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版本更新 · v2arXiv:2603.03405

Surprisal-Rényi Free Energy

Shion Matsumoto, Ankur Arjun Mali

原文摘要

The forward and reverse Kullback-Leibler (KL) divergences arise as limiting objectives in learning and inference, yet they induce markedly different inductive biases that cannot be explained by expectation terms alone. We introduce the Surprisal-Rényi Free Energy (SRFE), a normalized Rényi-Chernoff log-moment functional of the likelihood ratio. Although SRFE is algebraically related to Rényi divergence for fixed values of the order parameter, its endpoint-symmetric normalization yields a distinct free-energy viewpoint for analyzing how forward- and reverse KL behavior emerges near singular limits. We show that SRFE recovers the forward and reverse KL divergences as endpoint limits and derive local expansions around both regimes in which the variance of the log-likelihood ratio appears as a first-order correction away from the KL objective. These expansions reveal an explicit mean-variance tradeoff governing departures from KL-dominated behavior. We further establish a Gibbs-type variational characterization in terms of weighted KL projections and prove that SRFE controls large deviations of excess code length through Chernoff-type bounds, giving a Minimum Description Length interpretation of the functional. We evaluate SRFE on two synthetic settings (two-dimensional Gaussian-mixture variational inference and multi-class classification) and the long-tailed CIFAR-10 and CIFAR-100 (CIFAR-LT) datasets. On CIFAR-LT, retraining the best configuration of each loss over five seeds, SRFE matches cross-entropy accuracy on CIFAR-10-LT and gives mixed results on CIFAR-100-LT, improving few-shot accuracy at one of three imbalance factors. Together, these results position SRFE as a variance- and tail-sensitive, normalized Rényi-Chernoff free energy that clarifies the geometric, statistical, and large-deviation structure underlying the transition between forward- and reverse-KL limits.

stat.ML · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.03876

An Open Book, On Whose Terms? Power Dynamics in Sensor-Derived, LLM-Generated Summaries Sharing Between Older Adults and Remote Families

Jiachen Li, Reina Szeyi Chan, Shihan Fu, Zijian Wang, Akshat Choube, Yingtian Shi, Rosemarie Santa Gonzalez, Thomas Ploetz 等 12 位作者

原文摘要

As older adults (OAs) increasingly favor aging in place, remote family members (RFMs) need meaningful awareness of their loved ones without intrusive monitoring. Large language models can provide rich interpretations of non-intrusive sensing data, yet recent work has largely emphasized technical accuracy over how families use or benefit from this information. We conducted formative interviews with 12 RFMs and 2 OAs, followed by a three-month deployment case study with one OA-RFM dyad. Our system provided daily AI-generated summaries of multimodal sensing data, which we iteratively refined with both parties using a research-through-design approach. Although the summaries integrated well into the dyad's daily routine, sustaining over 90% engagement, their lived experience revealed evolving tensions around agency, reassurance, privacy, trust, and communication, shaped by sensor limitations and existing communication misalignments. Our findings inform the design of future AI-mediated sharing systems that account for real-world sensing constraints and complex family care dynamics.

cs.HC · cs.AI · cs.MA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.19652

On Global Joint Lower Bounds in Zeroth-Order Nonconvex Optimization

Yuxuan Xia, Xiao-Tong Yuan

原文摘要

Must the dimension and accuracy costs of zeroth-order optimization multiply? Global upper bounds for smooth stochastic nonconvex optimization scale as $O(dε^{-4})$, with both factors separately necessary. We show that their product is unavoidable. We establish an $Ω(dε^{-4})$ lower bound for smooth stochastic nonconvex optimization, yielding an $Ω(dδ^{-1}ε^{-3})$ bound for nonsmooth Goldstein stationarity. Both match the best-known full-space upper bounds, establishing joint minimax optimality in dimension and accuracy. These bounds hold against fully adaptive randomized algorithms with unrestricted queries and outputs, even under arbitrary sample reuse. As a byproduct, we establish noiseless exact-value lower bounds for both smooth and Goldstein stationarity, even against randomized algorithms.

cs.LG · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05074

Nested Field-of-View Barriers for Teleoperated Multi-Robot Relay Chains

Arthur Vangasse, Theo Cruz, Thales Soares, André Cid, Vinícius Gonçalves, Gustavo Freitas, Luciano Pimenta

原文摘要

Teleoperation of mobile robots in communication-denied environments requires maintaining a communication link while allowing the operator to maneuver freely. We propose a shared-autonomy framework in which a teleoperated leader is followed by a chain of autonomous robots that relays a line of sight to a fixed base station, a geometric proxy for the link. Connectivity is enforced geometrically through visibility-based Control Barrier Functions (CBFs), with each agent computing its control input locally through a quadratic program. A strict visibility constraint guarantees connectivity, but a follower with no independent motion objective remains stationary, stalling the leader at the boundary of the safe set. Relaxing it preserves mobility at the cost of the guarantee. Our framework resolves this conflict through nested hard-soft visibility constraints: a full field-of-view (FoV) constraint enforced strictly to guarantee connectivity and a contracted FoV constraint imposed on each follower as a relaxed behavioral objective that drives it inward before the connectivity boundary is reached. We prove feasibility of the local quadratic programs and forward invariance of the connected, collision-free set under the resulting distributed controller. The contraction parameter shapes follower behavior without affecting the guarantee, which follows from the strict constraint alone. The framework is evaluated in confined and cluttered environments on two simulated systems: a kinematic integrator and software-in-the-loop Crazyflie quadrotors. Compared with a baseline tuned to reach the goal in comparable time, the framework keeps the links far closer to the connected set and moves the followers less.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05073

Toward Constant-Factor Maximin Share Guarantees for Subadditive Valuations

Bo Li, Ankang Sun

原文摘要

For $n$ agents and $m$ indivisible goods, an agent's maximin share (MMS) is the largest minimum bundle value she can obtain by partitioning the goods into $n$ bundles. Constant MMS approximations are known for additive, submodular, or XOS valuations, but whether such a guarantee exists for general subadditive valuations remains open. We advance this question in two directions. For general subadditive valuations, we exponentially improve the guarantee from $Ω(1/\log\log n)$ to $Ω(1/\log^* n)$, where $\log^* n$ is the iterated logarithm. We then seek constant MMS guarantees under additional structure on subadditive valuations by studying $q$-partitioning valuations: every partition of a bundle into at most $q$ groups admits nonnegative group prices that sum to the bundle's value and whose sum on any collection of groups is at most the value of their union. This hierarchy ranges from subadditivity at $q=2$ to XOS for $q\ge m$. We give the first study of MMS in the intermediate classes and prove constant guarantees whenever $q/n$ has a fixed positive lower bound, including $1/6$ for $q\ge n$ and $1/4$ for $q\ge2n$. For XOS valuations, we further improve the universal guarantee from $4/17\approx 0.2353$ to $5/16=0.3125$. For subadditive valuations, we prove a bidding amplification theorem that gives an exponential bound on each agent's probability of receiving less than half her MMS, while satisfying her budget in every outcome. For $q$-partitioning valuations, we give a scalar criterion that converts bounds on the value lost from disjoint deletions within a bundle into bounds on potential losses, even when an agent contributes zero below the target bundle value. For XOS valuations, we prove a reservation theorem that selects goods for integral assignment together with auxiliary additive valuations and accounts for the loss in rounding.

cs.GT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05072

Trajectory Evaluation via Rollout-Free World Model for End-to-end Autonomous Driving

Junzhe Wang, Jinqing Zhang, Qingjie Liu

原文摘要

Although existing candidate trajectory evaluators have substantially improved end-to-end planning accuracy, their underlying mechanism remains suboptimal. Model-based evaluators typically make scoring future-aware by explicitly predicting candidate-conditioned future states over the planning horizon, which we term rollout. However, rollout incurs high inference cost and accumulates prediction errors. We argue that planning does not require reconstructing a full future scene for each candidate, but only future-relevant information for reliable comparison. We therefore propose RFWorld, a Rollout-Free World model for end-to-end trajectory evaluation. RFWorld constructs a shared scene memory and shapes it into a future-readable representation via lightweight training-time temporal readout with semantic supervision, keeping training overhead low. At inference time, candidate trajectories directly query the shared memory via sparse spatial readout for scoring, reducing computation by roughly the planning horizon times the number of candidate trajectories, while avoiding recursive rollout errors. Experiments on NAVSIM and Bench2Drive show that RFWorld learns a more planning-aligned world representation and achieves state-of-the-art performance.

cs.RO · cs.AI · cs.CV

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2609.13969

Zero-Shot Cross-Material Ptychographic Phase Reconstruction Using Deep Learning

Wen-Chun Lin, Kuan-Peng Chen, Nan-Yow Chen, Yu-Chee Tseng, Jen-Jee Chen

原文摘要

Ptychographic phase reconstruction is commonly formulated as an iterative inverse problem, requiring repeated object-probe updates and resulting in substantial computational cost for large-scale 4D-STEM data. We present a direct local-to-global learning framework that reconstructs full-field phase maps from diffraction measurements without iterative refinement during inference. The proposed network predicts local wrapped-phase patches from individual diffraction patterns using a sine-cosine representation, and the predictions are assembled into a full-field reconstruction using calibrated scan positions and Gaussian-weighted stitching. To evaluate generalization beyond the training domain, the model is trained on one material and directly applied to another in a zero-shot setting without target-domain fine-tuning. Experiments on AuPd and MoS$_2$ demonstrate consistent cross-material transfer in both directions, with the proposed method achieving the best full-field MSE, PSNR, and MS-SSIM among the evaluated learning-based methods. Compared with the iterative ePIE approach, the proposed direct local-to-global pipeline reduces end-to-end reconstruction time by approximately 10$\times$, demonstrating its potential for efficient and transferable ptychographic reconstruction.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.11984

Example-driven Parametrisations for Bayesian Shape Optimisation

Gabriel Diaz-Aylwin, Joseph Neighbour, Abiel Malkani Talwar, Rui-Yang Zhang, Henry B. Moss

原文摘要

Bayesian optimisation is the natural tool for shape design when objectives are expensive and non-differentiable, but it needs a compact yet expressive parameterisation of the search space. Hand-crafting one is a complex endeavour requiring domain expertise, and often yields implicit infeasible regions, artificial bounds, and coupled, unordered coordinates. We instead learn the parameterisation from a collection of existing designs, applying principal component analysis to the deformations between shapes. The result is a linear, interpretable search space in which the number of components explicitly trades expressivity against dimensionality. Across aerofoils, wings, and radio-frequency cavities, spanning 2D geometry to 3D aerodynamics and electromagnetics, we show improved sample efficiency and the ability to explore beyond the confines of hand-crafted baselines.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05071

Dataset Recommendation for Heterogeneous and Dynamic Scholarly Graphs via Multimodal Representation Learning

Ornella Irrera, Matteo Lissandrini, Daniele Dell'Aglio, Gianmaria Silvello

原文摘要

Researchers struggle to find relevant datasets amid the growing volume of data across repositories and scholarly publications. Manual dataset retrieval is time-consuming and often fails to identify valuable resources that are not easily discoverable through direct links or popularity metrics. Dataset recommendation, which aims to identify datasets relevant to a paper, author, or query, has become essential for improving dataset discoverability and reuse. However, this task remains challenging because Scholarly Knowledge Graphs (SKGs) are large-scale, heterogeneous, dynamic, and incomplete. Although openly available SKGs organize scientific knowledge by linking scholarly outcomes, they often lack meaningful connections between datasets, leaving them isolated and underutilized. These graphs contain millions of evolving relations but still suffer from missing or inaccurate metadata, duplicate records, and weak dataset integration. This work presents the Multimodal Scholarly Attention Network (MM-SAN), a heterogeneous multimodal graph representation learning method for recommending datasets relevant to research publications within real-world SKGs. MM-SAN combines topology-aware and text-based embeddings, enabling effective recommendations even when textual metadata is limited or absent. It operates in transductive, semi-inductive, and fully inductive settings, adapting to evolving SKGs without requiring retraining. Experiments show that MM-SAN outperforms state-of-the-art methods, providing a robust and scalable solution for dataset recommendation.

cs.IR

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.34507

The Price of Scale in Proactive Memory Recall

Yichuan Chen, Xin Wang

原文摘要

A long-lived assistant that brings up memories without being asked must decide at every turn whether anything in its store is worth an interruption. We show that this decision is a multiple-testing problem whose difficulty grows with the store size N. On replays of LongMemEval, a similarity threshold tuned to 5% false alarms at N=8 reaches 84-100% at N=2048 across embedding models and cue types; LoCoMo and LLM judges show it too. We prove that a gate which thresholds the best of N independent irrelevant scores at false-alarm rate alpha operates at a per-item false-positive rate of about -ln(1-alpha)/N, which pins its hit rate to the far left of the ROC and makes it regularly varying in N under regular variation. Replayed hit rates stay within its bounds for mixed cues up to 0.3 points, and hit rates carry over to held-out questions to within 6.0 points. At matched false alarms the deployed score recalls 14-26 points more than raw cosine. A cue-swap conformal gate, which ranks the current cue among other cues on the same store, has a finite-sample false-alarm guarantee under exchangeability. With a background from the user's unfiltered history it keeps false alarms at 3-6% on LongMemEval at the hit rate of a label-tuned threshold; on LoCoMo it overshoots or turns conservative depending on the source of its reference cues. In a chronological replay with the user's own earlier messages as references, it fires on 5-8% of questions on stores without their answer and on 6-8% of session-opening turns, against 57% for the deployed gate at N=2048, at a median 0.4 s CPU per decision. A cross-encoder pays the same price of scale, and on a prospective-memory benchmark, similarity gates recall at most 9% of constraints at controlled false alarms. One human and two LLM annotators judge most false alarms unhelpful for a response. Code and data: https://github.com/RiverChenStillCompiling/price-of-scale

cs.CL · cs.AI · cs.IR · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.14423

Scaling Laws for Multimodal Data Mixtures

Aditi Khandelwal, Ayush K. Tarun, Yixuan Xu, Imanol Schlag, Golnoosh Farnadi, Siva Reddy, Luke Zettlemoyer, Antoine Bosselut

原文摘要

Frontier AI systems are increasingly natively multimodal, jointly pretrained on multiple modalities, such as text, vision, and audio. However, the scientific understanding of the multimodal data mixtures used to pretrain these systems --- how much of each modality to mix, and at what potential interference cost to the others --- is largely absent or gatekept. In this work, we derive data mixing scaling laws for three modalities: text, vision, and audio. Multimodal Large Language Models (MLLMs) increasingly adopt Mixture-of-Experts (MoE) architectures, as MoEs scale efficiently and partially limit cross-modal interference. We therefore use MoEs to conduct 268 experiments ranging from 457M to 8.3B parameters, on up to 150 billion tokens. Our work leads to multiple actionable insights into multimodal data mixing for training MLLMs. First, compute optima are modality-specific: audio modality is model-heavy, favoring scaling parameters over tokens, while text and vision require balanced scaling of model size and data. Second, repeating vision or audio data beyond 2x yields negligible benefits. Finally, cross-modal interactions are highly asymmetric: vision strongly interferes with audio, whereas audio's interference with learning text and vision is negligible. Overall, we provide a principled foundation for understanding the multimodal data mixtures needed to train frontier MLLMs.

cs.CL · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05066

VIGOR: Visual Gain Ordering for Hallucination Mitigation in Multimodal Discrete Diffusion Language Models

Junzhe Chen, Tian Qin, Eugenie Shi, Tianshu Zhang, Qiang Ju, Lijie Wen

原文摘要

Multimodal discrete diffusion language models (dLLMs) generate responses through iterative mask prediction and offer a promising alternative to autoregressive large vision-language models. However, they still suffer from hallucination, producing outputs that are plausible under language priors but insufficiently grounded in the image. We argue that hallucination in multimodal dLLMs is not only a token prediction problem, but also a premature commitment problem: during iterative denoising, some masked positions are unmasked too early because their confidence is driven more by language priors than by visual evidence, and these early commitments can bias later generation. To address this issue, we propose Visual Gain Ordering (VIGOR), a training-free and plug-and-play inference strategy that reorders masked positions according to counterfactual visual evidence. At each denoising step, VIGOR compares the confidence of each candidate token under the original image-conditioned pass and an additional visual-ablation pass, and prioritizes positions whose predictions are more strongly supported by the image. Experiments on LLaDA-V and Lumina-DiMOO show that VIGOR consistently reduces hallucination and improves multimodal reasoning performance across benchmarks. Code is available at https://github.com/THU-BPM/Vigor.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05065

Process-zone condensation: a discretization-exact reduced cell model for FE$^2$ analysis of composites with fibre-matrix debonding and matrix plasticity

Leonilson Toffoli Pedrini, Maycoln Conci, Rafael Marin Ferro

原文摘要

Periodic composites with cohesive interfaces and matrix plasticity require nonlinear cell solutions in multiscale analysis. Process-zone condensation eliminates the linear elastic remainder of the cell by a Schur complement, retaining all interfacial and process-zone internal variables. Influence matrices recover macroscopic stress and consistent tangent. One back-substitution per increment checks that the matrix outside the retained zone remains elastic. While this holds, the reduction is exact relative to the full discretisation, also with a plastic matrix, and requires no training. In an E-glass/epoxy cell, eight of nine two-dimensional paths agree with the full cell to 1e-11 in stress and tangent; the check flags the ninth. Three paths on each of two three-dimensional meshes need zone expansion. Excluding setup and checks, two-dimensional path timings show an aggregate speed-up of 3.3; certified three-dimensional zones show none. In adaptive finite element squared (FE$^2$) simulations of a perforated plate, the reduced solver reproduces the full-cell response within solver tolerances, 1.51 times faster for glass/polypropylene and slower for glass/epoxy. Independent Ansys models agree to below 1e-3 in cell stress and about 0.6% in plate reactions, although local damage and plasticity onsets differ. The advantage depends on zone size, active set and adaptation costs.

cs.CE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05061

Semi-Streaming Reachability and Shortest Paths in $n^{1/3+o(1)}$ Passes

Gary Hoppenworth, George Z. Li, Sepideh Mahabadi, Jakub Tarnawski

原文摘要

In the semi-streaming model, the edges of an $n$-vertex graph arrive in a stream, and an algorithm makes multiple passes over that stream while using only $\widetilde{O}(n)$ space. The number of passes needed to compute single-source reachability in directed graphs is a long-standing open question in streaming algorithms, going back to Henzinger, Raghavan, and Rajagopalan (1998). The best known semi-streaming algorithm uses $n^{1/2+o(1)}$ passes due to Jambulapati, Liu, and Sidford (2019), while the best lower bound is $Ω(\log n/\log\log n)$ passes due to Guruswami and Onak (2016). We give streaming algorithms for computing single-source reachability and shortest paths on $n$-vertex directed graphs using $\widetilde{O}(n)$ space and $n^{1/3+o(1)}$ passes. Our algorithms are based on implicit hopsets, which are large hopsets that can be represented implicitly in $\widetilde{O}(n)$ space. We introduce this notion and give new constructions for implicit hopsets with small hopbound. Along the way, we obtain new work-depth tradeoffs for parallel shortest paths in directed graphs, improving the recent result of Ashvinkumar, Bernstein, Probst Gutenberg, and Saranurak (2026).

cs.DS

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2503.19288

Design of an Underwater Vehicle With Orientation-Adjustable Thrusters and Adaptive Tracking Control

Yifei Wang, Shihan Kong, Zhanhua Xin, Kaiwei Zhu, Junzhi Yu

原文摘要

Unmanned underwater vehicles (UUVs) are essential for marine exploration and research. However, conventional designs often struggle with limited maneuverability in complex and dynamic underwater environments. This paper presents an orientation-adjustable thruster UUV (OAT-UUV) with vector thrusters, enabling full six-degree-of-freedom (6-DOF) motion. and composite maneuvers. To overcome challenges associated with uncertain model parameters and environmental disturbances, a vision-based tracking control system based on a novel feedforward adaptive model predictive controller (FF-AMPC) is proposed to achieve robust object tracking, which integrates realtime state feedback with adaptive parameter updating. Extensive experiments, including closed-loop tracking and composite motion tests in real-world environment, have been conducted. The obtained results demonstrate that the orientation-adjustable propulsion design significantly improves the underwater maneuverability, while the FF-AMPC controller achieves a 49.1% hovering improvement compared to conventional controller.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05059

Rank-metric intersecting codes

Martino Borello, Martin Scotti, Lune Vila

原文摘要

Intersecting codes are codes in which any two nonzero codewords share a common nonzero coordinate, and they have been extensively studied due to their applications in communication theory, cryptography, and combinatorics. Recently, geometric interpretations have provided new insights into intersecting codes, both in the Hamming metric and, more recently, in the rank metric for linear codes. In this paper, we introduce and study matrix rank-metric intersecting codes. By defining the support of a matrix as its row space, we extend the notion of intersecting codes to the matrix rank-metric setting. Using the slice space associated with the generator tensor of a rank-metric code, we derive a geometric characterization that is equivalent to the intersecting property. This characterization generalizes the known results for linear rank-metric intersecting codes and highlights both analogies and differences with the linear case. In particular, it yields bounds on the parameters of intersecting codes, one of which is sharp and differs from the one in the linear case, together with first constructions. We then consider codes that are closed under multiplication, i.e. matrix algebras. In this setting, the intersecting property can be read off from the algebraic structure, namely the Jacobson radical and the socle. This provides a simple and flexible way to construct many intersecting codes, including codes that do not satisfy the classical sufficient condition on the minimum distance.

cs.IT · math.CO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05058

Do Agentic Policies Need to Look at Every Step? Geometric Abstraction for Reasoning and Action

Yihang Liu, Yiming Chen, Mingzhe Zheng

原文摘要

Recent multimodal language models can reason directly about robot scenes, but it is unclear whether they need to repeatedly reinterpret raw visual observations at every step of an interaction. We propose shifting routine spatial reasoning from images to text and symbols through a 3D geometric abstraction that is refreshed locally from observations and read directly by the language models. Specifically, our interface represents scene geometry via typed, evidence-aware symbolic references to object parts, grounds them from calibrated RGB-D, and returns compact textual checkpoints with metric residuals after each action. Symbolic task structure can thus be reused across decisions while metric values are rebound to the current scene, with direct visual reasoning invoked only on demand. Across offline policy synthesis and continuous online execution, reasoning over grounded text and symbols improves multi-step manipulation completion while substantially reducing repeated multimodal queries, positioning explicit geometric state as an effective substrate for agentic robot policies.

cs.RO · cs.AI · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05056

Equal Access, Unequal Gains? GitHub Copilot and Productivity Among Developers with Disabilities

Emeralda Sesari, Daria Ana-Maria Mogoş, Federica Sarro, Ayushi Rastogi

原文摘要

Generative AI tools are increasingly used in software development and have been associated with benefits such as faster task completion and improved perceived productivity. However, whether these benefits extend across different developer populations remains unclear. This is especially relevant for developers with disabilities, whose work can already be impacted by accessibility barriers. Yet, evidence remains limited across a broader range of disability groups. This study investigates whether the wider availability of GitHub Copilot is associated with changes in productivity among GitHub users who self-identify as having a disability. Using publicly available profile and repository data, we curated a dataset of 199 developers across multiple disability groups and compared their activity before and after GitHub Copilot became freely available. We found that most disability groups showed a descriptive pattern of fewer commits but more code modified. In several disability groups, the distribution of developers across commit activity levels shifted toward both lower and higher level, leaving fewer developers at moderate level. However, we found no evidence that changes in commits or net lines modified differed across disability groups. These findings suggest that the wider availability of GitHub Copilot cannot be associated with a broad increase or decrease in observable development activity, while patterns of change varied across developers and disability groups.

cs.SE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.16351

PALM: Physically-Anchored Latent-timescale Mamba for Stable Representations under Distribution Shift

Sangyoon Bae, Shinjae Yoo, Jiook Cha

原文摘要

Scientific foundation models are built to reuse representations across datasets and acquisition protocols, yet their representations degrade under distribution shift. We trace one source of this failure to selective state-space models (SSMs). Their memory depends on the step Delta and the state matrix A only through their product, so data do not identify the physical timescale of memory, and under a shift the resulting memory no longer matches the signal's timescales. We call this failure memory-timescale misalignment. To resolve it, the split between Delta and A must be tied to physical units. We therefore propose Physically-Anchored Latent-timescale Mamba (PALM), which disentangles A from Delta by anchoring both to acquisition units: Delta through spectrum-estimated knee frequencies and A through a physical memory range. We evaluate PALM against parameter-matched baselines in two domains, under temporal-context truncation, low-resource transfer, and brain-state shift on fMRI and under held-out-station forecasting on Weather-5K. Under fMRI context truncation with run-level spectral calibration, PALM keeps its latent timescales in physical units and retains 11% higher representation similarity than Mamba2. At geographically isolated weather stations never seen during training, PALM achieves the lowest error on every variable and horizon, 15.2% below the best baseline on average, and outperforms zero-shot forecasting foundation models. Across both domains, the gains come from where the backbone places memory in physical time rather than from model size. We therefore argue that physically anchored latent timescales are a design requirement for scientific foundation models that must transfer across acquisition conditions.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05055

Ablating Design Choices for Protein Ensemble Generators Across Structural Regimes

Eoin Quinn, Oliver Bent

原文摘要

Generative models of protein conformational ensembles inherit much of their design from structure prediction for folded proteins. Ordered and disordered proteins, however, occupy distinct structural regimes, and we investigate how design choices hold up across them. Using a minimal C$α$ flow-matching model, we ablate choices of architecture, sequence conditioning, rigid-body alignment and training source, and evaluate each variant against experimental SAXS and NMR chemical-shift data partitioned into ordered, disordered, multi-domain and transitional proteins. Mixture-of-experts routing, a static pair representation and a disorder probe have no detectable effect, a smaller protein language model performs worse on ordered proteins but not on disordered ones, and alignment in the loss degrades agreement with SAXS. Replacing coarse-grained simulations of disordered proteins with fragment-assembled ensembles improves agreement with chemical shifts at a cost to SAXS, while an equal mixture of the two keeps most of the gain at a smaller cost. This points to the training data, rather than the architecture, as the main constraint.

q-bio.BM · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.34527

LLM4Robot-Bench: How Far Can GPT-6 Astra Go in Robotic Manipulation? A Hierarchical, Paired Sim-Real Benchmark

Haibao Yu, Jiaqi Meng, Zijian Zhang, Bowen Jing, Lei Yang, Yue Hu, Chenchen Ge, Yiming Hou 等 19 位作者

原文摘要

We introduce LLM4Robot-Bench, a hierarchical, paired Sim-Real benchmark for evaluating physical interaction capabilities in contact-rich manipulation. Its 16 household tasks span three cumulative levels: contact selection and geometric alignment (L1), force regulation within sustained contact (L2), and coordinated contact reconfiguration and force regulation (L3). Each task is instantiated in physically and visually matched simulated and real-world environments, with shared task definitions and success criteria. We evaluate GPT-6 Astra zero-shot with three trials per task in each environment. Real-world success rates decline from 80.0% at L1 to 47.6% at L2 and 0.0% at L3, with corresponding simulation rates of 66.7%, 47.6%, and 0.0%. Ten of the 16 tasks have identical success counts across environments; the remaining six differ by one success out of three trials. Across both environments, recurring difficulties in spatial reasoning, contact selection, force regulation, and coordination often prevent local physical progress from accumulating into task completion. These findings characterize Astra's observed capability boundary across increasing interaction demands and, for this model, support matched simulation as a repeatable, scalable testbed for studying the sustained, coordinated physical interactions that limit reliable manipulation.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2506.02982

Bounded Discrete Bridges

Pierre Nicodeme

原文摘要

In 2010 Banderier and Nicodeme consider the height of bounded discrete bridges and conclude to a limiting Rayleigh distribution. This result is correct although their proof is partly erroneous. They make asymptotic simplifications based upon dominance properties of the roots of the kernel of the walk within a disk centered at the origin, but these dominance properties apply only upon a positive real segment. However the very good agreement of simulations with their asymptotic expansion of the probability distribution in case of Łukasiewicz bridges let us think that their proof could be corrected. This is the scope of the present article which provides a proof using the dominance property only in its domain of validity. We also consider the case of periodic walks, a topic not considered in Banderier-Nicodeme2010..

math.PR · cs.DM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.17864

TStruct: Learning Shared Temporal Structures for Long-Term Time Series Forecasting

Yangze Li, Zhengyang Zhou, Qihe Huang, Pengkun Wang, Binwu Wang, Yang Wang

原文摘要

Long-term time series forecasting requires learning temporal dependencies that remain stable and generalizable under temporal distribution shifts. However, real-world time series are often noisy, non-stationary, and heterogeneous, making deep learning models prone to overfitting incidental temporal interactions in the training data. This raises a question: when observations are difficult to align, could temporal relationships be easier to reuse? To investigate this possibility, we analyze patch-level temporal relationship matrices across commonly used time series datasets, revealing dominant patterns shared across different temporal windows and variables. Comparisons with patch-shuffled and IID Gaussian controls further support the presence of reusable temporal relationship structures. Motivated by these findings, we propose \textbf{T}emporal \textbf{Struct}ure Network (\textbf{TStruct}), a simple yet effective network that learns reusable temporal dependency structures and adaptively combines them for each sample and variable. Specifically, TStruct directly parameterizes a set of shared temporal dependency matrices and generates normalized combination weights using dynamic sample information and static variable priors. These weights represent a soft allocation of each sample-variable pair over shared temporal dependency structures, enabling the model to generate adaptive temporal dependency matrices under shared structural constraints, rather than fitting incidental interactions in a fully unconstrained manner. Notably, TStruct consistently outperforms several strong baselines across all evaluated datasets, achieving superior accuracy while maintaining high computational efficiency. Our code is available at https://github.com/yangzeli/TStruct.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2508.09741

Project Submission Games in Participatory Budgeting

Piotr Faliszewski, Łukasz Janeczko, Andrzej Kaczmarczyk, Grzegorz Lisowski, Grzegorz Pierczyński

原文摘要

We introduce the framework of project submission games, capturing the behavior of project proposers in participatory budgeting (and multiwinner elections). Here, each proposer submits a subset of project proposals, aiming at maximizing the total cost of those that get funded. We focus on finding conditions under which pure Nash equilibria (NE) exist in our games, and on the complexity of checking whether they exist. We also seek algorithms for computing best responses for the proposers

cs.GT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05049

On the Learnability of Footwear Robustness: Footstep Recognition Under Unseen Conditions

Robyn Larracy, Erik Scheme

原文摘要

Footstep patterns, captured using pressure or force-sensitive walkways during natural walking, offer a convenient and passive approach to biometric recognition. However, footwear variability poses a major challenge to practical deployment, as it impacts both the foot-sensor interface and the user's underlying gait mechanics. This study considers the particularly difficult scenario where users enroll under one fixed footwear condition but must later be recognized in arbitrary, unseen footwear. Using the StepUP-P150 dataset, we show that footwear-induced shift, while substantial and heterogeneous across individuals, contains shared structure that can be learned from a training population and transferred, zero-shot, to previously unseen users and footwear styles. We do this using BiStepNet, a novel architecture that decomposes spatial and temporal identity cues into independently trainable streams and outperforms compact 3D spatiotemporal baselines for cross-footwear recognition. We demonstrate that cross-footwear recognition performance scales systematically with the volume and diversity of footwear representation in the training set, with gains transferring to footwear styles not directly observed during training. Moreover, we show that auxiliary gait datasets with only partial modality overlap (incorporated through the architecture's decomposed design) can be leveraged to further improve robustness to unseen footwear conditions.

cs.CV · eess.SP

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.30192

SAGE: Mitigating Long-Horizon Reasoning Biases via Topological Guidance

Xinyue Zeng, Jiawei Zhang, Yujun Yan, Dawei Zhou

原文摘要

Long-horizon reasoning remains a central challenge for large language models (LLMs) under sparse-reward regimes. We argue that this brittleness arises from two biases induced by complex reasoning spaces: an exploration bias, where models are drawn toward locally plausible but structurally unstable branches, and a compounding bias, where small local deviations accumulate across depth and suppress rare rewards. We introduce Symbolic Closure Analysis (SCA) as a theoretical lens characterizing how branching structures and sparse rewards induce these biases in long-horizon reasoning with local admissibility, and as a design principle for structural priors in less formal reasoning tasks. Motivated by this analysis, we propose SAGE (Structural Admissibility-Guided Exploration), a unified framework that injects structural guidance to alleviate exploration bias and compounding bias in long-horizon reasoning. SAGE combines two complementary structural guidance: algebraic sparsification, which projects locally admissible candidates onto operator-indexed algebraic subspaces to suppress spurious branching and mitigate exploration bias, and hyperbolic structural guidance, which embeds reasoning states into a negatively curved space to provide dense depth-wise signals and mitigate compounding bias. Across 12 benchmarks and 7 model families, SAGE outperforms competitive baselines. In particular, SAGE achieves up to an 8-fold improvement on the Andrews-Curtis problem, an open real-world long-horizon task. Code is available at: https://github.com/Susan571/SAGE-NeurIPS2026.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2610.05366

The ÌròyìnSpeech Text Corpus: 24,905 Curated Yorùbá Sentences for Speech and Language Technology

Kola Tubosun, Anuoluwapo Aremu, Tolulope Ogunremi, Iroro Orife, David Ifeoluwa Adelani

原文摘要

ÌròyìnSpeech is a 42-hour, 80-speaker Yorùbá read-speech corpus whose audio has been distributed by ELRA since 2024. This paper describes the release of its text component: 24,905 unique, hand-verified, tone-marked Yorùbá sentences (275,897 tokens; 15,687 types), curated in 2022 as recording prompts. Roughly 11,000 sentences were adapted from openly licensed news material; the remainder were written in-house to broaden coverage beyond the religious translation that dominates existing Yorùbá corpora. Every sentence was checked by hand for tone-mark accuracy, edited for read-aloud clarity and a neutral register, and localised so that non-Yorùbá personal and place names appear in Yorùbá form. Preparing the text for release surfaced systematic Unicode normalisation failures affecting more than 60% of lines (with precomposed and decomposed forms of the same letter co-occurring within single sentences) which we document and correct. The corpus supports diacritic restoration, grapheme-to-phoneme conversion, TTS front-end development and orthographic research, and serves as a validated prompt set for new recording.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05043

A Low-Diameter Nash-Williams--Tutte Theorem

Pin-Hsian Lee, Peng-Ting Lin, Meng-Tsung Tsai, Ting-An Wu

原文摘要

The Nash-Williams--Tutte theorem characterizes the existence of $k$ edge-disjoint spanning trees by a condition on every vertex partition. We prove that, for simple graphs, the same condition guarantees $k$ edge-disjoint spanning trees, each of diameter $O(n/δ)$ and with $O((n/δ)\log(δ+1))$ internal vertices, where $n$ is the number of vertices and $δ$ is the minimum degree. Both bounds hold simultaneously for every tree, with absolute implicit constants independent of $k$, and each is asymptotically optimal in general. Our proof is constructive and yields a randomized algorithm with expected running time $\tilde{O}(n^5)$. We derive applications to short edge-disjoint routes and weak linkages, small Steiner trees, and survivable networks with short paths. The joint bounds also yield low-diameter spanning Eulerian subgraphs and short tours for postman and general routing problems.

math.CO · cs.DM · cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05042

Effects of Parton Pair Correlations on Direct-Photon Yields and Elliptic Flow in Heavy-Ion Collisions

Xingjian Lu, Shuzhe Shi

原文摘要

Direct photons escape heavy-ion collisions with little rescattering, carrying information from the full emission history. Yet describing the measured direct-photon yield and the sizable elliptic flow $v_2$ together remains a long-standing challenge. Here we explore how correlations between emitting partons may affect both observables. The local $2\to2$ photon rate depends on the joint distribution of incoming partons, so a connected correlation between them can change photon emission without changing the one-particle distribution of medium particles. Once folded over the evolving medium in Au+Au and Pb+Pb collisions, the correlation raises the photon yield toward the fitted PHENIX and ALICE data and increases $v_2$ mainly at intermediate $p_T$. The fitted temperature dependence accounts for most of the net flow increase in this region, suggesting that the correlation changes the relative contributions between hotter and cooler regions.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05041

Positional Encoding is All You Need for Scalable Equivariance Constraint Relaxation

Hagay Michaeli, Haggai Maron, Daniel Soudry

原文摘要

Equivariant neural networks incorporate known symmetries of the data, such as permutations, rotations, or translations, directly into their architecture, often improving sample efficiency and generalization. However, strict equivariance can make optimization difficult, for example, when the data only approximately satisfy the assumed symmetry, or when the equivariance constraints induce a challenging loss landscape. Recent studies have shown that relaxing equivariance during training by introducing non-equivariant dense linear layers can ease optimization in such cases and improve the performance of the resulting strictly equivariant model, after removing the relaxation layers at test time. While effective, this approach incurs a significant computational overhead, limiting its applicability in scalable architectures. In this work, we propose a more efficient constraint-relaxation method based on positional encoding (PE). Our framework injects a positional encoding with learnable scale that breaks the model's original symmetry. This positional signal is gradually annealed to zero, recovering the original equivariant model by the end of training. Empirically, we demonstrate significant improvements for translational and rotational equivariant models with only marginal compute and memory overhead.

cs.LG

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arXiv ↗PDF ↗