Search: “framework”

X-Ragent: Tool-Augmented Reinforcement Learning for Evidence-Grounded Chest X-Ray Reasoning

arXiv:2610.16811v1 Announce Type: new Abstract: Chest X-ray (CXR) interpretation often requires integrating visual findings with task-specific evidence rather than relying on direct visual recognition

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14 Oct 2026

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Model Releases

Zeroth-Order Fine-Tuning of LLMs in Learnable Low-Rank Subspaces

DGX agent

arXiv:2610.15225v1 Announce Type: new Abstract: Zeroth-order (ZO) optimization enables memory-efficient fine-tuning of large language models (LLMs). However, ZO optimization suffers from high variance

model-releasesarxiv-cs-lg

14 Oct 2026

ZOIF: Zeroth-Order Influence Function Estimation for LLM Data Selection Using Only Forward Passes

arXiv:2610.15336v1 Announce Type: new Abstract: Influence functions enable principled data selection for large language models (e.g., identifying influential training samples for model editing, debugg

14 Oct 2026

Applications

Acoustic Biomarkers for Alzheimer's Disease Detection from Spontaneous Speech

arXiv:2610.12500v1 Announce Type: cross Abstract: Alzheimer's disease (AD) remains a major public health challenge, and early identification is crucial for timely intervention and care planning. Recen

applicationsarxiv-cs-lg

Adaptive Whole-Body MPC with Optimal Posture Planning for Humanoid Robots under Unknown Payloads

arXiv:2610.12972v1 Announce Type: new Abstract: Humanoid robots performing lifting and material-handling tasks must maintain balance despite unknown and varying payloads. However, payload uncertainty

researcharxiv-cs-ro

AlphaNIST: Emergent Assembly Skills from Binary Task Rewards

arXiv:2610.13354v1 Announce Type: new Abstract: The NIST Assembly Task Board #1 remains a challenging benchmark for learning-based methods in robotic manipulation. Over short horizons, it demands prec

ARTIS: Agentic Risk-Aware Test-Time Scaling via Iterative Simulation

arXiv:2602.01709v3 Announce Type: replace Abstract: Current test-time scaling (TTS) techniques enhance large language model (LLM) performance by allocating additional computation at inference time, ye

agentsarxiv-cs-cl

ATHLETE: Learning Reactive Humanoid Tennis via Trajectory-Guided Motion Matching

arXiv:2610.13572v1 Announce Type: new Abstract: Humanoid tennis requires reactive whole-body coordination under rapidly changing ball dynamics, yet collecting task-specific motion-capture demonstratio

safetyarxiv-cs-ro

AudioTETRIS: Token Elimination via Transitional Importance Signals for Efficient Audio LLMs

arXiv:2610.13600v1 Announce Type: cross Abstract: Large Audio Language Models (LALMs) achieve strong performance on speech-text tasks, but standard audio encoders produce hundreds of tokens for even s

Bandits with Anytime Knapsacks

arXiv:2501.18560v2 Announce Type: replace Abstract: We consider bandits with anytime knapsacks (BwAK), a novel version of the BwK problem where there is an extit{anytime} cost constraint instead of a

Tutorials

Belief-Overlap Partial Inversion: Object-Centric Flow Replanning for Humanoid Loco-Manipulation

arXiv:2610.13087v1 Announce Type: new Abstract: People often learn how an unfamiliar object moves through a small exploratory push, using its response to guide their next motion. Inspired by this beha

tutorialsarxiv-cs-ro

Beyond Prediction-Set Size: Quantifying Conformal Uncertainty through Credal Geometry

arXiv:2602.01667v2 Announce Type: replace Abstract: Prediction-set size offers a coarse summary of conformal uncertainty. We develop a richer perspective through the geometry of credal sets: families

Beyond Successful Robot Demonstrations: Learning from Human Interactions and Modeling Failure Dynamics

arXiv:2610.13522v1 Announce Type: new Abstract: Action-conditioned world models offer a promising foundation for generalist robotic intelligence by predicting the consequences of actions. However, exi

safetyarxiv-cs-ro

Beyond Temporal Proximity: Explanatory World Models for Credit Assignment in Reinforcement Learning

arXiv:2610.13504v1 Announce Type: cross Abstract: Generative Recommenders increasingly use Reinforcement Learning (RL) to optimize outcomes that may appear hundreds or thousands of interactions after

BLEND: Balancing Personalization vs. Generalization in Federated Vision-Language Models

arXiv:2610.12907v1 Announce Type: cross Abstract: Federated vision-language models (FedVLMs) represent one of the emerging frontiers of machine learning, aiming to integrate federated learning within

Tutorials

BoundaryEvo: Data-Model Co-Evolution at the Capability Boundary for Long-Chain Reasoning Distillation

arXiv:2610.13285v1 Announce Type: new Abstract: Distilling long-chain reasoning trajectories from large teacher models provides an effective way to improve the reasoning ability of small language mode

tutorialsarxiv-cs-ai

Byzantine-Resilient Federated Large Language Model via Hypergraph Message Passing

arXiv:2610.12711v1 Announce Type: new Abstract: Federated Large Language Models (FedLLMs) are promising to deliver privacy-preserving, collaborative intelligence. The decentralized training process is

Calibration in One Go: Jointly Learning Accurate and Calibrated Neural Networks

arXiv:2610.13693v1 Announce Type: cross Abstract: Neural networks (NNs), while highly flexible and versatile, are often poorly calibrated. Post-hoc calibration methods adjust the outputs of an already

researcharxiv-cs-ai

Can Failed Trajectories Reveal Where Agents Go Wrong? Agentic Failure Attribution Without Step-Level Labels

arXiv:2610.13105v1 Announce Type: new Abstract: Failure attribution for LLM agents aims to identify decisive steps responsible for task failure, which is critical for debugging and improving agentic s

agentsarxiv-cs-lg

CAST: Credit Alignment for Cross-Tokenizer On-Policy Distillation

model-releasesarxiv-cs-cl

arXiv:2610.13257v1 Announce Type: cross Abstract: Cross-tokenizer on-policy distillation (OPD) requires aligning teacher and student sequences before their scores can be compared. Decoded-text alignme

CB-Orchestrator: Adaptive Workflow Optimization for LLM Agents via Contextual Bandits

arXiv:2610.13064v1 Announce Type: new Abstract: Large Language Model (LLM) agents have demonstrated remarkable capabilities in tackling complex tasks through agentic workflows. However, manually desig

agentsarxiv-cs-lg

CeDR-Nav: Cross-Embodiment Mapless Navigation via Offline Demonstration Recasting

arXiv:2610.13007v1 Announce Type: new Abstract: Learning-based methods have demonstrated strong performance in mapless robot navigation. However, a learned policy remains restricted to the embodiment

safetyarxiv-cs-ro

CLEAT: Learning Value-Guided Clarification for Interactive Agents

arXiv:2610.12655v1 Announce Type: new Abstract: LLM agents can execute increasingly complex tasks, but effective assistance also requires aligning their actions with goals and constraints that users m

safetyarxiv-cs-ai

CliQ-RRG: Clinical-Knowledge Guided Disease-aware Visual-Textual Alignment for QA-Style Radiology Report Generation

arXiv:2610.13059v1 Announce Type: new Abstract: Radiology reports are essential for diagnostic reasoning and patient care, yet their manual preparation is time-consuming and cognitively demanding. Aut

safetyarxiv-cs-cl

Co-Modifying Application Code and Deployment Infrastructure: A Causal Study of AI Agent Pull Requests

arXiv:2610.12849v1 Announce Type: cross Abstract: Current software engineering agents are evaluated predominantly inside isolated sandboxes against synchronous local tests. However, deploying features

agentsarxiv-cs-ai

CoGameReg: Cooperative-Game-Based Collaborative Semi-Supervised Regression for Seismic Reservoir Property Prediction

arXiv:2610.12884v1 Announce Type: new Abstract: Accurate estimation of reservoir properties from seismic data remains challenging when well-log labels are sparse but three-dimensional seismic observat

Collaborative Theory of Mind: Learning to Self-Revise by Anticipating User Responses

arXiv:2610.12872v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate strong individual task performance, yet still struggle to collaborate with users over extended, multi-turn inte

safetyarxiv-cs-ai

Competence-Aware Orchestration for LLM-Based Multi-Agent Systems

arXiv:2610.13144v1 Announce Type: new Abstract: LLM-based multi-agent systems (MAS) have emerged as a promising paradigm for enhancing complex reasoning through role specialization and collaboration,

agentsarxiv-cs-ai

Controlled-Minimax: Stabilizing Adversarial Optimization via Control Theory

arXiv:2610.12915v1 Announce Type: new Abstract: Minimax optimization is fundamental to adversarial learning yet suffers from severe stability challenges, often manifesting as persistent limit cycles o

Cooperative Aerial Transportation of a Payload with a Sliding Cable Attachment

arXiv:2610.13665v1 Announce Type: new Abstract: This paper presents a dual-quadrotor hook- suspended transport system for cooperative aerial manipulation of a payload that is not rigidly attached to a

researcharxiv-cs-ro

Local Ai

CORAL-KV: Compact Cross-Layer Prediction and Residual Coding for Autoregressive Video KV Caches

arXiv:2610.12514v1 Announce Type: cross Abstract: Autoregressive video diffusion models maintain a growing attention key-value (KV) cache, making memory a key bottleneck for long video generation. Rec

local-aiarxiv-cs-lg

CORE-Seg: Reasoning-Driven Segmentation for Complex Lesions via Reinforcement Learning

arXiv:2603.05911v2 Announce Type: replace-cross Abstract: Medical image segmentation is undergoing a paradigm shift from conventional visual pattern matching to cognitive reasoning analysis. Although

Credit Attribution in Timestamped Soccer Networks Using Multivariate Hawkes Processes

arXiv:2610.12775v1 Announce Type: cross Abstract: The two main traditional player statistics used in soccer are goals scored and assists, with an assist credited when a player's pass immediately leads

CRM: Retrieval Model with Controllable Condition

arXiv:2412.13844v2 Announce Type: replace-cross Abstract: Retrieval models for recommendation need to account for outcomes such as watch time while searching large item corpora efficiently. We present

researcharxiv-cs-ai

DEAF: A Benchmark for Diagnostic Evaluation of Acoustic Faithfulness in Audio Language Models

arXiv:2603.18048v3 Announce Type: replace Abstract: Recent Audio Multimodal Large Language Models (Audio MLLMs) demonstrate impressive performance on speech benchmarks, yet it remains unclear whether

DeFiRank: Federated Pairwise Ranking for Governance in Decentralized Financial Networks

arXiv:2610.12916v1 Announce Type: new Abstract: Existing governance approaches in decentralized financial networks rely on manual review processes conducted by human committees, which introduces subje

safetyarxiv-cs-ai

Local Ai

DeltaLLM: Delta-Key Sparse Attention for Edge LLMs

arXiv:2507.19608v2 Announce Type: replace Abstract: On-device LLM inference is increasingly important for edge platforms but remains challenging due to high computational and memory demands and tight

local-aiarxiv-cs-ai

Applications

Diffusion Precipitation Generation with Medium-range Weather Forecasting

arXiv:2610.12976v1 Announce Type: new Abstract: Accurate daily precipitation forecasts are important for weather-sensitive decisions. Although recent Weather Foundation Models (WFMs) have achieved sig

applicationsarxiv-cs-lg

Local Ai

DP-MIND: Differentially private memory-guided Long context Inference

arXiv:2610.13132v1 Announce Type: new Abstract: LLM-as-a-Service has become a dominant paradigm for accessing large language models without requiring local deployment. However, the process raises seve

local-aiarxiv-cs-ai

DRYLAB-Bench: Benchmarking the Biosecurity Risk of Agentic LLM Mutation Design

arXiv:2610.13116v1 Announce Type: new Abstract: As large language models (LLMs) acquire advanced biological design capabilities, they offer substantial research benefits but simultaneously introduce s

DualQuant: Dual-scale Calibration-free Weight-and-Activation Quantization for LLMs

arXiv:2610.13767v1 Announce Type: cross Abstract: Low-bit inference on accelerators with native 4-bit support requires quantizing both weights and activations to realize the full throughput gains thes

DyssomniaNN: Multi-Scale Spiking Neural Modeling of Human Sleep Disorders and Perceived Restedness

arXiv:2610.13918v1 Announce Type: cross Abstract: Clinical sleep medicine repeatedly documents profound divergences between objective sleep duration and subjective daytime impairment, notably exemplif

Applications

Easy-IIL: Reducing Human Operational Burden in Interactive Imitation Learning via an Assistant Expert

arXiv:2603.12769v2 Announce Type: replace Abstract: Interactive Imitation Learning (IIL) typically relies on extensive human involvement for both offline demonstration and online interaction. Prior wo

applicationsarxiv-cs-ro

EchoTab: Learning Measurement-Grounded Representations for Echocardiographic Diagnosis through Tabular Reconstruction

arXiv:2610.13599v1 Announce Type: new Abstract: Diagnostic information in multi-view medical imaging studies, like echocardiography, is distributed across views, each capturing different aspects of ca

Tutorials

EgoReveal: Learning Active Vision and Object Manipulation under Partial Occlusions from Egocentric Demonstrations

arXiv:2610.12999v1 Announce Type: new Abstract: Learning from Demonstrations (LfD) enables robots to learn object manipulation directly from human demonstrations. In contrast to resource-intensive and

tutorialsarxiv-cs-ro

Electron-Density-Informed Molecular Property Prediction Using Surrogate Functionals

arXiv:2610.13649v1 Announce Type: cross Abstract: Machine learning methods are increasingly used to predict chemical properties directly from the molecular structure to bypass the often prohibitive co

EmoGavel: Grounding-Aware Reinforcement Learning with Verifiable Rewards for Multimodal Emotion Preference Learning

arXiv:2610.13353v1 Announce Type: cross Abstract: Multimodal emotion preference learning uses pairwise comparisons to identify which open-ended emotion descriptions align more closely with human judgm

Evidential Multimodal Transformers with Dempster-Shafer Fusion for Emotion Recognition

arXiv:2610.13013v1 Announce Type: new Abstract: Softmax-based multimodal emotion recognition methods often assign high confidence even when sensor data are corrupted or contradictory, for example when

safetyarxiv-cs-lg