectives on the Gemma-2-2b-it model.",
    "• On the Qwen2.5-0.5B-Instruct model, it showed a +4.7% improvement in overall quality.",
    "• Demonstrated effective handling of gradient conflicts, leading to faster convergence."
  ],
  "limitations": [
    "• Computational overhead from gradient Gram matrix calculations.",
    "• Performance gains are relatively small due to high baseline model performance.",
    "• The UltraFeedback dataset may limit gradient conflict density.",
    "• Future work needed to explore direct use of raw numerical scores in optimization."
  ],
  "why_useful": "This paper is significant for researchers and practitioners in AI alignment, providing a practical method for optimizing LLMs across multiple objectives. It emphasizes fairness and efficiency, making it relevant for developing responsible AI systems.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code & data available at https://arxiv.org/abs/2604.20685v1"
  }
}
            

arXiv Page | PDF

Published: 2026-04-22 15:07:54

Authors: Shivani Kumar, Adarsh Bharathwaj, David Jurgens

Categories: cs.CL

Abstract:
Multi-agent systems built from teams of large language models (LLMs) are increasingly deployed for collaborative scientific reasoning and problem-solving. These systems require agents to coordinate under shared constraints, such as GPUs or credit balances, where cooperative behavior matters. Behavioral economics provides a rich toolkit of games that isolate distinct cooperation mechanisms, yet it remains unknown whether a model's behavior in these stylized settings predicts its performance in realistic collaborative tasks. Here, we benchmark 35 open-weight LLMs across six behavioral economics games and show that game-derived cooperative profiles robustly predict downstream performance in AI-for-Science tasks, where teams of LLM agents collaboratively analyze data, build models, and produce scientific reports under shared budget constraints. Models that effectively coordinate games and invest in multiplicative team production (rather than greedy strategies) produce better scientific reports across three outcomes, accuracy, quality, and completion. These associations hold after controlling for multiple factors, indicating that cooperative disposition is a distinct, measurable property of LLMs not reducible to general ability. Our behavioral games framework thus offers a fast and inexpensive diagnostic for screening cooperative fitness before costly multi-agent deployment.

            {
  "tldr": "This paper demonstrates that cooperative profiles derived from behavioral economics games can predict the performance of multi-agent LLM teams in scientific workflows. Models that exhibit effective coordination and resource sharing outperform others in generating accurate scientific reports.",
  "whats_new": [
    "• Introduces a framework using behavioral economics games to assess LLM cooperation.",
    "• Evaluates 35 LLMs across six games, revealing systematic differences in cooperative behavior.",
    "• Shows that game-derived metrics predict performance in AI-for-Science tasks.",
    "• Provides a cost-effective method for screening LLMs for multi-agent deployment."
  ],
  "method": "The study benchmarks 35 open-weight LLMs across six behavioral economics games to assess their cooperative profiles. It then evaluates these models in collaborative scientific tasks under shared budget constraints, using Bayesian hierarchical models to analyze the predictive power of cooperative behaviors on task performance.",
  "results": [
    "• Models that coordinate effectively in games produce scientific reports with 15% higher accuracy.",
    "• Cooperative profiles significantly predict report quality, with CPR game metrics correlating with quality scores (β = 0.827).",
    "• Models that withdraw more in O-Ring games show a 21% decrease in completion rates.",
    "• Behavioral games provide a diagnostic tool that is up to 44x faster and 27x cheaper than full multi-agent evaluations."
  ],
  "limitations": [
    "• The study does not establish causal relationships between cooperative behavior and task performance.",
    "• Results may not generalize to all types of collaborative tasks beyond those tested.",
    "• The impact of model size on cooperation is significant but not fully explored.",
    "• The effectiveness of theory-of-mind prompting in complex tasks remains uncertain."
  ],
  "why_useful": "This research is crucial for developing efficient multi-agent systems in scientific workflows, as it provides a method to predict LLM performance based on cooperative behavior. It can inform the design and deployment of LLMs in collaborative environments, enhancing their effectiveness in real-world applications.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code & data available at http://jurgens.people.si.umich.edu/agent-cooperation."
  }
}
            

arXiv Page | PDF

Published: 2026-04-22 14:54:39

Authors: Yijia Zeng, Xiang Pan, Hailong Ma

Categories: hep-ex

Abstract:
Experimental measurements of different decays of charmed mesons have been extensively performed at BESIII. Precision measurements of absolute branching fractions of different decays, the decay constants of $D^+$ and $D^+_s$ mesons, hadronic form factors of $D$ transitions to light hadrons ($K$, $π$, $η$, $η^\prime$, $K^*(892)$, $ρ$, $ω$, $φ$, $K_1(1270)$, $f_0(980)$), $c\to s(d)$ Cabibbo-Kobayashi-Maskawa (CKM) matrix elements, tests of lepton flavor universality with various (semi)leptonic $D$ decays, precision measurements of strong phase difference between $D^0$ and $\bar D^0$ decays, amplitude analyses of multibody hadronic $D_{(s)}$ decays, search for rare $D$ decays have been reported. The reported results offer important information to test different theoretical calculations, to test the unitarity of the CKM matrix, and to search for new physics effects beyond the standard model (SM). This paper reviews experimental studies of different decays of $D^0$, $D^+$, and $D^+_s$ as well as their excitations at BESIII as of April 15, 2026. Based on existing results of (semi)leptonic $D$ decays from all experiments, we have presented the most precise averages for the CKM matrix elements $|V_{cs}|=0.9648\pm0.009\pm0.0036$ and $|V_{cd}|=0.2259\pm0.0014\pm0.0013$, the decay constants of $D^+$ and $D^+_s$ $f_{D^+}=(213.1\pm2.0\pm1.5)$ MeV and $f_{D^+_s}=(253.2\pm1.2\pm1.6)$ MeV, as well as the hadronic form factors $f^{D\to K}_+(0)=0.7342\pm0.0007\pm0.0008$, $f^{D\to π}_+(0)=0.6337\pm0.0053\pm0.0037$, $f^{D\to η}_+(0)=0.351\pm0.009\pm0.005$, $f^{D\to η^\prime}_+(0)=0.263\pm0.025\pm0.006$, $f^{D_s\to η}_+(0)=0.4653\pm0.0058\pm0.0069$, $f^{D_s\to η^\prime}_+(0)=0.535\pm0.020\pm0.011$, and $f^{D_s\to K^0}_+(0)=0.627\pm0.036\pm0.009$, where the first and second uncertainties are statistical and systematic, respectively.

arXiv Page | PDF

Published: 2026-04-22 14:48:10

Authors: Abdul Rahman

Categories: math.AG, hep-th

Abstract:
We study finite-node conifold degenerations of Calabi--Yau threefolds from the point of view of interacting light sectors. Although each ordinary double point contributes a rank-one local vanishing sector, the corrected global object need not assemble as a freely independent sum of nodewise pieces. Using the corrected perverse and mixed-Hodge-module degeneration package, the global gluing law for corrected extension classes, and the rigid-flexible atom decomposition on the \(F\)-bundle side, we define an interacting multi-node light-sector package and prove a block-reduced structure theorem. In the block-separated cycle family, the finite-node package separates into two logically distinct layers: relation collapse, controlled by a common relation lattice on the corrected-extension, smoothing, and resolution sides, and residual interaction among the surviving global sectors, controlled by a reduced block interaction matrix on the transport and atom sides. The result isolates the geometric and Hodge-theoretic precursor of coupled conifold light states and provides the mathematical input for a later multi-node reformulation of Strominger's conifold mechanism.

            {
  "tldr": "This paper introduces a framework for understanding finite-node conifold degenerations in Calabi-Yau threefolds, focusing on the interactions between light sectors. It establishes a mathematical package that captures the complexities of these interactions.",
  "whats_new": [
    "• Defines an interacting multi-node light-sector package for finite-node conifold degenerations.",
    "• Proves a block-reduced structure theorem that separates relation collapse and residual interaction.",
    "• Organizes multiple realizations (corrected-extension, transport, atom-side) into a unified framework.",
    "• Provides a mathematical precursor for a later reformulation of Strominger’s conifold mechanism."
  ],
  "method": "The paper employs corrected perverse and mixed-Hodge-module degeneration techniques to analyze finite-node conifold degenerations. It defines a package that encapsulates local rank-one sectors, their global assembly, and interaction behaviors through a structured comparison of realizations.",
  "results": [
    "• Each ordinary double point contributes a local rank-one vanishing sector.",
    "• The corrected global light-sector package does not always assemble as a free direct sum of local sectors.",
    "• A relation-controlled subspace exists for the globally realized light-sector package.",
    "• Noncommutativity of Picard–Lefschetz operators indicates finite-node interactions.",
    "• The interaction matrix governs the coupling of surviving sectors."
  ],
  "limitations": [
    "• The paper does not provide explicit geometric realizations for all discussed models.",
    "• The relationship between corrected-extension interaction and transport interaction remains partially unexplored.",
    "• Further work is needed to fully realize the multi-node Strominger recast."
  ],
  "why_useful": "This paper is significant for researchers in algebraic geometry and string theory, as it lays the groundwork for understanding complex interactions in conifold degenerations. Its findings may have implications for effective theories in string theory, particularly in the context of multi-node systems.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-04-22 14:45:00

Authors: Md. Shaddam Hossain Bagmar, Hua Shen

Categories: stat.ME

Abstract:
Missing confounders are common in observational studies and present fundamental challenges for causal effect estimation by weakening identification and increasing sensitivity to model misspecification. Within the missing-indicator framework, existing methods rely on a single working model and achieve consistency only when that model is correctly specified, and are therefore singly robust. In this article, we develop a doubly robust missing indicator weighted ordinary least squares (MI-WOLS) estimator with partially observed confounders. The MI-WOLS estimator incorporates the treatment assignment mechanism, commonly known as the propensity score model, into the weighting structure of the outcome regression. Building on the missing-indicator framework, we define propensity score based regression weights that satisfy a covariate-balancing condition in the presence of confounder missingness. Under the missingness-strongly-ignorable treatment allocation assumption and assuming either a Conditionally Independent Treatment or Conditionally Independent Outcome structure, the MI-WOLS estimator is consistent when at least the treatment or the outcome model is correctly specified. Simulation studies support the theoretical robustness of the MI-WOLS estimator, demonstrating negligible bias, accurate sandwich-based variance estimation, and near-nominal coverage probability across a wide range of data-generating scenarios. An illustrative application to kidney function outcomes further demonstrates the interpretability and practical feasibility of the method, offering a flexible, doubly robust alternative to existing singly robust estimators.

            {
  "tldr": "This paper introduces a doubly robust estimator, MI-WOLS, for causal inference in the presence of missing confounders, improving upon existing singly robust methods. It ensures consistent estimation when either the treatment or outcome model is correctly specified.",
  "whats_new": [
    "• Proposes the MI-WOLS estimator that integrates missing-indicator and weighted regression frameworks.",
    "• Achieves double robustness under the missingness-strongly-ignorable treatment allocation assumption.",
    "• Demonstrates negligible bias and accurate variance estimation through extensive simulations.",
    "• Provides a practical application to kidney function outcomes, showcasing the method's feasibility."
  ],
  "method": "The MI-WOLS estimator uses a missing-indicator framework to incorporate treatment assignment mechanisms into the outcome regression. It defines propensity score-based weights that satisfy covariate-balancing conditions, ensuring consistency when either the treatment or outcome model is correctly specified.",
  "results": [
    "• Simulation studies show MI-WOLS maintains negligible bias across various scenarios.",
    "• Achieves near-nominal coverage probability for variance estimates under correct model specifications.",
    "• Outperforms existing singly robust estimators, recovering true causal effects even with model misspecification.",
    "• Illustrative application to ACEI/ARB treatment shows consistent estimates of treatment effects."
  ],
  "limitations": [
    "• Assumes missingness only in confounders, not in treatment or outcome data.",
    "• Bias may arise if the mSITA assumption is violated or if both CIT and CIO conditions fail.",
    "• Efficiency can be reduced with extreme propensity scores."
  ],
  "why_useful": "The MI-WOLS estimator provides a flexible and interpretable method for causal inference in observational studies with missing confounders. Its double robustness enhances reliability in estimating treatment effects, making it valuable for researchers in health and social sciences.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-04-22 14:41:56

Authors: Aravind Venugopal, Jiayu Chen, Xudong Wu, Chongyi Zheng, Benjamin Eysenbach, Jeff Schneider

Categories: cs.LG, cs.RO

Abstract:
The temporal lag between actions and their long-term consequences makes credit assignment a challenge when learning goal-directed behaviors from data. Generative world models capture the distribution of future states an agent may visit, indicating that they have captured temporal information. How can that temporal information be extracted to perform credit assignment? In this paper, we formalize how the temporal information stored in world models encodes the underlying geometry of the world. Leveraging optimal transport, we extract this geometry from a learned model of the occupancy measure into a reward function that captures goal-reaching information. Our resulting method, Occupancy Reward Shaping, largely mitigates the problem of credit assignment in sparse reward settings. ORS provably does not alter the optimal policy, yet empirically improves performance by 2.2x across 13 diverse long-horizon locomotion and manipulation tasks. Moreover, we demonstrate the effectiveness of ORS in the real world for controlling nuclear fusion on 3 Tokamak control tasks. Code: https://github.com/aravindvenu7/occupancy_reward_shaping; Website: https://aravindvenu7.github.io/website/ors/

            {
  "tldr": "This paper introduces Occupancy Reward Shaping (ORS), a method that enhances credit assignment in offline goal-conditioned reinforcement learning by extracting temporal information from generative world models. ORS improves performance by 2.2× on various tasks without altering the optimal policy.",
  "whats_new": [
    "• Proposes a novel reward-shaping method, ORS, leveraging generative world models.",
    "• Demonstrates significant performance improvements (2.2×) across 13 diverse tasks.",
    "• Validates effectiveness in real-world applications, including nuclear fusion control.",
    "• Provides theoretical guarantees on policy optimality under ORS."
  ],
  "method": "ORS formalizes the extraction of temporal information from learned occupancy measures using optimal transport. It transforms this information into a reward function that captures goal-reaching dynamics while preserving the optimal policy.",
  "results": [
    "• Achieves a 2.2× improvement over sparse-reward baselines.",
    "• Outperforms state-of-the-art methods by 24%-248% on various tasks.",
    "• Demonstrates superior performance on 3 Tokamak control tasks.",
    "• ORS consistently yields higher success rates across diverse long-horizon tasks."
  ],
  "limitations": [
    "• May struggle in highly combinatorial state spaces with few successful paths.",
    "• Performance could be limited in long-horizon tasks with sparse rewards.",
    "• Requires careful tuning and may have higher computational overhead."
  ],
  "why_useful": "ORS addresses the critical challenge of credit assignment in offline reinforcement learning, making it applicable to complex tasks with sparse rewards. Its ability to extract meaningful temporal information can enhance learning efficiency and effectiveness in real-world scenarios.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code available at https://github.com/aravindvenu7/occupancy_reward_shaping"
  }
}
            

arXiv Page | PDF

Published: 2026-04-22 14:33:12

Authors: Dario Giandinoto, Boris Shapiro

Categories: math.PR, math.SP

Abstract:
Twisted Toeplitz matrices constitute a generalization of Toeplitz matrices in the sense that the entries on each diagonal no longer need to be constant, but are given by the values of a continuous function on a partition of $[0,1]$. We study the limiting statistical distribution of the eigenvalues of matrices of the form $R_n(a) = T_n(a) + σ_n X_n$, where $T_n(a)$ is a sequence of non-Hermitian tridiagonal twisted Toeplitz matrices, $X_n$ is a sequence of tridiagonal random matrices whose entries have mean $0$ and finite variance, and $σ_n\to0$. The limiting distribution turns out to be a two-dimensional measure which is in general different from the push-forward of the Lebesgue measure by the symbol. We also explain how the results could extend to banded non-Hermitian twisted Toeplitz matrices.

            {
  "tldr": "The paper investigates the spectral asymptotics of structured perturbations of tridiagonal twisted Toeplitz matrices, revealing that their eigenvalue distributions differ from those of full-matrix perturbations.",
  "whats_new": [
    "• Introduces structured random perturbations of twisted Toeplitz matrices.",
    "• Establishes weak convergence of eigenvalue distributions for tridiagonal matrices.",
    "• Provides numerical examples demonstrating distinct eigenvalue accumulation sets.",
    "• Extends results to conjecture about banded twisted Toeplitz matrices."
  ],
  "method": "The authors analyze the limiting statistical distribution of eigenvalues for matrices of the form R_n(a) = T_n(a) + σ_n X_n, where T_n(a) is a tridiagonal twisted Toeplitz matrix and X_n is a random matrix. They employ a 'frozen blocks' technique to approximate eigenvalues and derive asymptotic distributions.",
  "results": [
    "• The limiting distribution of eigenvalues is a two-dimensional measure differing from the Lebesgue measure push-forward.",
    "• Weak convergence of eigenvalue-counting measures μ_n to a specific measure μ is established.",
    "• Numerical examples show eigenvalues of R_n(a) can accumulate on sets disjoint from the symbol's range.",
    "• The results suggest that structured perturbations yield different asymptotics than full-matrix perturbations."
  ],
  "limitations": [
    "• Results are primarily focused on tridiagonal matrices; extensions to banded cases remain conjectural.",
    "• The paper does not provide proofs for all conjectured extensions to banded matrices.",
    "• Further exploration is needed to fully understand the spectral behavior in non-normal settings."
  ],
  "why_useful": "This research enhances understanding of the spectral properties of twisted Toeplitz matrices, which have applications in physics and differential equations. The findings could inform future studies on random perturbations and their effects on matrix spectra.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-04-22 14:20:00

Authors: Matthew Collinson

Categories: math.CT, cs.LO, math.LO

Abstract:
We display a family of Stone-type dualities linking categories of frames carrying pairs of modal operators to categories of spaces carrying a binary relation. Different notions of morphism used on the relational side lead to significant variations in the point construction. We show how the situation simplifies in the case of semicontinuous relations, allowing for straightforward correspondences between modal axioms and relational properties.

            {
  "tldr": "This paper presents Stone-type dualities connecting modal algebras and relational spaces, simplifying the correspondence between modal axioms and relational properties, especially under semicontinuity conditions.",
  "whats_new": [
    "• Introduces continuous pq-morphisms, enhancing the understanding of modal frame points.",
    "• Establishes a clearer link between modal axioms and relational properties through semicontinuous relations.",
    "• Provides a family of adjunctions between categories of modal frames and relational spaces.",
    "• Discusses the implications of these dualities for intuitionistic modal logics."
  ],
  "method": "The paper develops a framework linking categories of modal algebras with relational spaces using adjunctions. It refines existing morphism concepts and explores the impact of semicontinuity on these relationships.",
  "results": [
    "• Demonstrates that continuous pq-morphisms allow for better control over relational properties.",
    "• Shows that under semicontinuity, modal correspondence theory becomes tractable.",
    "• Establishes that all points in certain modal frames satisfy specific restrictions, enhancing the understanding of modal logic semantics.",
    "• Provides examples of how these dualities apply to intuitionistic modal logics."
  ],
  "limitations": [
    "• The applicability of semicontinuity assumptions may not be universally accepted.",
    "• Some results may be fragile and require careful handling of additional axioms.",
    "• The exploration of correspondence theory without semicontinuity remains incomplete."
  ],
  "why_useful": "This paper is significant for researchers in modal logic and category theory, providing new tools for understanding the interplay between modal algebras and relational semantics. Its findings can enhance the development of intuitionistic modal logics and their applications in computer science.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-04-22 14:11:40

Authors: Anif N. Shikder, Ramit Dey, Sayantan Auddy, Luisa Liboni, Alexandra N. Busch, Arthur Powanwe, Ján Mináč, Roberto C. Budzinski, Lyle E. Muller

Categories: cs.NE, cs.LG, nlin.AO

Abstract:
We establish a mathematical correspondence between state space models, a state-of-the-art architecture for capturing long-range dependencies in data, and an exactly solvable nonlinear oscillator network. As a specific example of this general correspondence, we analyze the diagonal linear time-invariant implementation of the Structured State Space Sequence model (S4). The correspondence embeds S4D, a specific implementation of S4, into a ring network topology, in which recent inputs are encoded, as waves of activity traveling over the one-dimensional spatial layout of the network. We then derive an exact operator expression for the full forward pass of S4D, yielding an analytical characterization of its complete input-output map. This expression reveals that the nonlinear decoder in the system induces interactions between these information-carrying waves that enable classifying real-world sequences. These results generalize across modern SSM architectures, and show that they admit an exact mathematical description with a clear physical interpretation. These insights enable a new level of interpretability for these systems in terms of nonlinear oscillator networks.

            {
  "tldr": "This paper establishes a mathematical link between state space models and nonlinear oscillator networks, providing an explicit operator for understanding computations in modern neural networks used for sequence and language modeling.",
  "whats_new": [
    "• Introduces a mathematical correspondence between state space models and nonlinear oscillator networks.",
    "• Derives an exact operator expression for the Structured State Space Sequence model (S4D).",
    "• Reveals how traveling waves in S4D's recurrent layer classify inputs effectively.",
    "• Provides a framework for understanding the forward pass in SSMs analytically."
  ],
  "method": "The authors establish a formal correspondence between diagonal state space models and nonlinear oscillator networks. They derive an operator expression for S4D, allowing for an analytical characterization of its input-output mapping and revealing the dynamics of traveling waves in the network.",
  "results": [
    "• S4D generates traveling waves that effectively distinguish between different input classes.",
    "• The linear dynamics account for 83% of classification performance, while second-order interactions improve this to 94%.",
    "• The complete GELU activation captures 100% of the classification performance.",
    "• The framework enables a new level of interpretability for SSMs."
  ],
  "limitations": [
    "• The analysis primarily focuses on diagonal implementations of SSMs.",
    "• The implications for more complex architectures remain to be fully explored.",
    "• The performance metrics are based on specific datasets and may vary with different inputs."
  ],
  "why_useful": "This paper enhances the interpretability of state space models in neural networks, providing a mathematical framework that can lead to better understanding and control of model outputs. It has potential applications in designing more efficient architectures for sequence processing tasks.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code & data available at https://mullerlab.github.io"
  }
}
            

arXiv Page | PDF

Published: 2026-04-22 14:11:27

Authors: Qian Chen, Yuehao Chen, Qiang Wang, Lei Zhu, Yanye Lu, Qiushi Ren

Categories: cs.CV

Abstract:
Retinal laser speckle contrast imaging (LSCI) is a noninvasive optical modality for monitoring retinal blood flow dynamics. However, conventional temporal LSCI (tLSCI) reconstruction relies on sufficiently long speckle sequences to obtain stable temporal statistics, which makes it vulnerable to acquisition disturbances and limits effective temporal resolution. A physically informed reconstruction framework, termed RetinaDiff (Retinal Diffusion Model), is proposed for retinal tLSCI that is robust to motion and requires only a few frames. In RetinaDiff, registration based on phase correlation is first applied to stabilize the raw speckle sequence before contrast computation, reducing interframe misalignment so that fluctuations at each pixel primarily reflect true flow dynamics. This step provides a physics prior corrected for motion and a high quality multiframe tLSCI reference. Next, guided by the physics prior, a conditional diffusion model performs inverse reconstruction by jointly conditioning on the registered speckle sequence and the corrected prior. Experiments on data acquired with a retinal LSCI system developed in house show improved structural continuity and statistical stability compared with direct reconstruction from few frames and representative baselines. The framework also remains effective in a small number of extremely challenging cases, where both the direct 5-frame input and the conventional multiframe reconstruction are severely degraded. Overall, this work provides a practical and physically grounded route for reliable retinal tLSCI reconstruction from extremely limited frames. The source code and model weights will be publicly available at https://github.com/QianChen113/RetinaDiff.

arXiv Page | PDF

Published: 2026-04-22 05:11:59

Authors: Zehong Ke, Yanbo Jiang, Jinhao Li, Zhiyuan Liu, Yiqian Tu, Qingwen Meng, Heye Huang, Jianqiang Wang

Categories: cs.CV, cs.AI, cs.RO

Abstract:
Interpretable driver attention prediction is crucial for human-like autonomous driving. However, existing datasets provide only scene-level global gaze rather than fine-grained object-level annotations, inherently failing to support text-grounded cognitive modeling. Consequently, while Vision-Language Models (VLMs) hold great potential for semantic reasoning, this critical data limitations leads to severe text-vision decoupling and visual-bias hallucinations. To break this bottleneck and achieve precise object-level attention prediction, this paper proposes a novel dual-branch gaze prediction framework, establishing a complete paradigm from data construction to model architecture. First, we construct G-W3DA, a object-level driver attention dataset. By integrating a multimodal large language model with the Segment Anything Model 3 (SAM3), we decouple macroscopic heatmaps into object-level masks under rigorous cross-validation, fundamentally eliminating annotation hallucinations. Building upon this high-quality data foundation, we propose the DualGaze-VLM architecture. This architecture extracts the hidden states of semantic queries and dynamically modulates visual features via a Condition-Aware SE-Gate, achieving intent-driven precise spatial anchoring. Extensive experiments on the W3DA benchmark demonstrate that DualGaze-VLM consistently surpasses existing state-of-the-art (SOTA) models in spatial alignment metrics, notably achieving up to a 17.8% improvement in Similarity (SIM) under safety-critical scenarios. Furthermore, a visual Turing test reveals that the attention heatmaps generated by DualGaze-VLM are perceived as authentic by 88.22% of human evaluators, proving its capability to generate rational cognitive priors.

            {
  "tldr": "This paper introduces a dual-branch gaze prediction framework that enhances driver attention prediction by using a new object-level dataset and a multimodal model, achieving significant improvements in spatial alignment metrics.",
  "whats_new": [
    "• Proposes G-W3DA, a novel object-level driver attention dataset.",
    "• Introduces DualGaze-VLM, a dual-branch architecture for gaze prediction.",
    "• Achieves up to 17.8% improvement in spatial alignment metrics under safety-critical scenarios.",
    "• 88.22% of human evaluators perceive generated attention heatmaps as authentic."
  ],
  "method": "The approach involves constructing the G-W3DA dataset by decoupling scene-level gaze maps into object-level masks using a multimodal large language model and the Segment Anything Model. The DualGaze-VLM architecture then utilizes these masks to generate precise gaze predictions based on text queries.",
  "results": [
    "• DualGaze-VLM outperforms existing models in spatial alignment metrics.",
    "• Achieves a 17.8% improvement in Similarity (SIM) metrics.",
    "• Human evaluators found 88.22% of generated heatmaps to be authentic.",
    "• Average attention intensity improved by 65% in the G-W3DA dataset."
  ],
  "limitations": [
    "• The model's performance may vary in non-critical driving scenarios.",
    "• Further optimization of object-level attention prediction is needed.",
    "• The dataset construction process may still be susceptible to some annotation errors."
  ],
  "why_useful": "This paper addresses the critical gap in driver attention prediction by providing a robust dataset and a novel model architecture. Its findings can enhance the interpretability and safety of autonomous driving systems, making it relevant for researchers and practitioners in the field.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-04-22 05:08:10

Authors: Le Tuan Hoa, Doan Quang Tien

Categories: math.AC

Abstract:
A monomial curve $C$ is defined by a sequence of coprime integers $0 = a_0 < a_1 < \cdots < a_k =: d$. One gap of this sequence is $a_{i+1} - a_i - 1$. Gruson--Lazarsfeld--Peskine bound (1983) says that $reg (C) \le d - k +2$, which is equal to the sum of all gaps plus 2. Lvovsky (1996) showed that it is enough to take the sum of two largest gaps plus 2. In this paper, under some specific conditions, we give several new bounds which are better than Lvovsky's bound. Our method relies on the study of Apery sets and Frobenius numbers. From this we can give new criteria to check the (arithmetically) Cohen--Macaulay and Buchsbaum property of $C$. Algorithms are provided to check these properties as well as to compute $ reg(C)$ and other invariants. We also give an application to study the structure of sumsets.

            {
  "tldr": "This paper presents new bounds on the Castelnuovo–Mumford regularity of monomial curves, improving upon existing results. It also explores applications to sumsets and provides algorithms for checking properties of these curves.",
  "whats_new": [
    "• Introduces several new bounds that outperform L’vovsky’s bound under specific conditions.",
    "• Provides new criteria for checking Cohen–Macaulay and Buchsbaum properties of monomial curves.",
    "• Develops algorithms for computing regularity and other invariants efficiently.",
    "• Establishes a connection between Castelnuovo–Mumford regularity and sumsets regularity."
  ],
  "method": "The authors utilize combinatorial techniques involving Apéry sets and Frobenius numbers to derive new bounds. They also provide algorithms for computing the Castelnuovo–Mumford regularity and checking properties of monomial curves.",
  "results": [
    "• New bounds on reg(C) are established that are better than L’vovsky’s bound under certain conditions.",
    "• Algorithms are developed that do not require Gröbner bases, allowing for faster computations.",
    "• The paper shows that L’vovsky’s bound applies to sumsets regularity σ(A).",
    "• New classes of monomial curves are classified based on Cohen–Macaulay and Buchsbaum properties."
  ],
  "limitations": [
    "• The bounds obtained are not always explicit in terms of the sequence of integers defining the monomial curves.",
    "• Some results depend on specific assumptions that may not hold in all cases.",
    "• The complexity of computing Apéry sets and degrees can be high."
  ],
  "why_useful": "This paper enhances the understanding of Castelnuovo–Mumford regularity in algebraic geometry, particularly for monomial curves. The results have implications for both theoretical research and practical applications in additive number theory.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-04-22 04:28:45

Authors: Ihor Vitenki, Noha Ibrahim, Sihem Amer-Yahia

Categories: cs.LG

Abstract:
Reinforcement learning (RL) policies are typically trained for fixed objectives, making reuse difficult when task requirements change. We study inference-time policy reuse: given a library of pre-trained policies and a new composite objective, can a high-quality policy be constructed entirely offline, without additional environment interaction? We introduce lever (Leveraging Efficient Vector Embeddings for Reusable policies), an end-to-end framework that retrieves relevant policies, evaluates them using behavioral embeddings, and composes new policies via offline Q-value composition. We focus on the support-limited regime, where no value propagation is possible, and show that the effectiveness of reuse depends critically on the coverage of available transitions. To balance performance and computational cost, lever proposes composition strategies that control the exploration of candidate policies. Experiments in deterministic GridWorld environments show that inference-time composition can match, and in some cases exceed, training-from-scratch performance while providing substantial speedups. At the same time, performance degrades when long-horizon dependencies require value propagation, highlighting a fundamental limitation of offline reuse.

            {
  "tldr": "This paper presents Lever, a framework for reusing pre-trained reinforcement learning policies without additional interaction, focusing on offline policy composition under support constraints. It demonstrates that effective reuse depends on the availability of relevant transitions in the policy library.",
  "whats_new": [
    "• Introduces Lever, an end-to-end framework for inference-time policy reuse.",
    "• Explores the impact of support constraints on policy composition effectiveness.",
    "• Proposes three composition strategies: Targeted, Hybrid, and Exhaustive.",
    "• Provides empirical results showing performance trade-offs in support-limited and planning-enabled regimes."
  ],
  "method": "Lever retrieves relevant pre-trained policies based on a natural-language task description, evaluates them using behavioral embeddings, and composes new policies offline. It operates under strict constraints, allowing no new data collection or environment interaction.",
  "results": [
    "• In the support-limited regime (γ=0), Lever's composition strategies can match or exceed training-from-scratch performance.",
    "• Targeted Composition (TC) is efficient but sensitive to retrieval errors, while Hybrid Composition (HC) balances performance and computational cost.",
    "• Exhaustive Composition (EC) can improve performance but incurs high computational costs.",
    "• Performance degrades significantly in the planning-enabled regime (γ>0) due to the inability to propagate value."
  ],
  "limitations": [
    "• The effectiveness of reuse is highly dependent on the coverage of the policy library.",
    "• Performance can suffer from approximation errors in offline evaluations.",
    "• Some composition strategies require access to environment dynamics for optimal evaluation.",
    "• The framework's reliance on existing policies limits its adaptability to new tasks."
  ],
  "why_useful": "This work advances the field of reinforcement learning by enabling efficient policy reuse, reducing the need for retraining in dynamic environments. It has potential applications in robotics and other decision-making systems where retraining is costly or impractical.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code available at https://github.com/Strongich/LEVER"
  }
}
            

arXiv Page | PDF

Published: 2026-04-22 03:53:14

Authors: Qiuye Jia

Categories: math.AP, math-ph, math.DG

Abstract:
For a time dependent Schrödinger equation, the scattering map is the map sending the asymptotic profile of solution as $t\to-\infty$ to its asymptotic profile as $t\to+\infty$. In this paper we show that, for certain class of metrics, the scattering maps associated to two Schrödinger operators with two time dependent metrics only differ by a compact operator if and only if these two metrics are related by a pull-back of a diffeomorphism.

            {
  "tldr": "This paper establishes that the scattering map of the time-dependent Schrödinger equation can determine the underlying metric, specifically showing that two metrics related by a diffeomorphism yield equivalent scattering maps.",
  "whats_new": [
    "• Introduces a method to determine metrics from scattering maps in time-dependent Schrödinger equations.",
    "• Establishes conditions under which scattering maps differ only by a compact operator.",
    "• Provides a framework for analyzing geodesics in time-dependent metrics.",
    "• Connects geometric properties of metrics to scattering data."
  ],
  "method": "The approach involves analyzing the scattering map, which relates asymptotic profiles of solutions to the Schrödinger equation under varying metrics. The paper employs advanced microlocal analysis and pseudodifferential operator theory to derive conditions for metric determination.",
  "results": [
    "• Demonstrates that scattering maps are equivalent if metrics differ by a diffeomorphism.",
    "• Establishes that the scattering map can determine geometric properties of metrics, including lengths of geodesics.",
    "• Shows that if two metrics have the same scattering map, their geodesics have the same lengths.",
    "• Provides a criterion for compactness of operators derived from scattering maps."
  ],
  "limitations": [
    "• The results are contingent on specific geometric conditions being met.",
    "• The analysis primarily focuses on metrics that are compactly supported in spacetime.",
    "• Further exploration is needed for metrics without compact support."
  ],
  "why_useful": "This paper contributes to the field of inverse problems by providing a method to recover metrics from scattering data, which has implications in mathematical physics and geometry. It enhances understanding of how geometric properties influence wave propagation in varying media.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-04-22 03:50:52

Authors: Zeyu Shen, Peter Henderson

Categories: cs.LG

Abstract:
Mixture-of-Experts models, now popular for scaling capacity at fixed inference speed, switch experts at nearly every token. Once a model outgrows available GPU memory, this churn can render optimizations like offloading and pre-fetching ineffective. We make the case that the options framework in reinforcement learning is a perfect match to tackle this problem, and argue for temporally extended mixture-of-experts layers. Building on the option-critic framework with deliberation costs, we add a controller to each layer that learns when to switch expert sets and which to load. By applying this to gpt-oss-20b with low-rank adapters and a self-distillation reward, our method reduces switch rates from over 50% to below 5% while retaining up to 90% of base-model accuracy on MATH, MMLU, and MMMLU. This shows that even existing pre-trained models can be converted to temporally extended MoEs with lightweight training, with the deliberation cost allowing model trainers to trade off switching rates against capability. We hope this opens a principled path, grounded in the options framework, for memory-efficient serving and continual learning in ever-growing MoE models.

            {
  "tldr": "This paper introduces temporally extended mixture-of-experts (MoE) models that reduce expert switching rates from over 50% to below 5% while maintaining high accuracy. This approach leverages reinforcement learning principles to optimize memory usage in large language models.",
  "whats_new": [
    "• Proposes a novel framework for temporally extended MoEs using reinforcement learning.",
    "• Introduces a lightweight controller that learns when to switch expert sets.",
    "• Demonstrates significant reduction in expert switching rates with minimal accuracy loss.",
    "• Provides a principled approach for memory-efficient serving and continual learning."
  ],
  "method": "The authors implement a controller that operates within the options framework to manage expert switching in MoE layers. This controller learns to optimize when to switch expert sets based on a deliberation cost, effectively treating expert selection as a temporally extended decision-making process.",
  "results": [
    "• Reduced expert switching rates from over 50% to below 5% on gpt-oss-20b.",
    "• Maintained up to 90% accuracy on benchmarks like MATH, MMLU, and MMMLU.",
    "• Achieved significant memory savings, reducing GPU memory requirements by up to 55%.",
    "• Demonstrated effective continual learning capabilities with expandable expert pools."
  ],
  "limitations": [
    "• The deliberation cost is a tunable hyperparameter, not grounded in actual hardware latency.",
    "• The evaluation is limited to three benchmarks, lacking broader performance assessments.",
    "• Future work is needed to integrate temporal continuity into pre-training objectives.",
    "• The method's reliance on self-distillation complicates isolating the effects of dynamic routing."
  ],
  "why_useful": "This paper addresses critical challenges in scaling large language models by optimizing memory usage and reducing latency through expert management. Its findings could significantly enhance the efficiency of deploying large models in resource-constrained environments.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-04-22 03:36:52

Authors: Xi Chen, Arian Maleki, Shirin Jalali

Categories: eess.IV, cs.CV, cs.LG

Abstract:
Multi-look acquisition is a widely used strategy for reducing speckle noise in coherent imaging systems such as digital holography. By acquiring multiple measurements, speckle can be suppressed through averaging or joint reconstruction, typically under the assumption that speckle realizations across looks are statistically independent. In practice, however, hardware constraints limit measurement diversity, leading to inter-look correlation that degrades the performance of conventional methods. In this work, we study the reconstruction of speckle-free reflectivity from complex-valued multi-look measurements in the presence of correlated speckle. We model the inter-look dependence using a first-order Markov process and derive the corresponding likelihood under a first-order Markov approximation, resulting in a constrained maximum likelihood estimation problem. To solve this problem, we develop an efficient projected gradient descent framework that combines gradient-based updates with implicit regularization via deep image priors, and leverages Monte Carlo approximation and matrix-free operators for scalable computation. Simulation results demonstrate that the proposed approach remains robust under strong inter-look correlation, achieving performance close to the ideal independent-look scenario and consistently outperforming methods that ignore such dependencies. These results highlight the importance of explicitly modeling inter-look correlation and provide a practical framework for multi-look holographic reconstruction under realistic acquisition conditions. Our code is available at: https://github.com/Computational-Imaging-RU/MLE-Holography-Markov.

            {
  "tldr": "This paper presents a method for reconstructing speckle-free images from multi-look digital holography measurements, addressing the challenge of correlated speckle noise using a Markov model. The proposed approach significantly improves reconstruction quality compared to traditional methods that assume independent speckle realizations.",
  "whats_new": [
    "• Introduces a first-order Markov process to model inter-look speckle correlation.",
    "• Develops a constrained maximum likelihood estimation framework for reconstruction.",
    "• Proposes an efficient projected gradient descent algorithm with deep image priors.",
    "• Demonstrates robustness under strong inter-look correlation, outperforming conventional methods."
  ],
  "method": "The authors model speckle correlation using a first-order Markov process and derive a corresponding likelihood function. They formulate a constrained maximum likelihood estimation problem and solve it using a projected gradient descent algorithm that incorporates deep image priors and Monte Carlo approximations for efficient computation.",
  "results": [
    "• Achieves reconstruction performance close to ideal independent-look scenarios.",
    "• Outperforms baseline methods that ignore inter-look dependencies by up to 0.5 dB in PSNR.",
    "• Maintains scalability to high-resolution imaging systems through efficient matrix-free implementations.",
    "• Successfully estimates correlation coefficients with high accuracy under various conditions."
  ],
  "limitations": [
    "• The method's performance may vary with different hardware setups and noise levels.",
    "• Future work is needed to validate the approach on real-world holography data.",
    "• Assumes Gaussian noise, which may not hold in all practical scenarios."
  ],
  "why_useful": "This paper provides a practical framework for improving image quality in digital holography by addressing speckle noise, which is crucial for applications in coherent imaging systems. The proposed method can enhance the performance of holographic displays and wavefront imaging, making it relevant for researchers and practitioners in optical imaging.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code available at https://github.com/Computational-Imaging-RU/MLE-Holography-Markov."
  }
}
            

arXiv Page | PDF

Published: 2026-04-22 03:32:40

Authors: M. U. Ashraf, A. M. Khan, M. Shahid, Faraz Mohd Mehdi

Categories: hep-ph, nucl-th

Abstract:
We present a systematic study of particle production in $Ne+Ne$ collisions at $\sqrt{s_{\mathrm{NN}}} = 5.36$ TeV using the A Multi-Phase Transport (AMPT) model with string melting (SM) configuration. The analysis compares spherical and deformed configurations of ${}^{20}\mathrm{Ne}$ to investigate the influence of initial-state nuclear deformation on bulk observables. Charged-particle pseudorapidity ($\langle dN_{\mathrm{ch}}/dη\rangle$) densities, identified particle yields ($dN/dy$), transverse momentum ($p_T$) spectra, mean transverse momentum ($\langle p_{\mathrm{T}} \rangle$), and $p_{\mathrm{T}}$-differential particle ratios ($K/π$ and $p/π$) are studied as functions of multiplicity and centrality. The results show that all observables exhibit the expected dependence on event activity, including smooth multiplicity scaling, mass ordering in $\langle p_{\mathrm{T}} \rangle$, and characteristic features associated with radial flow and quark coalescence. Differences between the two configurations on bulk observables remain small across all observables, typically at the level of a 2\%--6\% percent, with slightly enhanced sensitivity observed in peripheral collisions. These findings suggest that, within the AMPT-SM framework, the collective dynamics and hadrochemical composition are primarily governed by the overall system density and interaction dynamics, while the influence of initial-state deformation is subleading. This study provides a baseline for understanding deformation effects in light-ion collision systems and highlights the limited sensitivity of bulk observables to initial nuclear geometry in transport-based approaches.

            {
  "tldr": "This study examines particle production in Ne + Ne collisions at √sNN = 5.36 TeV, comparing spherical and deformed nuclear configurations. Results show that initial-state nuclear deformation has a minimal impact on bulk observables, with differences typically around 2-6%.",
  "whats_new": [
    "• Systematic analysis of nuclear deformation effects in light-ion collisions.",
    "• Utilizes the AMPT model with string melting configuration for detailed simulations.",
    "• Provides a baseline for understanding deformation effects in particle production.",
    "• Highlights limited sensitivity of bulk observables to initial nuclear geometry."
  ],
  "method": "The study employs the AMPT model with string melting to simulate Ne + Ne collisions. It compares spherical and deformed configurations of 20Ne nuclei, analyzing various observables like charged-particle densities, transverse momentum spectra, and particle ratios as functions of multiplicity and centrality.",
  "results": [
    "• Charged-particle multiplicity shows minimal differences (2-6%) between spherical and deformed configurations.",
    "• Average charged-particle yield decreases smoothly from central to peripheral collisions, reflecting system size effects.",
    "• Transverse momentum spectra exhibit mass ordering, with heavier particles showing harder distributions.",
    "• Particle ratios K/π and p/π remain similar across configurations, with deviations of 1-3%."
  ],
  "limitations": [
    "• The study focuses on bulk observables, potentially overlooking more sensitive measures like anisotropic flow.",
    "• Limited to the AMPT-SM framework, which may not capture all dynamics of nuclear deformation.",
    "• Future experimental data may refine the parameters used in the simulations."
  ],
  "why_useful": "This paper provides insights into the role of nuclear deformation in particle production during light-ion collisions, which is crucial for understanding the dynamics of quark-gluon plasma formation. The findings can inform future research on the effects of initial conditions in high-energy nuclear physics.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-04-22 03:28:26

Authors: Baolong Cheng, Linlin Ye, Zhaoqi Wu

Categories: quant-ph

Abstract:
Quantum coherence is an important quantum resource which plays a pivotal role in the field of quantum information. Based on metric adjusted skew information, we define a measure of quantum uncertainty to study average coherence under conical 2-designs generalized equiangular measurements, and prove the equivalence of this measure to the scaled average coherence based on metric adjusted skew information under a set of unitary groups, operator orthonormal bases, and mutually unbiased bases. We also derive two trade-off relations by this measure and solve a conjecture. Furthermore, we give two entanglement criteria by this measure and conical 2-designs generalized equiangular measurement, respectively, and illustrate the effectiveness of them by explicit examples.

            {
  "tldr": "This paper defines a measure of quantum uncertainty based on metric adjusted skew information to study average coherence under conical 2-designs generalized equiangular measurements. It proves the equivalence of this measure to scaled average coherence and presents new entanglement criteria.",
  "whats_new": [
    "• Introduces a measure of quantum uncertainty using metric adjusted skew information.",
    "• Proves equivalence to scaled average coherence under various measurement frameworks.",
    "• Derives two new entanglement criteria based on the proposed measure.",
    "• Provides explicit examples demonstrating the effectiveness of the criteria."
  ],
  "method": "The authors define a measure of quantum uncertainty and average coherence using metric adjusted skew information. They analyze coherence under conical 2-designs generalized equiangular measurements and derive trade-off relations between coherence and quantum entropy.",
  "results": [
    "• The average coherence under conical 2-designs GEAMs is expressed as a function of quantum uncertainty.",
    "• Two new entanglement criteria are established, showing effectiveness with isotropic and Werner states.",
    "• Trade-off relations between average coherence and quantum entropy are derived, indicating limits on coherence."
  ],
  "limitations": [
    "• The applicability of the results to broader classes of quantum states remains to be explored.",
    "• The complexity of the proofs may limit practical implementation in quantum information tasks.",
    "• No experimental validation of the proposed criteria is provided."
  ],
  "why_useful": "This paper contributes to the understanding of quantum coherence and uncertainty, which are crucial for quantum information science. The new entanglement criteria may enhance methods for detecting entanglement in quantum systems, potentially impacting quantum computing and communication technologies.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-04-22 02:58:51

Authors: Melanie Subbiah, Haaris Mian, Nicholas Deas, Ananya Mayukha, Dan P. McAdams, Kathleen McKeown

Categories: cs.CL

Abstract:
Increasingly, studies are exploring using Large Language Models (LLMs) for accelerated or scaled qualitative analysis of text data. While we can compare LLM accuracy against human labels directly for deductive coding, or labeling text, it is more challenging to judge the ethics and effectiveness of using LLMs in abstractive methods such as inductive thematic analysis. We collaborate with psychologists to study the abstractive claims LLMs make about human life stories, asking, how does using an LLM as an interpreter of meaning affect the conclusions and perspectives of a study? We propose a summarization-based pipeline for surfacing biases in perspective-taking an LLM might employ in interpreting these life stories. We demonstrate that our pipeline can identify both race and gender bias with the potential for representational harm. Finally, we encourage the use of this analysis in future studies involving LLM-based interpretation of study participants' written text or transcribed speech to characterize a positionality portrait for the study.

            {
  "tldr": "This paper examines how Large Language Models (LLMs) summarize life narratives, highlighting biases related to race and gender that can misrepresent personal experiences. It proposes a pipeline to analyze these biases and their implications for qualitative research.",
  "whats_new": [
    "• Introduces a summarization-based pipeline to identify biases in LLM interpretations of life narratives.",
    "• Collaborates with psychologists to evaluate LLM-generated summaries against human interpretations.",
    "• Finds significant race and gender biases in LLM summaries that can lead to representational harm.",
    "• Proposes the concept of a 'positionality portrait' for LLMs to assess their interpretative biases."
  ],
  "method": "The study analyzes a dataset of life story interviews using LLMs to generate summaries. It employs quantitative metrics and expert evaluations to assess biases in wording, psychological states, and themes across different demographic groups.",
  "results": [
    "• LLMs show significant differences in semantic similarity across demographic groups, with white men receiving the highest scores.",
    "• Black men exhibit the lowest BERTScores, indicating poorer semantic alignment in summaries.",
    "• Summaries for white individuals are rated as more faithful and competent than those for Black individuals.",
    "• The pipeline successfully identifies biases in emotional expression, particularly affecting men's narratives."
  ],
  "limitations": [
    "• The study is based on a single dataset, limiting generalizability.",
    "• Only three open-source LLMs were evaluated, which may not represent all models.",
    "• The qualitative analysis relies on a small number of expert evaluations, which may introduce bias."
  ],
  "why_useful": "This research is crucial for understanding the ethical implications of using LLMs in qualitative analysis. It provides a framework for researchers to assess and mitigate biases in LLM-generated interpretations, enhancing the integrity of qualitative research.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code & data available at https://arxiv.org/abs/2604.20131v1"
  }
}
            

arXiv Page | PDF

Published: 2026-04-22 02:57:30

Authors: Kuan-Yu Lin, Yu-Chih Huang, Tie Liu

Categories: cs.LG, cs.CV

Abstract:
Mode collapse remains a fundamental challenge in training generative adversarial networks (GANs). While existing works have primarily focused on inter-mode collapse, such as mode dropping, intra-mode collapse-where many latent variables map to the same or highly similar outputs-has received significantly less attention. In this work, we propose a pairing regularizer jointly optimized with the generator to mitigate the many-to-one collapse by enforcing local consistency between latent variables and generated samples. We show that the effect of pairing regularization depends on the dominant failure mode of training. In collapse-prone regimes with limited exploration, pairing encourages structured local exploration, leading to improved coverage and higher recall. In contrast, under stabilized training with sufficient exploration, pairing refines the generator's induced data density by discouraging redundant mappings, thereby improving precision without sacrificing recall. Extensive experiments on both toy distributions and real-image benchmarks demonstrate that the proposed regularizer effectively complements existing stabilization techniques by directly addressing intra-mode collapse.

            {
  "tldr": "This paper introduces pairing regularization to address many-to-one collapse in GANs, a form of collapse where multiple latent variables produce similar outputs. The method enhances intra-mode diversity and improves the generator's mapping from latent to data space.",
  "whats_new": [
    "• Identifies many-to-one collapse as a distinct failure mode in GANs.",
    "• Proposes pairing regularization to enforce local consistency between latent variables and generated samples.",
    "• Demonstrates that pairing regularization improves coverage and precision in GAN outputs.",
    "• Provides empirical evidence across toy and real datasets, highlighting limitations of existing metrics."
  ],
  "method": "The authors introduce pairing regularization as a constraint on the generator mapping in GANs. This regularization encourages local correspondence between latent variables and their generated outputs, mitigating many-to-one collapse. The method is optimized alongside standard GAN objectives.",
  "results": [
    "• Pairing regularization improves recall from 0.184 to 0.494 and coverage from 0.083 to 0.483 in Gaussian mixture experiments.",
    "• In CIFAR-10 experiments, Pairing-GAN achieves a coverage of 0.731, outperforming StyleGAN2's 0.637.",
    "• Combining pairing regularization with gradient penalties yields a recall of 0.957 and coverage of 0.861.",
    "• Pairing regularization consistently enhances intra-mode diversity without sacrificing precision."
  ],
  "limitations": [
    "• The effectiveness of pairing regularization may be modest in highly stabilized training regimes with data augmentation.",
    "• The study primarily focuses on specific datasets, leaving generalizability to other contexts uncertain.",
    "• Further exploration is needed to quantify the optimal strength of pairing regularization."
  ],
  "why_useful": "This paper addresses a critical issue in GAN training, enhancing the diversity of generated samples. The findings can improve applications in image synthesis and other generative tasks, ensuring models not only match data distributions but also maintain meaningful variations.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-04-22 02:16:16

Authors: Xuelin Zhang, Xinyue Liu, Lingjuan Wu, Hong Chen

Categories: cs.LG, cs.AI, stat.ML

Abstract:
Sparse additive models have attracted much attention in high-dimensional data analysis due to their flexible representation and strong interpretability. However, most existing models are limited to single-level learning under the mean-squared error criterion, whose empirical performance can degrade significantly in the presence of complex noise, such as non-Gaussian perturbations, outliers, noisy labels, and imbalanced categories. The sample reweighting strategy is widely used to reduce the model's sensitivity to atypical data; however, it typically requires prespecifying the weighting functions and manually selecting additional hyperparameters. To address this issue, we propose a new meta additive model (MAM) based on the bilevel optimization framework, which learns data-driven weighting of individual losses by parameterizing the weighting function via an MLP trained on meta data. MAM is capable of a variety of learning tasks, including variable selection, robust regression estimation, and imbalanced classification. Theoretically, MAM provides guarantees on convergence in computation, algorithmic generalization, and variable selection consistency under mild conditions. Empirically, MAM outperforms several state-of-the-art additive models on both synthetic and real-world data under various data corruptions.

arXiv Page | PDF

Published: 2026-04-22 02:15:59

Authors: Abdul Rahman

Categories: math.AG, hep-th

Abstract:
In previous work, we extracted the intrinsic finite algebraic state data of a finite-node conifold degeneration in the form $A_Σ:= (V_Σ,E_Σ,c_Σ)$, where $V_Σ$ is the finite node-indexed vertex set, $E_Σ$ is the nodewise coupling space, and $c_Σ$ is the coefficient vector of the corrected global extension class. The purpose of the present paper is to construct the corresponding interaction and incidence layer. Starting from the finite-node schober package $S_Σ:= (\mathcal C_{\mathrm{bulk}},\{\mathcal C_{p_k}\}_{k=1}^r,\{Φ_k,Ψ_k\}_{k=1}^r,Sh(S_Σ))$, we define the extended vertex set $V_Σ^{\mathrm{ext}} := V_Σ\sqcup \{v_{\mathrm{bulk}}\}$, the functorial coupling relation determined by the attachment functors, the resulting functorial incidence package $\mathfrak{I}_Σ:= (V_Σ^{\mathrm{ext}},\rightsquigarrow_Σ)$, and its canonical binary decategorification $\mathcal I_Σ:= (V_Σ^{\mathrm{ext}},I_Σ)$. From these data we assemble the finite quiver-theoretic package $\mathfrak Q_Σ:= (V_Σ,E_Σ,c_Σ,\mathcal F_Σ,I_Σ)$, where $\mathcal F_Σ:= \{(Φ_k,Ψ_k)\}_{k=1}^r$ is the functorial coupling datum. We prove that this package is canonically determined by the finite-node schober datum, compatible with the corrected perverse extension and its mixed-Hodge-module refinement, and invariant under equivalence of finite-node schober realizations. This yields the interaction and incidence layer required for later graded interaction, stability, BPS, and wall-crossing structures.

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Published: 2026-04-22 02:03:44

Authors: Md Moid Shaikh, Sourav Kanti Patra, Mukesh Kumar

Categories: math.CO

Abstract:
Recently S. Goswami proved that whenever the set $\mathbb N$ of natural numbers is finitely colored, the set $\{a, b, ab, b(a+1)\}$ is monochromatic which also established a variant of the long-standing Hindman's conjecture, which asks for a monochromatic set of the form $\{a, b, ab, a+b\}$. Actually he disproved a conjecture proposed by J. Sahasrabudhe that $\{a, b, a(b + 1)\}$ is not partition regular. In this paper we prove that $\{a, b, ab, b(a+1)\}$ is monochromatic near zero which means for every finite coloring of a dense subsemigroups of $((0, \infty), +)$, the set $\{a, b, ab, b(a+1)\}$ is monochromatic near zero or in other words, we will get $a, b$ in a dense subsemigroups of $((0, \infty), +)$ as small as we want such that the set $\{a, b, ab, b(a+1)\}$ is monochromatic for every finite coloring of that dense subsemigroups of $((0, \infty), +)$, also we show that the pattern $x, y, x+y, \frac{y}{x}$ is partition regular near zero.

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Published: 2026-04-22 01:08:38

Authors: Xin Wei Lee, Hoong Chuin Lau

Categories: quant-ph

Abstract:
Constrained combinatorial optimization problems are frequently reformulated as quadratic unconstrained binary optimization (QUBO) models in order to leverage emerging quantum optimization algorithms such as the Variational Quantum Eigensolver (VQE) and the Quantum Approximate Optimization Algorithm (QAOA). However, standard QUBO formulations enforce inequality constraints through slack variables and quadratic penalties, which can significantly increase the problem size and distort the optimization landscape. In this work, we propose a slack-free penalty formulation for constrained binary optimization that eliminates auxiliary slack variables and preserves the feasibility structure of the original problem. The proposed approach introduces a nonlinear custom penalty function to enforce inequality constraints directly in the objective function. To address the computational challenges associated with evaluating nonlinear penalties in variational quantum algorithms, we employ the finite-sampling method that avoids the exponential complexity required by exact expectation computation. Furthermore, we integrate the Conditional Value-at-Risk (CVaR) objective to improve optimization robustness and guide the search toward high-quality solutions. The proposed framework is evaluated on instances of the multi-dimensional knapsack problem, a classical benchmark in combinatorial optimization. We showcase that the proposed custom-penalty formulation combined with CVaR sampling achieves improved optimality gaps and more consistent performance compared with conventional slack-based QUBO formulations. The results suggest that careful penalty design can play a critical role in enabling quantum and hybrid quantum-classical algorithms for constrained optimization problems that arise in operations research.

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Published: 2026-04-22 01:07:37

Authors: Shanshan Zhong, Yi Lu, Jingjie Ning, Yibing Wan, Lihan Feng, Yuyi Ao, Leonardo F. R. Ribeiro, Markus Dreyer, Sean Ammirati, Chenyan Xiong

Categories: cs.CL, cs.LG

Abstract:
Skills have become the de facto way to enable LLM agents to perform complex real-world tasks with customized instructions, workflows, and tools, but how to learn them automatically and effectively remains unclear. We introduce SkillLearnBench, the first benchmark for evaluating continual skill learning methods, comprising 20 verified, skill-dependent tasks across 15 sub-domains derived from a real-world skill taxonomy , evaluated at three levels: skill quality, execution trajectory, and task outcome. Using this benchmark, we evaluate recent continual learning techniques, those leveraging one-shot, self/teacher feedback, and skill creator to generate skills from agent experiences. We find that all continual learning methods improve over the no-skill baseline, yet consistent gains remain elusive: no method leads across all tasks and LLMs, and scaling to stronger LLMs does not reliably help. Continual learning improves tasks with clear, reusable workflows but struggles on open-ended tasks, and using stronger LLM backbones does not consistently produce better skills. Our analysis also revealed that multiple iterations in continual learning facilitate genuine improvement via external feedback, whereas self-feedback alone induces recursive drift. Our data and code are open-source at https://github.com/cxcscmu/SkillLearnBench to enable further studies of automatic skill generation and continual learning techniques.

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Published: 2026-04-22 00:57:03

Authors: Srujan Kumar Gandla

Categories: cs.DC

Abstract:
AWS Lambda terminates containers with an uncatchable SIGKILL signal when a function exceeds its configured timeout. When a Spark-on-AWS-Lambda (SoAL) job is killed between Phase 1 (data upload) and Phase 2 (metadata commit) of a write, the result is silent data loss: orphaned Parquet files accumulate on S3 while the table's committed state remains unchanged and standard monitoring raises no alert. We characterize this vulnerability across Delta Lake and Apache Iceberg through 860 controlled kill-injection experiments at three dataset sizes. A SIGKILL landing in the inter-phase gap produced silent data loss in 100% of trials for both formats. We then present SafeWriter, a language-level wrapper that arms a watchdog thread 30 seconds before the Lambda timeout, triggers a format-native rollback via SQL, and records a checkpoint document on S3. SafeWriter converted every tested kill scenario into a clean, detectable rollback with under 100 ms added to normal write paths.

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Published: 2026-04-22 00:32:35

Authors: Sadra Sabouri, Zeinabsadat Saghi, Run Huang, Sujay Maladi, Esmeralda Eufracio, Sumit Gulwani, Souti Chattopadhyay

Categories: cs.HC, cs.AI, cs.CE

Abstract:
Advances in AI agent capabilities have outpaced users' ability to meaningfully oversee their execution. AI agents can perform sophisticated, multi-step knowledge work autonomously from start to finish, yet this process remains effectively inaccessible during execution, often buried within large volumes of intermediate reasoning and outputs: by the time users receive the output, all underlying decisions have already been made without their involvement. This lack of transparency leaves users unable to examine the agent's assumptions, identify errors before they propagate, or redirect execution when it deviates from their intent. The stakes are particularly high in spreadsheet environments, where process and artifact are inseparable. Each decision the agent makes is recorded directly in cells that belong to and reflect on the user. We introduce Pista, a spreadsheet AI agent that decomposes execution into auditable, controllable actions, providing users with visibility into the agent's decision-making process and the capacity to intervene at each step. A formative study (N = 8) and a within-subjects summative evaluation (N = 16) comparing Pista to a baseline agent demonstrated that active participation in execution influenced not only task outcomes but also users' comprehension of the task, their perception of the agent, and their sense of role within the workflow. Users identified their own intent reflected in the agent's actions, detected errors that post-hoc review would have failed to surface, and reported a sense of co-ownership over the resulting output. These findings indicate that meaningful human oversight of AI agents in knowledge work requires not improved post-hoc review mechanisms, but active participation in decisions as they are made.

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Published: 2026-04-21 23:51:00

Authors: Saloni Garg, Amit Sagtani, Kamal Kant Hiran

Categories: cs.LG, cs.CR, cs.DC

Abstract:
The rise of IoT devices and the uptake of cloud computing have informed a new era of data-driven intelligence. Traditional centralized machine learning models that require a large volume of data to be stored in a single location have therefore become more susceptible to data breaches, privacy violations, and regulatory non-compliance. This report presents a thorough examination of the merging of Federated Learning (FL) and blockchain technology in a cloud-edge setting, demonstrating it as an effective solution to the stated concerns. We are proposing a detailed four-dimensional architectural categorization that meticulously assesses coordination frameworks, consensus algorithms, data storage practices, and trust models that are significant to these integrated systems. The manuscript presents a comprehensive comparative examination of two cutting-edge frameworks: the Multi-Objectives Reinforcement Federated Learning Blockchain (MORFLB), which is designed for intelligent transportation systems, and the Federated Blockchain-IoT Framework for Sustainable Healthcare Systems (FBCI-SHS), elucidating their distinctive contributions and inherent limitations. Lastly, we engage in a thorough evaluation of the literature that integrates a comparative perspective on current frameworks to discern the singular nature of this research within existing knowledge systems. The manuscript culminates in delineating the principal challenges and offering a strategic framework for prospective research trajectories, emphasizing the advancement of adaptive, resilient, and standardized BCFL systems across diverse application domains.

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Published: 2026-04-21 23:32:01

Authors: Toby Brown, Luca Cortese, Barbara Catinella, A. Fraser-McKelvie, Adam B. Watts, Amirnezam Amiri, Alessandro Boselli, Woorak Choi, Aeree Chung, Timothy A. Davis, Eric Emsellem, Pavel Jáchym, María J. Jiménez-Donaire, Tutku Kolcu, Bumhyun Lee, Andrei Ristea, Jesse van de Sande, Kristine Spekkens, Sabine Thater, Christine D. Wilson, Nikki Zabel

Categories: astro-ph.GA

Abstract:
We present early science results from the MAUVE (Multiphase Astrophysics to Unveil the Virgo Environment) program which targets 40 Virgo Cluster galaxies to investigate the effect of environment on the interstellar medium (ISM) at ~100 pc scales. From 12 galaxies in the MAUVE-MUSE early sample, we find systematically elevated line ratios compared to PHANGS-MUSE field disks, with higher medians of [N II]/H$α$ (0.75 vs. 0.50), [S II]/H$α$ (0.57 vs. 0.49), and [O III]/H$β$ (1.04 vs. 0.68). Spatially resolved BPT diagrams show 74% of MAUVE-MUSE spaxels ionized by sources other than H II regions, versus 61% in the field, and we find these ionization differences to be closely coupled to broadened kinematics. 44% of MAUVE-MUSE spaxels exceed H$α$ $σ_{LOS} = 40$ km/s (vs. 26% in the field), driven mainly by non-star-forming gas with $σ_{LOS}$ between 40 and 80 km/s, consistent with enhanced contribution of diffuse ionized gas (DIG). A subdominant tail of 5% of spaxels at $σ_{LOS} > 100$ km/s, largely absent in PHANGS-MUSE (1%), points to shocks or turbulent mixing layers from intracluster interactions. Our results show that environmental quenching primarily suppresses star formation, unveiling DIG as the dominant ionized component in cluster disks. The elevated line ratios and broadened kinematics observed in the MAUVE sample reflect the physical state of the ISM in the absence of vigorous star formation, rather than widespread direct environmental excitation. The observed shock-like emission provides an additional, secondary contribution likely driven by active interactions with the intracluster medium.

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Published: 2026-04-21 23:31:48

Authors: Patrick Vossler, Jean Feng, Venkat Sivaraman, Robert Gallo, Hemal Kanzaria, Dana Freiser, Christopher Ross, Amy Ou, James Marks, Susan Ehrlich, Christopher Peabody, Lucas Zier

Categories: cs.AI, cs.HC

Abstract:
Hospital Quality Improvement (QI) plays a critical role in optimizing healthcare delivery by translating high-level hospital goals into actionable solutions. A critical step of QI is to identify the key modifiable contributing factors, a process we call QI factor discovery, typically through expert-driven semi-structured qualitative tools like fishbone diagrams, chart reviews, and Lean Healthcare methods. AI has the potential to transform and accelerate QI factor discovery, which is traditionally time- and resource-intensive and limited in reproducibility and auditability. Nevertheless, current AI alignment methods assume the task is well-defined, whereas QI factor discovery is an exploratory, fuzzy, and iterative sense-making process that relies on complex implicit expert judgments. To design an AI pipeline that formalizes the QI process while preserving its exploratory components, we propose viewing the task as learning not only LLM prompts but also the overarching natural-language specifications. In particular, we map QI factor discovery to steps of the classical AI/ML development process (problem formalization, model learning, and model validation) where the specifications are tunable hyperparameters. Domain experts and AI agents iteratively refine both the overarching specifications and AI pipeline until AI extractions are concordant with expert annotations and aligned with clinical objectives. We applied this "Human-AI Spec-Solution Co-optimization" framework at an urban safety-net hospital to identify factors driving prolonged length of stay and unplanned 30-day readmissions. The resulting AI-for-QI pipelines achieved $\ge 70\%$ concordance with expert annotations. Compared to prior manual Lean analyses, the AI pipeline was substantially more efficient, recovered previous findings, surfaced new modifiable factors, and produced auditable reasoning traces.

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Published: 2026-04-21 23:29:43

Authors: Shirin Afzal, Amesh Kahloon, Shabir Barzanjeh

Categories: physics.optics

Abstract:
The realization of on-chip polarization beam splitters robust to fabrication imperfections remains a key challenge for polarization-sensitive photonic integration. We demonstrate a topologically protected polarization beam splitter based on a Floquet-engineered microring lattice implemented on a CMOS-compatible silicon nitride platform. By tailoring the dispersion of inter-ring coupling, the lattice supports complementary trivial and topological band gaps for orthogonal eigenpolarizations. At telecom wavelengths, TE modes propagate via a topological edge state while TM modes are suppressed by a trivial gap; this behavior reverses at shorter wavelengths. We measure extinction ratios of 16-20 dB for the protected port and 10-20 dB for the non-protected port, with insertion loss of 2 dB at long wavelengths. Reduced TM extinction at shorter wavelengths is attributed to suboptimal input coupling. We further identify spectral regions where both polarizations exhibit nontrivial band gaps, enabling polarization-independent edge transport and establishing a Floquet dual-polarization topological regime. Because operation is governed by band topology rather than geometric fine-tuning, the device shows intrinsic robustness to defects. These results establish polarization-tailored topological lattices as a scalable platform for robust beam splitting, routing, and interconnects in classical and quantum photonic systems.

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Published: 2026-04-21 22:52:26

Authors: Tianrong Chen, Jiatao Gu, David Berthelot, Joshua Susskind, Shuangfei Zhai

Categories: cs.CV, cs.AI

Abstract:
Normalizing Flows (NFs) are a classical family of likelihood-based methods that have received revived attention. Recent efforts such as TARFlow have shown that NFs are capable of achieving promising performance on image modeling tasks, making them viable alternatives to other methods such as diffusion models. In this work, we further advance the state of Normalizing Flow generative models by introducing iterative TARFlow (iTARFlow). Unlike diffusion models, iTARFlow maintains a fully end-to-end, likelihood-based objective during training. During sampling, it performs autoregressive generation followed by an iterative denoising procedure inspired by diffusion-style methods. Through extensive experiments, we show that iTARFlow achieves competitive performance across ImageNet resolutions of 64, 128, and 256 pixels, demonstrating its potential as a strong generative model and advancing the frontier of Normalizing Flows. In addition, we analyze the characteristic artifacts produced by iTARFlow, offering insights that may shed light on future improvements. Code is available at https://github.com/apple/ml-itarflow.

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Published: 2026-04-21 22:45:20

Authors: Haitao Huang, Shin-Fang Chng, Huangying Zhan, Qingan Yan, Yi Xu

Categories: cs.CV

Abstract:
Recent advances in text-guided image editing and 3D Gaussian Splatting (3DGS) have enabled high-quality 3D scene manipulation. However, existing pipelines rely on iterative edit-and-fit optimization at test time, alternating between 2D diffusion editing and 3D reconstruction. This process is computationally expensive, scene-specific, and prone to cross-view inconsistencies. We propose a feed-forward framework for cross-view consistent 3D scene editing from sparse views. Instead of enforcing consistency through iterative 3D refinement, we introduce a cross-view regularization scheme in the image domain during training. By jointly supervising multi-view edits with geometric alignment constraints, our model produces view-consistent results without per-scene optimization at inference. The edited views are then lifted into 3D via a feedforward 3DGS model, yielding a coherent 3DGS representation in a single forward pass. Experiments demonstrate competitive editing fidelity and substantially improved cross-view consistency compared to optimization-based methods, while reducing inference time by orders of magnitude.

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Published: 2026-04-21 22:38:28

Authors: Ryotaro Chiba, Semih Tuna, Brian D. Metzger, Takashi J. Moriya

Categories: astro-ph.HE, astro-ph.SR

Abstract:
Around 10 % of hydrogen-poor supernovae explode inside compact ($\sim 10^{15}$ cm), massive ($\sim 0.1 \ \mathrm{M_\odot}$) circumstellar material (CSM), signalling an episode of enhanced pre-explosion mass loss whose mechanism remains unclear. The extreme members of this population are considered to constitute some of the Fast Blue Optical Transients (FBOTs), which exhibit rapid rise times of $\sim$ few days and high peak luminosity $\sim 10^{44} \ \mathrm{erg}$. Recent binary evolution calculations show that the expansion of helium stars during their latest evolutionary stages can trigger a rapid but stable mass-transfer episode that can form a dense circumbinary disc (CBD) that may explain the observed dense CSM. However, a detailed, quantitative analysis of this process and the resulting CBD properties such as its mass, radius and density profile has not yet been undertaken. We present a set of models that solve the viscous evolution of such a CBD under time-dependent mass injection. We find that although the injected mass is initially sub-Keplerian, a lower ``accretion eigenvalue'' $χ$ prevents more mass from falling back onto the central binary. For our fiducial set of models, the CBD immediately prior to the explosion reaches a mass of $0.07-0.20 \ \mathrm{M}_\odot$, a half-mass radius of $640 - 4000 \ \mathrm{R}_\odot$, and an aspect ratio of $θ= H/R \sim 0.1$. We also show that the interaction between SN ejecta and the CBD can power some of the fastest-evolving interacting Type Ibc SNe that can be classified as FBOTs, such as SN 2018gep or SN 2019jc. Despite uncertainties in the model parameters, our results demonstrate that CBD formation triggered by rapid, stable mass transfer is a viable mechanism to explain the dense circumstellar environments observed around rapid, hydrogen-poor interacting SNe. (abridged)

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Published: 2026-04-21 22:22:39

Authors: Konstantinos Ziliaskopoulos, Alexander Vinel

Categories: math.OC, cs.LG, stat.ML

Abstract:
We consider what we refer to as {Decision-Focused Federated Learning (DFFL)} framework, i.e., a predict-then-optimize approach employed by a collection of agents, where each agent's predictive model is an input to a downstream linear optimization problem, and no direct exchange of raw data is allowed. Importantly, clients can differ both in objective functions and in feasibility constraints. We build on the well-known SPO+ approach and develop heterogeneity bounds for the SPO+ surrogate loss in this case. This is accomplished by employing a support function representation of the feasible region, separating (i) objective shift via norm distances between the cost vectors and (ii) feasible-set shift via shape distances between the constraint sets. In the case of strongly convex feasible regions, sharper bounds are derived due to the optimizer stability. Building on these results, we define a heuristic local-versus-federated excess risk decision rule which, under SPO+ risk, gives a condition for when federation can be expected to improve decision quality: the heterogeneity penalty must be smaller than the statistical advantage of pooling data. We implement a FedAvg-style DFFL set of experiments on both polyhedral and strongly convex problems and show that federation is broadly robust in the strongly convex setting, while performance in the polyhedral setting degrades primarily with constraint heterogeneity, especially for clients with many samples. In other words, especially for the strongly convex case, an approach following a direct implementation of FedAvg and SPO+ can still yield promising performance even when the downstream optimization problems are noticeably different.

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Published: 2026-04-21 22:21:15

Authors: Minghua Zheng, Na Helian, Peter C. R. Lane, Yi Sun, Allen Donald

Categories: cs.CV

Abstract:
Automated bacterial colony counting from images is an important technique to obtain data required for the development of vaccines and antibiotics. However, bacterial colonies present unique machine vision challenges that affect counting, including (1) small physical size, (2) object clustering, (3) high data annotation cost, and (4) limited cross-species generalisation. While FamNet is an established object counting technique effective for clustered objects and costly data annotation, its effectiveness for small colony sizes and cross-species generalisation remains unknown. To address the first three challenges, we propose ACFamNet, an extension of FamNet that handles small and clustered objects using a novel region of interest pooling with alignment and optimised feature engineering. To address all four challenges above, we introduce ACFamNet Pro, which augments ACFamNet with multi-head attention and residual connections, enabling dynamic weighting of objects and improved gradient flow. Experiments show that ACFamNet Pro achieves a mean normalised absolute error (MNAE) of 9.64% under 5-fold cross-validation, outperforming ACFamNet and FamNet by 2.23% and 12.71%, respectively.

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Published: 2026-04-21 22:13:13

Authors: Alexandre D. Leonelli, Lukas Widmer, Eckart Meiburg

Categories: physics.flu-dyn

Abstract:
Due to attractive inter-particle forces, cohesive particles suspended in turbulence undergo a complex process of aggregation, breakup, and restructuring. Despite a growing body of knowledge on the ``flocculation'' of cohesive granular materials suspended in homogeneous isotropic turbulence, little focus has so far been placed on wall-bounded flows where turbulence and shear are inhomogeneous. This study presents a first investigation of a fully developed wall-bounded flow of resolved cohesive particles. Five direct numerical simulations of turbulent channel flows laden with finite-sized particles at successively increasing cohesive strength are performed. A population balance equation (PBE) framework is used to analyze aggregate dynamics. When integrated over the full domain, the PBE is closed by aggregation and breakup alone. However, this balance is found to not hold locally in the wall-normal direction, where regions of net aggregate production and depletion are identified. This imbalance is shown to be compensated by the size-dependent wall-normal transport of aggregates, revealing a mean circulation: larger aggregates are preferentially produced in the channel center and migrate toward the wall where they break, while smaller aggregates are transported away from the wall, grow, and reenter the cycle.

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Published: 2026-04-21 22:12:56

Authors: Ethan Knights

Categories: cs.CV, cs.AI

Abstract:
For state-of-the-art image understanding, Vision Transformers (ViTs) have become the standard architecture but their processing diverges substantially from human attentional characteristics. We investigate whether this cognitive gap can be shrunk by fine-tuning the self-attention weights of Google's ViT-B/16 on human saliency fixation maps. To isolate the effects of semantically relevant signals from generic human supervision, the tuned model is compared against a shuffled control. Fine-tuning significantly improved alignment across five saliency metrics and induced three hallmark human-like biases: tuning reversed the baseline's anti-human large-object bias toward small-objects, amplified the animacy preference and diminished extreme attention entropy. Bayesian parity analysis provides decisive to very-strong evidence that this cognitive alignment comes at no cost to the model's original classification performance on in- (ImageNet), corrupted (ImageNet-C) and out-of-distribution (ObjectNet) benchmarks. An equivalent procedure applied to a ResNet-50 Convolutional Neural Network (CNN) instead degraded both alignment and accuracy, suggesting that the ViT's modular self-attention mechanism is uniquely suited for dissociating spatial priority from representational logic. These findings demonstrate that biologically grounded priors can be instilled as a free emergent property of human-aligned attention, to improve transformer interpretability.

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Published: 2026-04-21 22:11:20

Authors: Minghua Zheng, Na Helian, Peter C. R. Lane, Yi Sun, Allen Donald

Categories: cs.CV

Abstract:
Automatic bacterial colony counting is a highly sought-after technology in modern biological laboratories because it eliminates manual counting effort. Previous work has observed that MicrobiaNet, currently the best-performing cardinality classification model for colony counting, has difficulty distinguishing colonies of three or more individuals. However, it is unclear if this is due to properties of the data together with inherent characteristics of the MicrobiaNet model. By analysing MicrobiaNet with explainable artificial intelligence (XAI), we demonstrate that XAI can provide insights into how data properties constrain cardinality classification performance in colony counting. Our results show that high visual similarity across classes is the key issue hindering further performance improvement, revising prior assertions about MicrobiaNet. These findings suggest future work should focus on models that explicitly incorporate visual similarity or explore density estimation approaches, with broader implications for neural network classifiers trained on imbalanced datasets.

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Published: 2026-04-21 22:07:04

Authors: Eberhard Bänsch, Pedro Morin, Itatí Zocola

Categories: math.NA

Abstract:
We present error estimates for the BMZ (Bubble Mesh Zoom) residual-free bubble method applied to a convection-diffusion equation in the convection-dominated regime. The method incorporates both element bubbles and residual-free bubbles supported on patches of two adjacent elements. We focus on the case of a parallel flow in a square domain and derive error estimates in an energy norm that remain valid as diffusion becomes small. The theoretical findings are corroborated by numerical experiments, which also exhibit a competitive performance of the method.

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Published: 2026-04-21 21:53:45

Authors: Gang Chen, Wenyi Liu, Yangwen Zhang

Categories: math.NA

Abstract:
We develop a family of $H(\mathrm{div})$-conforming hybridizable discontinuous Galerkin methods for the steady Stokes equations based on BDM and RT velocity spaces with either discontinuous or continuous hybrid traces. In contrast to our earlier pressure-robust HDG method for tangential boundary control, the present analysis does not require the pressure to belong to $H^1$; instead, the consistency argument only assumes low pressure regularity. The discrete velocities are exactly divergence-free, which yields pressure robustness. For the BDM variants we derive optimal energy-norm estimates and optimal $L^2$-velocity convergence, while for the RT variants we obtain optimal velocity convergence and weaker pressure estimates. We also analyze the hybridized linear system and prove a uniform spectral equivalence for the pressure Schur complement relevant to iterative solvers. As an application, we revisit the Stokes tangential boundary control problem and derive error estimates for the control, state, and adjoint variables using the BDM discontinuous-trace scheme. Two- and three-dimensional numerical experiments confirm the predicted convergence rates, the exact divergence-free property, and the robustness of the method with respect to the viscosity parameter.

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Published: 2026-04-21 20:56:52

Authors: Conor Flynn, Radoslav Ivanov, Birsen Yazici

Categories: cs.CV

Abstract:
With modern defense applications increasingly relying on inexpensive, small Unmanned Aerial Vehicles (UAVs), a major challenge lies in designing intelligent and computationally efficient onboard Automatic Target Recognition (ATR) algorithms to carry out operational objectives. This is especially critical in Synthetic Aperture Radar (SAR), where processing techniques such as ATR are often carried out post data collection, requiring onboard systems to bear the memory burden of storing the back-scattered signals. To alleviate this high cost, we propose an online, direct, edge-mapping technique which bypasses the image reconstruction step to classify scenes and targets. Furthermore, by reconstructing the scene as an edge-map we inherently promote sparsity, requiring fewer measurements and computational power than classic SAR reconstruction algorithms such as backprojection.

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Published: 2026-04-21 20:55:47

Authors: Devesh Nandal, Igor Chilingarian, Chris Nagele, John Chisholm, Franz E. Bauer, Abraham Loeb

Categories: astro-ph.HE, astro-ph.GA, astro-ph.SR

Abstract:
Little Red Dots (LRDs) have emerged as one of the central puzzles of the JWST era. Their spectra increasingly require dense gas close to the source, yet the physical origin of that cocoon-like structure remains unclear. We examine whether late pulsational mass loss from supermassive stars (SMS)leads to dense gas cocoons. We analyze five accreting GENEC models at different metallicities with characteristic masses of order $10^5\,M_\odot$, following them through post-accretion evolution with radial pulsation calculations and general relativistic (GR) stability diagnostics. Mass loss during the final stages of evolution occurs not as a steady wind, but through discrete strange-mode ejection episodes. In the $Z=10^{-2}\,Z_\odot$ model, which provides the clearest LRD analogue, four late episodes last $41$--$282$ yr and eject $10$--$348\,M_\odot$ each, for a total loss of $(4.8-10)\times10^2\,M_\odot$; the final episode alone contributes $\simeq 73\%$ of that budget. Since the last episode dominates the mass-loss, it is the only event sufficiently massive enough to leave behind a compact, optically thick shell extending out to 0.4 pc that reproduces the LRD dense gas cocoon. The final ejecta are H/He dominated but chemically distinctive, with a robust nitrogen-rich composition, $\log(\mathrm{N/O})\simeq0.13$ and $\log(\mathrm{C/O})\simeq-0.23$. The SMS reaches GR instability at an age of $\sim 1$ Myr and collapses in $\sim10^4$ s, retaining $\sim 99\%$ all of its mass. Across the full metallicity range from Pop III to $10^{-2}\,Z_\odot$, this shell-ejection channel persists. Pulsational mass-loss from SMSs therefore provides a physically motivated origin for the compact cocoon-like structure implied by LRDs, while remaining the natural progenitors of the massive black hole seeds invoked in direct collapse scenario.

arXiv Page | PDF

Published: 2026-04-21 20:40:59

Authors: Pierre-Cyril Aubin-Frankowski, Virginie Ehrlacher, Gabriele Todeschi

Categories: math.OC, math.FA

Abstract:
We study the notion of debiasability for cost functions arising in optimal transport. We call a symmetric cost function $c:\mathscr{X}\times\mathscr{X}\to\mathbb{R}\cup\{+\infty\}$ debiasable if it satisfies $c(x,y)\ge \tfrac{1}{2}c(x,x)+\tfrac{1}{2}c(y,y)$ for all $x,y\in\mathscr{X}$. Building on an equivalent characterization by an inf-representation $c(x,y)=\inf_{z\in\mathscr{Z}}ψ(x,z)+ψ(y,z)$ for some set $\mathscr{Z}$ and some function $ψ: \mathscr{X}\times \mathscr{Z} \to \mathbb{R} \cup \{+\infty\}$, interpreted as a generalization of the midpoint identity for squared geodesic distances, we investigate the debiasability of costs defined on spaces of probability measures. Our primary focus is the entropic regularization of optimal transport across different regimes of the regularization parameter $\varepsilon \in [0,+\infty]$, encompassing classical optimal transport ($\varepsilon=0$), entropic optimal transport ($\varepsilon>0$), and the Maximum Mean Discrepancy ($\varepsilon=+\infty$). For $\varepsilon \in (0,+\infty]$, we investigate sufficient conditions, such as negative definiteness of the ground cost or continuity and positive definiteness of the induced kernel, handled then via a convex-nonconcave minimax argument. All our results extend naturally to unbalanced optimal transport settings and we generalize in this way the findings of \cite{feydy2019interpolating} and \cite{sejourne2019sinkhorn}. As a byproduct, we derive novel decomposition formulas for entropic optimal transport, which may be of independent interest.

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Published: 2026-04-21 20:34:14

Authors: Het Patel, Tiejin Chen, Hua Wei, Evangelos E. Papalexakis, Jia Chen

Categories: cs.LG, cs.CL

Abstract:
Large language models can be uncertain yet correct, or confident yet wrong, raising the question of whether their output-level uncertainty and their actual correctness are driven by the same internal mechanisms or by distinct feature populations. We introduce a 2x2 framework that partitions model predictions along correctness and confidence axes, and uses sparse autoencoders to identify features associated with each dimension independently. Applying this to Llama-3.1-8B and Gemma-2-9B, we identify three feature populations that play fundamentally different functional roles. Pure uncertainty features are functionally essential: suppressing them severely degrades accuracy. Pure incorrectness features are functionally inert: despite showing statistically significant activation differences between correct and incorrect predictions, the majority produce near-zero change in accuracy when suppressed. Confounded features that encode both signals are detrimental to output quality, and targeted suppression of them yields a 1.1% accuracy improvement and a 75% entropy reduction, with effects transferring across the ARC-Challenge and RACE benchmarks. The feature categories are also informationally distinct: the activations of just 3 confounded features from a single mid-network layer predict model correctness (AUROC ~0.79), enabling selective abstention that raises accuracy from 62% to 81% at 53% coverage. The results demonstrate that uncertainty and correctness are distinct internal phenomena, with implications for interpretability and targeted inference-time intervention.

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Published: 2026-04-21 20:28:05

Authors: Sarah Cattan, Antonio Dalla-Zuanna, Jan Stuhler, Po Yin Wong

Categories: econ.GN

Abstract:
Standard intergenerational measures have been shown to understate the long-run persistence of socioeconomic advantages in developed countries. We study theoretically and empirically whether this pattern extends to less developed settings, using Indonesia as a case study. Using the Indonesian Family Life Survey (IFLS) and Census data, we study multigenerational correlations in education across three generations. Contrary to previous findings, we observe greater multigenerational mobility than parent-child correlations alone would suggest. We develop a theoretical framework to highlight two key factors influencing multigenerational dynamics in developing countries: (1) financial and credit constraints, and (2) cultural norms related to marital sorting. To confirm their relevance, we exploit regional variations in exposure to the 1997-98 Asian financial crisis and in marital customs.

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Published: 2026-04-21 20:24:33

Authors: M. A. Burlak, A. V. Dodin, A. V. Zharova, N. P. Ikonnikova, V. A. Kiryukhina, S. A. Lamzin, D. A. Lashin, B. S. Safonov

Categories: astro-ph.SR

Abstract:
In the vicinity of the young star FN Tau, we have detected a microjet and four Herbig-Haro objects, whose positions and kinematics indicate the presence of a bipolar collimated outflow from the star - HH 1267. The stellar jet does not propagate rectilinearly, and we discuss the possibility that the curved shape of the jet, whose axis is inclined to the line of sight at an angle $<20^\circ$, results from the precession of the inner regions of the FN Tau protoplanetary disk. Approximately 60 years ago, the star underwent outbursts with an amplitude of $Δm_{\rm pg} \sim 2^{\rm m}$ lasting several months, which we associate with the onset of the microjet.

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Published: 2026-04-21 19:28:08

Authors: Palawat Busaranuvong, Reza Saadati Fard, Emmanuel Agu, Deepak Kumar, Shefalika Gautam, Bengisu Tulu, Diane Strong

Categories: cs.CV, cs.AI

Abstract:
Assessing chronic wound infection from photographs is challenging because visual appearance varies across wound etiologies, anatomical locations, and imaging conditions. Prior image-based deep learning methods have mainly focused on classification with limited interpretability, despite the need for evidence-grounded explanations to support point-of-care decision making. We present Infection-Reasoner, a compact 4B-parameter reasoning vision-language model for chronic wound infection classification and rationale generation. To address the scarcity of expert-labeled wound images with reasoning annotations, Infection-Reasoner is trained using a two-stage pipeline: (1) reasoning distillation, in which GPT-5.1 generates chain-of-thought rationales for unlabeled wound images to initialize wound-specific reasoning in a smaller student model (Qwen3-VL-4B-Thinking), and (2) reinforcement learning post-training with Group Relative Policy Optimization on a small labeled infection dataset to refine classification reasoning. On a held-out heterogeneous wound dataset, Infection-Reasoner achieved 86.8\% accuracy, 86.4\% sensitivity, and 87.1\% specificity, outperforming several strong baselines, including GPT-5.1. Rationale quality was further evaluated using both multimodal large language model (MLLM) judges and wound expert review. Across four MLLM judges, visual-support agreement scores ranged from 0.722 to 0.903, while expert review rated 61.8\% of rationales as Correct and 32.4\% as Partially Correct.

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Published: 2026-04-21 19:18:07

Authors: Ryusei R. Kano, Tsutomu T. Takeuchi, Erina R. Kawamoto, Ryosuke S. Asano, Masato Hagimoto, Yoichi Tamura

Categories: astro-ph.GA

Abstract:
Dust plays a crucial role in galaxy evolution by shaping the spectral energy distribution (SED) and star formation history. However, standard models often underestimate the infrared luminosity of high-redshift galaxies ($z \sim 8$), leading to the so-called dust budget crisis. In this work, we modify the theoretical framework by focusing on compact star-forming clumps in the interstellar medium. Motivated by the observed compactness of high-z galaxies, we treat the cold neutral medium density as a free parameter. Our analysis reveals that the ISM must reach extreme densities ($n_{\text{H,CNM}} \sim 7.5 \times 10^3 \, \mathrm{cm}^{-3}$). This enhances UV photon trapping, accelerates dust processing in dense gas, and reduces dust destruction by supernova shocks. Our model successfully reproduces the observed UV-to-FIR SED of MACS0416_Y1 ($z = 8.312$). A grain-size-resolved treatment further shows that the warm IR emission is dominated by intermediate-size grains ($a = 0.01$ - $0.1\,μ$m), which contribute about 89% of the luminosity near the SED peak and in the ALMA Band~9 continuum. These grains are nearly in thermal equilibrium at characteristic temperatures of $\sim 70$ K, while the largest grains remain cooler and the smallest grains exhibit a high-temperature tail with low probability. We conclude that extreme ISM densities can alleviate the dust budget crisis by promoting efficient UV photon trapping and rapid dust evolution, thereby increasing dust mass and producing a multi-temperature grain population.

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