Published: 2026-06-23 14:30:49
Authors: Meisam Ghasemi Bostanabad, Mojtaba Mohammadi Najafabadi
Categories: hep-ph
Abstract:
The Large Hadron Collider (LHC) produces an enormous volume of data in which the identification and characterization of hadronic jets is a central challenge. Determining the electric charge of the parton initiating a light-quark jet; a task known as jet-charge discrimination; is highly valuable for both precision tests of the Standard Model (SM) and searches for physics beyond it. In this work, we benchmark a range of classical and quantum machine-learning models for the task of distinguishing up-quark from anti-up-quark jets in a controlled QCD environment. Among the approaches tested, a Graph Neural Network achieved the best performance, with an AUC of 0.883. Jet-charge tagging of this kind has broad phenomenological applications, from improving measurements of charge asymmetries to enhancing sensitivity in searches for new particles from beyond the SM where quark versus antiquark discrimination is essential. Our study provides a methodological foundation for deploying modern machine-learning techniques in jet-charge analyses at the LHC experiments.
Published: 2026-06-23 14:28:11
Authors: Lisa Schirch, Beth Goldberg
Categories: cs.HC, cs.AI, cs.CY, cs.ET, cs.GR
Abstract:
Traditional visual data storytelling relies on binary graphics that depict two simplified groups in conflict. This can increase political polarization by oversimplifying intra-group disagreements and erasing ambiguity and shared ideas or values. This can inadvertently foster "us versus them" thinking. Intentional, pluralistic design choices for AI-enabled digital platforms can produce visualizations that emphasize nuance, opinion distribution, and intergroup commonalities. To demonstrate this potential, we examine deliberative technologies that map high-dimensional opinion spaces and highlight areas of both consensus and dissensus. The paper highlights the We the People deliberation conducted by Jigsaw and the Napolitan Institute in September 2025, which engaged over 2,400 Americans across all 435 congressional districts in an AI-supported, asynchronous dialogue regarding freedom and equality. By utilizing AI to synthesize long-form, text-based participant inputs into interactive "opinion landscapes," the initiative provided an alternative format for pluralistic data storytelling that humanized diverse viewpoints and revealed hidden areas of substantial broad consensus. The paper concludes that shifting from divisive, contrast-heavy visual frameworks to distribution-focused, interactive models represents a highly scalable, low-cost intervention capable of bridging perceptual gaps and cultivating a more resilient, collaborative democratic culture.
Published: 2026-06-23 14:23:56
Authors: Arka Ujjal Dey, John Collomosse
Categories: cs.CL
Abstract:
Fact-checking systems built on LLMs achieve high verdict accuracy on standard benchmarks, yet routinely output Supports labels whose cited evidence does not license the claim. Structured decomposition is the natural way to inspect those warrants, but rigid extraction protocols strip the full-claim context that facets need. We introduce SIFT -- claim-conditioned re-scoring of extracted evidence spans against the full claim -- paired with WSP (Warranted Supports Proportion), an automatic NLI check that the cited warrant entails the claim. We evaluate on FEVER, SciFact, 5PILS, and DP across four open-source backbones. SIFT recovers accuracy on cells where naive decomposition costs up to 27.6 points, while raising WSP above direct prompting; WSP itself calibrates against human gold evidence at AUC 0.92 and precision 0.98.
Published: 2026-06-23 14:21:41
Authors: Yuanhe Zhao, Tianyu Zhang, Huafei Xing, Derek F. Wong, Jianbin Li, Tao Fang
Categories: cs.CL, cs.AI
Abstract:
Retrieval-Augmented Generation enhances large language models by incorporating external knowledge, but deploying it in sensitive scenarios risks privacy leakage via malicious prompts. To address this, we propose a multi-agent framework that sanitizes retrieved content through semantic rewriting. By employing three specialized agents for privacy extraction, semantic analysis, and reconstruction, our approach collaboratively removes sensitive identifiers while preserving the semantic core. We evaluate the framework on the ChatDoctor and Wiki-PII datasets across six large language models. Experimental results demonstrate a significant reduction in privacy leakage under targeted attacks. For instance, we reduced targeted information exposure in LLaMA-3-8B from 144 instances in the baseline to just 1. Furthermore, we maintain strong contextual fidelity with a BLEU-1 score of 0.122, outperforming the existing SAGE method's 0.117. Finally, the framework operates as an asynchronous preprocessing module, introducing no additional latency to online inference, as all rewriting is executed as a one-time offline preprocessing step. To promote reproducibility, the source code of this work is publicly available at https://github.com/foursoils/Privacy-Preserving-RAG.
Published: 2026-06-23 14:17:14
Authors: Anna Sofia Lippolis, Mohammad Javad Saeedizade, Robin Keskisärkkä, Aldo Gangemi, Eva Blomqvist, Andrea Giovanni Nuzzolese
Categories: cs.AI
Abstract:
Competency Questions (CQs) are the central component of CQ-verification, an established process in which an ontology is evaluated against a set of natural language questions to determine whether the intended purpose of the ontology has been properly modelled. However, CQ-verification is often time-consuming and error-prone, as it requires careful interpretation of linguistic nuances and precise alignment with formal ontology constructs. Ambiguities and complexity in CQs can further complicate this process, leading to inconsistent modelling decisions and verification outcomes. In this paper, we investigate what makes a CQ challenging and possible solutions to enhance the users' performance in the CQ-verification process. We experimented with the data of 19 participants who performed CQ-verification on 20 tasks using an LLM assistant to support ontology evaluation. The results show the necessity of a tool to refine CQs before publishing them to avoid ambiguity or excessive complexity in later phases of the ontology engineering process.
Published: 2026-06-23 14:12:12
Authors: Sattwik Sadhu, Nitin Kriplani, Raghunath Chelakkot
Categories: cond-mat.soft, nlin.AO
Abstract:
The dynamics of flexible polymers and chains under follower activity is known to produce diverse nonequilibrium states. A prominent feature of such systems is the emergence of periodic motion arising from the coupling between internal activity and chain conformation. Recently, it has been shown that flexible and extensible chains of active particles exhibit rich dynamical patterns in the overdamped limit, where inertia is negligible. Here, we study the complex dynamics of a flexible and extensible chain of active particles under follower activity when inertia is significant. Using numerical simulations, we quantify the chain dynamics as a function of chain length ($N$), segment mass, and activity. To rationalize the numerical results, we develop theoretical descriptions in the limit of short chains ($N=3$) and long chains ($N \gg 1$). In both these limits, we derive approximate expressions for the bond lengths and bond angles along the contour, which show excellent agreement with the numerical results. In addition, for short chains, we derive the stability conditions for a periodic motion as a function of segment mass and activity. For long chains ($N\gg1$) we identify parameter regime in which the circular, periodic solution becomes structurally unstable. Our theoretical and numerical analysis provides insights into the emergence of ordered and periodic behaviour in active chains.
Published: 2026-06-23 14:09:09
Authors: Ludovick Bouthat, Javad Mashreghi, Raphaël Vo
Categories: math.FA, math.OA
Abstract:
In finite dimensions, every doubly stochastic matrix has the $\ell^p$-operator norm equal to $1$ for all $1 \le p \le \infty$. However, in the infinite-dimensional setting, this property may fail since the norm can be strictly smaller than $1$ when $1
Published: 2026-06-23 13:53:50
Authors: Federico Marcuzzi, Xuefei Ning, Roy Schwartz, Iryna Gurevych
Categories: cs.CL
Abstract:
As Large Language Models are increasingly deployed in critical applications, robustly evaluating their social biases is paramount. However, the current literature suffers from widespread methodological fragmentation, which yields contradictory conclusions. This stems largely from ignoring the structural framing of benchmark-level evaluations. To resolve this, we introduce a unified and controllable framework that standardizes heterogeneous benchmarks to systematically contrast isolated demographic assessments with forced-choice comparative settings. Crucially, this allows us to disentangle the confounding effects of Chain-of-Thought reasoning, neutral fallback options, and other structural artifacts in social bias evaluations. Our evaluation across multiple model families reveals a massive, systematic paradigm gap: while isolated assessments limit prejudice activation, comparative settings act as aggressive catalysts for latent discrimination, a shift primarily driven by underspecified contexts. Alarmingly, CoT reasoning exacerbates social biases under comparative settings, and this systemic bias persists as a deterministic prejudice even when models are provided neutral fallback options or claim to answer randomly. Finally, we demonstrate that this comparative prejudice is a generalized phenomenon that scales positively with model size. Ultimately, we offer a crucial methodological guideline: while researchers must leverage comparative settings to robustly audit hidden biases, practitioners cannot safely rely on comparative deployments in ambiguous real-world tasks.
Published: 2026-06-23 13:52:46
Authors: Enze Ma, Yufan Zhou, Wei-Chieh Huang, Jie Yang, Huanhuan Ma, Zixuan Wang, Chengze Li, Chunyu Miao, Philip S. Yu, Zhen Wang
Categories: cs.CL
Abstract:
Long-term memory promises LLM agents that grow more capable across sessions, maintaining an accurate, evolving understanding of the user that interaction forms. In practice, however, this memory is evaluated mostly through downstream behavior, such as later answers, personalization quality, or task success, which tests that understanding only indirectly and leaves the memory artifact itself largely unaudited. We argue that long-term memory should instead be evaluated as an auditable post-interaction artifact: after ordinary assistance, what structured user state can be reconstructed from the memory the agent leaves behind? We instantiate this view in MEMPROBE, a benchmark in which a memory-equipped agent assists simulated users, each carrying a hidden, taxonomy-anchored user-state bank, across a trajectory of leak-controlled tasks, after which that bank is reconstructed from the agent's resulting memory under both full-store and top-k access. Built on synthetic ground truth for efficient, scalable measurement, MEMPROBE spans 50 simulated users with 31 hidden dimensions each (1,550 recovery targets) and tests 5 representative memory systems. Testing state-of-the-art memory agents, we find that successful assistance and recoverable memory behave as distinct capabilities. Task completion nearly saturates, even for a memoryless baseline, while category-balanced recovery stays moderate (about 0.6) and drops further under top-k retrieval. MEMPROBE is the first benchmark to study memory recovery directly, reconstructing the user state a system retains and scoring it against ground truth. We see recovery as a concrete objective for future memory agents to optimize, and MEMPROBE as a step toward an environment where agents are trained to remember their users, growing more faithful the longer they know them.
Published: 2026-06-23 13:50:45
Authors: Ravid Achituv, reem sari
Categories: astro-ph.SR
Abstract:
We derive a simple analytical description for the structure and evolution of $3$--$10 M_\odot$ stars throughout main-sequence hydrogen burning. We obtain an analytical relation for the convective core mass, $\frac{M_\star}{M_c}=1+2.1\left(\frac{μ_c}{μ_e}\right)^2$, where $μ$ is the mean molecular weight of the core and envelope. Using this relation, we analytically derive the hydrogen abundance profile outside the convective core. We find that $μ(m)\propto m^{-0.7}$, and show that this profile is important for an analytical description of these stars. Within this region of variable $μ$, the temperature, density, and pressure are well approximated by power laws of radius. We derive analytical expressions for the core and stellar radii, stellar luminosity, and effective temperature as functions of $μ_c$.
We provide a simple physical explanation for the main-sequence hook, defined by the minimum in effective temperature. We show that the hook occurs when the hydrogen mass fraction in the core is $x_c\simeq0.045$, and stress that the same convective-core burning physics governs the subsequent evolution. In that sense, at the hook hydrogen is not yet fully exhausted. During late main-sequence evolution, we find that the ratio of nuclear luminosity between the core and the surrounding hydrogen-rich shell is $\simeq4000x_c$. Hence, the main sequence terminates only once $x_c\simeq2.5\times10^{-4}$, when the surrounding layers become as luminous as the core itself and $M_c\simeq0.11 M_\star$.
Although this terminal core mass is numerically similar to the Sch"onberg--Chandrasekhar limit, we show that the two are physically unrelated, since the core remains far from isothermal even at this stage. We validate all analytical results using MESA simulations.
Published: 2026-06-23 13:47:42
Authors: Nahuel Gonzalez, Marta Robledo-Moreno, Ivan DeAndres-Tame, Ruben Vera-Rodriguez, Ruben Tolosana
Categories: cs.CV, cs.LG
Abstract:
Deep learning approaches to biometric verification are commonly trained by optimizing indirect objectives, creating a misalignment between the optimization process and the primary evaluation metric, typically the Equal Error Rate (EER). This paper introduces EERLoss: a subdifferentiable, arbitrarily accurate approximation to EER for training deep biometric models. Furthermore, this framework has the potential to be adapted to optimize any specific operating point on the DET curve, enhancing its generalizability. To validate this approach, EERLoss is evaluated on a particularly demanding behavioral biometric modality: keystroke dynamics verification. This task is characterized by its high intra-class and low inter-class variability. Experiments are conducted on the large-scale KVC-onGoing benchmark, incorporating data from over 185,000 subjects across different scenarios. A comprehensive ablation study initially demonstrates the superiority of EERLoss in comparison to existing state-of-the-art loss functions. It also converges substantially faster compared to other losses, reducing the overall training cost. Additionally, a comparison is made between the proposed loss and the KVC-winning architecture by re-training it with EERLoss, demonstrating that the proposed approach significantly outperforms the original SoTA, achieving a relative EER reduction of up to approx. 30\%. This improvement on a challenging, large-scale benchmark validates the effectiveness of EERLoss as a task-aligned training objective specifically suited for high-variance biometric traits.
Published: 2026-06-23 13:42:23
Authors: Belle, Belle II Collaborations, :, M. Abumusabh, I. Adachi, A. Aggarwal, H. Ahmed, Y. Ahn, H. Aihara, M. Akdag, N. Akopov, S. Alghamdi, M. Alhakami, N. Althubiti, K. Amos, M. Angelsmark, N. Anh Ky, C. Antonioli, K. Arai, H. Atmacan, T. Aushev, V. Aushev, R. Ayad, V. Babu, H. Bae, N. K. Baghel, S. Bahinipati, P. Bambade, Sw. Banerjee, M. Barrett, M. Bartl, J. Baudot, A. Beaubien, F. Becherer, J. Becker, G. F. Benfratello, J. V. Bennett, F. U. Bernlochner, V. Bertacchi, M. Bertemes, E. Bertholet, M. Bessner, S. Bettarini, V. Bhardwaj, B. Bhuyan, F. Bianchi, T. Bilka, D. Biswas, D. Bodrov, G. Bonvicini, A. Boschetti, A. Bozek, M. Bračko, P. Branchini, R. A. Briere, T. E. Browder, A. Budano, S. Bussino, F. Callet, Q. Campagna, M. Campajola, M. Carminati, G. Casarosa, C. Cecchi, P. Cheema, C. Chen, L. Chen, B. G. Cheon, C. Cheshta, H. Chetri, K. Chilikin, K. Chirapatpimol, H. -E. Cho, K. Cho, S. -J. Cho, S. -K. Choi, S. Choudhury, S. Chutia, J. Cochran, J. A. Colorado-Caicedo, I. Consigny, L. Corona, S. Cuccuini, J. X. Cui, E. De La Cruz-Burelo, S. A. De La Motte, G. De Nardo, G. De Pietro, R. de Sangro, M. Destefanis, S. Dey, R. Dhayal, A. Di Canto, J. Dingfelder, Z. Doležal, X. Dong, M. Dorigo, G. Dujany, P. Ecker, J. Eppelt, R. Farkas, P. Feichtinger, T. Ferber, T. Fillinger, C. Finck, G. Finocchiaro, F. Forti, A. Frey, B. G. Fulsom, A. Gabrielli, P. Gagneja, E. Ganiev, R. Garg, A. Garmash, G. Gaudino, V. Gaur, V. Gautam, A. Gaz, A. Gellrich, G. Ghevondyan, D. Ghosh, H. Ghumaryan, R. Giordano, A. Giri, P. Gironella Gironell, B. Gobbo, R. Godang, O. Gogota, W. Gradl, E. Graziani, D. Greenwald, K. Gudkova, Y. Han, K. Hayasaka, H. Hayashii, S. Hazra, C. Hearty, M. T. Hedges, A. Heidelbach, G. Heine, I. Heredia de la Cruz, T. Higuchi, M. Hoek, M. Hohmann, R. Hoppe, P. Horak, X. T. Hou, C. -L. Hsu, T. Humair, T. Iijima, K. Inami, N. Ipsita, A. Ishikawa, R. Itoh, M. Iwasaki, P. Jackson, D. Jacobi, W. W. Jacobs, E. -J. Jang, S. Jia, Y. Jin, A. Johnson, K. K. Joo, H. Kakuno, K. H. Kang, G. Karyan, T. Kawasaki, F. Keil, C. Ketter, C. Kiesling, C. Kim, D. Y. Kim, H. Kim, J. -Y. Kim, K. -H. Kim, H. Kindo, K. Kinoshita, P. Kodyš, T. Koga, S. Kohani, A. Korobov, S. Korpar, E. Kovalenko, R. Kowalewski, P. Križan, P. Krokovny, T. Kuhr, Y. Kulii, R. Kumar, K. Kumara, T. Kunigo, S. Kurokawa, A. Kuzmin, Y. -J. Kwon, S. Lacaprara, Y. -T. Lai, T. Lam, J. S. Lange, T. S. Lau, R. Leboucher, H. Lee, M. J. Lee, P. Leo, P. M. Lewis, C. Li, L. K. Li, Q. M. Li, S. X. Li, W. Z. Li, Y. Li, Y. B. Li, Y. P. Liao, J. Libby, J. Lin, Z. Liptak, V. Lisovskyi, C. Liu, G. Liu, M. H. Liu, Q. Y. Liu, D. Liventsev, S. Longo, A. Lozar, T. Lueck, C. Lyu, J. L. Ma, Y. Ma, M. Maggiora, S. P. Maharana, R. Maiti, G. Mancinelli, R. Manfredi, E. Manoni, M. Mantovano, D. Marcantonio, S. Marcello, M. Marfoli, C. Marinas, C. Martellini, A. Martens, T. Martinov, L. Massaccesi, M. Masuda, T. Matsuda, D. Matvienko, S. K. Maurya, M. Maushart, J. A. McKenna, Z. Mediankin Gruberová, R. Mehta, F. Meier, D. Meleshko, M. Merola, C. Miller, M. Mirra, K. Miyabayashi, H. Miyake, R. Mizuk, G. B. Mohanty, S. Moneta, A. L. Moreira de Carvalho, H. -G. Moser, N. Mudgal, Th. Muller, H. Murakami, R. Mussa, K. R. Nakamura, M. Nakao, Y. Nakazawa, Z. Natkaniec, A. Natochii, M. Nayak, M. Neu, S. Nishida, R. Nomaru, S. Ogawa, R. Okubo, H. Ono, Y. Onuki, G. Pakhlova, S. Pardi, J. Park, K. Park, S. -H. Park, A. Passeri, S. Patra, T. K. Pedlar, L. E. Piilonen, P. L. M. Podesta-Lerma, T. Podobnik, L. Polat, A. Prakash, R. pramanik, V. Prasad, C. Praz, S. Prell, E. Prencipe, M. T. Prim, I. Prudiiev, H. Purwar, P. Rados, S. Raiz, K. Ravindran, J. U. Rehman, M. Reif, S. Reiter, M. Remnev, L. Reuter, D. Ricalde Herrmann, I. Ripp-Baudot, S. H. Robertson, J. M. Roney, A. Rostomyan, N. Rout, G. Russo, S. Saha, D. A. Sanders, S. Sandilya, L. Santelj, C. Santos, V. Savinov, B. Scavino, J. Schmitz, S. Schneider, G. Schnell, K. Schoenning, C. Schwanda, Y. Seino, K. Senyo, J. Serrano, C. Sfienti, W. Shan, C. P. Shen, X. D. Shi, T. Shillington, T. Shimasaki, J. -G. Shiu, D. Shtol, B. Shwartz, A. Sibidanov, F. Simon, J. B. Singh, J. Skorupa, A. Soffer, A. Sokolov, E. Solovieva, S. Spataro, K. Špenko, B. Spruck, M. Starič, P. Stavroulakis, S. Stefkova, R. Stroili, M. Sumihama, M. Takahashi, M. Takizawa, U. Tamponi, S. S. Tang, K. Tanida, F. Testa, A. Thaller, D. V. Thanh, T. Tien Manh, O. Tittel, R. Tiwary, E. Torassa, F. F. Trantou, I. Tsaklidis, M. Uchida, I. Ueda, T. Uglov, K. Unger, Y. Unno, K. Uno, S. Uno, Y. Ushiroda, R. van Tonder, K. E. Varvell, M. Veronesi, A. Vinokurova, V. S. Vismaya, L. Vitale, V. Vobbilisetti, R. Volpe, M. Wakai, S. Wallner, M. -Z. Wang, A. Warburton, M. Watanabe, S. Watanuki, C. Wessel, X. P. Xu, B. D. Yabsley, S. Yamada, W. Yan, W. P. Yan, J. Yelton, K. Yi, J. H. Yin, K. Yoshihara, C. Z. Yuan, J. Yuan, L. Yuan, Y. Yusa, L. Zani, F. Zeng, M. Zeyrek, B. Zhang, X. Zhao, V. Zhilich, J. S. Zhou, Q. D. Zhou, X. Y. Zhou, L. Zhu, R. Žlebčík
Categories: hep-ex
Abstract:
We search for the pionic transitions $X(3872)\toπ^0χ_{cJ}(1P)$ $(J = 0,~1,~2)$ and $X(3915)\toπ^0χ_{c1}$ in $B^+\to π^0χ_{cJ}K^+$ decays using the Belle and Belle~II data samples collected at the $Υ(4S)$ resonance, corresponding to integrated luminosities of $711~\mathrm{fb}^{-1}$ and $492~\mathrm{fb}^{-1}$, respectively. We report the first evidence for the decay $X(3872)\toπ^0χ_{c0}$ with a significance of $3.4σ$, including systematic uncertainties. We measure the product of branching fractions ${\cal B}(B^+\to X(3872)K^+)\times{\cal B}(X(3872)\toπ^0χ_{c0})=(20.0\pm6.8\pm2.3)\times10^{-6}$ and the branching fraction ratio ${\cal B}(X(3872)\toπ^0χ_{c0})/{\cal B}(X(3872)\toπ^+π^-J/ψ)=2.3\pm0.8\pm0.4$, where the first and second uncertainties are statistical and systematic, respectively. The upper limits at 90\% credibility on the products of branching fractions for the $π^0χ_{c1}$ and $π^0χ_{c2}$ modes are $7.5\times10^{-6}$ and $15.3\times10^{-6}$, respectively. The corresponding upper limits on the branching fraction ratios relative to the $π^+π^-J/ψ$ decay are $0.9$ and $1.8$. The measured branching fractions for $X(3872)\toπ^0χ_{cJ}$ are consistent with several theoretical predictions based on the hadronic molecular interpretation of the $X(3872)$. No significant signal is seen for the $X(3915)\toπ^0χ_{c1}$ decay, and we set the 90\% credibility upper limit of ${\cal B}(B^+\to X(3915)K^+)\times{\cal B}(X(3915)\toπ^0χ_{c1})<6.6\times10^{-6}$, while the decays for $J=0$ and 2 are forbidden by parity conservation.
Published: 2026-06-23 13:40:03
Authors: Nikita Ushakov, Bayarto Lubsandorzhiev, Arslan Lukanov, Andrei Sidorenkov, Dmitrii Voronin
Categories: physics.ins-det
Abstract:
We report the observation of anomalously long-delayed afterpulses in photomultipliers of the Baksan Large Neutrino Telescope project$~-$ 10-inch R7081-100, 8-inch R5912-100, 20-inch R12860 photomultipliers produced by Hamamatsu Photonics, and 20-inch N6205 photomultipliers produced by NNVT. The mean delay times relative to the main pulses are approximately $85~μ$s, $73~μ$s, $260~μ$s, and $90~μ$s, respectively. The probability of such afterpulses does not exceed 0.1% per photoelectron, and their amplitudes are strictly confined to the single-photoelectron level, regardless of the amplitude of the main pulse. The delay time of these afterpulses shows no significant dependence on the PMT operating voltage.
Published: 2026-06-22 19:32:57
Authors: Deepanjhan Das, Vishwesh Ramanathan, Shankar Narasimhan
Categories: eess.SY
Abstract:
The dynamic behavior of numerous engineering processes is effectively characterized through differential-algebraic equations (DAEs), commonly referred to as descriptor systems. While substantial progress has been achieved in identifying dynamic models governed by ordinary differential equations (ODEs), limited research has addressed the identification of descriptor systems from measured data. This work presents a systematic methodology for identifying the DAE model of a linear descriptor system in discrete difference equation form under errors-in-variables (EIV) setting, where both input and output measurements are corrupted by random noise. The proposed methodology generalizes the identification framework to handle scenarios where the system contains multiple algebraic and different ordered differential relations. The key innovation involves a partial stacking procedure of lagged data matrix with a sequentially increasing lag window that identifies all the differential relations individually. This is preceded by an iterative estimation of the measurement error covariance matrix that is diagonal and heteroskedastic, under large sample conditions. The algorithm simultaneously estimates the number of differential and algebraic relations, observability indices and delay parameters of the differential equations, and all the model coefficients directly from measured data without requiring prior specification from the user. The framework addresses the increased complexity arising from multiple dynamic coupled interactions while maintaining computational tractability through systematic decomposition of the identification problem. Effectiveness of the proposed methodology is demonstrated through several simulation studies.
Published: 2026-06-22 19:28:27
Authors: Dragi Anevski, ElBatoul Manel Merai
Categories: math.ST
Abstract:
In this paper we propose estimators of the parameter functions in an Aalen additive hasard regression model. The estimators are the individual and componentwise $l^2$ projections of the naive estimators resulting from the ordinary least squares estimator in the Aalen additive hazard model on the space of monotone functions. We provide pointwise limit distribution results for the resulting estimators, that exhibit $n^{-1/3}$ rate of convergence and the Chernoff distribution as the limit distribution.
Published: 2026-06-22 19:23:59
Authors: Lukas Ottenhof, Thibaud Lutellier
Categories: cs.SE, cs.AI
Abstract:
Jupyter Notebooks are an increasingly popular coding environment used across many domains, especially in Python-based data science and scientific computing. Originally used for prototyping and interactive exploration, notebooks are increasingly used to develop more complex programs, leading to a rapid rise in buggy notebooks on platforms like GitHub. To address this trend, we present JupOtter, a bug detection system designed specifically for Jupyter Notebooks. JupOtter features three novel contributions: (1) a notebook-specific tokenization strategy that preserves cell structure, (2) a cell-level bug prediction technique, and (3) a new labeled dataset, OtterDataset, containing over 21,000 notebooks annotated for fine-grained cell-level bug detection. JupOtter achieves cell-level bug detection F1 scores that surpass static analyzers and large language models in two out of three evaluation datasets.
Published: 2026-06-22 19:21:41
Authors: Ratan Bahadur Thapa, Steffen Staab
Categories: cs.DB
Abstract:
We extend SPARQL with a generative query construct, called \tx{GenOp}, whose evaluation calls a language model and produces typed solution mappings. We define the semantics of the GenOp in the query in a way that maintains the fixed-dataset assumption, on which formal semantics of SPARQL build, and extend solution mappings with values generated by the language model. We formalize the semantics of the extended language over these mappings using a compatibility relation that generalizes equality and supports similarity-based matching between RDF terms and generated values. We analyze the semantic consequences of generative query patterns, focusing on mapping-level recursion induced by the reuse of generated bindings. Under deterministic bounded generation and finite candidate coverage assumptions, we characterize acyclic and stratified fragments with fixpoint semantics, establish algebraic equivalence and semantics-preserving rewrite rules, and provide an executable evaluation method; and we show that data and combined complexity coincide with those of standard SPARQL.
Published: 2026-06-22 19:14:51
Authors: Natalia Moreno-Blasco, Anusha Ihalapathirana, Pekka Siirtola, Miguel Fernandez-de-Retana
Categories: cs.LG, q-bio.QM, stat.ME, stat.ML
Abstract:
Survival analysis is central to clinical decision-making, yet reliable time-to-event models require large, diverse cohorts that are rarely available at a single institution, while privacy regulations restrict the centralization of patient data. Federated learning (FL) offers a privacy-preserving alternative by training shared models without exchanging raw data, but its effectiveness for survival modeling under realistic, heterogeneous conditions remains insufficiently understood. This paper presents a systematic, multi-model evaluation of federated survival analysis on a cross-institutional breast cancer cohort with naturally heterogeneous distributed clients. Three representative survival models, the Cox Proportional Hazards model, DeepSurv, and Random Survival Forest (RSF), are compared across centralized, local, and federated training, and three federated optimization strategies (FedAvg, FedProx, and FedAdam) are assessed for the gradient-based models. Results show that FL consistently outperforms local training and approaches, and occasionally exceeds, centralized performance, while RSF offers the best overall balance of discrimination, calibration, and robustness across heterogeneous clients. We further find that performance depends on the diversity of client distributions, and that FedAvg and FedProx are stronger and more stable than FedAdam. Based on these findings, we derive practical, decision-oriented guidelines mapping data, privacy, interpretability, and resource constraints to recommended model and training-paradigm choices for federated survival modeling in healthcare.
Published: 2026-06-22 18:52:57
Authors: Barbara Giunti, Elizabeth Munch
Categories: cs.CG
Abstract:
In this paper, we provide a new construction for studying parameterized persistence, called a canopy. We give two versions of this construction: the A-canopy, retaining all information about points on the diagonal of the persistence diagram; and the D-canopy, encoding the information of the "standard" persistence diagram. We do this by making a simple but major modification in the persistence bundle representation information: namely, rather than tracking a point in the persistence diagram, we instead track some choice of pairs of simplices that created said point. This viewpoint is a combinatorial version of tracking the chain complex information rather than just the output of persistence. We show how to construct the canopies from any filtered filtration function, proving, using the algebraic structure of filtered chain complexes, that different choices of pairs result in homeomorphic structures. Finally, we showcase the power of our approach by using canopies to define vines even in the presence of points with multiplicity; to discuss monodromy; and to obtain some immediate results linking non-trivial monodromy in the persistent homology transform with the existence of non-Hausdorff points in the canopy.
Published: 2026-06-22 18:48:10
Authors: Konstantin Yatsenko, Arvind Thiagarajan
Categories: cs.LG
Abstract:
Generative molecular models for drug design are a promising direction with much active research. In the next phase of computational drug design, such models will need to understand small molecule structure and protein-ligand interactions, and they will need to possess the machinery to generate molecules \textit{de novo}. Incorporating each feature poses a critical challenge. Equally important, yet often treated as secondary, is the ability to grow a molecule from a partial starting point -- a scaffold or fragment supplied by a chemist -- which is the central operation of lead optimization. We present Sesame (Spatial Evoformer for a Structure-Aware Molecular Engine), a diffusion-based molecular generation model that leverages a novel spatial pairformer module to condition on partial molecular structure and the surrounding protein pocket, both expressed as continuous spatial density maps. This single conditioning mechanism supports both \textit{de novo} generation and fragment-conditioned lead optimization, letting a medicinal chemist prune a hit to a scaffold and have Sesame grow it in productive ways. In addition to this module, we also introduce a diffusion framework for joint denoising of atom types, bond types, and positions, along with a trajectory finetuning scheme that trains on the model's own sampling rollouts to improve generation quality. Sesame is trained on a large corpus of ligand-only and protein-ligand datasets.
Published: 2026-06-22 18:27:59
Authors: Odylo Costa
Categories: math.DS
Abstract:
We construct a smooth nonsingular periodic flow on a compact manifold with high emergence, in sharp contrast with the low statistical complexity of periodic self-maps. The construction is based on a modification of the Epstein--Vogt counterexample to the Periodic Orbit Conjecture and on the high-emergence mechanism of Berger--Bochi.
Published: 2026-06-22 18:22:53
Authors: Marvin Pafla, Jesse Hoey, Kate Larson, Mark Hancock
Categories: cs.HC
Abstract:
Explainability is often framed as a property of an AI model, with explanations extracted from its internals and shown to users. In this argument paper, we instead provide an embodied account of explainability based on Dourish and enactivist cognition: understanding is created in use as people act on affordances in shared practice. Using demonstrations and conceptual analysis, we reveal ontological obstacles when "looking inside" large language models: surrogates import external abstractions that can be mistaken for the model's, and focusing on internal reasoning misses that explainers participate in their own understanding. We discuss these obstacles in XAI practice, arguing that many explanations are misnamed, which skews their purpose and can increase overreliance. Finally, we highlight how embodied explanations reorganize sense-making by making what matters publicly available for action, and argue that explainability claims should be reserved for designs that provide affordances to probe, coordinate, and repair behaviour in situated practice.
Published: 2026-06-22 18:07:17
Authors: Molly Kaplan, Antoine Tilloy
Categories: quant-ph, cond-mat.str-el, hep-th
Abstract:
We introduce a variational method to solve continuum quantum models with discrete tensor network techniques. The method leverages wavelet matrix product states (wMPS): matrix product states built on top of sufficiently regular ($N\geq 6$) Daubechies scaling functions. These states live in the continuum field theory Fock space, have finite energy density, and can be optimized with standard algorithms, without restriction to free theories. Further, exploiting the multi-resolution analysis built into wavelets, and its quantum circuit description, we can iteratively refine wMPS to obtain accurate approximations at arbitrarily fine length-scales. We showcase the efficiency of the method on the Lieb-Liniger model, computing energy density and correlation functions.
Published: 2026-06-22 18:01:57
Authors: J. S. G. Mombarg, S. Mathis
Categories: astro-ph.SR
Abstract:
Asteroseismology of gravity (g)-mode pulsators covering BAF-type stars have shown that angular momentum is redistributed during the main sequence. The efficiency of the transport, however, remains largely uncalibrated. This paper aims at exploiting a sample of 2937 characterized g-mode pulsators (the largest one to-date) to place constraints on the efficiency of angular momentum transport by assuming an effective viscosity or an Eddy-viscosity based on the Tayler-Spruit dynamo within a fully-diffusive framework. We compute grids of rotating stellar evolution models that we then use to simulate a population of stars by sampling from these grids with prior distributions on the mass, age and initial rotation rate. We then compare these simulated distributions of rotation frequencies and specific angular momentum ($J/M$) to the ones of the sample of observed stars. We find that a fully-diffusive framework for the transport of angular momentum during the main sequence is sufficient to explain the observed evolution of near-core rotation frequencies, the observed differential rotation, and the observed mass-dependence of $J/M$ when the effective viscosity (assumed constant) is $10^6\,{\rm cm^2\,{\rm s^{-1}}}$ or larger. Viscosities predicted by the Tayler-Spruit dynamo are in general far above this value and can explain the data as well. Future observational studies of main sequence g-mode pulsators are encouraged to measure core-to-surface rotation rates, particularly of B-type stars. In this work we have exploited the constraining potential of near-core rotation frequencies alone, while the contrast with the surface rotation would allow us to unravel the mechanisms driving the transport further.
Published: 2026-06-22 18:01:01
Authors: AmirFarzan Esmaeili, Arman Esmaili, Pasquale Dario Serpico
Categories: hep-ph, astro-ph.HE
Abstract:
The joint observation of astrophysical sources in gamma rays and neutrinos can provide invaluable insight into the physical conditions of the source, including its size, particle densities, and acceleration and production mechanisms. In this work, we investigate the role of nuclear composition in high-energy astrophysical environments. Using NGC 1068 as a representative example, we perform detailed Monte Carlo simulations of nuclear and electromagnetic cascades within the source and study the imprints of the injected nuclear composition on the resulting neutrino and gamma-ray emissions. We further discuss the importance of MeV-GeV gamma-ray observations for constraining the source composition in the context of future gamma-ray experiments. A dedicated re-analysis of archival COMPTEL observations is also presented.
Published: 2026-06-22 18:00:20
Authors: Nachiket Karve, Nathan Rose, David Campbell, Anatoli Polkovnikov
Categories: cond-mat.stat-mech, nlin.CD, quant-ph
Abstract:
We propose a unified perspective on classical and quantum chaos based on the stability of a system's stationary states under slow driving. We probe this sensitivity via the system's susceptibility to the average protocol speed, which we call the ``speed-Fisher information," and relate it to irreversible entropy production in the system. We show that chaotic dynamics manifests as a divergence of the speed-Fisher information with the protocol time, and that this response is controlled by the perturbation's low-frequency spectral weight. This approach to chaos applies to both classical and quantum Hamiltonian systems, and naturally extends to non-Hamiltonian classical flows. We illustrate this framework with simple classical and quantum examples, along with a non-Hamiltonian flow that qualitatively exhibits analogous low-frequency spectral behavior.
Published: 2026-06-22 18:00:10
Authors: Hadrien Paugnat, Tommaso Treu, Anna M. Nierenberg, Anowar J. Shajib, Shawn Knabel, Daniel Gilman
Categories: astro-ph.GA, astro-ph.CO
Abstract:
Strong gravitational lensing is a powerful probe of dark matter (DM) structure on subgalactic scales: in particular, statistics of flux-ratio anomalies (discrepancies between mass model predictions and observed flux ratios) in quadruply imaged quasars are sensitive to perturbations by low-mass DM halos down to $\sim 10^6 M_\odot$. Studies leveraging these anomalies require high-quality flux-ratio measurements from an emission region insensitive to stellar microlensing. In this paper, we present the first measurement of narrow-line flux ratios for a gravitationally lensed quasar using JWST/NIRSpec with Integral Field Spectroscopy (IFS), targeting the well-studied system RXJ1131$-$1231. Flux ratios are extracted from the [S III] 9071/9533 $Å$ narrow-line doublet - the first use of this doublet for substructure studies - by performing a full lens model reconstruction to isolate the unresolved nuclear emission from extended narrow-line emission. The resulting spectra are jointly modeled using $\texttt{lensqso-specfit}$, a publicly available software package introduced in this work for the simultaneous spectral fitting of multiple lensed quasar images. We achieve $\sim$ 5% uncertainties on the flux ratios, comparable to the precision of JWST/MIRI warm dust measurements, and detect a clear anomaly in the cusp images relative to a standard smooth lens model. Our results are in good agreement with previous narrow-line measurements and broadly consistent with JWST/MIRI warm dust flux ratios, with marginal ($\sim 2-3σ$) deviations. We demonstrate how such shifts between differently sized emission regions may be enhanced by small ($\sim 10$ pc) spatial offsets. Our method is generalizable to other systems with existing or future IFS observations, and the combination of narrow-line and warm dust flux ratios offers a new avenue for improving DM constraints with flux-ratio anomaly statistics.
Published: 2026-06-22 18:00:08
Authors: Ioana Coman, Myungbo Shim, Masahito Yamazaki, Yehao Zhou
Categories: math.AG, hep-th, math-ph, math.QA, math.RT
Abstract:
Given a quiver Q with gauge dimension $\bf v$ and framing dimension $\bf w$, one can define the extended quiver variety $\widetilde{\mathcal M}(\mathbf v,\mathbf w)$, which is a smooth family of deformations of the Nakajima quiver variety $\mathcal M(\mathbf v,\mathbf w)$. In this paper we discuss two vertex algebras which chiralize the geometry $\widetilde{\mathcal M}(\mathbf v,\mathbf w)$. We construct a sheaf of $\hbar$-adic vertex superalgebras $\mathscr D^{\mathrm{ch}}_{\widetilde{\mathcal M}(\mathbf v,\mathbf w),\hbar}$ on $\widetilde{\mathcal M}(\mathbf v,\mathbf w)$ which quantizes the jet bundle of $\widetilde{\mathcal M}(\mathbf v,\mathbf w)$, and define a vertex algebra $\mathsf D^{\mathrm{ch}}(\widetilde{\mathcal M}(\mathbf v,\mathbf w))$ to be the $\hbar=1$ specialization of the $\mathbb C^{\times}$-finite part of the vector space of global sections $Γ(\widetilde{\mathcal M}(\mathbf v,\mathbf w), \mathscr D^{\mathrm{ch}}_{\widetilde{\mathcal M}(\mathbf v,\mathbf w),\hbar})$. We define another vertex superalgebra $\mathcal V(\mathbf v,\mathbf w)$ by BRST reduction of the tensor product of the $βγbc$-system and Heisenberg VOA associated to the quiver Q, and show that there exists a natural vertex superalgebra map from $\mathcal V(\mathbf v,\mathbf w)$ to $\mathsf D^{\mathrm{ch}}(\widetilde{\mathcal M}(\mathbf v,\mathbf w))$. Under certain technical assumptions, we prove that the negative degree BRST cohomologies of the tensor product of $βγbc$-systems and Heisenberg VOA associated to the quiver Q are zero, and under stronger assumptions, that the aforementioned vertex superalgebra map is injective.
Physically, the vertex superalgebra $\mathcal V(\mathbf v,\mathbf w)$ is closely related to the boundary VOA of the H-twisted 3D $\mathcal N=4$ quiver gauge theory associated to the quiver Q with gauge and framing dimension vectors $\bf v$ and $\bf w$.
Published: 2026-06-22 18:00:03
Authors: Mariano Garralda-Barrio
Categories: cs.SE, cs.AI, cs.CL, cs.MA
Abstract:
Graph and multi-agent orchestration frameworks make production large language model (LLM) workflows practical, but they do not by themselves solve conversational continuity when users maintain several interdependent objectives. This conceptual systems paper focuses on the high-complexity end of that design space, where goals can be suspended, resumed, revised, and invalidated by actions in other goals. We introduce the Goal-Oriented Dialogue Runtime (GODR), a framework-neutral design pattern that treats goals, task frames, lifecycle state, invalidation rules, and resumption contracts as first-class runtime objects while delegating bounded execution to graph runtimes, agents, tools, or application programming interfaces (APIs). GODR is not proposed as a replacement for workflow graphs in simple guided processes; it is intended for complex, multi-domain, interruptible conversations where objective continuity cannot be recovered reliably from agent identity, chat history, or execution-graph position alone. The paper formalizes the problem, proposes runtime objects and architecture-selection criteria, and frames evaluation as an agenda for future empirical validation rather than as a measured performance claim.
Published: 2026-06-22 18:00:03
Authors: Alice Somigliana, Giulia Perotti, Nicolás T. Kurtovic, Thomas Henning, Myriam Benisty, Andrew D. Sellek, Melissa McClure, Zak L. Smith, Aditya M. Arabhavi, Alessio Caratti o Garatti, Valentin Christiaens, Ewine F. van Dishoeck, Danny Gasman, Sierra L. Grant, Manuel Güdel, Till Kaeufer, Inga Kamp, Lucas Stapper, Benoît Tabone, Milou Temmink, Marissa Vlasblom
Categories: astro-ph.EP, astro-ph.SR
Abstract:
[Abridged] HK Tau is a roughly equal mass pre-main sequence binary system consisting of a low-inclination primary (57 deg) and an edge-on (83 deg) secondary. We present JWST/MIRI observations targeting both sources, taken as part of the JWST GTO program MINDS. The spectra reveal a line-rich, CO2-dominated primary and a line-poor secondary; this evidence, albeit in line with the evolutionary-motivated trend uncovered by recent observations of binaries at MIRI wavelengths, is likely due to the different configuration of the two sources. Indeed, thermochemical disc models coupled with radiative transfer show that, at inclinations comparable to that of HK Tau B, only ionised atomic lines are expected to remain visible in the spectra. While blocking molecular emission lines, however, the edge-on configuration allows ice absorption bands to be visible against the continuum; in this framework, the HK Tau system provides an unprecedented opportunity to have a simultaneous view of the solid and gaseous component of a pair of coeval protoplanetary discs, thanks to the complementary inclination of the two sources. We detect water ice at 6.2 and 13.6um, CO2 ice at 15.2um, and NH4+ ice at 6.85um in the spectrum of HK Tau B; an additional absorption band between 8.3 and 9um is compatible with both silicate stretching and C-H bending. Neither of the two sources show signs of PAHs. Extended H2 emission is present around both discs, although much more elongated in HK Tau B. The distinctive 'X' shape centred in B, combined with the intensity, morphology, and spectral characteristics of the ionised atomic lines [Ar II], [Ne II], and [Ne III] suggests a low-velocity wind origin with a wide (~ 70 deg) semi-opening angle. The lower forbidden line fluxes and smaller extent of the H2 emission around A imply that, if a wind is launched from the primary as well, it is too cold or dense to be ionised.
Published: 2026-06-22 18:00:03
Authors: Minhao He, Yen-Chen Tsui, Ran Peng, Kenji Watanabe, Takashi Taniguchi, Oskar Vafek, Ali Yazdani
Categories: cond-mat.mes-hall, cond-mat.str-el
Abstract:
In the presence of a magnetic field, electronic states of moiré quantum materials develop a Hofstadter spectrum that provides a unique setting for studying the interplay between band topology and strong electron-electron interaction. Using scanning tunneling microscopy, we study Hofstadter's states in bilayer graphene aligned with hexagonal BN and directly visualize the formation of interaction-driven symmetry breaking Chern insulators. Our measurements reveal the formation of phases that double, triple or quadruple the moiré unit cell at fractional filling of the Hofstadter bands, as well as states with complex intra-unit-cell wave functions. We visualize two distinct quantum phenomena in different Chern states, including quantum melting driven by the appearance and proliferation of topological defects, and a quantum transition co-occurring with phase competition and separation.
Published: 2026-06-22 18:00:02
Authors: Debasish Borah, Nayan Das
Categories: hep-ph, astro-ph.CO
Abstract:
We study the possibility of probing non-thermal leptogenesis with multi-peaked high-frequency gravitational waves (GW) by considering heavy right-handed neutrino (RHN) produced from primordial black hole (PBH) evaporation to be responsible for generating the required lepton asymmetry. The decay of RHN also produces a GW spectrum due to graviton bremsstrahlung with the corresponding amplitude being enhanced for heavier RHN. The presence of an ultra-light PBH dominated epoch not only ensures sufficient production of RHNs, but also keeps the leptogenesis scenario free from strong washout problem of thermal leptogenesis at very high scale. In addition, the PBH dominated epoch also helps in generating a gravitational bremsstrahlung spectrum distinct from the stochastic GW background from the thermal bath. Finally, PBH evaporation also brings two separate sources of GW via density perturbation and graviton emission via Hawking evaporation. For the most optimistic scenario with very high scale seesaw consistent with neutrino mass and leptogenesis, this leads to a multi-peaked GW spectrum with peak frequencies lying in the MHz-EHz range.
Published: 2026-06-22 18:00:01
Authors: Conor L. Ransome, Bhagya M. Subrayan, David J. Sand, Brian Hsu, Xander J. Hall, Jeniveve Pearson, K. Azalee Bostroem, Jennifer E. Andrews, Joszef Vinko, J. Craig Wheeler, Phillip Noel, Lei Hu, Tomas Cabrera, Stefano Valenti, Wynn V. Jacobson-Galan, Nathan Smith, Alexei V. Filippenko, Mojgan Aghakhanloo, Igor Andreoni, Moira Andrews, Iair Arcavi, Raphael Baer-Way, Emma R. Beasor, Edo Berger, Federica B. Bianco, Peter Blanchard, Thomas G. Brink, Siddarth Chaini, Adrian Crawford, Yize Dong, Joseph Farah, Noah Franz, Sebastian Gomez, Melissa L. Graham, Daichi Hiramatsu, Agoston Horti-David, Griffin Hosseinzadeh, D. Andrew Howell, Philip A. James, Saurabh W. Jha, Charles D. Kilpatrick, Lindsey A. Kwok, Gavin P. Lamb, Amanda R. Lopes, Michael Lundquist, Clara E. Martinez-Vasquez, Thomas Matheson, Curtis McCully, Maryam Modjaz, Gregory S. H. Paek, Antonella Palmese, Avi Patel, Joanne L. Pledger, Aravind P. Ravi, Jeonghee Rho, Krisztian Sarneczky, Manisha Shrestha, Richard Smith, Analia V. Smith Castelli, Monika Soraisam, Jay Strader, Francisco Valdes, Sergiy Vasylyev, V. Ashley Villar, A. Katherina Vivas, Lifan Wang, Samuel D. Wyatt, Kathryn Wynn, WeiKang Zheng
Categories: astro-ph.HE, astro-ph.GA, astro-ph.SR
Abstract:
The Legacy Survey of Space and Time (LSST) will start in late-summer 2026, revolutionizing transient astronomy. Here, we present the Dark Energy Camera (DECam) Shadow Survey, which is designed to maximize the science potential of LSST by shadowing LSST observations of local galaxy-cluster fields, producing a nightly cadence of these fields. The Shadow Survey will discover extremely young supernovae (SNe), SN precursors, as well as other explosive transients and exotic phenomena, helping to characterize such transients at unprecedented cadence and depth when combined with LSST. We describe our workflow, pipeline, public data releases, and candidate vetting. As an early result of Shadow, we present the fitful luminous blue variable (LBV) eruptions of AT2017des in the Virgo-Cluster galaxy NGC4532. AT2017des has short-timescale variability (of order 10 days), peaking at around $M_r=-12.5$mag, brighter than normal LBVs, and similar to the more extreme flaring of hot LBVs/SN impostors such as SN2000ch, AT2016blu, and the precursor activity of SN2009ip. Our spectral time-series reveals features typical of these hot LBVs and SN impostors/precursors. Combining our data with long-baseline photometry from additional observatories, we find that the peaks of the outbursts of AT2017des are getting brighter over time, with 2026 peak fluxes being up to 5 times greater than in 2023 and an average brightening of $\sim0.05$ mag yr$^{-1}$. The peaks of AT2017des are more luminous than those of most other LBVs, only being fainter than bright precursors such as SN2009ip, and extreme SN impostors such as AT2016blu. AT2017des may therefore be ``ramping up'' to a terminal explosion.
Published: 2026-06-22 18:00:00
Authors: Michael Zhang, Qiao Xue, Jeehyun Yang, Vighnesh Nagpal, Michael R. Line, Guangwei Fu, Matthew C. Nixon, Jacob L. Bean, Peter Gao, Eliza M. -R. Kempton, Luis Welbanks, Edward M. Bryant, Daniel Bayliss, Madison Brady, Jean-Michel Désert, Vincent Van Eylen, Jonathan J. Fortney, Andrés Jordán, Vivien Parmentier, Caroline Piaulet-Ghorayeb, Elyar Sedaghati, Kevin B. Stevenson, Amaury H. M. J. Triaud
Categories: astro-ph.EP
Abstract:
We report the JWST NIRSpec/PRISM transit spectrum of TOI-6894b, an exceptional 420 K sub-Saturn that is the only known giant planet transiting a late M dwarf. Remarkably, both the light curve and the transit spectrum exhibit almost no stellar contamination. The spectrum is dominated by prominent absorption features from CH$_4$ and the photochemical product CS$_2$. For the first time in an exoplanet spectrum, NH$_3$ is visually evident, while subtler features from H$_2$O, and CO$_2$ can also be seen. We significantly improve upon state-of-the-art photochemical reaction networks, and use our new network to run radiative-convective photochemical models at different metallicities. These models show that the spectrum--in particular the size of the NH$_3$ and CO$_2$ features relative to the CH$_4$ and H$_2$O features--is most consistent with a metallicity of 3--10$\times$ solar. Using a semi-free retrieval framework that perturbs the self-consistent model's abundance and temperature profiles to fit the data, we find that the planet's C/O, N/O, and S/O ratios are broadly consistent with solar values. A grid retrieval on 1D radiative-convective photochemical equilibrium (RCPE) models reveals a similar result: $[M/H]=0.46 \pm 0.08$ and C/O=$0.69 \pm 0.06$. The planet's atmospheric metallicity, abundance ratios, and bulk metal fraction are all strikingly similar to that of Jupiter, Saturn, and other gas giant exoplanets, despite orbiting a very low-mass star.
Published: 2026-06-22 17:59:54
Authors: Yehonathan Litman, Xiaoxuan Ma, Manan Shah, Nicolas Ugrinovic, Kris Kitani, Fernando De la Torre, Shubham Tulsiani
Categories: cs.CV
Abstract:
Reconstructing dynamic non-rigid objects from monocular video requires integrating visual cues from direct observations with data-driven priors over geometry and appearance. Prior approaches either learn to directly predict 4D representations from visual input or initialize a 3D representation that is subsequently deformed and refined based on video evidence. However, the former are constrained by the scarcity of 4D training data, while the latter leverage priors only for the initial reconstruction and rely solely on video supervision thereafter; neither handles complex in-the-wild scenarios with large deformations and occlusions well. We present Lift4D, a test-time optimization framework that addresses both limitations. First, we adapt an existing single-view 3D reconstruction model to yield temporally consistent per-frame predictions via causal latent conditioning, providing a coherent initialization for a deformable 3D Gaussian Splatting representation. We then ``sculpt'' this representation to match the input video through an occlusion-aware optimization that faithfully recovers visible surface details while completing unobserved regions using a view-conditioned diffusion prior. We demonstrate that Lift4D clearly improves over prior 4D reconstruction methods, particularly on challenging in-the-wild sequences with severe occlusions and non-rigid motion.
Published: 2026-06-22 17:59:53
Authors: Manas Mehta, Fangcong Yin, Greg Durrett
Categories: cs.CL
Abstract:
Large language models (LLMs) are typically pretrained on short sequences and then extended to work on longer sequences with additional training. However, such LLMs still struggle to further generalize to very long sequences. We propose Randomized YaRN, a training method that improves length generalization by combining YaRN-based positional extrapolation with randomized positional encoding and a length curriculum. During training on short context data, tokens are assigned YaRN positional encodings sampled from a larger position range, exposing the model to out-of-distribution positional representations even on short-context inputs. We evaluate Randomized YaRN on two challenging long-context reasoning benchmarks, BABILong and Multi-Round Coreference Resolution (MRCR). When training on data with <8K context, Randomized YaRN consistently improves reasoning performance on context lengths from 16K to 128K and outperforms standard fine-tuning, with the largest gains appearing at far out-of-distribution lengths. Our results suggest that progressively exposing models to OOD positional distributions provides an effective recipe for generalizable long-context reasoning.
Published: 2026-06-22 17:59:53
Authors: Rongxu Cui, Zongzheng Zhang, Jingrui Pang, Haohan Chi, Jinbang Guo, Saining Zhang, Shaoxuan Xie, Xin Jin, Yao Mu, Jiaolong Yang, Guocai Yao, Xianyuan Zhan, Ya-Qin Zhang, Hao Zhao
Categories: cs.RO
Abstract:
Despite the impressive manipulation capabilities of Vision-Language-Action (VLA) models, their operational safety under strict constraints remains largely unverified. To address this, we introduce a parametric safety benchmark to procedurally generate safety-critical scenarios with comprehensive stochasticity. To overcome the scalability bottlenecks of human teleoperation, we develop a novel keypose-driven data generation pipeline. Leveraging this infrastructure, we curate a large-scale dataset of 19,664 strictly collision-free demonstrations with extensive domain randomization. We then conduct a systematic cross-paradigm evaluation of eight VLA and two embodied foundation models. Our analysis reveals a critical generalization-safety tension: although high-diversity training fosters safer trajectories, task success remains fundamentally bottlenecked by sub-optimal trajectory synthesis and semantic misalignment. By providing a scalable pipeline, a robust dataset, and profound failure-mode insights, LIBERO-Safety establishes a crucial foundation for developing safe and reliable VLA models.
Published: 2026-06-22 17:59:52
Authors: Jiaming Liu, Yinxi Wang, Chenyang Gu, Siyuan Qian, Xiangju Mi, Hao Chen, Jiawei Chen, Qingpo Wuwu, Xiaoqi Li, Nuowei Han, Yiming Zhang, Xuheng Zhang, Yang Yue, Yeqing Yang, Lei Wang, Peng Jia, Hao Tang, Shanghang Zhang
Categories: cs.RO
Abstract:
Human-hand demonstrations provide a direct and scalable source of physical interaction data for robot learning. While manual retargeting is indispensable for establishing kinematic action correspondence across different morphologies, robust transfer requires going beyond geometry to address the underlying alignment of physical dynamics between human and robot manipulation. To address this, we introduce LaST-HD, a novel human-to-robot action learning paradigm that extends reasoning-before-acting VLA by aligning human-hand and robot demonstrations in a shared latent reasoning space. Rather than mimicking human kinematics, LaST-HD trains an auxiliary action-conditioned world model on unpaired human-hand and robot trajectories to synthesize unified latent targets. After aligning cross-embodiment representations in this shared forward-dynamics space, these targets supervise LaST-HD's latent reasoning process, enabling it to internalize shared physical dynamics and drive efficient human-hand action learning. Moreover, we develop Out-of-Lab (OOL) Glove, a low-cost motion-capture glove tailored to LaST-HD for human-hand data collection. The captured human data provide precise keypoints and serve as universal action supervision across grippers and dexterous hands. Armed with the aligned latent space and high-fidelity human-hand data, we develop a progressive mixed-to-human training recipe comprising mixed human-robot co-training and human-hand online correction post-training. Through mixed co-training, LaST-HD improves generalization to novel objects, scenes, and positions using only human-hand demonstrations. With online correction, LaST-HD further adapts to novel environments and achieves over 90\% accuracy using only 20 minutes of OOL glove data.
Published: 2026-06-22 17:59:28
Authors: Rishubh Parihar, Ayush Raina, R. Venkatesh Babu, Or Patashnik
Categories: cs.CV
Abstract:
Reference-based diffusion models enable highly controllable image generation by leveraging elements from input images to guide prompt-driven synthesis. However, these models are computationally expensive in runtime, and their cost scales severely with the number of input references. While the efficiency of diffusion models has been extensively studied in the context of prompt-driven generation, it remains largely under-explored in the realm of reference-based models. This setting presents unique challenges not addressed by methods focusing solely on generation. In particular, the wasteful representation of references as dense token grids offers significant opportunities for improvement. In this work, we present Sparse Context, a method for constructing sparse reference representations by retaining only a reduced subset of reference tokens. We observe that even without modifying the model, dropping a significant portion of reference tokens at inference time largely preserves its generation capabilities. To fully realize this potential, we fine-tune the model with random token dropping at varying ratios, encouraging robustness to partial reference representations. Crucially, this training strategy decouples the model from any specific token selection rule, allowing flexible control at inference time. At inference time, instead of random dropping, we apply task-aware token selection strategies that prioritize the most informative regions of the reference images, adapting the token budget to the input and task requirements. Extensive experiments show our method achieves a 4x increase in inference speed for multi-reference generation and an 2x for single reference generation. Importantly, this efficiency is achieved without compromising visual quality across both spatially-aligned editing and subject-driven generation.
Published: 2026-06-22 17:56:25
Authors: Reza Bayat, Ali Behrouz, Aaron Courville
Categories: cs.LG, cs.AI, cs.CL
Abstract:
Modern language models, including transformer, recurrent, and memory-based variants, share a common chassis: a stack of identical layers in which parameters are allocated uniformly across depth. This is a default inherited from the original transformer and largely unchanged since, yet a growing body of evidence suggests that layers contribute non-uniformly to the final output, with later layers refining the residual stream rather than transforming it. We ask whether parameter capacity should reflect this asymmetry. Our controlled experiment shows that, under a fixed budget, allocating more capacity to earlier layers and less to later layers improves perplexity over a uniform-width baseline, while the reverse allocation hurts. Building on this result, we introduce Tapered Language Models (TLMs), an architectural principle in which a parameter-bearing component is monotonically tapered across depth under a fixed total budget. MLPs are the natural site for this instantiation: they dominate parameter count across all modern LM families and expose width as a single, clean axis of variation. Across three model scales and four architectures (Transformer, Gated Attention, Hope-attention, and Titans), tapering MLP width via a smooth cosine schedule consistently improves perplexity and downstream benchmark performance over uniform baselines, at no additional parameter or compute cost. These findings establish depth-aware capacity allocation as a simple, architecture-agnostic axis of language model design, a free lever hidden in plain sight.
Published: 2026-06-22 17:52:59
Authors: David Mguni, Julian Ma, Jun Wang
Categories: cs.LG
Abstract:
Large Language Models (LLMs) are frequently portrayed as general-purpose solvers capable of solving arbitrary tasks. We argue that this view overlooks a fundamental constraint: language is a compressed and capacity-limited interface for conveying task information. Modelling User--System interaction as a bilevel \emph{cheap-talk} game, we analyse how latent tasks are encoded into prompts and reinterpreted under alignment and safety constraints. We introduce a conceptual decomposition separating task inference from execution and derive PAC-Bayes bounds that distinguish finite-sample estimation error from irreducible structural limitations. Our first main result establishes an \emph{expressivity floor}: language acts as a capacity-limited communication channel, and whenever the informational complexity of a task family exceeds the capacity of that channel, distinct tasks become unavoidably indistinguishable to the Solver, inducing a strictly positive error floor that cannot be eliminated by additional data, optimisation, or model scaling alone. We then establish an \emph{objective-misalignment floor}: when alignment constraints restrict the admissible output set, the User-ideal distribution may lie outside the feasible class, inducing an irreducible distortion. Together, these results yield a formal negative conclusion: prompt-conditioned LLMs are not universal problem solvers through prompting alone, as there exist task families for which correct behaviour is provably unattainable even in the infinite-data regime. More broadly, our analysis shows the limits of prompt-based generalisation arise from information-constrained communication and alignment-constrained objectives. This suggests that interfaces beyond natural language, including multimodal observations and, external memory, may reduce the inherent LLM limitations by increasing the task-relevant information available to the System.
Published: 2026-06-22 17:48:32
Authors: Dylan Cant
Categories: math.SG
Abstract:
Following the idea of Jungsoo Kang and Jun Zhang, we prove the strong Arnol'd chord conjecture for the boundary of a uniformly convex domain in $\mathbb{R}^{4}$, using an ellipsoid embedding construction due to Oliver Edtmair. We prove a general structural result for Legendrians $L$ which are eventually equivariantly essential (E3), in the sense that the $k$th Gutt-Hutchings capacity $c_{k}(D^{*}TL)$ is infinite for $k$ large enough. We show that any E3 Legendrian in the boundary of a Liouville domain $Ω$ bounds a chord of length at most $\liminf c_{k}(Ω)/k$.
Published: 2026-06-22 17:45:05
Authors: Eric Li
Categories: math.CO
Abstract:
We resolve Erdős Problem 550, originally asked as question (2) of Erdős, Faudree, Rousseau, and Schelp. Precisely, for fixed integers $k\geq 2$ and $1\leq m_1\leq \cdots \leq m_k$, we prove that, for every sufficiently large $n$ and every $n$-vertex tree $T$, $R(T,K_{m_1,\ldots,m_k}) \leq (k-1)(R(T,K_{m_1,m_2})-1)+m_1$. The proof combines a new off-Turán tree-embedding theorem with a compactness-and-rounding theorem for represented bounded-rank hypergraph obstructions. The embedding theorem follows from Szemerédi regularity and a local regular-matching embedding lemma of Hladký and Piguet. The compactness argument uses shadow hypergraphs to retain obstructions whose vertices escape along the limiting sequence.
Published: 2026-06-22 17:39:18
Authors: Diego E. Farchione, Ramzi Idoughi, Peter Wonka
Categories: cs.CV
Abstract:
Accurate volume and surface area estimation is critical for diverse applications, from marine ecology to medical diagnostics. However, existing methods often suffer from high computational costs and poor performance with sparse and noisy data. We propose a fully feed-forward framework that regresses scale-normalized volume and surface area and their associated uncertainties directly from multi-view images. By fusing 3D point cloud reconstructions with view-aligned 2D features through a graph-based decoder, our model bypasses iterative optimization, ensuring exceptional scalability and rapid inference. Experimental results demonstrate that our approach outperforms state-of-the-art methods, particularly when operating with a low number of input images. Validated across coral monitoring, dietary analysis, and anthropometry, our proposed framework provides a robust, adaptable solution for quantitative shape analysis. This architecture provides a high-speed, scalable alternative for precise geometric estimation from visual data, maintaining high performance even in resource-constrained or sparse-view scenarios.
Published: 2026-06-22 17:32:40
Authors: Tristan M. Lawrie
Categories: math-ph
Abstract:
We introduce an analytically invertible framework for wavefront construction based on the scattering properties of periodic waveguide networks governed by a gauge-shifted Helmholtz operator. By determining the exact transmission coefficients of the network, we express the lattice reactance as a Fourier expansion whose coefficients are analytically mapped onto the underlying graph architecture, allowing the required bond connections, refractive indices, lengths, and gauge phases to be determined directly from a prescribed target transmission coefficient. In contrast to conventional inverse-design approaches, the present formulation provides a closed-form route from desired wave transmission profiles to physically realisable structures. The framework extends naturally from one-dimensional angular filtering to two-dimensional image synthesis, where arbitrary transmitted intensity patterns are reconstructed through exact spectral control of the network scattering response.
Published: 2026-06-22 17:30:29
Authors: Haochen Zhang, Yonatan Bisk
Categories: cs.RO
Abstract:
Vision-language-action (VLA) models have the potential for open-world generalization by leveraging pretrained vision-language representations, yet downstream finetuning on limited robot data often degrades these representations, leading to brittle policies that ignore language instructions in favor of visual shortcuts, a failure mode we term instruction blindness. We hypothesize that standard finetuning with limited data applies gradients to a sparse set of points, which manifests as a sharp loss landscape with high-curvature minima. We propose to address this directly through flatness-preserving optimization while finetuning on the exact same data, where learning a flatter landscape results in a model more robust to perturbations in the weight space. Specifically, we demonstrate that simply applying sharpness-aware minimization during VLA finetuning significantly improves instruction following by over 60% across multiple simulation and real-world benchmarks without additional data, architectural modification, or retraining. We further analyze the effect of selective sharpness, quantify its effects, and show that our approach is complementary to existing guidance techniques. Project page can be found at https://haochenz11.github.io/papers/flatness-vla/.
Published: 2026-06-22 17:30:24
Authors: Raymond Tsao, Andrew Wagenmaker, Sergey Levine
Categories: cs.LG, cs.AI, cs.RO, stat.ML
Abstract:
In many modern applications of reinforcement learning (RL), the natural reward for a task of interest is inherently sparse: a reward of 0 is given everywhere except when the task is completed, when a reward of +1 is given. Training a policy to maximize such a sparse reward requires solving a challenging credit assignment problem, leading to slow or ineffective RL improvement. We propose a simple approach to transform a sparse outcome reward into a dense process reward. Our approach relies on training a discriminator to distinguish between previous successful and unsuccessful episodes, and using this discriminator to incentivize the RL-learned policy to match the state-action visitations of successful episodes, while avoiding those of unsuccessful episodes. By incentivizing the policy to match the visitations over all states, not just those that correspond to task success, this reward provides dense feedback on whether progress is being made towards task completion, and, we show, provably achieves this without changing the optimal policy. Focusing on finetuning of robotic control policies, we demonstrate that our approach leads to significantly faster RL finetuning performance on both simulated and real-world manipulation tasks, as compared to simply maximizing the sparse outcome reward.
Published: 2026-06-22 17:27:28
Authors: Amine Marrakchi
Categories: math.OA
Abstract:
We show that the relative bicentralizer flow of a type $\mathrm{III}_1$ irreducible subfactor with expectation is always ergodic. As a consequence, every irreducible subfactor with expectation in a factor with separable predual contains a maximal abelian subalgebra. This completes the solution to Kadison's problem on maximal abelian subalgebras from 1967.
Published: 2026-06-22 17:23:57
Authors: Hongli Xu, Jiaqi Hu, Junwen Huang, Boyang Zhong, Peter KT Yu, Nassir Navab, Benjamin Busam, Slobodan Ilic
Categories: cs.CV
Abstract:
Estimating the 6D pose of unseen objects is a fundamental yet challenging problem for open-world robotics and embodied perception. Model-based methods are accurate but depend on CAD assets or heavy onboarding, while most model-free approaches are still limited to pairwise single-anchor matching and thus fail under occlusion and large viewpoint changes with low query-reference overlap. Therefore, we present PANY, a unified model-free framework that seamlessly supports both RGB and RGB-D inputs, operates on one or sparse pose-free reference views, and generalizes effectively to novel objects. Built on a multi-view transformer geometry backbone, PANY moves beyond pairwise matching by learning view-consistent geometry and cross-view alignment cues that remain stable under wide baselines and limited overlap. When additional unposed assist views are available, PANY aggregates them via pose-graph canonical registration to increase geometric coverage and reinforce the final pose. Extensive experiments show that PANY achieves state-of-the-art performance across multiple benchmarks, substantially outperforming existing model-free methods, improving pose accuracy by +12% on YCB-V and over +20% on LM-O. Furthermore, PANY consistently performs well under both single-reference and sparse-reference settings, demonstrating strong robustness in real-world environments.
Published: 2026-06-22 17:22:22
Authors: Pei-Yu Tseng, Mahir Akgun, Peng Liu
Categories: cs.HC
Abstract:
Designing effective pretest questions is challenging at scale: high-quality questions require careful calibration of openness, cognitive depth, and alignment with learning objectives, yet generating and evaluating them manually is time-consuming. We present an AI-assisted workflow for pretest question development that combines automated generation, rubric-based evaluation, and iterative selection. Because the workflow relies on machine evaluation to filter questions at scale, we investigate the alignment between human and machine judgments across a 2x2 design varying rubric operationalization and evaluation mode. Our findings show that human-machine disagreements are systematic rather than random, that rubric revision has a larger effect on alignment than rationale-first evaluation, and that the two interventions are complementary. These findings highlight that scalable AI-assisted pretesting depends not only on generation capability but on how pedagogical quality is operationalized for machine interpretation.