Search: “tools”

OA-MAP: Evidence-Grounded Multi-Agent Multimodal Framework for Interpretable Knee Osteoarthritis Progression

arXiv:2610.12134v1 Announce Type: new Abstract: Knee osteoarthritis (KOA) progression prediction can support patient monitoring, requiring the integration of multimodal data and multidomain expertise.

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Agents

OneSearch-VL: Unified Multimodal Deep Research Agent for Image and Video

DGX agent

arXiv:2610.12419v1 Announce Type: new Abstract: Single-image, multi-image, and video deep research require different visual operations but share a workflow of visual grounding, external retrieval, and

agentsarxiv-cs-cv

9 Oct 2026

Reasoning-Informed Visual Editing

arXiv:2610.12343v1 Announce Type: new Abstract: Large Multi-modality Models (LMMs) have made significant progress in visual understanding and generation, but still face challenges in visual editing, p

RESETTLE: Robotic Recovery through Disagreement-Triggered Retrieval and Efficient Corrective Control

arXiv:2610.12185v1 Announce Type: new Abstract: Reliable robotic manipulation requires timely intervention to correct emerging deviations and restore progress after execution errors. However, recovery

safetyarxiv-cs-ro

ReTeach: Building a Self-Teacher through Multi-Round Reflection and Retry

arXiv:2610.11529v1 Announce Type: new Abstract: Self-distillation can improve reasoning without a separately trained, more capable teacher, but its effectiveness depends on how the self-teacher gains

safetyarxiv-cs-ai

SAIL: Scientific Agentic Intelligence via a Science-Aware Loop

arXiv:2610.11451v1 Announce Type: new Abstract: We introduce SAIL, an open model with 35B total and 3B active parameters for literature research, scientific coding, and multi-step research workflows.

safetyarxiv-cs-cl

Stochastic Teacher Intervention for Agentic On-Policy Distillation

model-releasesarxiv-cs-ai

arXiv:2610.10878v1 Announce Type: cross Abstract: On-policy distillation (OPD) efficiently transfers capabilities from a stronger teacher to a student language model through dense token-level supervis

Local Ai

The Harness as the Only Mutable Surface: Compliance-Bounded Self-Evolution of LLM Agents in Credit Pipelines, with a Measured Admission Gate

arXiv:2610.10629v1 Announce Type: new Abstract: Self-improving LLM agents can adapt a credit pipeline to a changed rule, but an agent that rewrites itself destroys the artefact a supervisor reviews: a

local-aiarxiv-cs-ai

Unlocking the Regulatory Genome by ARGUS: An Evidence-Constrained Agentic Framework for Interpreting Single Nucleotide Variants

arXiv:2610.12281v1 Announce Type: cross Abstract: Over 90% of disease-associated variants from genome-wide association studies fall in noncoding regulatory regions, yet their functional interpretation

A Good Self-Teacher Meets the Student Where They Are: Joint On-Policy Learning and Teaching

arXiv:2610.10447v1 Announce Type: new Abstract: Reinforcement Learning (RL) from outcome rewards suffers from sparse supervision, particularly on difficult, long-horizon tasks where successful traject

safetyarxiv-cs-lg

Agentic Scene Policies

arXiv:2509.19571v2 Announce Type: replace-cross Abstract: Designing or learning robot policies that generalize zero-shot across a range of language instructions and objects is a core problem in roboti

safetyarxiv-cs-cv

Best Arm Identification for Bandits with Shifting Means

arXiv:2610.10488v1 Announce Type: cross Abstract: We study the best arm identification problem in a stochastic environment with a novel form of adversarial perturbations, which we coin Shifting Means.

Beyond Nominal Equilibria: Risk-Averse Multi-Population Mean-Field Games

arXiv:2610.09244v1 Announce Type: cross Abstract: Recent advances in mean-field games and its multi-population variants enable large-scale heterogeneous multi-agent systems to be modeled through repre

agentsarxiv-cs-lg

COPC: Coupled Off-Policy Correction for Asynchronous LLM Reinforcement Learning

arXiv:2610.09597v1 Announce Type: new Abstract: Asynchronous RL accelerates large language model post-training by decoupling rollout generation from optimization, but trains on stale trajectories. Exi

Deep Learning for Longitudinal Medical Imaging: A Scoping Review

arXiv:2610.08838v1 Announce Type: cross Abstract: Longitudinal medical imaging analysis is a cornerstone of modern medical practice and patient care. Deep learning applied to longitudinal imaging offe

researcharxiv-cs-cv

Applications

Distributed Motion Planning for Multi-Robot Systems under Topological Constraints

arXiv:2610.10065v1 Announce Type: new Abstract: Efficient and distributed coordination of mobile robots is one of the main challenges in multi-robot systems. Topological constraints, often expressed a

applicationsarxiv-cs-ro

Efficient 3D Gaussian Head Avatars for Edge Devices

arXiv:2610.09821v1 Announce Type: new Abstract: Generative 3D Gaussian head avatars provide high-quality, efficient rendering, but synthesising the Gaussian representation remains computationally expe

FaceKit: a Toolkit for Interpretable Facial Phenotyping, Synthetic Image Generation and Privacy Analysis in Rare Diseases

arXiv:2610.09288v1 Announce Type: cross Abstract: Many rare genetic diseases are associated with recognizable craniofacial features. However, traditional approaches for describing facial morphology re

researcharxiv-cs-cv

From Wearable Interfaces to Dexterous Policies: Contact Shifts and Tactile Representations

arXiv:2610.08870v1 Announce Type: new Abstract: Unlike conventional teleoperation, wearable interfaces allow operators to collect dexterous demonstrations through their own hand motions while directly

safetyarxiv-cs-ro

GraphOPD: Graph-Augmented On-Policy Distillation for LLM Agents

arXiv:2610.08959v1 Announce Type: new Abstract: On-policy distillation post-trains large language model agents by supplying dense, step-level guidance from a teacher policy when the reinforcement-lear

Hidden in Plain Sight: Benchmarking Agent Safety Against Decomposition Attacks with DECOMPBENCH

arXiv:2606.13994v2 Announce Type: replace-cross Abstract: LLM-based Agents are becoming increasingly capable and widely deployed, creating growing incentives for adversarial misuse in the real-world.

LiveMACE: Process-Aware Evaluation of LLM Agent Capabilities in Evolving Markets

model-releasesarxiv-cs-cl

arXiv:2610.09872v1 Announce Type: cross Abstract: Evaluating agents by outcomes alone can obscure the capabilities that produce them. This problem is especially pronounced in evolving environments, wh

Machine learning modularity

arXiv:2601.01779v2 Announce Type: replace-cross Abstract: Based on a transformer based sequence-to-sequence architecture combined with a dynamic batching algorithm, this work introduces a machine lear

NeRFifyMesh: Optimizing Neural Radiance Fields from Textured Meshes for Robotics Scene Building

arXiv:2610.10387v1 Announce Type: new Abstract: In robotics, scene representation plays a pivotal role in understanding and interacting with the environment. The advent of Neural Radiance Fields (NeRF

researcharxiv-cs-ro

NeuralBES: A Differentiable, Control-Aware Emulator for Scalable Building Energy Modeling

arXiv:2610.10459v1 Announce Type: new Abstract: Demand-side flexibility i.e. forecasting, shifting, and curtailing residential energy loads, depends on thermal models trusted across millions of hetero

On KL-Regularized Policy Optimization

arXiv:2610.08963v1 Announce Type: cross Abstract: Asynchronous reinforcement learning (RL) for large language model (LLM) agents trains one policy on trajectories generated by another: rollouts come f

safetyarxiv-cs-cl

On the Necessity of Attention-FFN Split in Vision Transformers

arXiv:2610.10303v1 Announce Type: new Abstract: The standard Transformer architecture relies on a rigid pattern that alternates Attention and Feed-Forward Network (FFN) layers. Despite its widespread

Performance at What Cost? A Sustainability-Aware Performance Index for Cell and Nucleus Instance Segmentation

arXiv:2610.10324v1 Announce Type: new Abstract: Pretrained models for cell and nuclear instance segmentation differ substantially in architecture, pretraining data and objectives, parameter count, inf

Quadratic Weak-to-Strong Generalization in Random Feature Networks via Random Matrix Theory

arXiv:2610.09044v1 Announce Type: cross Abstract: Weak-to-strong generalization is the phenomenon where a strong student model trained with labels produced by a weak teacher model is able to generaliz

RoboQuest: Generalist Physical Agents that Search, Inspect and Test

arXiv:2610.10388v1 Announce Type: cross Abstract: Recent advances in multimodal foundation models have made them capable generalist physical agents for a range of manipulation tasks. However, successf

RollVerify: Bridging Efficiency and Accuracy in Long-Tail Rollout Reinforcement Learning

arXiv:2610.09914v1 Announce Type: new Abstract: Reinforcement learning is crucial for improving large language models' reasoning and generalization. It relies on massive rollouts whose lengths become

safetyarxiv-cs-lg

Route-Verify-Vote: Procedure-Conditioned Self-Consistency for Mixed-Domain Reasoning

model-releasesarxiv-cs-cl

arXiv:2610.08814v1 Announce Type: cross Abstract: Compositional generalization remains challenging when language models must combine familiar reasoning operations in unfamiliar ways. The Scenario-Base

SEER: Self-Enhancing Chain-of-Thought Compression for Reasoning Models

model-releasesarxiv-cs-cl

arXiv:2509.14093v3 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting can substantially improve the reasoning ability of large language models (LLMs), but it often comes with high

Self-attention summary networks for subsurface velocity-model building from common-image gathers

arXiv:2610.09282v1 Announce Type: new Abstract: Common-image gathers (CIGs) contain physically meaningful information about velocity-model errors through reflector focusing and residual moveout, but i

STORK: Spatio-Temporal Observation of uterine contRactions via neural networKs

arXiv:2610.09598v1 Announce Type: new Abstract: Uterine contractions in fetal MRI are typically identified manually and discarded, limiting insights into contraction dynamics. We formalize Uterine Con

researcharxiv-cs-cv

Toward Evidence-Driven Human-Agent-Robot Teaming for Earth-Independent Anomaly Triage

arXiv:2610.08933v1 Announce Type: new Abstract: Deep-space crews cannot rely on real-time ground support for urgent off-nominal events. Initial alerts may underdetermine cause, while discriminating ev

safetyarxiv-cs-ro

A Query Is Not a Commitment: Learning to Correct Expert Answers in Online Deferral

arXiv:2610.07084v1 Announce Type: cross Abstract: An inaccurate expert can still provide useful information after correction. We study online learning to defer in which the learner chooses an expert a

safetyarxiv-cs-lg

A self-learning scientific agent for X-ray diffraction

arXiv:2610.07862v1 Announce Type: cross Abstract: A central challenge for scientific agents is to turn analytical experience into reusable expertise grounded in physical evidence. Here we introduce Ga

AdvSim2Real : Training Web Agents Against Adaptive Prompt Injection in a Web World Model

arXiv:2610.08773v1 Announce Type: cross Abstract: Web agents complete user requests by reading and acting on pages that third parties write, so an instruction planted on a page can redirect the agent

AegisFlow: A Multi-Agent Agentic AI Framework for Autonomous Remediation and Self-Healing in Fragile Data Ecosystems

arXiv:2610.06971v1 Announce Type: new Abstract: Traditional data pipelines are notoriously brittle, often failing due to upstream schema drift, API contract changes, or website DOM modifications. Pres

An Information-Theoretic Evaluation Framework for Benchmark and Model Diagnosis in Knowledge Tracing

arXiv:2610.06988v1 Announce Type: new Abstract: Knowledge tracing (KT) models are predominantly evaluated using aggregate metrics such as area under the curve (AUC) and accuracy. However, these global

APEX: Active Protection at Execution Boundaries for LLM Agents

model-releasesarxiv-cs-ai

arXiv:2610.06966v1 Announce Type: cross Abstract: Indirect prompt injection (IPI) hides adversarial instructions in content that large language model (LLM) agents read at runtime. As agents compose he

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

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

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

model-releasesarxiv-cs-ai

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

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

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

Coupled but Late: Turn-Taking Between Full-Duplex Speech Models in Unscripted Dialogue

arXiv:2610.08683v1 Announce Type: new Abstract: Full-duplex speech models are trained to converse with a person, but they are increasingly made to converse with each other, in self-play data generatio

CuratorMAS: Automating Dataset Curation via Multi-Agent Orchestration

arXiv:2610.07075v1 Announce Type: new Abstract: High-quality datasets are essential for reliable machine learning, but dataset curation remains costly and hard to generalize across domains. Existing m