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版本更新 · v2arXiv:2609.17837

Adaptive hybrid coupling with operator inference, the overlapping Schwarz alternating method and reinforcement learning

Trishit Mondal, Irina Tezaur, Anthony Gruber

原文摘要

Hybrid domain decomposition methods provide a flexible framework for coupling full order models (FOMs) and reduced order models (ROMs), but typically assume the model assigned to each subdomain is fixed throughout a simulation. This is limiting for transient problems in which localized features propagate through the domain and the regions requiring high-fidelity resolution change over time. We introduce a reinforcement learning (RL)-based approach for online adaptation of FOM-ROM models coupled via the overlapping Schwarz alternating method (O-SAM), an iterative domain decomposition method that solves subdomain-local problems while exchanging solution information through transmission boundary conditions on overlapping interfaces. Deep Q-networks (DQNs) are trained offline to select among subdomain-local FOMs and pre-trained Operator Inference (OpInf) ROMs using a reward balancing accuracy, cost, and model-switching frequency. Once trained, the policies are deployed predictively on problem instances not seen during training, without requiring a reference FOM solution. We demonstrate the approach on two examples: a 1D advection-diffusion problem with a moving front, and a 3D linear elastic wave propagation problem implemented in the Norma.jl solid mechanics code. For the advection-diffusion benchmark, the learned policy dynamically allocates high-fidelity resolution as the front propagates and outperforms static FOM/ROM assignments; letting the agent also adapt the domain decomposition provides no further benefit. For the elastic wave benchmark, learned policies for two and three subdomain decompositions track the propagating wave by assigning FOMs to subdomains containing the wave and ROMs elsewhere, as expected. Our results demonstrate the potential of RL to enable predictive online adaptation of model fidelity within Schwarz-based hybrid simulations.

cs.LG · cs.AI · math-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39008

Fed-IGTD: A federated learning-based framework for tabular-to-image encoding

David Chushig-Muzo, Eva Milara, Francisco J. Lara Abelenda, Luis Carlos Estrada-Petrocelli, Diego H. Peluffo-Ordóñez

原文摘要

Tabular-to-image encoding methods, which transform tabular data into image-like representations, have shown promising results in predictive tasks. Despite their benefits, these methods follow a centralized paradigm, in which the data are collected and models are trained on a central device. This raises privacy and security concerns, particularly the risk of data leakage, unauthorized data collection and access to users' private information. Federated Learning (FL) provides a decentralized framework in which multiple clients jointly optimize a shared model without directly sharing training data. We introduce Fed-IGTD, a horizontal FL-based approach in which clients share a common feature space and upload only local statistics, from which the server reconstructs the global correlation matrix and obtains a feature-to-pixel mapping to train a convolutional neural network via Federated Averaging (FedAvg). We studied two differential privacy (DP) mechanisms: (i) a one-shot Gaussian mechanism protecting the correlation-aggregation step; and (ii) a client-level DP-FedAvg variant protecting the training with privacy loss composed via a Renyi-DP accountant. We evaluated Fed-IGTD on five datasets under a systematic sweep of non-IID severity and privacy budgets. Privacy protection applied to the mapping-construction step has little impact on predictive performance. By contrast, DP-FedAvg substantially degrades utility at moderate privacy budgets with a small client cohort (K=5). Ablation study indicates that this degradation is driven mainly by the injected noise rather than by uniform averaging or update clipping. Macro-F1 exhibits sensitivity to performance degradation than accuracy under non-IID heterogeneity and class imbalance.

cs.LG · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.19354

Between Commons and Property: Individual Image-Generation Model Creators' Perceived Accountability for Derivative AI Model on Open Model Marketplaces

Eun Jeong Kang, Fengyang Lin, Angel Hsing-Chi Hwang

原文摘要

Within the open model ecosystem, AI model marketplaces enable non-AI experts to create and distribute derivative AI models. These platforms establish RAI approaches that creators are expected to follow to prevent negative downstream effects from model deployment, yet such governance approaches often rely on creators' intrinsic motivation and integrity. Little is known about how individual creators conceptualize their accountability for derivative models and how these conceptualizations shape their RAI practices throughout the development cycle. Through semi-structured interviews with 19 creators of derivative image-generation models on open model marketplaces, we identify two distinct accountability logics underlying creators' development practices: commons-based peer production and personal creative property. Guided by these logics, creators engage with platform RAI approaches in ways that diverge from their intended purposes: they strategically repurpose attribution tools for model visibility rather than provenance transparency, displace platform authority in favor of community-based governance, restrict model access to manage downstream accountability costs, and treat RAI-related work as undervalued labor to be minimized. We discuss how self-regulatory use of RAI tools, shaped by individuals' perceptions, produces shortcomings, and how open model marketplaces might mitigate these limitations by structurally supporting creators' engagement with RAI practices.

cs.HC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.15220

Always Learning, Always Mixing: Efficient and Simple Data Mixing All the Time

Michael Y. Hu, Apurva Gandhi, Kyunghyun Cho, Tal Linzen, Pratyusha Sharma

原文摘要

Data mixing decides how to combine different sources or types of data and is a consequential problem throughout language model training. In pretraining, data composition is a key determinant of model quality; in continual learning and adaptation, it governs what is retained and acquired. Yet existing data mixing methods address only one phase of this lifecycle at a time: some require smaller proxy models tied to a single training phase, others assume a fixed domain set, and continual learning lacks principled guidance altogether. We argue that data mixing is fundamentally an online decision making problem---one that recurs throughout training and demands a single, unified solution. We introduce OP-Mix (On-Policy Mix), a data mixing algorithm that operates across pretraining, midtraining, and instruction tuning. Our main insight is that candidate data mixtures can be cheaply simulated by interpolating between low-rank adapters trained directly on the current model, eliminating separate proxy models and ensuring the search is always grounded in the model's actual learning dynamics. Across the language model training lifecycle, OP-Mix consistently finds near-optimal mixtures while using a fraction of the compute of the baselines. In pretraining, OP-Mix improves upon training without mixing by 6.3% in average perplexity. For continual learning, OP-Mix matches the performance of both retraining and on-policy distillation while using 66% and 95% less overall compute, respectively. OP-Mix suggests a different view of language model training: not a sequence of distinct phases, but a single continuous process of learning from data.

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39007

Formal Specifications with Data-Driven Signal Analysis

Roderick Bloem, Benedikt Maderbacher, Cristinel Mateis, Dejan Ničković, Sergii Tkachov, Björn Ziegler

原文摘要

Developing analog and hybrid systems requires substantial domain knowledge that is hard to articulate in a formal specification; without additional effort, this domain knowledge is also inaccessible to machine learning methods. We propose a hybrid approach for analyzing and monitoring real-valued signals that integrates specification-based and data-driven techniques. We use regular expressions with interval operations to identify regions of interest in a signal, classical signal processing methods to extract semantically meaningful features, and semi-supervised machine learning to infer complex relationships that distinguish correct from faulty behaviors. The relationships learned from normal behavior data can automatically detect incorrect behavior in the same system or variants thereof. We evaluate the method on a voltage regulator, demonstrating its effectiveness in detecting undesired system behaviors.

cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39006

Blind Spots: Where Small Language Models Fall Behind Frontier Models on Terminal Tasks

Ashima Suvarna, Ruta Desai, Ahmed Elgohary

原文摘要

Small language models (SLMs) offer an attractive alternative to frontier models (FMs) because they can be deployed with lower cost and latency. This makes SLMs especially appealing for terminal tasks on edge devices, where responsiveness, local execution, and limited connectivity are important considerations. However, they continue to lag behind FMs on terminal tasks, as measured by standard task success rates. Yet these rates conflate several capabilities that may contribute to this gap. In this work, we characterize the SLM-FM gap along three axes: solvability, whether additional test-time compute allows an SLM to reach a correct solution; verification, whether it can identify a successful solution among candidate trajectories; and confidence, whether it can accurately anticipate its probability of success. Across two standard terminal benchmarks, we find that repeated sampling narrows, but does not eliminate, the gap in solvability. In contrast, a persistent gap remains in verification. Given the same candidate trajectory pool, FMs recover substantially more of the gap between random and oracle selection than SLMs. SLMs are also more overconfident in their prospective success estimates and exhibit poorer calibration. These results suggest that the SLM-FM gap is multidimensional, spanning not only the ability to generate successful solutions, but also the ability to recognize successful solutions and anticipate success. We hope this decomposition can guide future work on developing stronger and smaller terminal agents.

cs.CL · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39005

Self-Supervised Doppler-Guided RF Odometry

Kunzhe Song, Jialuo Du, Jingkai Lin, Huacheng Zeng

原文摘要

Radio-frequency (RF) sensing enables robot ego-motion estimation in darkness, smoke, and fog, yet RF odometry remains challenging because multipath and moving reflectors make cross-frame matching highly ambiguous. Existing methods often rely on auxiliary inertial sensors or ground-truth pose supervision. We present RFWalk, a self-supervised, RF-only odometry framework that models dense correspondences across range-azimuth frames as random walks on a space-time graph and learns them from unlabeled sequences through temporal cycle consistency. To rule out correspondences that are cycle-consistent but physically implausible, we introduce a Doppler consistency loss that constrains correspondence-induced motion using radial velocity measured by the RF sensor itself. Ego-motion is then recovered through robust $\mathrm{SE}(2)$ registration that suppresses uncertain correspondences and those arising from independently moving objects. Without pose labels or auxiliary sensors, RFWalk remains competitive with supervised baselines in static scenes and reduces absolute trajectory error by 58% in dynamic scenes with moving pedestrians.

cs.RO · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.05415

Executable Schema Contracts: From Automatic Ingestion to Multi-Source Retrieval

Padmaja Jonnalagedda, Yuguang Yao, Xiang Gao, Hilaf Hasson, Kamalika Das

原文摘要

Real-world data spans tables, documents, and semi-structured files with implicit semantics. Querying this data requires integrating evidence across inconsistent schemas and formats, yet existing approaches either demand costly manual engineering or bypass structure entirely. We present a system that automatically discovers an executable schema from raw multi-source data and uses it as a shared contract for knowledge graph construction and query-time retrieval. A closed-world field catalog constrains LLM-based schema discovery to attested fields; deterministic structural analysis infers identity keys, foreign keys, and source hierarchy; and the resulting schema drives extraction, deduplication, and cross-source linking into a provenance-aware knowledge graph. At query time the schema -- optionally extended via a monotonic protocol -- conditions a multi-tool agent routing retrieval across structured lookup, graph traversal, and vector search, returning grounded answers with traceable citations. In controlled zero-shot comparisons using the same LLM, data, and evaluation harness, the system improves over retrieval-only and decomposition-based baselines across four QA benchmarks, with ablations showing that schema-conditioned routing, structural intelligence, and schema-guided construction each contribute to the gains.

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39003

Agentic AI Reasoning and Learned Sampling for Task-Aware Projection Selection in Sparse-View CT: Predicting Hemodynamics

Mahmoud Elhadidy, Roshan M. D'Souza, Amirhossein Arzani

原文摘要

Sparse-view computed tomography (CT) reduces the number of acquired projections, but conventional sampling strategies are designed primarily for image reconstruction rather than for downstream physical inference. Here, we investigate optimal task-aware projection selection for directly predicting hemodynamics from sparse CT angiography (CTA) measurements. Using computational fluid dynamics simulations of intracranial aneurysms, we generate synthetic fan-beam CTA projections and train a Fourier neural operator (FNO) to predict two targets: the spatially resolved time-averaged wall shear stress (TAWSS) field and the spatially averaged TAWSS. The FNO was coupled with optimal input selection for simultaneous optimal sampling and training. Our goal was to investigate if agentic AI could discover optimal sampling strategies that outperform current methods. We compared fixed uniform and golden-angle sampling with three task-aware optimal selection strategies: Gumbel-Softmax, a Soft top-K method, and an agentic AI selector. The methods were compared at different view selection budgets. A detailed analysis of the AI agent was performed to understand its reasoning and performance. For spatially resolved TAWSS, Agentic and Soft top-K achieve the lowest mean errors (Agentic performs slightly better) and substantially outperform fixed sampling under extreme sparsity. For spatially averaged TAWSS, optimized selection becomes important at extremely sparse acquisitions. Analysis of the selected masks reveals multiple distinct subsets concentrated around informative regions. Moreover, masks optimized for hemodynamic prediction yield poor image reconstructions, showing that task-optimal acquisition differs fundamentally from reconstruction-optimal acquisition. This work suggests the possibility of designing sparse acquisitions directly around downstream physical quantities.

cs.AI · physics.flu-dyn

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.36477

Proposing Better Rollouts for On-Policy Distillation

Sirui Chen, Yunzhe Qi, Haobo Xu, Jiaru Zou, Mengting Ai, Zihao Li, Jingrui He

原文摘要

On-policy distillation (OPD) improves language-model post-training by letting the student learn from its own sampled trajectories under teacher supervision, aligning the training process with inference-time behavior. However, the rollout policy in OPD is usually treated as fixed: each round samples directly from the current student. We show that this choice can create a compounding teacher--student mismatch, because it requires the teacher to provide effective supervision on whichever trajectories the student samples, including ones less reachable by the teacher itself. On such trajectories, teacher supervision can become noisy or even misleading, leading to undesirable student patterns such as repetition. Moreover, since the updated student in turn defines the rollout policy of subsequent rounds, these patterns persist and potentially amplify over training. This failure mode suggests that improving OPD requires treating the rollout policy itself as an optimization target, rather than only modifying the distillation objective. We propose Mixture Proposal On-Policy Distillation (MPOPD), which derives a student-anchored, teacher-guided rollout distribution from a local trust-region objective based on relative teacher support. The resulting target is a geometric mixture of the student and teacher policies, and we introduce a proposal policy that approximates this mixture, enabling rollout generation in a single forward pass. Experiments on 16 math, knowledge, and medical reasoning benchmarks show consistent gains over OPD and recent variants. On math reasoning, our method improves average accuracy by 6.91 over OPD on Qwen3-4B-Base while reducing average generation length by 41%, suggesting that rollout policy design provides an effective way to improve OPD.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39001

AlbumVSR: Identity-Preserving Face Video Super-Resolution from a Personal Photo Album

Nianyao Du, Xijun Wang, Stanley H. Chan

原文摘要

Severe blur, downsampling, noise, and compression can erase the identity cues needed to faithfully restore a person's face. We therefore use photographs of the same person as an external source of identity. AlbumVSR conditions a frozen, one-step streaming diffusion transformer on a personal album. We use four MagFace/CLIP set tokens to encode the album, and 64 texture tokens which come from a fused UV atlas rendered at the current window's estimated face pose. These tokens are appended to the transformer's cached cross-attention context. On an identity-disjoint VoxCeleb2 benchmark with heavy synthetic degradation, AlbumVSR raises ArcFace similarity to the ground truth from 0.586 to 0.670 and face PSNR from 24.98 to 26.71 dB. On the 91 test identities, its mean identity similarity is higher than that of all 14 baselines.

cs.CV · eess.IV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38999

A Spectral Boundary for Independence Learnability

Senhan Yao, Yuxin Zhou

原文摘要

Learning independence is fundamental to statistical inference and machine learning, yet learning independence between entire time series by a test remains challenging when only a single observation record is available. Existing guarantees often rely on structural or mixing assumptions, while stationarity and ergodicity alone do not permit consistent testing. In this work, we identify a class-level boundary governed by shared nontrivial Koopman eigenvalues, which represent common persistent oscillatory frequencies of the two processes. When no such eigenvalues are shared, we extend shift-based rank calibration with finite-window dependence witnesses and a data-adaptive threshold to construct a deterministic, weakly consistent test with finite-sample false-positive control, without quantitative mixing assumptions. Conversely, we show that no possibly randomized test can achieve vanishing false-positive probabilities under every independent jointly totally ergodic law while consistently detecting every dependent jointly totally ergodic law with a shared nontrivial eigenvalue. This impossibility already holds for binary observations and persists within any prescribed nonempty open eigenvalue window, identifying a spectral obstruction to universal learning of full-process independence.

math.ST · stat.ML

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.13047

Revealing the Gap in Human and VLM Scene Description through Counterfactual Semantic Saliency

Ziqi Wen, Parsa Madinei, Miguel P. Eckstein

原文摘要

Evaluating whether large vision-language models (VLMs) align with human perception for high-level semantic scene comprehension remains a challenge. Traditional white-box interpretability methods are inapplicable to closed-source architectures and passive metrics fail to isolate causal features. We introduce Counterfactual Semantic Saliency (CSS). This black-box, model-agnostic framework quantifies the importance of objects by measuring the semantic shift induced by their causal ablation from a scene. To evaluate AI-human semantic alignment, we tested prominent VLMs against a human psychophysics baseline comprising 16,603 valid responses across 307 complex natural scenes and 1,306 high-fidelity counterfactual variants. Our analysis reveals a pervasive scene comprehension gap: models exhibit an over-reliance (relative to humans) on object size, object centrality, and low-level saliency of objects. In contrast, models are less sensitive to the presence of people in the scenes than our human participants when describing the images. The over-reliance on object size is a primary driver explaining variations in model-human semantic divergence. Code and data are available at https://github.com/starsky77/Counterfactual-Semantic-Saliency-Data .

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38994

Rethinking GFlowNet Training: Negative Reinforce Dynamics

Pedro Dall'Antonia, Daniel Csillag, Diego Mesquita

原文摘要

Generative Flow Networks (GFlowNets) are models that learn to sample discrete objects proportional to a target reward. They are trained by minimizing balance errors over sampled trajectories, which inherently ties correctness to exploration: if a region is not visited, balance cannot be enforced there. Adaptive exploration is therefore crucial, yet standard training relies on a semi-gradient update that treats the sampling distribution as fixed at each optimization step. On the other hand, the full gradient reveals a second mechanism that reduces the observed errors in an anti-exploratory manner. In this paper, we establish a connection between the full gradient and min-min dynamics between the GFlowNet and a decoupled sampler, showing that the sampler reduces observed violations by avoiding high-error regions. This insight motivates a bridge between two-player games and single-policy dynamics, which underpins the design of our proposed method, Negative Reinforce Dynamics (NRD). By tying the parameters of an adversarial game together, we recover a single-policy dynamic that extends exploration while maintaining correctness. In standard benchmarks, our method outperforms existing approaches in both convergence speed and exploration efficiency, without training a separate teacher or auxiliary exploration policy.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38993

Open-World Object Motion Anticipation via Meta-Learned Semantic Adaptation

Mason Peterson, Gage MacLin, Camilla Tabasso, Jonathan P. How, Joshua Migdal, Tye Brady, Taskin Padir

原文摘要

Anticipating the motion of nearby objects is critical for safe and efficient robot navigation. Without a proper understanding of what may move, a robot may plan overly conservative, inefficient trajectories to guarantee safety, or it may be overly risky, resulting in unacceptable collisions. While tracking and predicting the motion of known objects has seen significant progress, little research addresses how a robot should adapt online to novel objects, including anticipating how one object may move another (e.g., a person operating a forklift to move a pallet). To enable this capability, we propose a meta-learned graph neural network that leverages a semantic, geometric, and relational history of objects to accurately predict, from a few online observations, when objects will begin to move in new environments. We demonstrate that our method, which is trained only in simulation, accurately anticipates the motion of objects unseen at training time, transfers to hardware without any fine-tuning, and outperforms a vision-language model and a class-frequency baseline in both simulation and hardware experiments. Our method's accurate prediction enables collision-free traversals in a closed-loop navigation simulation, while remaining within four seconds of the fastest, collision-prone planner.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.17342

Learning a Maximum Entropy Model for Visual Textures using Diffusion

Xinyuan Zhao, Eero P. Simoncelli

原文摘要

Visual textures -- spatially homogeneous image regions containing repeated elements (e.g. a field of grass, the bark of a tree) -- are ubiquitous in visual scenes and provide important cues for recognizing and analyzing materials, surface geometry and objects. A number of existing texture models extract essential statistics from a single texture image, and can then generate high-quality samples that are visually similar to the original by matching these statistics. However, their statistics are either hand-designed or based on a network pretrained for another purpose (e.g., object recognition). Here, we develop the first principled method for unsupervised learning of a set of statistics that are used to constrain a maximum entropy probability model for texture. We leverage methods developed for generative diffusion models to derive training and sampling procedures, and compare these to the traditional method of sampling via matching the statistics. Despite the compactness of our trained model (512 statistics), it generates texture images whose quality is as good as or better than the current state-of-the-art model (~177k statistics), both visually and by quantitative perceptual scoring using DreamSim. Finally, we show that images generated from parameters lying along a line between the parameter vectors corresponding to two different reference textures are homogeneous textures with features that smoothly transition between those of the two references.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38990

Exploring AI Peer in Collaborative Mathematics Learning

Yiran Lin, Ye Deng, Ziqi Pan, Yongan Yu, Alexandra I. Cristea

原文摘要

Artificial intelligence (AI) agents are increasingly explored as collaborative learning partners in mathematics education, yet it remains unclear what makes an AI recognizable as a peer. This study examines how domain experts perceive and interact with an adaptive LLM-based peer agent combining a Big Five-informed persona with motivational state modulation for collaborative mathematics problem solving. Through thematic analysis of expert interviews, we identify conditions shaping peer perceptions, including emotional safety, shared control, and collaborative reasoning. Participants primarily judged the AI through its social behavior rather than mathematical capabilities. The AI was perceived more as a peer when it created a non-judgmental space, engaged in tentative reasoning, displayed appropriate imperfection, and respected participants' pace. Conversely, blind agreement and over-instruction reduced trust and shifted perceptions toward a tutor role. Based on these findings, we propose human-centered design dimensions for AI peer agents to guide future peer-oriented learning systems.

cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38987

Syntactic Segmentation and Collage Generation for Videogame Levels with Perceptual Distance

Ross Mawhorter, Matthew Guzdial

原文摘要

In procedural videogame level design, many existing techniques combine hand-authored level chunks. Some existing analysis techniques also rely on a higher-level graph structure which abstracts the level design. In this paper, we explore techniques for computing both of these things automatically from an existing design. We introduce the concept of perceptual distance, based on analysis techniques from the architecture subfield of Space Syntax. We use this perceptual distance to segment level designs with hierarchical clustering. We compare with past work on StarCraft analysis, show results on rooms from Super Metroid, and also demonstrate a proof-of-concept level generation algorithm based on this segmentation method.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38985

Benchmarking Graph Neural Networks for AC Power Flow: The Role of Inductive Biases and Physical Constraints

Federico Clerici, Ferran Bohigas-Daranas, Hamid Latif-Martínez, Eduardo Prieto-Araujo, Oriol Gomis-Bellmunt, Pere Barlet-Ros

原文摘要

Fast and accurate models for AC power flow (ACPF) are increasingly important as modern electrical grids require more frequent system analyses. Graph neural networks (GNNs) naturally exploit electrical grid topology, yet the effects of architectural design choices on predictive performance remain poorly understood given differences in graph representations and evaluation settings across studies. We present a unified benchmark of representative GNN architectures for ACPF prediction under a common experimental framework. All models use identical preprocessing and training procedures and comparable parameter budgets, isolating the impact of message-passing mechanisms and inductive biases. We also analyze errors by distance from the slack bus and systematically ablate key design components. We evaluate predictive accuracy and physical consistency on the IEEE 14-bus and IEEE 118-bus systems. Results indicate that anisotropic message-passing architectures offer the most favorable trade-off between predictive accuracy and physical consistency under the studied parameter and topology constraints. Explicit edge features encoding line impedances and global context aggregation are particularly important for representing electrical interactions and system-wide dependencies, especially for voltage-angle predictions on the topologically bottlenecked IEEE-118 grid. Under a budget with approximately fixed parameters, two or three message-passing layers generally provide the best balance between depth and representation width, while additional depth offers no consistent improvement. Ablations also show that standard residual and root connections are not necessarily beneficial in shallow models, and that aggregation operators involve trade-offs between statistical accuracy and physical consistency. These results provide practical guidelines for designing graph-based power system models.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.34321

TopX: Text-to-Motion for Unseen Skeletons from the Skeleton File Alone

Tengjiao Sun, Pengcheng Fang, Xiaoyu Zhan, Dongjie Fu, Xiaohao Cai, Hansung Kim

原文摘要

Text-to-motion models generate motion only for the skeletons they were trained on, while a new character arrives as a skeleton file and nothing else. We study text-to-motion for skeletons unseen in training, without clips of the character, templates, retargeting or fine-tuning. We present TopX, a 73M-parameter flow-matching transformer that generates joint rotations and a root trajectory for any skeleton from its kinematic tree, rest pose and joint names. Motion is normalised by the rest pose and the size of the skeleton, and the rest pose is encoded as a world-frame descriptor that is independent of how the rig's author oriented each joint, while rest conventions are randomised during training. Attention is factorised over the joints and the frames of a motion. Across joints, queries and keys are rotated by HK-RoPE, a heat-kernel rotary encoding of the skeleton graph that is defined for every tree and does not change with the order of its joints or the eigenbasis of its Laplacian; across frames, they are rotated by a rotary encoding of frame order. To evaluate this generalisation, we build SkelShift, which unifies four motion corpora under one codec and holds out whole skeletons, tree classes and species, so that each test bucket isolates one kind of skeleton shift: topology, joint count, body plan, species or rig source. It scores motion quality without a learned evaluator. A single model animates every benchmark skeleton (4--117 joints) directly from its skeleton file and generates up to 240 frames. On held-out game species its motion is about as close to the real clips as two halves of the real clips are to each other (kernel distance 0.127 against 0.124), and text-to-motion retrieval reaches R@1 0.953, compared with 0.929 for real motion. Project page: https://chdtevior.github.io/TopX-page/.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38984

Wreath products of distributive quasi relation algebra frames and their application to representability

Wilmari Morton, Claudette Robinson

原文摘要

We adapt a construction by Comer (1983) for polygroups to define the wreath product of the dual frames of two distributive quasi relation algebras satisfying certain conditions and show that it is itself a frame. Frames play a role analogous to that of atom structures for relation algebras. After introducing the notion of a frame network, we establish a correspondence between frame networks satisfying certain conditions and representability of finite distributive quasi relation algebras. Next we describe a frame network for the wreath product of two finite representable distributive quasi relation algebras that satisfies the aforementioned conditions, as well as additional conditions, and use this to conclude that the complex algebra of the wreath product is a representable distributive quasi relation algebra. Thus, our construction provides a method for obtaining representable DqRAs from certain finite DqRAs that are already known to be representable.

cs.LO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38983

Beyond atol and rtol: Sound Test Oracles for Low-Precision GPU Kernels

Xinyi Shi

原文摘要

Low-precision GPU kernels are commonly tested against a reference using hand-picked absolute and relative tolerances. Legitimate numerical error varies with the input, format and precision plan, so a fixed tolerance trades false alarms for missed faults. We present numoracle, which derives per-element acceptance regions from precision-annotated reference programs. Its calculus tracks shared rounding errors, provenance-ordered products, block-scaled quantizers and calibrated math-library errors. A rounding-interval check detects faulty stores that symmetric bounds can admit. Soundness is conditional on explicit rounding, hardware, domain and implementation assumptions; a fixed-input mode removes the zero-mean rounding assumption. We evaluate 13 Triton operators from Liger-Kernel, Unsloth and vLLM on two GPU generations. The main campaigns produce no false alarm on 33,960 correct-kernel and behavior-preserving-variant runs per GPU. Under input shift, the GPU campaigns record no false alarm while tuned tolerances flag up to 30% of correct runs; final-version reference checks identify inputs without complete finite-range certificates. On the RTX PRO 6000, numoracle kills 116 of 124 effective mutants, versus 59-83 for four fixed baselines. Among 400 tuned tolerance configurations, requiring at most 1% false alarms in every evaluated Liger input group permits at most 31 of 53 main-mutant kills, versus numoracle's 50. Historical replays expose accuracy faults in eight change pairs without rejecting their matched corrected controls; a ninth change concerns rounding parity.

cs.MS · cs.PL · cs.SE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.26257

Group Isomorphism and the Polylogarithmic-Time Hierarchy: Depth-2$\frac{1}{2}$ Circuits and Lower Bounds

Joshua A. Grochow, Gülce Kardeş, Michael Levet

原文摘要

In this paper, we investigate the low-depth circuit complexity of Group Isomorphism in the multiplication (Cayley) table model. We prove the first circuit lower bounds for Group Isomorphism: namely, we show that every family of depth-$2$ Boolean circuits deciding Group Isomorphism requires quasipolynomial-size. (We also extend this to a limited class of depth-3 circuits, namely $\textsf{CNF}$-of-$\textsf{XOR}$s.) We complement this with upper bounds of uniform depth-$2\frac{1}{2}$ circuits of quasipolynomial-size: depth 3 circuits with poly-logarithmic bottom fan-in. A sequence of previous results from 1970-2025 progressively reduced the circuit depth from polynomial to $3\frac{1}{2}$; all of these results relied on the generator-enumerator strategy and, in fact, applied more generally to quasigroups. In contrast, our depth-$2\frac{1}{2}$ construction follows a fundamentally different strategy that exploits structure more specific to groups. We guess a composition series for each group, together with generators for its terms and the isomorphism types of its composition factors. We then inductively verify that the corresponding extensions at each level of the two composition series are compatible. A central part in this approach brings to bear the extensive work on the Short Presentation Conjecture, in tandem with the algorithmic theory of group extensions and cohomology.

cs.CC · cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38981

Why Speculative Decoding Works Better Than Predicted on Sparse MoE Models

Ekagra Ranjan, Komal K. Teru, Bharat Venkitesh, Acyr Locatelli

原文摘要

Speculative decoding (SD) accelerates autoregressive inference by leveraging the memory bound nature of decoding, verifying multiple draft tokens in a single forward pass. For mixture-of-experts (MoE) models, prior work predicts a concave speedup-batch size relationship, with gains peaking at intermediate batch sizes due to expert saturation. We evaluate this across 10 sparse MoE models (30B--1T parameters, 8--512 experts) and find recent fine-grained models break this trend, showing higher speedup at low batch size. We find verification cost is governed not only by activated-expert bytes, but also by temporal overlap across draft tokens and GPU execution dynamics. At low batch size, MoE layers in fine-grained models account for a much smaller share of runtime than of weights; on H100, kernel profiling attributes this mainly to higher bandwidth utilization of MoE kernels relative to attention, with L2 absorbing repeated reads of co-routed experts. Across draft tokens, routing overlap further reduces the unique experts activated during verification relative to independence assumptions. We model this effect using a maximum-entropy Iterative Proportional Fitting (IPF) framework that predicts unique expert counts from routing-overlap statistics with less than 4\% error, and substantially narrows the gap to real-model curves and captures the qualitative low-batch trend from synthetic routing.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38980

Context Compaction for World Models

Hayder Tirmazi

原文摘要

World models let observers explore and interact with generated worlds. Recent work shows that when observers revisit a region, current models often fail to regenerate it consistently. We view this as a context retrieval problem. LLM agents manage context retrieval at two layers of their stack. At the model layer, the KV cache design determines how much context fits in memory. At the agent harness layer, harnesses such as Claude Code pick the context sent to the LLM. Prior work on world models has mostly focused only on the model layer. In this paper, we make three contributions. First, we build a benchmark to measure world model context retrieval. It generates new worlds for every episode, preventing a model from recalling rooms using training data. It also separates context retrieval from guesses based on nearby frames. Second, we build an agent harness that performs context compaction for world models, which to our knowledge no prior work has studied. Third, we use our benchmark to study four world model designs. Context compaction lets our world models retrieve rooms up to 4 times beyond their context window without retraining. However, world models with sliding-window attention lose most of their context retrieval when dreaming. Therefore, a dreaming world agent benefits more from extending the reach of its world model using context compaction than switching to sliding window attention at the model layer.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38977

Beyond Passing Tests: A Systematic Review and Controlled Re-evaluation of Techniques for Improving the Quality of LLM-Generated Code

Prashank Kadam

原文摘要

Large language models pass most functional-correctness benchmarks, but passing tests says little about whether their code is readable, efficient or secure. Many techniques claim to improve these qualities, yet their evidence is scattered, usually covers one quality dimension and is rarely compared at an equal compute budget. We contribute a systematic review and a controlled re-evaluation. A systematic literature review of 628 primary studies (July 2021 to October 2026), with disclosed, author-reviewed LLM assistance, organizes the techniques by stage, signal source and quality target. Most studies measure only the dimension they target, gains often come with unchanged or unreported correctness, single automated quality signals are gamed, and compute, variance and contamination are rarely reported. A controlled re-evaluation runs ten techniques on four models at matched budgets on correctness, maintainability, efficiency and security benchmarks, extends it to thirteen current models and repository-level tasks, and compares 66 highlighted techniques head-to-head on Gemma 4 31B. At one call, quality prompting raises analyzer issue counts and leaves complexity and judge scores almost unchanged. At matched budgets, best-of-n sampling with an analyzer-weighted ranker matches or beats prompting, planning, self-refinement, a reviewer agent and retrieval on measures the ranker does not optimize, while an analyzer feedback loop and fine-tuning beat it only on analyzer issue counts. Large correctness losses on HumanEval+, a likely contaminated benchmark near its ceiling, do not appear on contests held after the models' training cutoffs. In the head-to-head, 1 of 66 techniques beats best-of-n on its primary target beyond the 95% interval, a rate consistent with chance. Gains should be reported against a budget-matched sampling baseline and on measures independent of the technique's own signal.

cs.SE · cs.CL · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.36340

CrowdCC: Jointly Benchmarking Corruption Robustness and Deployment Quantization in Crowd Counting

S Mahmudul Hasan, Lu Peng

原文摘要

Crowd-counting models deployed on edge devices face two challenges: inputs are degraded along the capture and transmission pipeline, while models are often quantized to satisfy resource constraints of the edge. Yet corruption robustness and deployment quantization are typically evaluated separately. We introduce CrowdCC, a controlled benchmark that jointly evaluates these effects across 23 deployment-motivated corruptions at three severity levels, four crowd-counting datasets, and 26 models. We evaluate TensorRT engines at FP32, FP16, and post-training INT8 precision on an A100 GPU, alongside throughput and energy measurements on corresponding Jetson Orin~NX configurations. The corruptions span six families, with head annotations adjusted when geometry or visibility changes. Our results show that capture-chain corruptions consistently cause the largest degradation across evaluated models, while crowd density affects absolute and relative errors differently. Accuracy on clean images does not reliably predict performance on the corresponding corrupted images. INT8 changes corruption robustness more than FP16, without a consistent direction, and does not consistently improve latency or energy efficiency. INT8 inference efficiency is strongly associated with reformat overhead in the compiled engine, with slowdowns also observed under limited INT8 coverage. Precision changes corrupted-error rankings and robustness--cost frontiers, highlighting the need to consider corruption robustness and deployment cost jointly when selecting models and precision. The code is available at https://github.com/Tulane-Computer-Architecture-Systems/CrowdCC-Release.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2510.19793

A Logic-based Algorithmic Meta-Theorem for Treedepth: Single Exponential FPT Time and Polynomial Space

Benjamin Bergougnoux, Vera Chekan, Giannos Stamoulis

原文摘要

For a graph $G$, the parameter treedepth measures the minimum depth among all forests $F$, called elimination forests, such that $G$ is a subgraph of the ancestor-descendant closure of $F$. We introduce a logic, called neighborhood operator logic with acyclicity, connectivity and clique constraints ($\mathsf{NEO}_2[\mathsf{FRec}]+\mathsf{ACK}$ for short), that captures all NP-hard problems like Independent Set or Hamiltonian Cycle that are known to be tractable in time $2^{\mathcal{O}(k)}n^{\mathcal{O}(1)}$ and space $n^{\mathcal{O}(1)}$ on $n$-vertex graphs provided with elimination forests of depth $k$. We provide a model checking algorithm for $\mathsf{NEO}_2[\mathsf{FRec}]+\mathsf{ACK}$ with such complexity that unifies and extends these results. For $\mathsf{NEO}_2[\mathsf{FRec}]+\mathsf{k}$, the fragment of the above logic that does not use acyclicity and connectivity constraints, we get a strengthening of this result, where the space complexity is reduced to $\mathcal{O}(k\log(n))$. With a similar mechanism as the distance neighborhood logic introduced in [Bergougnoux, Dreier and Jaffke, SODA 2023], the logic $\mathsf{NEO}_2[\mathsf{FRec}]+\mathsf{ACK}$ is an extension of the fully-existential $\mathsf{MSO}_2$ with predicates for (1) querying generalizations of the neighborhoods of vertex sets, (2) verifying the connectivity and acyclicity of vertex and edge sets, and (3) verifying that a vertex set induces a clique. Our results provide $2^{\mathcal{O}(k)}n^{\mathcal{O}(1)}$ time and $n^{\mathcal{O}(1)}$ space algorithms for problems for which the existence of such algorithms was previously unknown. In particular, $\mathsf{NEO}_2[\mathsf{FRec}]$ captures CNF-SAT via the incidence graphs associated to CNF formulas, and it also captures several modulo counting problems like Odd Dominating Set.

cs.DS · cs.LO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.32915

Eye Tracking Signal Quality Evaluation: Meta Quest Pro vs HTC VIVE Pro Eye vs HTC VIVE Focus Vision

Aleksandr Chernysh, Mehedi Hasan Raju, Kamrul Hasan, Md Arif Shahriar, Proloy Karmakar, Isayas B. Adhanom, Oleg Komogortsev

原文摘要

Gaze research in virtual reality needs eye trackers of known error. Thirty participants completed the same protocol on the Meta Quest Pro (QP), HTC VIVE Pro Eye (PE) and HTC VIVE Focus Vision (FV); to our knowledge, this is the first evaluation of the FV eye tracker. In a random-saccade task, median participant mean error was 1.04° on QP, 1.50° on PE and 2.41° on FV, which remained least accurate after linear recalibration. In a later block with a darker background, PE error rose by 0.70°. FV samples were positively correlated: dispersion was 9.8 times the sample-to-sample root mean square. The device clocks showed losses that mean error hides. QP paused for up to 6.04 s, and FV skipped single samples. During instructed eye closures, QP flagged every sample valid while gaze moved 9.74°. Evaluations should report repeated timestamps, loss composition on the device clock and validity during eye closures.

cs.HC

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新论文 · v1arXiv:2610.38976

The Utility of Physics-Informed Machine Learning in Solar Power Production Forecasting

Peter Pavlík, Franz M. Rohrhofer, Bernhard C. Geiger

原文摘要

Physics-informed machine learning (PIML) and hybrid modeling have attracted significant attention as approaches for combining physical knowledge with the flexibility of deep learning. In this work, we investigate whether these approaches deliver their commonly claimed benefits for large-scale photovoltaic (PV) power forecasting. We develop two hybrid forecasting architectures that integrate the ADR physical PV efficiency model with the TSMixer neural forecasting architecture through feature augmentation and residual learning. The proposed models are evaluated on a dataset comprising over 3 million samples from 3,713 PV installations across 33 European countries. We assess convergence speed, forecasting accuracy, transferability across geographical regions, and data efficiency. Although the physics-informed models seem to exhibit slightly faster convergence during training, they do not achieve consistent improvements in test-set accuracy, generalization, or performance under limited training data compared to the purely data-driven baseline. These findings suggest that, for modern deep forecasting architectures trained on large datasets, explicitly incorporating the ADR physical model provides limited practical benefit for day-ahead PV forecasting.

cs.LG

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