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arXiv:2610.07384v1 Announce Type: cross Abstract: Individual animal re-identification from camera-trap imagery is an instance retrieval problem central to non-invasive wildlife monitoring: a query ima

tutorialsarxiv-cs-ai

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Human DGX agent

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Will the Judge Flip? Predicting Position-Sensitive LLM Judgments from Residual Stream Activations

DGX agent

arXiv:2610.07115v1 Announce Type: cross Abstract: The order in which candidate responses are presented can change an LLM judge's verdict. Detecting such a position flip ordinarily requires judging eac

model-releasesarxiv-cs-ai

7 Oct 2026

WiSPER: Pose-Supervised Predictive and Residual Flow Refinement For Multi-Person 3D Pose Estimation With WiFi CSI

arXiv:2610.07025v1 Announce Type: new Abstract: Multi-person 3D pose estimation with WiFi channel state information (CSI) is challenging because reflections from different people overlap without direc

local-aiarxiv-cs-cv

Agents

WorkflowOps: Learning Agent Collaboration Priors for Multi-Agent Workflow Orchestration

arXiv:2610.07860v1 Announce Type: new Abstract: Multi-agent systems are increasingly deployed for complex knowledge work, yet their orchestration layers remain largely memoryless: each new task is dec

agentsarxiv-cs-ai

World Models' Last Exam in Physics

model-releasesarxiv-cs-cv

arXiv:2610.08791v1 Announce Type: new Abstract: Video world models can produce visually convincing yet physically inconsistent sequences, raising concerns about their reliability for prediction and pl

World Properties without World Models: Distributional Associations and the Interpretation of Decoding Results from Language Models

arXiv:2603.04317v2 Announce Type: replace-cross Abstract: A growing literature shows that variables can be linearly decoded from the activations of large language models (LLMs). These range from prope

WorldSolver: Can LLM Agents Simulate the Physical Dynamics via Solver Generation?

arXiv:2610.08720v1 Announce Type: new Abstract: LLM-based agents are increasingly advancing scientific and engineering problem solving, with physics simulation emerging as a challenging yet practical

WorldSonus: Bringing Sound to Worlds

arXiv:2610.08760v1 Announce Type: cross Abstract: Recent advances in world models have enabled increasingly realistic visual synthesis. However, these generated environments remain largely silent. Bri

safetyarxiv-cs-ai

X-OPM: Explainable Automatic Digital On-Chip Power Modeling for Enhanced Robustness

arXiv:2610.08502v1 Announce Type: cross Abstract: Proactive power management systems reduce processor dynamic power through runtime power prediction and power-aware scheduling. Accurate, stable and lo

researcharxiv-cs-ai

Zero-Shot Lombard Speech Synthesis with Controllable Style Embeddings

arXiv:2601.12966v2 Announce Type: replace-cross Abstract: The Lombard effect plays a key role in natural communication, particularly in noisy environments or when addressing hearing-impaired listeners

researcharxiv-cs-cl

Zero-Shot Visualization: Exploring Text Corpora with User-Prompted Axes

arXiv:2610.06889v1 Announce Type: cross Abstract: We study the application of large language models (LLMs) to the visual exploration of textual corpora. We introduce zero-shot visualization (ZSV), a t

zkLLMPoT: Efficient Zero Knowledge Proof of Training for Large Language Models

arXiv:2610.08258v1 Announce Type: new Abstract: Auditing the claimed outcomes of large language model (LLM) training is challenging when model weights and training data are private, while cryptographi

Agents

1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities

arXiv:2503.14858v5 Announce Type: replace Abstract: Scaling up self-supervised learning has driven breakthroughs in language and vision, yet comparable progress has remained elusive in reinforcement l

agentsarxiv-cs-lg

A Biomimetic Gaze Control to Restore Head-Neck Movements

arXiv:2610.04022v1 Announce Type: new Abstract: Powered neck exoskeletons are developed to restore head-neck motions for those with neck muscle weakness. Current methods using hand-held input devices

researcharxiv-cs-ro

A Bird's-Eye View of Iterative Reward Design

arXiv:2610.04364v1 Announce Type: new Abstract: Designing effective reward functions in RL typically requires substantial expertise and trial and error. Recent work automates this process with LLM-bas

A Contrast-Source Inversion Scheme Based on Stochastic Optimization and Plug-and-Play Regularization

arXiv:2610.05130v1 Announce Type: cross Abstract: An electromagnetic inversion scheme that integrates stochastic optimization (STO) and plug-and-play (PNP) regularization into contrast-source inversio

A differentiable Lagrangian-coupled 3D Gaussian Splatting-SPH model for forward simulation and inverse analysis in solid mechanics

model-releasesarxiv-cs-cv

arXiv:2610.04336v1 Announce Type: new Abstract: Recent advances in generative world models have increased interest in digital models that reproduce both the appearance of real objects and their respon

A Fine-Grained Analysis of the LoRA Fine-Tuning Landscape with Implications for Data Selection

arXiv:2610.06542v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has become a standard approach for parameter-efficient fine-tuning, yet a fundamental practical question remains unresolved:

A General Pipeline for Dense Illuminant Estimation via Physically Based Synthetic Data

arXiv:2610.06508v1 Announce Type: new Abstract: Illuminant estimation is a fundamental problem in computational photography, as it enables the correction of color shifts induced by varying lighting co

A Geometric-Transformation Feature-Adaptive Manifold Restoration Method for Open-Vocabulary Semantic Segmentation of Remote Sensing Images

arXiv:2610.04300v1 Announce Type: new Abstract: The semantic information of objects in remote sensing images is typically invariant to geometric transformations from the dihedral group D4. However, SA

local-aiarxiv-cs-cv

A Graph-Based Inspection and Intervention Tool for Assessing Mechanistic Learning in PINNs

arXiv:2610.04939v1 Announce Type: new Abstract: We ask whether physically meaningful correspondences discovered inside a trained scientific model remain meaningful outside the conditions under which t

A Hand-Checkable Proof That Two Hidden ReLU Layers Compute the Maximum of Six Numbers

arXiv:2610.04256v1 Announce Type: new Abstract: Exactly computing the maximum function is a standard test case for studying depth in ReLU networks. Two hidden layers are known to suffice for up to twe

A KKL Observer Perspective on Reservoir Computing

arXiv:2610.04343v1 Announce Type: cross Abstract: Reservoir computing (RC) is a machine learning technique for data-driven modeling of dynamics for forecasting and control, primarily studied in comput

local-aiarxiv-cs-lg

A multi-stage probabilistic framework to estimate gas-fired generator performance during extreme winter weather

arXiv:2610.04368v1 Announce Type: new Abstract: Extreme winter weather has repeatedly disrupted gas-fired power generation in the United States, yet the plant-level data needed to systematically quant

A Multimodal Sequence-to-Sequence Model for Cross-Subject Prediction of Brain Responses to Naturalistic Stimuli

arXiv:2507.18104v3 Announce Type: replace Abstract: Brain encoding models predict time-resolved neural activity from computational representations of ongoing experience, providing a principled framewo

A new design of a fall detection system integrating landmark identification and deep learning techniques

arXiv:2610.05749v1 Announce Type: new Abstract: This article introduces an innovative system that integrates landmark identification with deep learning to enhance fall detection accuracy and reliabili

safetyarxiv-cs-cv

A Physics-Guided Transformer Framework for Electromigration Analysis in Multi-Segment Interconnects

arXiv:2610.06464v1 Announce Type: new Abstract: As technology scales to smaller nodes, increasing current densities make electromigration (EM) one of the dominant reliability challenges in on-chip int

A Response Theory Probe for Learned Stochastic AI Simulators, Tested on Lorenz-63

arXiv:2610.06798v1 Announce Type: cross Abstract: Machine-learning emulators of chaotic and stochastic systems are usually validated on forecast skill and long-run statistics. Neither certifies that a

A Safe Action Is Not Enough: Feasible-Future Decoding for Vision-Language-Action Policies

arXiv:2610.05166v1 Announce Type: cross Abstract: A safe action is not necessarily a viable one. Under a frozen vision-language-action (VLA) policy, an action can be likely and locally admissible yet

safetyarxiv-cs-ro

A Solvable Model of Adaptive Learning Rate Rescaling: Acceleration, Stability & Scaling

arXiv:2610.06701v1 Announce Type: cross Abstract: A recurring design principle in modern optimizers is to decouple update magnitude from the raw gradient norm, yet its consequences for learning-curve

A Spatiotemporal Semantic Importance-Guided Unified Compression and Editing Framework for AI-Generated Videos

arXiv:2610.05779v1 Announce Type: new Abstract: AI-generated videos are rapidly increasing in volume, duration, and resolution, creating growing demands for efficient storage and transmission. Unlike

local-aiarxiv-cs-cv

A State Based Dispatch Controller for Hospital Delivery Robots with Shared Human and Infrastructure Resources

model-releasesarxiv-cs-ro

arXiv:2610.05971v1 Announce Type: new Abstract: Robot delivery studies can overstate transport capacity when travel to pickups and human support fall outside the modeled schedule. We formulate a locat

A Statistical Inference Framework for PMI Estimation and SGNS Word Embeddings

arXiv:2610.05058v1 Announce Type: cross Abstract: Pointwise Mutual Information (PMI) is a core measure of testing word association, and Skip-gram with Negative Sampling (SGNS) is essentially a method

safetyarxiv-cs-lg

A Strong Baseline for Evaluating Vision Encoders in Multimodal Large Language Models

arXiv:2610.05413v1 Announce Type: new Abstract: Evaluating vision encoders requires metrics that reliably predict their downstream performance in multimodal large language models (MLLMs). Although rec

Applications

A Taxonomy on Collective Awareness

arXiv:2610.06427v1 Announce Type: new Abstract: As robotic teams tackle increasingly complex tasks in dynamic and unstructured environments, effective coordination requires agents to maintain accurate

applicationsarxiv-cs-ro

A Testable Theory of Atomic Features

arXiv:2610.05794v1 Announce Type: cross Abstract: We develop and test a theory of language model representations in which there exist atomic features. Our main theoretical insight is that in such a mo

Applications

A Theory of Shape Reconstruction from Heat Conduction and Shading

arXiv:2610.04052v1 Announce Type: new Abstract: Shape from shading using a single image of a Lam- bertian surface is inherently ambiguous. When the light source direction is known, the surface normal

applicationsarxiv-cs-cv

A Three-Dimensional Reverse-Projection Method for Sparse Point Cloud Completion and Its Application to High-Speed Train Nose Reconstruction

arXiv:2610.05758v1 Announce Type: new Abstract: Holes in LiDAR scans of environments with glass windows remain an unresolved problem in three-dimensional reconstruction. This study presents a pipeline

A Unified Dynamics Framework for Reinforcement Learning and Classical Control of a Six-DOF Pipeline-Tracking ROV in NVIDIA Isaac Sim

arXiv:2610.04949v1 Announce Type: new Abstract: Reinforcement learning controllers for underwater vehicles are usually trained against one physics representation and deployed against another, so repor

safetyarxiv-cs-ro

A Unified Scaling Law for Time Series Foundation Models

arXiv:2610.05269v1 Announce Type: new Abstract: We develop a Unified Scaling Law and a Unified Theory of Time Series Learning to understand how model capacity and historical information support foreca

A Unifying Perspective on Descent Directions for Constrained Minimization and Switching Criteria

arXiv:2006.08426v5 Announce Type: replace-cross Abstract: Descent directions are fundamental to first-order methods for constrained convex optimization. In this work, we provide a unified perspective

Active-DiNTS: Active Differentiable Network Topology Search

model-releasesarxiv-cs-cv

arXiv:2610.04787v1 Announce Type: new Abstract: Neural Architecture Search (NAS) has proved to be a strong alternative to manual network design, but applying it to 3D medical image segmentation is lim

AdaEva: Accelerating LLM-Driven Algorithm Design with Adaptive Partial Evaluation

arXiv:2610.03896v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used for automated algorithm design. However the computational cost of evaluating the generated algorithms

AdaptInfer: Adaptive Token Pruning for Vision-Language Model Inference via Dynamical Text Guidance

arXiv:2508.06084v3 Announce Type: replace Abstract: Vision-language models (VLMs) have achieved impressive performance on multimodal inference tasks, but the cost remains a significant challenge due t

Adapting prior-data fitted networks for tabular anomaly detection

arXiv:2610.06693v1 Announce Type: new Abstract: While deep features have transformed anomaly detection in images and video, their impact on tabular data has been less substantial, partly due to the li

Adaptive Bregman Alternating Projections for Feasible Gromov-Wasserstein Learning

arXiv:2610.04264v1 Announce Type: new Abstract: The Gromov-Wasserstein (GW) problem compares structured distributions without requiring a shared feature space or known correspondences, but its nonconv

safetyarxiv-cs-lg

Adaptive Expert Guidance for Efficient On-Policy Reinforcement Learning

arXiv:2610.06019v1 Announce Type: new Abstract: With massively parallel simulation, on-policy Reinforcement Learning methods such as PPO have become standard in many domains. However, learning from sc

Adaptive Mean Flow for Responsive Closed-Loop Robot Control

model-releasesarxiv-cs-ro

arXiv:2610.06089v1 Announce Type: new Abstract: Diffusion- and flow-based robot policies have recently become widespread in robotic Imitation Learning (IL) due to their high performance and ability to