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PACT: End-to-End Learning of Human Pose, Contacts, and Forces from Video

arXiv:2610.00451v1 Announce Type: cross Abstract: Human motion, environmental contacts, and interaction forces are governed by common physical laws, yet existing approaches typically separate visual p

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Human DGX agent

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PAGER: Partial-to-global Alignment via Geometric and Relational Distillation

DGX agent

arXiv:2610.01589v1 Announce Type: new Abstract: Pretrained 3D encoders are typically developed on globally reconstructed scenes expressed in a consistent world coordinate frame, whereas embodied syste

safetyarxiv-cs-cv

2 Oct 2026

Parameter-Efficient Distributionally Robust Adaptation of Tabular Foundation Models under Subpopulation Shift

arXiv:2610.01143v1 Announce Type: cross Abstract: Despite strong mean accuracy, tabular foundation models (TFMs) can perform poorly on underrepresented groups under subpopulation shift, where group pr

Applications

Partial AUC Maximization from Positive-unlabeled Data

arXiv:2610.00284v1 Announce Type: cross Abstract: The partial area under the receiver operating characteristic curve (pAUC) is an important performance metric for binary classification that summarizes

applicationsarxiv-cs-ai

Participation-Sensitive Convergence and the Fragment First, Converge Later Pattern in Asynchronous Online Learning: A Topological Analysis Across 22 OULAD Courses

arXiv:2610.01738v1 Announce Type: cross Abstract: Asynchronous online learning offers temporal flexibility at a structural cost: learning communities tend to fragment rather than cohere. eta_0, the nu

safetyarxiv-cs-lg

Pay for the Fault, Not the Flow: Label-Free In-Flow Multi-Agent Workflow Optimization

arXiv:2610.01017v1 Announce Type: new Abstract: Large language models (LLMs) increasingly construct multi-agent workflows that decompose a complex task and assign specialist agents from a pool. Howeve

Paying for Too Many Tokens? Valid and Cost-Efficient Multimodal LLM Annotation with Simple Heuristics

arXiv:2610.00809v1 Announce Type: new Abstract: Vision-Language Models (VLMs) enable video annotation at scale, but costs accumulate quickly: processing a typical 60-second short-form video at one fra

researcharxiv-cs-cv

pCoMole: Pareto-Constrained Molecule Editing with Discrete Flows

arXiv:2610.01663v1 Announce Type: new Abstract: Biomolecular therapeutics often start from known sequences and require targeted editing to improve multiple properties while satisfying hard biochemical

Per-Node Activation Function Evolution in Indirectly Encoded Substrates: Solvability, Limits, and Emergent Diversity

arXiv:2610.00149v1 Announce Type: cross Abstract: Biological neurons achieve computational diversity through specialized types: tonic, bursting, adapting, and fast-spiking cells coexist within the sam

Permutation-Robust Decision Modeling with Candidate-Independent Block-Causal Attention

arXiv:2610.01601v1 Announce Type: cross Abstract: Decision models often score a variable-sized set of candidate actions encoded in a single sequence. This setting is increasingly relevant for System 1

Persistent Depth Ordering amid Shifting Block-Bypass Responses in Language Model Pretraining

arXiv:2610.01165v1 Announce Type: new Abstract: Layer interventions are widely used to probe the internal organization of language models, yet most analyses examine a single training checkpoint even t

Personalized Image Generation with Reasoning and Reflection

arXiv:2610.00737v1 Announce Type: cross Abstract: Personalized image generation has remained narrowly focused on conditional synthesis from curated visual exemplars, rather than capturing who a user i

PG-SFT: Balancing Capability Acquisition and Retention in Offline Agent Fine-Tuning

arXiv:2610.00949v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) on offline agent trajectories is the standard approach for training specialized tool-using agents, but forcing models to im

PhaseAT: Fourier Phase Adversarial Training for Medical Image Domain Generalization

arXiv:2610.01807v1 Announce Type: new Abstract: Reliable clinical deployment of deep medical image models is hindered by distribution shifts across scanners, sites, and acquisition protocols. Existing

researcharxiv-cs-cv

PhGPO: Pheromone-Guided Policy Optimization for Long-Horizon Tool Planning

arXiv:2602.13691v2 Announce Type: replace Abstract: Recent advancements in Large Language Model (LLM) agents have demonstrated strong capabilities in executing complex tasks through tool use. However,

Applications

PhysDEM: Physics-Defined Energy-Matching Diffusion for Spatiotemporal Field Generation under Scarce Measurements

arXiv:2610.01759v1 Announce Type: new Abstract: Generating and predicting spatiotemporal physical fields from scarce measurements is challenging, as observations are insufficient to characterize a dis

applicationsarxiv-cs-cv

Local Ai

Physics-Refined Spatiotemporal Forecasting on Open-Boundary Hydrologic Graphs

arXiv:2610.01765v1 Announce Type: new Abstract: Spatiotemporal forecasting on hydrologic graphs is especially prone to instability in open-boundary systems, where the forecast domain exchanges fluxes

local-aiarxiv-cs-lg

PhysicsLENS: Diagnosing Physical Property Blindness in Video Generation Models

model-releasesarxiv-cs-cv

arXiv:2610.01162v1 Announce Type: new Abstract: Reliable video world models could provide scalable predictive environments for robot learning, planning, and evaluation. However, generated robot videos

PhysVista: Benchmarking Physical Intelligence in VLMs via a Perception-Reasoning-Assessment Loop

model-releasesarxiv-cs-cv

arXiv:2610.00559v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have shown strong multimodal reasoning capabilities, yet whether they truly capture the physical consistency underlying re

Tutorials

PI-AMFM: Permutation-Invariant Learning for Variable-Cardinality AM-FM Mode Decomposition in Biomedical Signal Analysis

arXiv:2610.00819v1 Announce Type: cross Abstract: Physiological recordings often contain nonstationary oscillatory components whose number and dynamics vary across signals. Amplitude- and frequency-mo

tutorialsarxiv-cs-lg

PickMoment: Continuous-Time Single-Image-to-Video via Learning Deblurring and Blur-to-Video

arXiv:2610.01279v1 Announce Type: cross Abstract: Motion blur arises from the temporal integration of a continuous sharp signal over a finite exposure window, yet existing learning-based methods sides

PixelDense: Dense Prediction as Representation Alignment for Pixel Diffusion

arXiv:2610.00483v1 Announce Type: new Abstract: Representation alignment (REPA) accelerates diffusion transformer training, but its alignment targets are almost exclusively semantic encoders such as D

safetyarxiv-cs-cv

Platonic Task Arithmetic

model-releasesarxiv-cs-cv

arXiv:2610.00929v1 Announce Type: cross Abstract: Models specialized for the same task converge to similar behavior, yet the parameter updates that produce it share no common coordinate system, so wei

Poincare Meets Bellman: Revisable Memory, Operational Quotients, and Evidence-Supported Learning in Changing Environments

arXiv:2512.05990v2 Announce Type: replace Abstract: Memory consolidation determines both what a learner can do now and which changes remain implementable later. We develop a finite-model synthesis of

Polylogarithmic Sparsity of Randomly Reweighted NPMLEs for Gaussian Mixtures

arXiv:2610.01088v1 Announce Type: cross Abstract: The nonparametric maximum likelihood estimator (NPMLE) of a Gaussian location mixture maximizes the likelihood over the infinite-dimensional space of

Pooling Helps, Learned Weighting Hurts In-Context: Decomposing Group Attention

arXiv:2610.01831v1 Announce Type: new Abstract: Group attention, introduced by the time series forecasting model Chronos-2, attends over the variates of a group at a fixed patch index and serves both

Port-Hamiltonian Neural Networks for Systems with Multiple Asymptotically Stable Equilibria

arXiv:2610.01356v1 Announce Type: new Abstract: Stable port-Hamiltonian neural networks certify asymptotic stability by construction. Yet, their Hamiltonian is a global Lyapunov function with a single

Posterior sampling by source-space MCMC via prior-based few-step transport maps

arXiv:2610.01034v1 Announce Type: cross Abstract: Bayesian inference increasingly uses informative but implicit priors represented only by samples, such as historical ensembles, simulator outputs, and

PPO-HRAP: Proximal Policy Optimization with a Hybrid Regime-Aware Policy for Risk-Controlled Trading

arXiv:2610.01325v1 Announce Type: new Abstract: Reinforcement learning for trading often struggles to balance upside participation with drawdown control. Profit-only policies can collapse toward passi

Pre-training interventions, ex post facto: Grafting model beliefs across checkpoints

arXiv:2610.00767v1 Announce Type: cross Abstract: Pre-training interventions are critical to alignment research, since beliefs formed during pre-training shape how a model generalizes from later train

Tutorials

Prediction-powered Neural Architecture Search

arXiv:2610.01317v1 Announce Type: new Abstract: Evaluating candidate architectures in neural architecture search (NAS) faces an inherent trade-off: on the one hand, reliable performance labels are lim

tutorialsarxiv-cs-lg

Predictive Credit: Measuring What Scientific Explanations Add to Experimental Forecasts

arXiv:2610.00314v1 Announce Type: new Abstract: Research agents explain planned experiments. We measure predictive credit with paired forecasts sharing an intervention, forecaster, and outcome while v

PRISM: A Category-Theoretic Framework for Measuring and Refining Multimodal Analogies

arXiv:2610.01383v1 Announce Type: new Abstract: Analogical reasoning involves identifying and preserving relational structures across domains. However, existing approaches to AI-driven multimodal anal

Probabilistic Plan Legibility with Off-the-shelf Planners

arXiv:2610.00065v1 Announce Type: cross Abstract: Legible planning is the creation of plans that best disambiguate their goals from a set of other candidates from an observer's perspective. In this pa

Tutorials

Program Learning with Verifiable Rewards: Symbolic Backpropagation for Post-Training LLMs

arXiv:2608.28421v2 Announce Type: replace Abstract: Post training a language model to reason means updating its weights. Supervised finetuning and reinforcement learning both place the acquired capabi

tutorialsarxiv-cs-ai

Progressive-Resolution Secure Aggregation for Federated Learning

arXiv:2610.00695v1 Announce Type: cross Abstract: Secure aggregation lets a server recover an aggregate of client updates without observing any individual update, but conventional protocols fix the ag

PROMO: Preference-conditioned Multi-Objective Reinforcement Learning for Quadrupedal Robots

arXiv:2610.01260v1 Announce Type: cross Abstract: Quadrupedal locomotion requires balancing conflicting objectives such as command tracking, stability, and energy efficiency, yet conventional reinforc

Proof-Gated Signing: Solver-Checked Transaction Guards that Hold Under State Drift for Onchain AI Agents

arXiv:2610.00354v1 Announce Type: cross Abstract: AI agents that control wallets read attacker-reachable content, so they can be steered into proposing harmful transactions. The usual last line of def

ProtoDCS: Towards Robust and Efficient Open-Set Test-Time Adaptation for Vision-Language Models

arXiv:2602.23653v3 Announce Type: replace-cross Abstract: Large-scale Vision-Language Models (VLMs) exhibit strong zero-shot recognition, yet their real-world deployment is challenged by distribution

ProtoFlow: Prototype-Guided Flow Matching for Multivariate Time Series Forecasting

arXiv:2610.01320v1 Announce Type: new Abstract: Generative modeling has shown strong promise for multivariate time mseries (MTS) forecasting, especially scale to high-dimensional settings. Diffusion-b

Provable FDR Control for Deep Feature Selection: Deep MLPs and Beyond

arXiv:2512.04696v3 Announce Type: replace-cross Abstract: We develop a flexible feature selection framework based on deep neural networks that approximately controls the false discovery rate (FDR), a

Pushing CPU Speech Synthesis to the Wall: Extreme Inference Tuning under Serverless Architecture and Billing

arXiv:2610.00063v1 Announce Type: cross Abstract: Instance-billed serverless platforms charge for CPU and memory over the lifetime of a warm instance, making idle inference state a direct serving cost

Applications

PyPottery: an AI-powered end-to-end suite for pottery processing and publication

arXiv:2610.02072v1 Announce Type: new Abstract: The study of ceramic materials constitutes a cornerstone of archaeological research, yet the post-production workflow for pottery documentation remains

applicationsarxiv-cs-ai

Q-Learning for Reachability in MEC-Free MDPs

arXiv:2610.01781v1 Announce Type: new Abstract: Reinforcement learning (RL) for reachability specifications is fundamental to sequential decision-making. Prior work establishes asymptotic convergence

Q-MINO: A Minimal-Norm Method for Quantization-Aware Training

arXiv:2610.00738v1 Announce Type: cross Abstract: The Straight-Through Estimator (STE) is a widely used heuristic for Quantization-Aware Training (QAT), but its surrogate gradients can exhibit substan

QK-Wanda: Coupling Queries and Keys for Unstructured Pruning

arXiv:2610.01554v1 Announce Type: new Abstract: Wanda (Sun et al., 2024) prunes large language models by scoring weights independently within each linear projection, although queries and keys interact

Quantifying Diversity of Thought: A Predictive Law of Weighted LLM Ensemble Lift

arXiv:2607.17384v3 Announce Type: replace Abstract: This paper provides an experimentally verified formal law for calculating the uplift that diversity of thought provides in Large Language Model (LLM

Quantifying the Impact of Ambulance Ramping: A Multi-Year Analysis of Victorian Emergency Medical Services Cases

arXiv:2610.00818v1 Announce Type: new Abstract: Ambulance ramping, the delay between hospital arrival and patient handover, is a critical operational bottleneck in Emergency Medical Services (EMS), ye