Search: “framework”

Beyond Uniform Compression: Budgeted Transmission Allocation for Extreme Federated Learning

arXiv:2609.39646v1 Announce Type: new Abstract: Federated learning faces severe communication bottlenecks when clients upload high-dimensional model updates. Existing methods often compress these upda

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Safety

BMASH: Ball-Motion-Aware Soccer Header Spotting

DGX agent

arXiv:2609.39300v1 Announce Type: new Abstract: Recent advances in computer vision have made broadcast sports videos increasingly useful for event analysis, performance assessment, and player-safety a

safetyarxiv-cs-cv

1 Oct 2026

Bounded Channel-Adaptive Spectral Learning for Forward-Consistent Inverse Flapping-Wing Aerodynamics

arXiv:2609.38730v1 Announce Type: new Abstract: Flapping-wing vehicles regulate aerodynamic forces and moments through coordinated variations in stroke, deviation, and pitch motion. Because the result

researcharxiv-cs-ro

Breaking Babel: A Self-Evolving Multi-Agent System for Long-Form Subtitle Translation

arXiv:2609.38660v1 Announce Type: cross Abstract: Long-form subtitle translation requires reasoning over discourse and cultural context spanning episodes or entire series, while maintaining consistent

Local Ai

BTC3D: Blended Tile Conditioning for Detail-Enhancing Image-to-3D Generation

arXiv:2609.39709v1 Announce Type: new Abstract: Recent diffusion-based pipelines have achieved promising progress in image-to-3D synthesis. However, generating high-fidelity details remains challengin

local-aiarxiv-cs-cv

CADeT: Causal-Aware Deformation Transmission for Indirect Robotic Manipulation of Soft Tissue

arXiv:2609.38483v1 Announce Type: new Abstract: Indirect manipulation of deep-seated deformable anatomy inaccessible to the robot is challenging in robot-assisted minimally invasive surgery (RAMIS) be

researcharxiv-cs-ro

Can Terminal Agents Trust Their Own Verification? Diagnosing and Improving Self-Verification

arXiv:2609.38812v1 Announce Type: new Abstract: Terminal agents rely on self-verification to assess and correct their solutions as they solve tasks through interaction with command-line environments.

agentsarxiv-cs-cl

CEER2: Directional and Tunable End-Effector and Root Compliance for Humanoid Loco-Manipulation

arXiv:2609.38709v1 Announce Type: new Abstract: Humanoids are increasingly capable of tracking complex whole-body motions, but physical interaction introduces a different challenge. When a robot makes

safetyarxiv-cs-ro

Certified Approximation for Interpretable Representer Landmarks

arXiv:2609.38901v1 Announce Type: new Abstract: Representer explanations rank the training landmarks that most influence a self-supervised representation. At scale, this ranking rests on up to four st

ChartDensity-Bench: Benchmarking MLLMs for Numerical Data Reconstruction under Visual Density

arXiv:2609.38781v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) offer a promising approach for recovering numerical data from scientific charts, but their ability to reconst

CodeMimicry: Exploiting Safety Generalization Lag in Large Language Models via Structured Code Completion

arXiv:2609.39902v1 Announce Type: cross Abstract: Large language models have achieved remarkable capabilities across diverse domains, yet their safety alignment remains vulnerable to jailbreak attacks

Community-Driven API and AI Writer Design for Openly Scaling Community Notes

arXiv:2609.40067v1 Announce Type: cross Abstract: Community Notes is a crowd-sourced approach for adding context to posts on X. Contributors propose and rate notes, forming the inputs to an open-sourc

Comparative study of adapting pre-trained models for driving behavior video captioning

arXiv:2609.39542v1 Announce Type: new Abstract: This report examines and compares some of the many fine tuning and prompting methods existing, applying them within the domain of autonomous driving. Th

agentsarxiv-cs-cv

Composing Task-specific Agent Harnesses at Test Time with Reusable Primitives

arXiv:2609.38912v1 Announce Type: new Abstract: Agent harnesses govern how large language models (LLMs) gather context, invoke tools, verify results, preserve state, and terminate, largely affecting a

agentsarxiv-cs-ai

ComputerSD: Online Self-Distillation from Real-Time Feedback for Computer-Use Agents

arXiv:2609.40253v1 Announce Type: cross Abstract: Online training enables computer-use agents (CUAs) to improve through interaction with executable environments. However, existing methods primarily re

Conditional Generation of Creative Chess Puzzles with Diffusion Models

arXiv:2609.38577v1 Announce Type: new Abstract: While modern language models demonstrate impressive generative capabilities, they often struggle with constrained, counter-intuitive creative tasks. To

Conformal Adversarial Generative Ensemble

arXiv:2609.38196v1 Announce Type: cross Abstract: Accurate time series forecasting is critical across various domains, yet traditional ensemble methods often suffer from the disproportionate influence

applicationsarxiv-cs-ai

Tutorials

Coverage Before Control: Route-Instruction Grounding and Steering for Controllable Retrosynthesis

arXiv:2609.39955v1 Announce Type: new Abstract: Single-step retrosynthesis models are commonly evaluated by their ability to recover recorded reactions. In practice, chemists may need to choose among

tutorialsarxiv-cs-ai

DAGent: Evaluate-then-Grow Planning for Deep Research Agents

arXiv:2609.39154v1 Announce Type: cross Abstract: Deep research tasks require agents to navigate large knowledge spaces, synthesize evidence across many sources, and adapt their plans as findings emer

Data-Driven Priors for Uncertainty-Aware Risk Prediction of Clinical Deterioration using Multimodal Data

arXiv:2603.08459v2 Announce Type: replace Abstract: Safe predictions are a crucial requirement for integrating predictive models into clinical decision support systems. One approach to improving trust

applicationsarxiv-cs-lg

Debias It Yourself: Teaching LLMs Cognitive Bias Mitigation Interventions

arXiv:2609.40124v1 Announce Type: new Abstract: Bias has long been studied in social psychology and cognitive science, where decades of research have produced a body of validated interventions that re

safetyarxiv-cs-cl

DecoMoE: Decoupling Visual Propagation and Expert Computation for Efficient Multimodal MoE Inference

arXiv:2609.38823v1 Announce Type: new Abstract: Multimodal mixture-of-experts (MoE) models combine sparse expert activation with visual-language capabilities, yet their inference remains costly becaus

researcharxiv-cs-cv

Decoupled and Distilled: Task-Adaptive LoRA-Teachers with Ensemble Knowledge Transfer for Few-Shot Class-Incremental Learning

arXiv:2609.39390v1 Announce Type: new Abstract: Few-Shot Class-Incremental Learning (FSCIL) addresses the challenge of learning new classes from very limited samples while retaining knowledge of previ

DensePed-Lite: Quality-Aware Adaptive Detection for Dense Pedestrians under Occlusion

arXiv:2609.39467v1 Announce Type: new Abstract: Pedestrian detection plays a crucial role in computer vision with applications in autonomous driving, surveillance, and public safety. However, real-wor

safetyarxiv-cs-cv

Depth Anything in 360^irc: Towards Scale Invariance in the Wild

arXiv:2512.22819v2 Announce Type: replace Abstract: Panoramic depth estimation captures the complete 360^irc scene geometry, being essential for robotics and AR/VR applications. While perspective dept

applicationsarxiv-cs-cv

DiFF: Doppler-informed Flow Matching for Human Motion Flow

model-releasesarxiv-cs-cv

arXiv:2609.39098v1 Announce Type: cross Abstract: Perceiving human motion via privacy-preserving 4D millimeter-wave (mmWave) radar is critical for next-generation human-robot interaction (HRI), where

Discrete Forcing: Infusing Discrete Guidance into Continuous Denoising for Few-Step Action Experts

model-releasesarxiv-cs-ro

arXiv:2609.39526v1 Announce Type: new Abstract: Efficient action generation in vision-language-action (VLA) models requires capturing both coarse action structure and fine-grained details. Discrete ac

Discrete Score Matching Enables Causal Discovery from Count Data

arXiv:2609.39326v1 Announce Type: cross Abstract: Count data pose a challenge for score-matching-based causal discovery: derivatives are unavailable, and simply replacing them with finite differences

Disentangling Self-Distillation: Measuring and Modeling Acquisition and Retention

arXiv:2609.39494v1 Announce Type: new Abstract: Self-distillation with privileged context adapts a language model from demonstrations by letting the model, once conditioned on a reference response, te

researcharxiv-cs-ai

Distributionally robust linear regression through the lens of adversarial training

arXiv:2609.39449v1 Announce Type: cross Abstract: Distributionally robust optimization (DRO) studies parameter estimation under uncertainty in the underlying probability distribution and has emerged a

Divide and Collapse: MAPF-Collapse via Exact Decomposition into Independent Sub-Instances

arXiv:2609.39559v1 Announce Type: new Abstract: In this work we study the problem of MAPFC, a post-optimization step for Multi-Agent Path Finding (MAPF) plans where we are given a feasible plan produc

agentsarxiv-cs-ai

DODGER: Safety-Guided Reinforcement Learning for Robot Navigation Among Dynamic Obstacles

arXiv:2609.38873v1 Announce Type: new Abstract: Robots operating in human-centered environments must safely navigate among multiple dynamic obstacles to avoid collisions with people and surrounding in

safetyarxiv-cs-ro

Dynamic LoRA-Experts and Prototype-Ensemble Matching for Class-Incremental Learning

arXiv:2609.39839v1 Announce Type: new Abstract: Class-Incremental Learning (CIL) aims to continuously learn new classes without forgetting previously acquired knowledge. Parameter-efficient fine-tunin

Tutorials

Dynamics-Inspired Diffusion for Foreground-Preserving Document Background Editing

arXiv:2601.21857v2 Announce Type: replace Abstract: We revisit diffusion-based generation for structured visual content and identify a fundamental limitation of existing approaches: foreground preserv

tutorialsarxiv-cs-cv

Dynamics to decision: A mathematical theory of Lyapunov spectra and decision boundaries in deep classifiers

arXiv:2609.39190v1 Announce Type: new Abstract: A deep classifier is defined not only by the decision it produces, but also by the sequence of transformations through which that decision is formed. Tr

researcharxiv-cs-lg

ECHO-G: Embodied Co-speech Humanoid mOtion Generation

arXiv:2609.39575v1 Announce Type: cross Abstract: Generating full-body co-speech motion for humanoid robots requires coordinating speech prosody, linguistic content, and embodiment-specific motion. To

Hardware

EDGC: Entropy-driven Dynamic Gradient Compression for Efficient LLM Training

arXiv:2511.10333v2 Announce Type: replace-cross Abstract: Training large language models (LLMs) at scale incurs substantial communication overhead, while static gradient compression cannot adapt to gr

hardwarearxiv-cs-ai

Ego4WAM: What Matters When Scaling Egocentric Human Data for Robot Learning?

arXiv:2609.40341v1 Announce Type: cross Abstract: Egocentric human data provides a scalable source of experience for robot learning, but varies substantially in human-robot alignment, behavioral cover

safetyarxiv-cs-cv

Enhancing Autoregressive Video Generation via Representation Adversarial Distillation

arXiv:2609.40037v1 Announce Type: new Abstract: Few-step autoregressive video generation enables efficient streaming synthesis, but errors introduced in early temporal blocks are reused as context and

safetyarxiv-cs-cv

Eulerian Motion Reconstruction for Water Scenery

arXiv:2609.38622v1 Announce Type: new Abstract: Reconstructing and animating water scenery from nature produces compelling and immersive visual experiences. Previous work examined this task from the p

applicationsarxiv-cs-cv

Existence Precedes Value: Joint Modeling of Observational Existence and Evolving States in Time Series Forecasting

arXiv:2606.13571v2 Announce Type: replace-cross Abstract: Real-world time series are often highly incomplete and irregular due to sensor dormancy, transmission delays, and event-driven sampling, makin

Exo2EgoHOI: Hand-Object-Interaction Aware Exocentric-to-Egocentric Video Generation

arXiv:2609.38615v1 Announce Type: cross Abstract: Egocentric videos of human manipulation provide valuable visual experience for embodied intelligence, yet collecting such data at scale is costly. Exo

researcharxiv-cs-ai

Experimental Experience Modeling for Autonomous Research

arXiv:2609.39392v1 Announce Type: new Abstract: Autonomous research agents can generate hypotheses and conduct experiments, but experimentation remains a major source of computational cost. A fundamen

agentsarxiv-cs-ai

Explicit Trajectory Diversity for RL-Based Post-Training of LLM Agents

arXiv:2609.38805v1 Announce Type: cross Abstract: LLM agents often admit multiple high-quality solutions to the same task, differing in reasoning structure, tool-use pattern, or interaction trajectory

Explore-on-Graph: Hybrid Embedding-LLM Reasoning for Knowledge Graph Question Answering under Incompleteness

model-releasesarxiv-cs-cl

arXiv:2609.39786v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly combined with knowledge graphs (KGs) to ground reasoning in structured evidence. However, most LLM-based K

Faithful Dual-constrained Erasure for Robust LLM Safety Alignment

arXiv:2609.39279v1 Announce Type: cross Abstract: Machine unlearning has emerged as a crucial mechanism for removing hazardous knowledge and enforcing safety alignment in Large Language Models (LLMs).

Tutorials

FIGS: Evaluating Multi-Turn Sycophancy Without Penalizing Empathy

arXiv:2609.39863v1 Announce Type: new Abstract: Large language models frequently fail to balance staying truthful with being supportive. They often exhibit sycophancy in responses to users, agreeing w

tutorialsarxiv-cs-ai

FlexRouter: Learning Complementary Model Sets for Flexible LLM Routing

arXiv:2609.38585v1 Announce Type: cross Abstract: Existing Large Language Model (LLM) routing methods score LLMs independently to select top-k models. However, this ignores model correlations and enfo