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BoT-Feedback: Grounding Multimodal Reasoning in Biomechanical Evidence for Explainable Human Action Feedback

model-releasesarxiv-cs-cv

arXiv:2610.06972v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in visual understanding and multimodal reasoning, yet they remain fun

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Research

BRACE: Adapting Whole-Body References for Force and Terrain Aware Humanoid Motion Tracking

DGX agent

arXiv:2610.07052v1 Announce Type: new Abstract: Whole-body tracking has become the interface through which operators drive humanoid robots, yet the references it consumes are recorded on level ground

researcharxiv-cs-ro

7 Oct 2026

Building Rome from a Single Image

arXiv:2610.08790v1 Announce Type: new Abstract: Single-image scene generation aims to produce a complete 3D scene mesh from a single image, including surfaces the camera did not observe. While pretrai

CACHEFORGE: LLM-Guided End-to-End Generative Cache Replacement Policy for Performance and Hardware Efficiency

arXiv:2610.07668v1 Announce Type: cross Abstract: Modern cache replacement designs saturate because they operate within fixed representational structures, hand-crafted and heuristic based feature-engi

Calibrated Answers About Randomized Trials From a 4-Billion-Parameter Open Model: A Registered Test and a License-Clean Release

model-releasesarxiv-cs-cl

arXiv:2610.07019v1 Announce Type: new Abstract: Fiorillo v0.5 is an open model that answers typed questions with a probability for each answer. Its main specialist reads a randomized trial's article,

Calibrated Uncertainty for Informative Path Planning in Aquatic Environmental Monitoring

arXiv:2609.34577v2 Announce Type: replace Abstract: Informative Path Planning for scalar field reconstruction uses predictive uncertainty to direct sensing vehicles toward maximally informative locati

Calibration Is Not Control: Intervention Value for LLM-Agent Oversight

arXiv:2606.21399v2 Announce Type: replace Abstract: Runtime oversight often intervenes when an LLM agent's calibrated failure score crosses a threshold. Yet states with the same failure risk can diffe

safetyarxiv-cs-ai

CALR: Continuous Anchored Latent Reasoning via Render-of-Thought Compression

arXiv:2610.07175v1 Announce Type: new Abstract: Visual latent reasoning compresses rendered derivations into compact intermediate states, reducing textual reasoning overhead. Existing approaches diffe

Agents

Can Agents Work for Everyone? Cross-User Reliability for Mobile GUI Agents in Personalized User Interfaces

arXiv:2610.07972v1 Announce Type: new Abstract: Mobile GUI agents increasingly operate on interfaces influenced by users' histories and preferences, but their reliability across different users remain

agentsarxiv-cs-ai

Can LLM-assisted regularization increase forecast accuracy for migration flows in low data regimes?

arXiv:2610.07208v1 Announce Type: new Abstract: Predicting migration flows remains a significant challenge for traditional gravity-based forecasting models, which primarily rely on structured socio-ec

Can phenotypic activity be predicted without experimental readouts?

arXiv:2610.07997v1 Announce Type: new Abstract: Molecular encoders contrastively pretrained on paired molecule-morphology data, such as CLOOME and CellCLIP, have been proposed as cheap surrogates for

Hardware

Can Power Draw Constrain Covert Compute? Limits of Analogue Verification for AI Governance

arXiv:2610.07476v1 Announce Type: cross Abstract: Frontier AI treaties or agreements on limiting computation require external verification; an external auditor must be able to confirm how much computa

hardwarearxiv-cs-ai

Can Semantic Geometry Teach an AI Judgement?

arXiv:2610.07249v1 Announce Type: new Abstract: How can an AI agent determine what rules to follow? One rule permits an action. Another imposes a condition, exception, or conflicting obligation. Deter

safetyarxiv-cs-ai

Can We Model the Artifacts Explicitly? Disentangle Artifacts via Pairwise Edit Relations for Image Manipulation Localization

arXiv:2610.07916v1 Announce Type: new Abstract: Image Manipulation Localization (IML) is commonly formulated as a fully supervised learning task that estimates the optimal manipulation mask y for a gi

CANDLE: Cortical Null-Space Decomposition for Noninvasive Brain Source Imaging

arXiv:2610.07824v1 Announce Type: new Abstract: Electrophysiological source imaging (ESI) aims to estimate cortical source activity from noninvasive electrophysiological measurements such as electroen

Local Ai

Capacity, Responsiveness and Alignment: What Makes a Latent Structure Actionable

arXiv:2610.06897v1 Announce Type: new Abstract: Localizing latent structures in the activation space of language models (LMs) is central to understanding and controlling their behavior. Yet, localized

local-aiarxiv-cs-cl

Cascadia: Resident 975B MoE Inference on Eleven AI PCs

arXiv:2610.07219v1 Announce Type: new Abstract: Mixture-of-experts models make nearly trillion-parameter capacity accessible with sparse per-token computation, provided that the serving system can dis

Catastrophic Forgetting in Sequential Thermal Anti-UAV Detection: The Role of Scale-Conditioned Gradient Imbalance

arXiv:2610.08315v1 Announce Type: new Abstract: Counter-UAV systems based on thermal infrared detection must stay accurate as operational datasets evolve, yet sequential fine-tuning causes catastrophi

Agents

Catching Developers in the Flow: Low-Latency Agentic Program Repair at Google Scale

arXiv:2610.07289v1 Announce Type: cross Abstract: Manual repair of program failures is time-consuming and disruptive for software developers, particularly during the pre-submit phase where test failur

agentsarxiv-cs-ai

Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumption

arXiv:2502.19741v4 Announce Type: replace Abstract: Estimating causal effects under networked interference from observational data is a crucial yet challenging problem. Most existing methods mainly re

Local Ai

CausalBind: Causal Modeling and Learning for Protein-Molecule Virtual Screening

arXiv:2610.07340v1 Announce Type: cross Abstract: Protein-molecule virtual screening is increasingly cast as a problem of representation learning in a shared embedding space. Existing methods rely on

local-aiarxiv-cs-ai

CCDF: A Benchmark Dataset for Deepfake Detection in Real-World Surveillance Footage

model-releasesarxiv-cs-cv

arXiv:2610.07939v1 Announce Type: new Abstract: Due to rapid advances in Generative AI, commercial video generation tools can be used to produce fabricated surveillance footage that can fool both huma

Tutorials

CETUS: How Far Do Representations Trained on Earth Transfer to Cassini SAR of Titan?

arXiv:2610.07576v1 Announce Type: new Abstract: Cassini synthetic aperture radar (SAR) images reveal the dunes, plains, and lake basins of Titan, providing an instance of representations learned from

tutorialsarxiv-cs-cv

ChartBmkAgent: Harness-Governed Multi-Agent Construction of Chart QA Benchmarks from Sparse Error-Taxonomy Specifications

arXiv:2610.08106v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) advance rapidly, while conventional benchmark development lags behind, delaying investigation of newly observed

CHARTER: Auditing Reference Substitution in Hierarchical Compact-Evidence Evaluation for Computational Pathology

arXiv:2610.07843v1 Announce Type: new Abstract: In digital pathology, compact evidence is often used to explain or audit predictions made by whole-slide image multiple instance learning models. In hie

CheckerBench: Can Long-Horizon Agents Synthesize Static-Analysis Checkers?

arXiv:2610.07557v1 Announce Type: cross Abstract: Static-analysis checker synthesis requires agents to interpret a defect specification, inspect a repository, implement analyzer-specific logic, and re

Cite What You Explore: Budget-Aware LLM Reasoning over Medical KGs with Verifiable Evidence

arXiv:2610.07739v1 Announce Type: new Abstract: Post-discharge risk prediction from electronic health records (EHRs) is difficult because many dependencies that link discharge-time observations to dow

safetyarxiv-cs-lg

Applications

Cleave: Scaling Tensor Program Optimization via Decoupled Algebraic Search and Operator Scheduling

arXiv:2610.07742v1 Announce Type: cross Abstract: Optimized kernels such as FlashAttention and FlashDecoding are crucial for accelerating today's large models. Most of them are handwritten by experts

applicationsarxiv-cs-ai

Climbing the Design Ladder: Sequential Knowledge Distillation for Early-Stage Circuit Timing Prediction

model-releasesarxiv-cs-lg

arXiv:2610.08457v1 Announce Type: new Abstract: Integrated circuit design involves multiple design stages: logic synthesis, floorplanning, placement, and routing, with each stage taking hours to weeks

CLM-as-a-Judge: Evaluating an Open Contrastive Decision Model on Public Judge Benchmarks

arXiv:2610.07177v1 Announce Type: cross Abstract: An open contrastive decision model is near chance as a judge on the hard public benchmarks: Contrastive-LM/CLM-v0.1-8B scores between 0.351 (best- of-

Closing Ambient Clinical Documentation Gaps with Automated Provider Queries

arXiv:2610.07502v1 Announce Type: new Abstract: Provider queries are clarifying requests sent by clinical documentation specialists to physicians to close gaps in the clinical note and ensure accurate

researcharxiv-cs-cl

CNet: A Complex-Valued Deep Learning Framework with Wirtinger Autodifferentiation and FFT--Hadamard Convolution

model-releasesarxiv-cs-lg

arXiv:2610.08592v1 Announce Type: new Abstract: CNet is a C++/CUDA framework for building and training deep complex-valued neural networks (CVNNs) and, more generally, for optimizing complex-valued fu

Co-Evolving Paths and Flows via Path-Flow Alignment

arXiv:2610.08717v1 Announce Type: new Abstract: We study path-flow alignment as a unified training objective for flow matching. Instead of fixing the interpolation path and learning only the velocity

safetyarxiv-cs-cv

CoDe-LoRA: Mitigating the Orthogonality Dilemma in Continual Learning of LLMs via Knowledge Consolidation and Decoupling

arXiv:2610.08312v1 Announce Type: new Abstract: Continual learning (CL) is essential for Large Language Models (LLMs) to sequentially adapt to evolving tasks. To mitigate catastrophic forgetting, rece

Commit While Futures Agree: Consequence-Aware Adaptive Action Chunking for Robot Manipulation

arXiv:2610.07949v1 Announce Type: new Abstract: Action-chunking policies predict multi-step control sequences, but a fundamental question remains: how much of a predicted action chunk should be commit

safetyarxiv-cs-ro

Common-Mode Errors Limit Low-Timestep Deep Spiking Q-Networks

arXiv:2610.07808v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) offer sparse and event-driven computation, making them attractive for energy-constrained reinforcement learning (RL) on

Communication-Free Obstacle Localization from Aggregate Wrench Measurements in Leader--Follower Cooperative Transport

arXiv:2610.08324v1 Announce Type: new Abstract: We consider obstacle localization for a team of robots cooperatively transporting a rigid payload without explicit inter-robot communication. A leader r

researcharxiv-cs-ro

Compact Robot Policies Need Fine-Grained Visual Representations

arXiv:2610.08183v1 Announce Type: cross Abstract: Multi-task manipulation policies differ in architecture, scale, and pretrained priors all at once, so published comparisons cannot attribute performan

Comparative review of hybrid forecasting models for short-term prediction of building thermal load

arXiv:2610.06881v1 Announce Type: cross Abstract: In this paper, a comparative review of different hybrid models for short-term forecasting of building thermal demand is carried out. Particularly, the

COMPASS: Finding Where Reasoning Lives in Language Models

arXiv:2610.07469v1 Announce Type: new Abstract: Explicitly eliciting reasoning substantially improves LLM performance. Existing approaches require a predefined characterization of reasoning, whether t

Complementary Feature Domains: Information Preservation Does Not Imply Predictive-Contribution Preservation

arXiv:2610.07565v1 Announce Type: cross Abstract: Complementary Feature Domains (CFD) theory characterizes predictive value as a context-indexed contribution system induced jointly by representations

Complementary Supervised and Self-Supervised Representations for Out-of-Distribution Graph Learning

arXiv:2610.07628v1 Announce Type: new Abstract: Out-of-distribution (OOD) generalization remains challenging for graph neural networks (GNNs), as graph distributions can vary substantially across time

Component and Dimension Sparsity in Transformer Refusal Mechanisms

arXiv:2610.06903v1 Announce Type: cross Abstract: Activation steering manipulates large language model behavior by intervening on internal activations, but the mechanistic basis of these interventions

Compositional Concept Erasure in Text-to-Image Diffusion Models via Hierarchically Grounded Semantic Surgery

arXiv:2610.07337v1 Announce Type: new Abstract: Removing copyrighted, unsafe, or user-specified concepts from a deployed text-to-image diffusion model is now a practical requirement. Weight-editing me

Comprehensive Evaluation and Fine-Tuning of Foundational Cell Nuclei Segmentation Models in Renal Pathology

arXiv:2610.07711v1 Announce Type: new Abstract: Accurate nuclei instance segmentation is essential for quantitative renal pathology, yet general-purpose models often struggle with low contrast, dense

Local Ai

Computationally efficient goodness-of-fit tests through kernelized Stein discrepancy

arXiv:2512.20007v3 Announce Type: replace-cross Abstract: Models with intractable normalizing constants are widely used in statistics and machine learning. Assessing the adequacy of such models poses

local-aiarxiv-cs-lg

Applications

Conditional Flow Matching for Transport Between Markov Processes

arXiv:2610.07229v1 Announce Type: new Abstract: Motivated by sequence-to-sequence transport in the context time-series domain adaptation, we study the problem of transportation between trajectories of

applicationsarxiv-cs-lg

Confidence-Ordering Reversal under Contextual Priors in Neural Decoding

arXiv:2610.08229v1 Announce Type: new Abstract: Contextual priors improve neural-to-language decoding by reshaping candidate scores. However, confidence is read from the same reshaped scores, so the e