Published: 2026-02-23 18:59:04
Authors: Zhenyao Ma, Yue Liang, Dongxu Li
Categories: cs.LG, cs.AI, stat.ML
Abstract:
Inspired by behavioral science, we propose Behavior Learning (BL), a novel general-purpose machine learning framework that learns interpretable and identifiable optimization structures from data, ranging from single optimization problems to hierarchical compositions. It unifies predictive performance, intrinsic interpretability, and identifiability, with broad applicability to scientific domains involving optimization. BL parameterizes a compositional utility function built from intrinsically interpretable modular blocks, which induces a data distribution for prediction and generation. Each block represents and can be written in symbolic form as a utility maximization problem (UMP), a foundational paradigm in behavioral science and a universal framework of optimization. BL supports architectures ranging from a single UMP to hierarchical compositions, the latter modeling hierarchical optimization structures. Its smooth and monotone variant (IBL) guarantees identifiability. Theoretically, we establish the universal approximation property of BL, and analyze the M-estimation properties of IBL. Empirically, BL demonstrates strong predictive performance, intrinsic interpretability and scalability to high-dimensional data. Code: https://github.com/MoonYLiang/Behavior-Learning ; install via pip install blnetwork.
Published: 2026-02-23 18:58:24
Authors: Wei-Cheng Huang, Jiaheng Han, Xiaohan Ye, Zherong Pan, Kris Hauser
Categories: cs.RO, cs.CV
Abstract:
Estimating simulation-ready scenes from real-world observations is crucial for downstream planning and policy learning tasks. Regretfully, existing methods struggle in cluttered environments, often exhibiting prohibitive computational cost, poor robustness, and restricted generality when scaling to multiple interacting objects. We propose a unified optimization-based formulation for real-to-sim scene estimation that jointly recovers the shapes and poses of multiple rigid objects under physical constraints. Our method is built on two key technical innovations. First, we leverage the recently introduced shape-differentiable contact model, whose global differentiability permits joint optimization over object geometry and pose while modeling inter-object contacts. Second, we exploit the structured sparsity of the augmented Lagrangian Hessian to derive an efficient linear system solver whose computational cost scales favorably with scene complexity. Building on this formulation, we develop an end-to-end real-to-sim scene estimation pipeline that integrates learning-based object initialization, physics-constrained joint shape-pose optimization, and differentiable texture refinement. Experiments on cluttered scenes with up to 5 objects and 22 convex hulls demonstrate that our approach robustly reconstructs physically valid, simulation-ready object shapes and poses.
Published: 2026-02-23 18:56:31
Authors: Andrei Galiautdinov
Categories: gr-qc
Abstract:
We introduce an exact, two-parameter family of static, spherically-symmetric, constant-curvature $Λ$-vacuum solutions within the four-dimensional Starobinsky $f(R)=R+αR^2$ model. When the bare cosmological constant is precisely fine-tuned to $Λ= 1/(8α)$, the scalar curvature is fixed such that the derivative $f'(R)=1+2αR$ identically vanishes, demonstrating that the family represents a pathological $R_0$-degenerate boundary of the viable physical states. This mathematical degeneracy decouples the modified field equations, permitting the existence of an arbitrary $1/r^2$ integration constant in the metric, which functions as a purely geometric, Reissner-Nordström hair mimicker. However, any infinitesimal deviation from this exact boundary instantaneously destroys the degeneracy, rigorously forcing the geometric hair to vanish and collapsing the spacetime back into the standard Schwarzschild-de Sitter family. We provide the exact algebraic derivation of this spacetime and highlight its physical pathologies, including the identically vanishing Wald entropy of the associated black hole horizons, the divergence of the effective gravitational coupling, the resulting backreaction catastrophe, and the onset of severe ghost instabilities. Ultimately, this exact solution functions as a rigorous no-go theorem within the Starobinsky model, pedagogically illustrating the extreme fragility and physical hostility of degenerate, purely mathematical solutions in highly non-linear $f(R)$ gravity theories.
Published: 2026-02-23 18:55:57
Authors: Kohta Murase, Shigeo S. Kimura, Mainak Mukhopadhyay, Mukul Bhattacharya
Categories: astro-ph.HE, astro-ph.GA, hep-ph
Abstract:
High-energy multimessenger background analyses over the past decade have provided evidence for a population of hidden neutrino sources that are opaque to GeV-TeV gamma rays, a picture bolstered by recent observations of the nearby active galaxy NGC 1068. The coronal regions in the hearts of active galactic nuclei (AGNs) have been proposed as the most promising sites for such hidden nonthermal particle production, and NGC 1068 is expected to be the most neutrino-active galaxy for IceCube. We demonstrate that the latest all-sky neutrino spectrum, exhibiting a spectral bend around 3-30 TeV, is consistent with predictions of the magnetically powered corona scenario, and the models for the all-sky neutrino flux can simultaneously explain the multimessenger data from NGC 1068 within observational and modeling uncertainties. We further show, in a largely model-independent way, that the contribution from NGC 1068-like sources does not overshoot the observed medium-energy neutrino flux. Finally, we highlight the key role of the Eddington ratio, which can drive substantial variations in the predicted neutrino fluxes of nearby AGNs, and we encourage systematic multimessenger searches for the neutrino-brightest AGNs.
Published: 2026-02-23 18:54:32
Authors: Zehao Wang, Mingzhe Han, Wei Cheng, Yue-Kai Huang, Philip Ji, Denton Wu, Mahdi Safari, Flemming Holtorf, Kenaish AlQubaisi, Norbert M. Linke, Danyang Zhuo, Yiran Chen, Ting Wang, Dirk Englund, Tingjun Chen
Categories: eess.SY, cs.AI, cs.NI
Abstract:
We present AgentOptics, an agentic AI framework for high-fidelity, autonomous optical system control built on the Model Context Protocol (MCP). AgentOptics interprets natural language tasks and executes protocol-compliant actions on heterogeneous optical devices through a structured tool abstraction layer. We implement 64 standardized MCP tools across 8 representative optical devices and construct a 410-task benchmark to evaluate request understanding, role-aware responses, multi-step coordination, robustness to linguistic variation, and error handling. We assess two deployment configurations--commercial online LLMs and locally hosted open-source LLMs--and compare them with LLM-based code generation baselines. AgentOptics achieves 87.7%--99.0% average task success rates, significantly outperforming code-generation approaches, which reach up to 50% success. We further demonstrate broader applicability through five case studies extending beyond device-level control to system orchestration, monitoring, and closed-loop optimization. These include DWDM link provisioning and coordinated monitoring of coherent 400 GbE and analog radio-over-fiber (ARoF) channels; autonomous characterization and bias optimization of a wideband ARoF link carrying 5G fronthaul traffic; multi-span channel provisioning with launch power optimization; closed-loop fiber polarization stabilization; and distributed acoustic sensing (DAS)-based fiber monitoring with LLM-assisted event detection. These results establish AgentOptics as a scalable, robust paradigm for autonomous control and orchestration of heterogeneous optical systems.
Published: 2026-02-23 18:53:09
Authors: Clarisse Wibault, Johannes Forkel, Sebastian Towers, Tiphaine Wibault, Juan Duque, George Whittle, Andreas Schaab, Yucheng Yang, Chiyuan Wang, Michael Osborne, Benjamin Moll, Jakob Foerster
Categories: cs.AI
Abstract:
Mean Field Games (MFGs) provide a principled framework for modeling interactions in large population models: at scale, population dynamics become deterministic, with uncertainty entering only through aggregate shocks, or common noise. However, algorithmic progress has been limited since model-free methods are too high variance and exact methods scale poorly. Recent Hybrid Structural Methods (HSMs) use Monte Carlo rollouts for the common noise in combination with exact estimation of the expected return, conditioned on those samples. However, HSMs have not been scaled to Partially Observable settings. We propose Recurrent Structural Policy Gradient (RSPG), the first history-aware HSM for settings involving public information. We also introduce MFAX, our JAX-based framework for MFGs. By leveraging known transition dynamics, RSPG achieves state-of-the-art performance as well as an order-of-magnitude faster convergence and solves, for the first time, a macroeconomics MFG with heterogeneous agents, common noise and history-aware policies. MFAX is publicly available at: https://github.com/CWibault/mfax.
Published: 2026-02-23 18:52:13
Authors: Akshay Subramanian, Elton Pan, Juno Nam, Maurice Weiler, Shuhui Qu, Cheol Woo Park, Tommi S. Jaakkola, Elsa Olivetti, Rafael Gomez-Bombarelli
Categories: physics.chem-ph
Abstract:
Organic molecular crystals underpin technologies ranging from pharmaceuticals to organic electronics, yet predicting solid-state packing of molecules remains challenging because candidate generation is combinatorial and stability is only resolved after costly energy evaluations. Here we introduce PackFlow, a flow matching framework for molecular crystal structure prediction (CSP) that generates heavy-atom crystal proposals by jointly sampling Cartesian coordinates and unit-cell lattice parameters given a molecular graph. This lattice-aware generation interfaces directly with downstream relaxation and lattice-energy ranking, positioning PackFlow as a scalable proposal engine within standard CSP pipelines. To explicitly steer generation toward physically favourable regions, we propose physics alignment, a reinforcement learning post-training stage that uses machine-learned interatomic potential energies and forces as stability proxies. Physics alignment improves physical validity without altering inference-time sampling. We validate PackFlow's performance against heuristic baselines through two distinct evaluations. First, on a broad unseen set of molecular systems, we demonstrate superior candidate generation capability, with proposals exhibiting greater structural similarity to experimental polymorphs. Second, we assess the full end-to-end workflow on two unseen CSP blind-test case studies, including relaxation and lattice-energy analysis. In both settings, PackFlow outperforms heuristics-based methods by concentrating probability mass in low-energy basins, yielding candidates that relax into lower-energy minima and offering a practical route to amortize the relax-and-rank bottleneck.
Published: 2026-02-23 18:47:51
Authors: Martin Sinnona, Valentin Bonas, Emmanuel Iarussi, Viviana Siless
Categories: cs.CV
Abstract:
Data visualization rules-derived from decades of research in design and perception-ensure trustworthy chart communication. While prior work has shown that large language models (LLMs) can generate charts or flag misleading figures, it remains unclear whether they can reason about and enforce visualization rules directly. Constraint-based systems such as Draco encode these rules as logical constraints for precise automated checks, but maintaining symbolic encodings requires expert effort, motivating the use of LLMs as flexible rule validators. In this paper, we present the first systematic evaluation of LLMs against visualization rules using hard-verification ground truth derived from Answer Set Programming (ASP). We translated a subset of Draco's constraints into natural-language statements and generated a controlled dataset of 2,000 Vega-Lite specifications annotated with explicit rule violations. LLMs were evaluated on both accuracy in detecting violations and prompt adherence, which measures whether outputs follow the required structured format. Results show that frontier models achieve high adherence (Gemma 3 4B / 27B: 100%, GPT-oss 20B: 98%) and reliably detect common violations (F1 up to 0.82),yet performance drops for subtler perceptual rules (F1 < 0.15 for some categories) and for outputs generated from technical ASP formulations.Translating constraints into natural language improved performance by up to 150% for smaller models. These findings demonstrate the potential of LLMs as flexible, language-driven validators while highlighting their current limitations compared to symbolic solvers.
Published: 2026-02-23 18:45:31
Authors: Mert Cemri, Shubham Agrawal, Akshat Gupta, Shu Liu, Audrey Cheng, Qiuyang Mang, Ashwin Naren, Lutfi Eren Erdogan, Koushik Sen, Matei Zaharia, Alex Dimakis, Ion Stoica
Categories: cs.NE, cs.AI, cs.CL
Abstract:
The paradigm of automated program generation is shifting from one-shot generation to inference-time search, where Large Language Models (LLMs) function as semantic mutation operators within evolutionary loops. While effective, these systems are currently governed by static schedules that fail to account for the non-stationary dynamics of the search process. This rigidity results in substantial computational waste, as resources are indiscriminately allocated to stagnating populations while promising frontiers remain under-exploited. We introduce AdaEvolve, a framework that reformulates LLM-driven evolution as a hierarchical adaptive optimization problem. AdaEvolve uses an "accumulated improvement signal" to unify decisions across three levels: Local Adaptation, which dynamically modulates the exploration intensity within a population of solution candidates; Global Adaptation, which routes the global resource budget via bandit-based scheduling across different solution candidate populations; and Meta-Guidance which generates novel solution tactics based on the previously generated solutions and their corresponding improvements when the progress stalls. We demonstrate that AdaEvolve consistently outperforms the open-sourced baselines across 185 different open-ended optimization problems including combinatorial, systems optimization and algorithm design problems.
Published: 2026-02-23 18:32:59
Authors: Yisi Liu, Nicholas Lee, Gopala Anumanchipalli
Categories: cs.SD, cs.AI
Abstract:
Voice style conversion aims to transform an input utterance to match a target speaker's timbre, accent, and emotion, with a central challenge being the disentanglement of linguistic content from style. While prior work has explored this problem, conversion quality remains limited, and real-time voice style conversion has not been addressed. We propose StyleStream, the first streamable zero-shot voice style conversion system that achieves state-of-the-art performance. StyleStream consists of two components: a Destylizer, which removes style attributes while preserving linguistic content, and a Stylizer, a diffusion transformer (DiT) that reintroduces target style conditioned on reference speech. Robust content-style disentanglement is enforced through text supervision and a highly constrained information bottleneck. This design enables a fully non-autoregressive architecture, achieving real-time voice style conversion with an end-to-end latency of 1 second. Samples and real-time demo: https://berkeley-speech-group.github.io/StyleStream/.
Published: 2026-02-23 18:32:11
Authors: M. Gage Plott, F. Ayca Cetinkaya, Rick Mukherjee
Categories: math.SP
Abstract:
Reconstructing a 1D quantum potential V(r) from a few bound-state energies is a long-standing inverse problem. We present a Laplace-moment reconstruction pipeline that ties the Bertlmann-Martin gap bound to generalized Bertlmann-Martin (GBM) even-moment ladders, continues the Laplace image with Pade approximants, and inverts the transform to recover rho(r) and V(r). Odd moments are supplied by a physically consistent interpolation scheme. The paper emphasizes mode ladders that isolate each approximation layer and a reproducibility spine that records benchmark settings and diagnostics. All numerical results are tied to archived configurations, and conclusions are reported empirically under the stated benchmark settings.
Published: 2026-02-23 18:14:13
Authors: Philippe Castillon, The-Cang Nguyen
Categories: gr-qc, math.AP, math.DG
Abstract:
Recent works by the second author and Dilts et al. have shown that the Einstein-scalar field conformal constraint equations are highly complex and generally intractable, even in the vacuum case. In this article, to gain a clearer understanding and offer a new perspective, we study these equations under special assumptions: the manifold $(M,g)$ is harmonic and data is radial.
In this setting, the system reduces to a single nonlinear equation and is completely resolved in the standard cases. In particular, on the sphere, our results reveal phenomena that contrast with the well-known achievements on compact manifolds without conformal Killing vector fields, including nonexistence of solutions in the near-CMC regime and instability when the mean curvature is non-constant. By contrast, on Euclidean or hyperbolic manifolds, the equations are always solvable, with all expected properties of solutions satisfied. These findings support the view that, although the conformal method appears to present some drawbacks on compact manifolds, it remains a promising tool for parametrizing solutions to the constraint equations on asymptotically flat and hyperbolic manifolds in arbitrary mean curvature regimes.
In this article, we also investigate the sign of mass, showing that the ADM and asymptotically hyperbolic mass can take arbitrary sign when the decay rate of symmetric $(0,2)$-tensor $k$ at infinity is critical. Finally, most solution classes in our framework are explicit, providing a variety of models in general relativity and offering insights into the structure and behavior of initial data, particularly in numerical applications.
Published: 2026-02-23 18:02:03
Authors: Weisong Dong
Categories: math.AP
Abstract:
We prove that every entire solution with quadratic growth, lying in a suitable cone, to the 2-Monge-Ampère equation on $\mathbb{R}^3$ is a quadratic polynomial. The proof proceeds by first establishing a concavity inequality, and then deriving a Pogorelov-type interior $C^2$ estimate.
Published: 2026-02-23 17:45:21
Authors: Xinya Chen, Christopher Wewer, Jiahao Xie, Xinting Hu, Jan Eric Lenssen
Categories: cs.CV
Abstract:
We present SemanticNVS, a camera-conditioned multi-view diffusion model for novel view synthesis (NVS), which improves generation quality and consistency by integrating pre-trained semantic feature extractors. Existing NVS methods perform well for views near the input view, however, they tend to generate semantically implausible and distorted images under long-range camera motion, revealing severe degradation. We speculate that this degradation is due to current models failing to fully understand their conditioning or intermediate generated scene content. Here, we propose to integrate pre-trained semantic feature extractors to incorporate stronger scene semantics as conditioning to achieve high-quality generation even at distant viewpoints. We investigate two different strategies, (1) warped semantic features and (2) an alternating scheme of understanding and generation at each denoising step. Experimental results on multiple datasets demonstrate the clear qualitative and quantitative (4.69%-15.26% in FID) improvement over state-of-the-art alternatives.
Published: 2026-02-23 17:24:18
Authors: Harry Anthony, Ziyun Liang, Hermione Warr, Konstantinos Kamnitsas
Categories: cs.CV, cs.LG
Abstract:
Deep Neural Networks achieve high performance in vision tasks by learning features from regions of interest (ROI) within images, but their performance degrades when deployed on out-of-distribution (OOD) data that differs from training data. This challenge has led to OOD detection methods that aim to identify and reject unreliable predictions. Although prior work shows that OOD detection performance varies by artefact type, the underlying causes remain underexplored. To this end, we identify a previously unreported bias in OOD detection: for hard-to-detect artefacts (near-OOD), detection performance typically improves when the artefact shares visual similarity (e.g. colour) with the model's ROI and drops when it does not - a phenomenon we term the Invisible Gorilla Effect. For example, in a skin lesion classifier with red lesion ROI, we show the method Mahalanobis Score achieves a 31.5% higher AUROC when detecting OOD red ink (similar to ROI) compared to black ink (dissimilar) annotations. We annotated artefacts by colour in 11,355 images from three public datasets (e.g. ISIC) and generated colour-swapped counterfactuals to rule out dataset bias. We then evaluated 40 OOD methods across 7 benchmarks and found significant performance drops for most methods when artefacts differed from the ROI. Our findings highlight an overlooked failure mode in OOD detection and provide guidance for more robust detectors. Code and annotations are available at: https://github.com/HarryAnthony/Invisible_Gorilla_Effect.
Published: 2026-02-23 17:22:19
Authors: Mohamed Abouagour, Eleftherios Garyfallidis
Categories: cs.GR
Abstract:
Spherical functions appear throughout computer graphics, from spherical harmonic lighting and precomputed radiance transfer to neural radiance fields and procedural planet rendering. Efficient evaluation is critical for real-time applications, yet existing approaches face a quality-performance trade-off: bilinear LUT sampling is fast but produces faceting, while bicubic filtering requires 16 texture samples. Most implementations use finite differences for normals, requiring extra samples and introducing noise. This paper presents Spherical Hermite Maps, a derivative-augmented LUT representation that resolves this trade-off. By storing function values alongside scaled partial derivatives at each texel of a padded cubemap, bicubic-Hermite reconstruction is enabled from only four texture samples (a 2x2 footprint) while providing continuous gradients from the same samples. The key insight is that Hermite interpolation reconstructs smooth derivatives as a byproduct of value reconstruction, making surface normals effectively free. In controlled experiments, Spherical Hermite Maps improve PSNR by 8-41 dB over bilinear interpolation and match 16-tap bicubic quality at one-quarter the cost. Analytic normals reduce mean angular error by 9-13% on complex surfaces while yielding stable specular highlights. Three applications demonstrate versatility: spherical harmonic glyph visualization, radial depth-map impostors for mesh level-of-detail, and procedural planet/asteroid rendering with spherical heightfields.
Published: 2026-02-23 17:17:53
Authors: Zoha Hayat Bhatti, Bakhtawar Ahtisham, Seemal Tausif, Niklas George, Nida ul Habib Bajwa, Mobin Javed
Categories: cs.CR
Abstract:
Large Language Models and commercial speech synthesis systems now enable highly realistic AI-generated voice scams (vishing), raising urgent concerns about deception at scale. Yet it remains unclear whether individuals can reliably distinguish AI-generated speech from human-recorded voices in realistic scam contexts and what perceptual strategies underlie their judgments. We conducted a controlled online study in which 22 participants evaluated 16 vishing-style audio clips (8 AI-generated, 8 human-recorded) and classified each as human or AI while reporting confidence. Participants performed poorly: mean accuracy was 37.5%, below chance in a binary classification task. At the stimulus level, misclassification was bidirectional: 75% of AI-generated clips were majority-labeled as human, while 62.5% of human-recorded clips were majority-labeled as AI. Signal Detection Theory analysis revealed near-zero discriminability (d' approx 0), indicating inability to reliably distinguish synthetic from human voices rather than simple response bias. Qualitative analysis of 315 coded excerpts revealed reliance on paralinguistic and emotional heuristics, including pauses, filler words, vocal variability, cadence, and emotional expressiveness. However, these surface-level cues traditionally associated with human authenticity were frequently replicated by AI-generated samples. Misclassifications were often accompanied by moderate to high confidence, suggesting perceptual miscalibration rather than uncertainty. Together, our findings demonstrate that authenticity judgments based on vocal heuristics are unreliable in contemporary vishing scenarios. We discuss implications for security interventions, user education, and AI-mediated deception mitigation.
Published: 2026-02-23 17:14:29
Authors: Sarath Shekkizhar, Adam Earle
Categories: cs.AI
Abstract:
As multi-agent architectures and agent-to-agent protocols proliferate, a fundamental question arises: what actually happens when autonomous LLM agents interact at scale? We study this question empirically using data from Moltbook, an AI-agent-only social platform, with 800K posts, 3.5M comments, and 78K agent profiles. We combine lexical metrics (Jaccard specificity), embedding-based semantic similarity, and LLM-as-judge validation to characterize agent interaction quality. Our findings reveal agents produce diverse, well-formed text that creates the surface appearance of active discussion, but the substance is largely absent. Specifically, while most agents ($67.5\%$) vary their output across contexts, $65\%$ of comments share no distinguishing content vocabulary with the post they appear under, and information gain from additional comments decays rapidly. LLM judge based metrics classify the dominant comment types as spam ($28\%$) and off-topic content ($22\%$). Embedding-based semantic analysis confirms that lexically generic comments are also semantically generic. Agents rarely engage in threaded conversation ($5\%$ of comments), defaulting instead to independent top-level responses. We discuss implications for multi-agent interaction design, arguing that coordination mechanisms must be explicitly designed; without them, even large populations of capable agents produce parallel output rather than productive exchange.
Published: 2026-02-23 16:49:37
Authors: Fahmida Liza Piya, Rahmatollah Beheshti
Categories: cs.CL, cs.AI
Abstract:
Large language models (LLMs) offer substantial promise for automating clinical text summarization, yet maintaining factual consistency remains challenging due to the length, noise, and heterogeneity of clinical documentation. We present AgenticSum, an inference-time, agentic framework that separates context selection, generation, verification, and targeted correction to reduce hallucinated content. The framework decomposes summarization into coordinated stages that compress task-relevant context, generate an initial draft, identify weakly supported spans using internal attention grounding signals, and selectively revise flagged content under supervisory control. We evaluate AgenticSum on two public datasets, using reference-based metrics, LLM-as-a-judge assessment, and human evaluation. Across various measures, AgenticSum demonstrates consistent improvements compared to vanilla LLMs and other strong baselines. Our results indicate that structured, agentic design with targeted correction offers an effective inference time solution to improve clinical note summarization using LLMs.
Published: 2026-02-23 16:45:56
Authors: Federico Manzoni
Categories: hep-th, math-ph
Abstract:
This paper studies the asymptotic gauge charges of the Curtright mixed-symmetry rank-3 field $φ_{[ρσ]ν}$ in Minkowski spacetime, interpreted in $ D = 5 $ as the dual graviton. In Bondi coordinates at future null infinity, we impose radiation fall-offs and fix a de Donder-like gauge together with an on-shell traceless condition, similarly to what happens in linearized gravity. Surface charges associated with the residual gauge transformations are constructed as boundary integrals via Nöther's 2-form. In $ D = 5 $, exploiting Hodge/Hodge-like decompositions on $ S^{3} $, the charge splits into a scalar sector $ Q_Φ $, a vector sector $ Q_{V} $ and a TT sector $Q_{y^{\text{TT}}}$. $ Q_Φ $ is parametrized by a single arbitrary scalar function $ Φ$ (interpreted as the supertranslation-like parameter), $ Q_{V} $ is parametrized by a vector field $ V^{i} \in \mathfrak{Diff}(S^{3}) $ and the TT sector $Q_{y^{\text{TT}}}$ is parametrized by a trasverse-traceless rank-2 tensor $y_{ij}^{\text{TT}} \in \mathfrak{TT}(S^3)$. The corresponding charge algebra closes only if $V_i \in \mathfrak{o}(4)$ as semidirect sum $ \mathfrak{o}(4) \loplus (C^{\infty}(S^3) \oplus \mathfrak{TT}(S^3)) $, i.e an abelian extension of a $\mathfrak{BMS}$-like algebra featuring a higher-spin-like supertranslation sector.
Published: 2026-02-23 16:45:16
Authors: Philemon Urbain Mballa
Categories: math.NT
Abstract:
This article develops a parametric approach to study the Diophantine equation \(\frac{k}{n} = \frac{1}{x} + \frac{1}{y} + \frac{1}{z}\), underlying the Erdős--Straus (\(k=4\)), Sierpiński (\(k=5\)), and their generalizations. We introduce and analyze the fundamental function \(F_{x,t}^{(k)}(n) = t^2(kx-n)^2 - 2nxt\), whose perfect square values are equivalent to solutions of the conjectures.
For any fixed pair \((x,t)\), we define its admissible domain \(\mathcal{D}_{x,t}^{(k)}\) and prove that on this domain, \(F\) is strictly decreasing, non-negative, and converges to its minimum. A key result is the Zero Lemma: if \(F(n_0)=0\) for some \(n_0\) in the domain, then \(n_0\) is necessarily the upper bound of \(\mathcal{D}_{x,t}^{(k)}\), and such zeros of \(F\) yield explicit symmetric solutions with \(y=z\).
As an illustration, in the classical Erdős--Straus case (\(k=4\)), we explicitly construct symmetric solutions \(y = z\) for all integers \(n \equiv 0,2,3 \pmod{4}\), covering already \(75\%\) of all integers. For the remaining class \(n \equiv 1 \pmod{4}\), which is traditionally more challenging, we construct explicit symmetric solutions based on the existence of a divisor \(b \equiv 3 \pmod{4}\), and we show that this condition is satisfied for almost all such integers: the set of exceptions has natural density zero.
Consequently, the Erdős--Straus conjecture is verified for a proportion of integers tending to \(1\) in this class. In particular, we obtain infinitely many new explicit families of symmetric solutions for numbers not covered by Mordell's theorem. These results elucidate the structural behavior of \(F\) and provide a unified framework for generating large families of solutions.
Published: 2026-02-23 16:39:30
Authors: J. Munárriz, F. Domínguez-Adame, R. P. A. Lima
Categories: quant-ph
Abstract:
We study theoretically the level shift of the Dirac oscillator perturbed by any sharply peaked potential approaching a surface delta potential. A Green function method is used to obtain closed expressions for all partial waves and parities.
Published: 2026-02-23 16:24:48
Authors: Jiechen Chen, Michele Polese, Osvaldo Simeone
Categories: eess.SP
Abstract:
Softwarized radio access networks (RANs), such as those based on the Open RAN (O-RAN) architecture, generate rich streams of key performance indicators (KPIs) that can be leveraged to extract actionable intelligence for network optimization. However, bridging the gap between low-level KPI measurements and high-level requirements, such as quality of experience (QoE), requires methods that are both relevant, capturing temporal patterns predictive of user-level outcomes, and interpretable, providing human-readable insights that operators can validate and act upon. This paper introduces conformal signal temporal logic learning (C-STLL), a framework that addresses both requirements. C-STLL leverages signal temporal logic (STL), a formal language for specifying temporal properties of time series, to learn interpretable formulas that distinguish KPI traces satisfying high-level requirements from those that do not. To ensure reliability, C-STLL wraps around existing STL learning algorithms with a conformal calibration procedure based on the Learn Then Test (LTT) framework. This procedure produces a set of STL formulas with formal guarantees: with high probability, the set contains at least one formula achieving a user-specified accuracy level. The calibration jointly optimizes for reliability, formula complexity, and diversity through principled acceptance and stopping rules validated via multiple hypothesis testing. Experiments using the ns-3 network simulator on a mobile gaming scenario demonstrate that C-STLL effectively controls risk below target levels while returning compact, diverse sets of interpretable temporal specifications that relate KPI behavior to QoE outcomes.
Published: 2026-02-23 16:23:49
Authors: Gaurav Najpande, Tampu Ravi Kumar, Manan Roy Choudhury, Neha Valeti, Yanjie Fu, Vivek Gupta
Categories: cs.CL
Abstract:
Real-world tables often exhibit irregular schemas, heterogeneous value formats, and implicit relational structure, which degrade the reliability of downstream table reasoning and question answering. Most existing approaches address these issues in a query-dependent manner, entangling table cleanup with reasoning and thus limiting generalization. We introduce QuIeTT, a query-independent table transformation framework that preprocesses raw tables into a single SQL-ready canonical representation before any test-time queries are observed. QuIeTT performs lossless schema and value normalization, exposes implicit relations, and preserves full provenance via raw table snapshots. By decoupling table transformation from reasoning, QuIeTT enables cleaner, more reliable, and highly efficient querying without modifying downstream models. Experiments on four benchmarks, WikiTQ, HiTab, NQ-Table, and SequentialQA show consistent gains across models and reasoning paradigms, with particularly strong improvements on a challenge set of structurally diverse, unseen questions.
Published: 2026-02-23 16:17:25
Authors: Stefano De Marco, Huyên Pham, Davide Zanni
Categories: q-fin.MF
Abstract:
We study generative modeling for time series using entropic optimal transport and the Schrödinger bridge (SB) framework, with a focus on applications in finance and energy modeling. Extending the diffusion-based approach of Hamdouche, Henry-Labordère, Pham, 2023, we introduce a jump-diffusion Schrödinger bridge model that allows for discontinuities in the generative dynamics. Starting from a Schrödinger bridge entropy minimization problem, we reformulate the task as a stochastic control problem whose solution characterizes the optimal controlled jump-diffusion process. When sampled on a fixed time grid, this process generates synthetic time series matching the joint distributions of the observed data. The model is fully data-driven, as both the drift and the jump intensity are learned directly from the data. We propose practical algorithms for training, sampling, and hyperparameter calibration. Numerical experiments on simulated and real datasets, including financial and energy time series, show that incorporating jumps substantially improves the realism of the generated data, in particular by capturing abrupt movements, heavy tails, and regime changes that diffusion-only models fail to reproduce. Comparisons with state-of-the-art generative models highlight the benefits and limitations of the proposed approach.
Published: 2026-02-23 16:16:37
Authors: Giorgia Sampò, Saverio Giallorenzo, Zelda Alice Franceschi
Categories: cs.SI, econ.GN
Abstract:
Why do some community-cooperation projects catalyse participation through durable, resilient collaboration networks while others result in negligible impact and leave the local social fabric unchanged? We argue outcomes hinge on participation architecture: simple, visible routines -- onboarding help, templated tasks, lightweight contribution/benefit tracking -- that create easy ``entry portals'' and route work across clusters without heavy hierarchy. We introduce Project Intervention Response Analysis (PIRA), a mixed anthropological-network-analysis framework that compares observed community networks with counterfactual networks absent from project-induced ties. PIRA also adds a new egocentric metric to detect ``architectural alters'' -- latent facilitators and boundary spanners. We begin validating PIRA in a three-month field study in Pomerini, Tanzania, where NGOs coordinated citizens, associations, and specialists. Findings indicate that sociotechnical participation architectures -- not charismatic hubs -- underwrite durable coordination. PIRA offers a reusable method to link organizational design mechanisms to formal network signatures.
Published: 2026-02-23 16:15:38
Authors: Louis Fabrice Tshimanga, Andrea Zanola, Federico Del Pup, Manfredo Atzori
Categories: cs.CV
Abstract:
We present Token-UNet, adopting the TokenLearner and TokenFuser modules to encase Transformers into UNets.
While Transformers have enabled global interactions among input elements in medical imaging, current computational challenges hinder their deployment on common hardware. Models like (Swin)UNETR adapt the UNet architecture by incorporating (Swin)Transformer encoders, which process tokens that each represent small subvolumes ($8^3$ voxels) of the input.
The Transformer attention mechanism scales quadratically with the number of tokens, which is tied to the cubic scaling of 3D input resolution.
This work reconsiders the role of convolution and attention, introducing Token-UNets, a family of 3D segmentation models that can operate in constrained computational environments and time frames.
To mitigate computational demands, our approach maintains the convolutional encoder of UNet-like models, and applies TokenLearner to 3D feature maps. This module pools a preset number of tokens from local and global structures.
Our results show this tokenization effectively encodes task-relevant information, yielding naturally interpretable attention maps. The memory footprint, computation times at inference, and parameter counts of our heaviest model are reduced to 33\%, 10\%, and 35\% of the SwinUNETR values, with better average performance (86.75\% $\pm 0.19\%$ Dice score for SwinUNETR vs our 87.21\% $\pm 0.35\%$).
This work opens the way to more efficient trainings in contexts with limited computational resources, such as 3D medical imaging. Easing model optimization, fine-tuning, and transfer-learning in limited hardware settings can accelerate and diversify the development of approaches, for the benefit of the research community.
Published: 2026-02-23 16:15:07
Authors: Andrew T. Karl
Categories: math.ST, stat.ME
Abstract:
In Individuals and Moving Range (I-MR) charts, the process standard deviation is often estimated by the span-2 average moving range, scaled by the usual constant $d_2$. Unlike the sample standard deviation, this estimator depends on the observation order: permuting the values can change the average moving range. We make this dependence explicit by modeling the order as an independent uniformly random permutation. A direct application of the law of total variance then decomposes its variance into a component due to ordering and a component due to the realized values. Averaging over all permutations yields a simple order-invariant baseline for the moving-range estimator: the sample Gini mean difference divided by $d_2$. Simulations quantify the resulting fraction of variance attributable to ordering under i.i.d. Normal sampling, and two NIST examples illustrate a typical ordering and an ordering with strong serial structure relative to random permutations of the same values.
Published: 2026-02-23 16:13:40
Authors: V. A. Okorokov
Categories: hep-ph
Abstract:
Energy dependence of global scattering parameters, mostly of total cross section, is studied for proton-proton and antiproton-proton collisions. Results are presented for physical analysis updated with taken into account the recent data from accelerator experiments as well as from cosmic ray measurements. The analytic parameterizations suggested within Axiomatic Quantum Field Theory (AQFT) provide the quantitative description of energy dependence of global scattering parameters for rather wide energy range. Detailed scan on low boundary of the fitting range for energy dependence of global scattering parameters allows the observation of the onsets for regions in which Pomeranchuk theorem and / or Froissart-Martin one is valid. It is obtained that global scattering parameters show the behavior corresponded to any formulations of Pomeranchuk theorem and closed to (modified) Froissart-Martin limit in functional sense in multi-TeV energy region. Bosonic condensation is considered as one of the possible dynamical mechanisms which would be provide the total cross section approaches to (modified) Froissart-Martin limit at quantitative level but not functionally only.
Published: 2026-02-23 16:08:32
Authors: Xianquan Wang, Zhaocheng Du, Jieming Zhu, Qinglin Jia, Zhenhua Dong, Kai Zhang
Categories: cs.IR, cs.LG
Abstract:
Large-scale online marketplaces and recommender systems serve as critical technological support for e-commerce development. In industrial recommender systems, features play vital roles as they carry information for downstream models. Accurate feature importance estimation is critical because it helps identify the most useful feature subsets from thousands of feature candidates for online services. Such selection enables improved online performance while reducing computational cost. To address feature selection problems in deep learning, trainable gate-based and sensitivity-based methods have been proposed and proven effective in industrial practice. However, through the analysis of real-world cases, we identified three bias issues that cause feature importance estimation to rely on partial model layers, samples, or gradients, ultimately leading to inaccurate importance estimation. We refer to these as layer bias, baseline bias, and approximation bias. To mitigate these issues, we propose FairFS, a fair and accurate feature selection algorithm. FairFS regularizes feature importance estimated across all nonlinear transformation layers to address layer bias. It also introduces a smooth baseline feature close to the classifier decision boundary and adopts an aggregated approximation method to alleviate baseline and approximation biases. Extensive experiments demonstrate that FairFS effectively mitigates these biases and achieves state-of-the-art feature selection performance.
Published: 2026-02-23 16:04:54
Authors: Jesper Glückstad
Categories: physics.optics
Abstract:
This paper presents a "reinvention" of Gabor Holography that does not suffer optically from the inherent twin-image problem originating back to Gabor's original Nobel Prize awarded invention. In-line or on-axis holography was ironically abandoned by its inventor Dennis Gabor himself and was effectively completely "re-placed" by so-called off-axis holography at the time when Gabor received the Nobel Prize in Physics in 1971. However, Gabor Holography is today the method of choice in modern digital holography due to its inherent on-axis, common-path robustness, lower requirements to resolution of the image sensor (or recording material), shorter exposure time, relaxed mechanical stability and temporal coherence requirements. However, it still inherently suffers from the aforementioned twin-image problem and, hence, one will find an abundance of papers trying to overcome this challenge by iterative phase retrieval or machine learning based approaches. Gabor Holography Reinvented overcomes this long-lasting twin-image problem for the first time by optical means.
Published: 2026-02-23 15:57:16
Authors: Yaya Sy, Dioula Doucouré, Christophe Cerisara, Irina Illina
Categories: cs.CL
Abstract:
Speakers of under-represented languages face both a language barrier, as most online knowledge is in a few dominant languages, and a modality barrier, since information is largely text-based while many languages are primarily oral. We address this for French-Wolof by training the first bilingual speech-text Matryoshka embedding model, enabling efficient retrieval of French text from Wolof speech queries without relying on a costly ASR-translation pipelines. We introduce large-scale data curation pipelines and new benchmarks, compare modeling strategies, and show that modality fusion within a frozen text Matryoshka model performs best. Although trained only for retrieval, the model generalizes well to other tasks, such as speech intent detection, indicating the learning of general semantic representations. Finally, we analyze cost-accuracy trade-offs across Matryoshka dimensions and ranks, showing that information is concentrated only in a few components, suggesting potential for efficiency improvements.
Published: 2026-02-23 15:53:17
Authors: Alastair B. Claringbold, Peter J. Wheatley, James Kirk, Eva-Maria Ahrer, Ian Skillen, Matteo Brogi, George W. King, James McCormac
Categories: astro-ph.EP
Abstract:
We present the low-resolution optical transmission spectrum of the inflated hot Saturn HAT-P-44b. The planet is a close sibling in radius (1.24 $\mathrm{R_{Jup}}$), temperature (1100 K), and mass (0.35 $\mathrm{M_{Jup}}$) to the exceedingly well-characterized WASP-39b. Using the ACAM instrument on the William Herschel Telescope (WHT), we obtain a transmission spectrum with sub-scale height precision of 246 ppm, with a wavelength range of 495 -- 874 nm and a 20 nm resolution, despite a relatively faint host star ($V\mathrm{_{mag} = 13.2}$). We detect absorption due to sodium with 3.9$σ$ confidence. Atmospheric retrieval of the transmission spectrum also reveals evidence for \ch{H2O} absorption and Rayleigh scattering from \ch{H2} gas consistent with a cool 800 K atmosphere and a super-solar metallicity of 7$\substack{+16 \\ -5}$$\times$solar. Comparison of retrieval models disfavour the inclusion of a super-Rayleigh scattering slope or high-altitude clouds (at $<1$ mbar) while being agnostic towards the presence of mid-altitude clouds. Our transmission spectrum of HAT-P-44b shows strong similarity to that of its sibling WASP-39b.} This is the tenth planet in the LRG-BEASTS (Low-Resolution Ground-Based Exoplanet Atmosphere Survey using Transmission Spectroscopy) survey.
Published: 2026-02-23 15:44:01
Authors: Eric Dannetun
Categories: math.AC
Abstract:
We study ideals generated by $n+1$ powers of general linear forms in $R= k[x_1,\dots,x_n]$. By generalizing the ideas in a recent paper of Diethorn et al., we determine the Betti numbers of such ideals when at least one generator is a square. It follows that all such ideals are level. As a consequence, we show that a generic ideal in $R$ generated by $n+1$ forms, with at least one quadric generator, is level. We also determine the Betti numbers of the Artinian Gorenstein algebras linked to these almost complete intersections. By describing the dual generators of these algebras, we obtain a family of forms, including the elementary symmetric polynomials, whose annihilator ideals have the strong Lefschetz property. Finally, we give explicit generators for the annihilator ideal of any elementary symmetric polynomial.
Published: 2026-02-23 15:43:45
Authors: Valerii Andreichev, Jindra Dušek, Markus Meuwly, Jeremy O. Richardson
Categories: physics.chem-ph
Abstract:
Accurate simulations of molecules require high-level electronic-structure theory in combination with rigorous methods for approximating the quantum dynamics. Machine-learning approaches can significantly reduce the computational expense of this workflow without any loss of accuracy. We discuss various methods for constructing potential energy surfaces including transfer learning, which requires a minimal number of expensive training points. In this way, we can study chemical reactions at a high level but a low cost. In particular, as the potentials are smooth and differentiable, they enable the use of more advanced semiclassical approximations to quantum dynamics, such as perturbatively corrected instanton theory, which can capture both tunnelling and anharmonicity.
Published: 2026-02-23 15:30:34
Authors: Frank Brückerhoff-Plückelmann, Anna P. Ovvyan, Akhil Varri, Hendrik Borras, Bernhard Klein, C. David Wright, Harish Bhaskaran, Ghazi Sarwat Syed, Abu Sebastian, Holger Fröning, Wolfram Pernice
Categories: physics.app-ph
Abstract:
Probabilistic computing excels in approximating combinatorial problems and modelling uncertainty. However, using conventional deterministic hardware for probabilistic models is challenging: (pseudo) random number generation introduces computational overhead and additional data shuffling, which is particularly detrimental for safety-critical applications requiring low latency such as autonomous driving. Therefore, there is a pressing need for innovative probabilistic computing architectures that achieve low latencies with reasonable energy consumption. Physical computing offers a promising solution, as these systems do not rely on an abstract deterministic representation of data but directly encode the information in physical quantities. Therefore, they can be seamlessly integrated with physical entropy sources, enabling inherent probabilistic architectures. Photonic computing is a prominent variant due to the large available bandwidth, several orthogonal degrees of freedom for data encoding and optimal properties for in-memory computing and parallel data transfer. Here, we highlight key developments in physical photonic computing and photonic random number generation. We provide insights into the realization of probabilistic photonic processors and lend our perspective on their impact on AI systems and future challenges.
Published: 2026-02-23 15:28:15
Authors: Zhengrong Xie, Zheng Li
Categories: math.NA
Abstract:
The flux-vector splitting scheme of Van Leer is a cornerstone of computational fluid dynamics, yet its original proof of the eigenvalue sign condition was presented in a condensed form. In this work, we provide a detailed and rigorous analysis of the eigenvalues of the Jacobian matrices associated with the Van Leer splitting for the one-dimensional Euler equations. By constructing the Sturm sequence of the discriminant, we prove that for the admissible parameter range $1 \le γ\le 3$, $|M|<1$, and $a>0$, the Jacobian $\partial F^+/\partial U$ has one zero eigenvalue and two positive real eigenvalues, confirming Van Leer's original claim. Furthermore, we extend our analysis to two variants of the original AUSM scheme (Advection Upstream Splitting Method) proposed by Liou and Steffen, considering both linear and second-order pressure splittings. For the linear pressure splitting we show that the eigenvalues are not all of the same sign, while for the second-order pressure splitting we prove that all coefficients of the characteristic equation are positive. Numerical experiments reported in the appendix confirm the non-negativity of the discriminant for the AUSM with the second-order pressure splitting, implying that its eigenvalues are real and positive.
Published: 2026-02-23 15:17:49
Authors: Peter Caradonna, Christopher Turansick
Categories: econ.TH, math.CO
Abstract:
We characterize the identified sets of a wide range of stochastic choice models, including random utility, various models of boundedly-rational behavior, and dynamic discrete choice. In each of these settings, we show two distributions over choice rules are observationally equivalent if and only if they can be obtained from one another via a finite sequence of simple swapping transforms. We leverage this to obtain complete descriptions of both the defining inequalities and extreme points of these identified sets. In cases where choice frequencies vary smoothly with some parameters, we provide a novel global-inverse result for practically testing identification.
Published: 2026-02-23 15:17:18
Authors: Ian Steenstra, Paola Pedrelli, Weiyan Shi, Stacy Marsella, Timothy W. Bickmore
Categories: cs.CL, cs.AI, cs.CY, cs.HC, cs.MA
Abstract:
Large Language Models (LLMs) are increasingly utilized for mental health support; however, current safety benchmarks often fail to detect the complex, longitudinal risks inherent in therapeutic dialogue. We introduce an evaluation framework that pairs AI psychotherapists with simulated patient agents equipped with dynamic cognitive-affective models and assesses therapy session simulations against a comprehensive quality of care and risk ontology. We apply this framework to a high-impact test case, Alcohol Use Disorder, evaluating six AI agents (including ChatGPT, Gemini, and Character.AI) against a clinically-validated cohort of 15 patient personas representing diverse clinical phenotypes.
Our large-scale simulation (N=369 sessions) reveals critical safety gaps in the use of AI for mental health support. We identify specific iatrogenic risks, including the validation of patient delusions ("AI Psychosis") and failure to de-escalate suicide risk. Finally, we validate an interactive data visualization dashboard with diverse stakeholders, including AI engineers and red teamers, mental health professionals, and policy experts (N=9), demonstrating that this framework effectively enables stakeholders to audit the "black box" of AI psychotherapy. These findings underscore the critical safety risks of AI-provided mental health support and the necessity of simulation-based clinical red teaming before deployment.
Published: 2026-02-23 15:15:47
Authors: Jin Liu, Yinbin Miao, Ning Xi, Junkang Liu
Categories: cs.LG, cs.AI
Abstract:
Balancing convergence efficiency and robustness under Differential Privacy (DP) is a central challenge in Federated Learning (FL). While AdamW accelerates training and fine-tuning in large-scale models, we find that directly applying it to Differentially Private FL (DPFL) suffers from three major issues: (i) data heterogeneity and privacy noise jointly amplify the variance of second-moment estimator, (ii) DP perturbations bias the second-moment estimator, and (iii) DP amplify AdamW sensitivity to local overfitting, worsening client drift. We propose DP-FedAdamW, the first AdamW-based optimizer for DPFL. It restores AdamW under DP by stabilizing second-moment variance, removing DP-induced bias, and aligning local updates to the global descent to curb client drift. Theoretically, we establish an unbiased second-moment estimator and prove a linearly accelerated convergence rate without any heterogeneity assumption, while providing tighter $(\varepsilon,δ)$-DP guarantees. Our empirical results demonstrate the effectiveness of DP-FedAdamW across language and vision Transformers and ResNet-18. On Tiny-ImageNet (Swin-Base, $\varepsilon=1$), DP-FedAdamW outperforms the state-of-the-art (SOTA) by 5.83\%. The code is available in Appendix.
Published: 2026-02-23 15:08:49
Authors: Ondřej Ježil, Dimitrios Tsintsilidas
Categories: cs.CC, cs.LO, math.LO
Abstract:
Student-Teacher Games are a model of computation in which a computationally restricted Student attempts to produce a string satisfying a refutable property, while an all-powerful Teacher refutes incorrect candidates by providing counterexamples. By the classical result of Krajíček, Pudlák, and Takeuti [KPT90], such games capture the witnessing of $\forall\exists\forall$-formulas in bounded arithmetic. In this paper, we introduce subclasses of total search problems in the polynomial hierarchy, classified by the number of rounds and candidate answers per round required for a Student at the corresponding level to solve them. Assuming the polynomial hierarchy does not collapse, we separate these classes for different number of rounds and queries. As applications we obtain the following results:
(a) We study theories of bounded arithmetic axiomatized by fine-grained variants of length induction and bounded collection. We prove a general witnessing theorem connecting their $\forall\exists\forall$-consequences to the appropriate classes of Student-Teacher Games. Under the non-collapse of the polynomial hierarchy, we separate these theories.
(b) These conditional separations resolve two open problems in bounded arithmetic: one by Buss and Ressayre [Bus85, CK93] on the strength of bounded collection, and one by Pollett [Pol97] on the difference between strict and non-strict double length induction.
(c) Finally, we extend the unprovability of circuit upper bounds due to Krajíček and Oliveira [KO17] to the theory $PV_1+BB(Σ^b_1)$, and the unprovability of strong co-nondeterministic circuit lower bounds due to Pich and Santhanam [PS21] to the theory $PV_1+LLIND(sΣ^b_1)$. By the preceding separations, both theories strictly extend $PV_1$ assuming $NP\nsubseteq P/poly$.
Published: 2026-02-23 15:08:42
Authors: Pelin Sekercioglu, Angela Fontan, Dimos V. Dimarogonas
Categories: eess.SY
Abstract:
We address the edge-based synchronization problem in first-order multi-agent systems containing both cooperative and antagonistic interactions with one or multiple leader groups. The presence of multiple leaders and antagonistic interactions means that the multi-agent typically does not achieve consensus, unless specific conditions (on the number of leaders and on the signed graph) are met, in which case the agents reach a trivial form of consensus. In general, we show that the multi-agent system exhibits a more general form of synchronization, including bipartite consensus and containment. Our approach uses the signed edge-based agreement protocol for signed networks described by signed edge-Laplacian matrices. In particular, in this work, we present new spectral properties of signed edge-Laplacian matrices containing multiple zero eigenvalues and establish global exponential stability of the synchronization errors. Moreover, we compute the equilibrium to which all edge states converge. Numerical simulations validate our theoretical results.
Published: 2026-02-23 14:55:28
Authors: Michael Trimboli, Mohammed Alsubaie, Sirani M. Perera, Ke-Gang Wang, Xianqi Li
Categories: cs.LG
Abstract:
Understanding and predicting microstructure evolution is fundamental to materials science, as it governs the resulting properties and performance of materials. Traditional simulation methods, such as phase-field models, offer high-fidelity results but are computationally expensive due to the need to solve complex partial differential equations at fine spatiotemporal resolutions. To address this challenge, we propose a deep learning-based framework that accelerates microstructure evolution predictions while maintaining high accuracy. Our approach utilizes a fully convolutional spatiotemporal model trained in a self-supervised manner using sequential images generated from simulations of microstructural processes, including grain growth and spinodal decomposition. The trained neural network effectively learns the underlying physical dynamics and can accurately capture both short-term local behaviors and long-term statistical properties of evolving microstructures, while also demonstrating generalization to unseen spatiotemporal domains and variations in configuration and material parameters. Compared to recurrent neural architectures, our model achieves state-of-the-art predictive performance with significantly reduced computational cost in both training and inference. This work establishes a robust baseline for spatiotemporal learning in materials science and offers a scalable, data-driven alternative for fast and reliable microstructure simulations.
Published: 2026-02-23 14:41:35
Authors: Junyi Wang, Yudong Guo, Boyang Guo, Shengming Yang, Juyong Zhang
Categories: cs.CV, cs.GR
Abstract:
While diffusion models have shown great potential in portrait generation, generating expressive, coherent, and controllable cinematic portrait videos remains a significant challenge. Existing intermediate signals for portrait generation, such as 2D landmarks and parametric models, have limited disentanglement capabilities and cannot express personalized details due to their sparse or low-rank representation. Therefore, existing methods based on these models struggle to accurately preserve subject identity and expressions, hindering the generation of highly expressive portrait videos. To overcome these limitations, we propose a high-fidelity personalized head representation that more effectively disentangles expression and identity. This representation captures both static, subject-specific global geometry and dynamic, expression-related details. Furthermore, we introduce an expression transfer module to achieve personalized transfer of head pose and expression details between different identities. We use this sophisticated and highly expressive head model as a conditional signal to train a diffusion transformer (DiT)-based generator to synthesize richly detailed portrait videos. Extensive experiments on self- and cross-reenactment tasks demonstrate that our method outperforms previous models in terms of identity preservation, expression accuracy, and temporal stability, particularly in capturing fine-grained details of complex motion.
Published: 2026-02-23 14:33:39
Authors: Soumen Pachal, Prashanth L. A., Shalabh Bhatnagar, Avinash Achar
Categories: cs.LG, stat.ML
Abstract:
We present a family of generalized Hessian estimators of the objective using random direction stochastic approximation (RDSA) by utilizing only noisy function measurements. The form of each estimator and the order of the bias depend on the number of function measurements. In particular, we demonstrate that estimators with more function measurements exhibit lower-order estimation bias. We show the asymptotic unbiasedness of the estimators. We also perform asymptotic and non-asymptotic convergence analyses for stochastic Newton methods that incorporate our generalized Hessian estimators. Finally, we perform numerical experiments to validate our theoretical findings.
Published: 2026-02-23 14:32:39
Authors: Y. Degerli, R. Aleksan, R. Casanova, Y. Gan, S. Grinstein, F. Guilloux, A. Hanlon, T. Hemperek, J. P. Meyer, J. Pinol, P. Schwemling, E. Vilella
Categories: physics.ins-det, hep-ex
Abstract:
MiniCACTUS-v2 is a monolithic sensor prototype designed in LF 150 nm CMOS process for time tagging of individual Minimum Ionizing Particles with an accuracy better than 100 ps. The sensing element is a deep n-well/p-substrate diode without internal amplification. To minimize detector capacitances, the analog front-ends and the discriminators for each pixel have been implemented outside the pixel, at the column level. After fabrication, the sensors have been thinned to 150 microns, 175 microns and 200 microns and then post-processed for backside biasing. The breakdown voltages measured on these sensors are higher than 500 V, ensuring the complete depletion of the charge collection volume. In this paper, we will focus on the time resolution measurements from a test-beam campaign conducted in July 2025 at SPS-CERN. During this period, several pixels from the 3 different sensor thicknesses have been tested at different bias voltages. The best time resolution measured is 48.88 ps on a 0.5 mm x 0.5 mm pixel from a 175 microns-thick sensor at 500 V, with nominal settings for the on-chip analog front-end and discriminator.
Published: 2026-02-23 14:29:06
Authors: Harry Fitchett, Jasmine Ritchie, Charles Fox
Categories: cs.AR
Abstract:
Computer architecture is searching for new ways to make use of increasingly available digital logic without the serial bottlenecks of CPU-based design. Recent work has demonstrated a fully CPU-less approach to executing functional programs, by exploiting their inherent parallelisability to compile them directly into parallel digital logic. This work uses lambda-calculus as a hyper simple functional language to prove the concept, but is impractical for real-world programming due to the well-known inefficiencies of pure lambda$-calculus. It is common in language design to extend basic lambda-calculus with additional primitives to short-cut common tasks such as arithmetic and lists. In this work, we build upon our previous research to examine how such extensions may be applied to CPU-less functional execution in digital logic, with the objective of advancing the approach toward practical implementation. We present a set of structures and algorithms for representing new primitives, describe a systematic process for selecting, implementing, and evaluating them, and demonstrate substantial reductions in execution time and node usage. These improvements are implemented in an open-source system, which is shown to correctly evaluate a range of representative lambda expressions.
Published: 2026-02-23 14:27:38
Authors: Cai Heng Li, Fu-Gang Yin, Jin-Xin Zhou
Categories: math.GR
Abstract:
A classification is given of finite $k$-set-homogeneous graphs for $k\geqslant 2$, leading to a striking result that each finite $k$-set-homogeneous graph is $k$-homogeneous. It shows that $3$-set-homogeneous graphs are rare, consisting of the following graphs and their complements: $\C_5$, $\K_n\square\K_n$, $n\K_m$, the Schläfli graph of order 27, the Higman-Sims graph, the MaLaughlin graph, {affine polar graphs, and elliptic orthogonal graphs}. As an ingredient for the proof, it is shown that all orbitals in a primitive permutation group of rank $4$ are self-paired, except for $\PSU_3(3)$ acting on 36 points.
Published: 2026-02-23 14:21:48
Authors: Thies Plassmann, Leon Schaefer, Meny Menashes, Guillaume Salomon
Categories: quant-ph, cond-mat.quant-gas, physics.atom-ph
Abstract:
Local Hilbert spaces with large dimension are of key interest for quantum information with applications in quantum computing and memories, quantum simulations and metrology. Thanks to its weak coupling to external perturbations, the large ground-state nuclear spin manifold of fermionic alkaline-earth atoms is an exciting resource to explore for quantum information. Simultaneous single atom and state-resolved detection however remains an outstanding challenge limiting the development of novel quantum computing and simulation schemes beyond qubits. Here, we report on a new imaging technique enabling the simultaneous detection of up to four quantum states encoded in the nuclear spin manifold of a single fermionic strontium atom within 100 microseconds, with state-resolved detection fidelities ranging from 0.936 to 0.997. This technique is further used to track the highly coherent nuclear spin dynamics after a quench highlighting the potential of this system for quantum information. These results offer fascinating perspectives for quantum science with multi-electron atoms ranging from qudit-based quantum computing to quantum simulations of the SU(N) Fermi-Hubbard model.
Published: 2026-02-23 14:15:56
Authors: Jizhou Han, Chenhao Ding, SongLin Dong, Yuhang He, Shaokun Wang, Qiang Wang, Yihong Gong
Categories: cs.CV, cs.AI
Abstract:
Continual Generalized Category Discovery (C-GCD) requires identifying novel classes from unlabeled data while retaining knowledge of known classes over time. Existing methods typically update classifier weights dynamically, resulting in forgetting and inconsistent feature alignment. We propose GOAL, a unified framework that introduces a fixed Equiangular Tight Frame (ETF) classifier to impose a consistent geometric structure throughout learning. GOAL conducts supervised alignment for labeled samples and confidence-guided alignment for novel samples, enabling stable integration of new classes without disrupting old ones. Experiments on four benchmarks show that GOAL outperforms the prior method Happy, reducing forgetting by 16.1% and boosting novel class discovery by 3.2%, establishing a strong solution for long-horizon continual discovery.