Search: “models”

Learning Across Temporal Graph Representation Silos

arXiv:2610.19576v1 Announce Type: new Abstract: Temporal graph data are recorded in different representations, but whether these differences also determine the scope of learned knowledge reuse remains

Learning From a Steady Hand: A Weakly Supervised Agent for Robot Assistance under Microscopy

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DGX agent

arXiv:2601.20776v2 Announce Type: replace Abstract: This paper rethinks steady-hand robotic manipulation by using a weakly supervised framework that fuses calibration-aware perception with admittance

agentsarxiv-cs-ro

15 Oct 2026

Learning in Strategic Queuing with Geometric Recharge: From Bandits to Systems

arXiv:2610.19179v1 Announce Type: cross Abstract: We study packet routing in queuing systems where queues act as selfish, myopic agents that use no-regret learning algorithms to select servers with un

Learning Perceptual Geometry of Natural Odor Mixtures from LLM-Derived Descriptors

arXiv:2610.17164v1 Announce Type: new Abstract: The relationship between odor chemistry and perception remains incompletely understood. Many existing studies focus on the perception of single molecule

Applications

Learning Who Benefits Most: Multi-Treatment Causal Ranking for Population Health Stratification

arXiv:2610.19782v1 Announce Type: new Abstract: Population health stratification frameworks, such as the Italian DM77, organize patients by clinical need and care pathways but offer limited guidance o

applicationsarxiv-cs-lg

LiFi: LiDAR Generation from Multi-View Images via Geometric and Semantic Collaborative Guidance

arXiv:2610.19786v1 Announce Type: new Abstract: Generating LiDAR point clouds from multi-view camera images is a valuable task with applications in controllable data synthesis, cross-modal simulation

researcharxiv-cs-cv

Limits of Statistical Assurance for Embodied and Agentic AI under Adaptive Adversaries

arXiv:2610.18948v1 Announce Type: cross Abstract: Finite safety assessments cannot exclude an adversary that preserves unattacked behavior until certification and activates afterward. We characterize

Litmus: Zero-Label, Code-Driven Metric Specification for Evaluating AI Systems

arXiv:2606.23403v2 Announce Type: replace Abstract: As agentic LLM systems move from prototypes to deployment, evaluating them has become more important and more difficult. Evaluation goals are often

Long-Horizon RL Needs Muon

arXiv:2610.20256v1 Announce Type: new Abstract: Long-horizon agents must coordinate reasoning, tool use, and environment feedback over extended interactions. Learning from sparse outcome rewards requi

Look Before You Restore: Learning to Predict Before Acting for Agentic Image Restoration

arXiv:2610.20384v1 Announce Type: new Abstract: Agents for mixed-degradation image restoration typically learn what a tool does to an image only after running it: failed calls waste computation and ha

agentsarxiv-cs-cv

MAEM: Mitigating Recency Bias via a Replay-Free, Capacity-Controlled Dynamic Mixture of Experts for Class-Incremental Graph Learning

arXiv:2610.20238v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) excel on graph-structured data but suffer catastrophic forgetting when classes arrive sequentially. In class-incremental gr

Making Language the Low-Rate Code of Vision: Successive Refinement from Words to Pixels

arXiv:2610.16552v1 Announce Type: new Abstract: An ordered visual code lets a short prefix stand in for an image, and the training loss decides what that prefix contains. Under pixel reconstruction th

researcharxiv-cs-ai

MIPS: Mitigating Mode Collapse in Reinforcement Learning for Large Outcome Spaces

arXiv:2610.19457v1 Announce Type: new Abstract: Expected-reward maximization in reinforcement learning concentrates probability mass on a few outcomes, even in tasks that require many distinct, high-q

Misinformation Propagation in Benign Multi-Agent Systems

arXiv:2606.16710v2 Announce Type: replace-cross Abstract: Multi-agent systems (MAS) are credited with solving complex problems, yet their agents routinely act on context that may be false. Prior work

Mixed-Reality Feedback Control: Towards Blending of Control and Digital Twinning

arXiv:2610.19759v1 Announce Type: new Abstract: Technologies, such as co-simulation, in particular, digital twins (DTs) are used for comprehensive and reliable prototyping, verification and validation

agentsarxiv-cs-ro

Tutorials

MMKG-R1: Constructing Multimodal Knowledge Graphs via Reinforcement Learning

arXiv:2610.19341v1 Announce Type: new Abstract: Multimodal knowledge graphs (MMKGs) organize information across different modalities to support multimodal Graph Retrieval-Augmented Generation (RAG), w

tutorialsarxiv-cs-cv

Local Ai

MobiGuide: Vocabulary-Guided Online Recovery for Mobile Manipulation

arXiv:2604.01567v2 Announce Type: replace Abstract: A central challenge in mobile manipulation is preserving multiple plausible action modes while remaining reactive during execution. For example, gra

local-aiarxiv-cs-ro

Neural Supervised Clustering for Learning Individualized Treatment Rules with Many Treatments

arXiv:2610.19104v1 Announce Type: cross Abstract: Learning an individualized treatment rule (ITR) with many treatments requires estimating treatment-specific effects from limited observations per arm.

Of Errors and Echoes: Responsibility-Aware Credit Assignment for Tool-Use Agents

arXiv:2610.16027v1 Announce Type: cross Abstract: Reinforcement learning improves tool-use agents, yet fine-grained credit assignment remains challenging even with step-level feedback. An observed err

On Symmetry for Characterizing Agent Heterogeneity in Multi-Agent Systems

arXiv:2610.18538v1 Announce Type: cross Abstract: Understanding agent heterogeneity can inform the design of shared and agent-specific policy representations, however existing criteria provide no unif

Local Ai

OntoAR-HAR: Ontology-Guided Agentic RAG with Personalized Memory for Human Activity Recognition

arXiv:2610.15292v1 Announce Type: new Abstract: LLMs increasingly support sensor-based human activity recognition (HAR) by linking motion signals with activity semantics and physical knowledge. Retrie

local-aiarxiv-cs-ai

Paraphrase Consensus Is Not Self-Consistency: Instruction Reliance and Robustness in VLA Policies

arXiv:2610.19438v1 Announce Type: new Abstract: Vision-language-action (VLA) policies fine-tuned on LIBERO are trained and evaluated on the same ten instruction strings, and rewording the instruction

safetyarxiv-cs-ro

Penumbra: Sample-Efficient Adversarial Search for Regulatory Obligations

arXiv:2610.04693v2 Announce Type: replace Abstract: Agents are entering finance, healthcare and law, sectors where a violation leaves no lexical signature and carries real penalties. Whether an omissi

Pollard: Governed Semantic Execution Trees for Framework-Independent AI Agent Control and Replay

arXiv:2610.16145v1 Announce Type: new Abstract: Agent frameworks can persist checkpoints, trace tool calls, and limit resource use, but these facilities often assign different identities to execution,

Posterior Frequencies and the Cost of Not Fitting: A Correction to a Memorization Lower Bound

arXiv:2610.20402v1 Announce Type: cross Abstract: Feldman's memorization lower bound assigns a frequency coefficient based on how often a point appears in the training sample. We give an exact three-p

researcharxiv-cs-lg

Price of Anarchy of First-Price Auctions in Agentic Markets

arXiv:2610.20587v1 Announce Type: cross Abstract: An agentic market is a decentralized serving-time market where AI agents bid to claim each user prompt. An agent's bid is based on its expected value

agentsarxiv-cs-lg

ProReviewer: Evidence-Driven Investigation for Scientific Peer Review

arXiv:2606.13349v2 Announce Type: replace Abstract: Effective peer review requires adaptive investigation that responds to evidence emerging from each paper. Existing LLM-based reviewers largely organ

Recall What Is Missing: Composing Evidence from Long-Term Agent Memory

arXiv:2610.15642v1 Announce Type: new Abstract: Under a limited context budget, a long-term agent must recall not merely memories that are individually relevant, but evidence that is collectively suff

Reclaiming Low-Quality Instruction Data with Rule-Guided LLM Reconstruction

arXiv:2510.23160v2 Announce Type: replace Abstract: Data selection for supervised fine-tuning (SFT) often discards low-scoring instruction examples that may retain useful supervision. We propose a rul

researcharxiv-cs-cl

Reinforcement Learning via Advantage Guidance

arXiv:2610.19783v1 Announce Type: new Abstract: Reinforcement learning (RL) holds the promise of learning a policy that can outperform what is shown in the data. However, in practice, such systems suf

Relational Semantic Grounding for Test-Time Discovery

arXiv:2610.14721v1 Announce Type: new Abstract: Test-Time Discovery (TTD) aims to recognize known classes and detect novel ones from a continuous unlabeled test stream after deployment. Existing TTD m

Risk-Controlled DFT Selection for Machine-Learned Interatomic Potentials

arXiv:2610.19630v1 Announce Type: cross Abstract: Automated materials discovery requires evaluating large pools of candidate configurations, with density functional theory (DFT) as a major throughput

researcharxiv-cs-lg

RoboSpan: Learning Modular Coordination Policies for High-DoF Whole-Body Mobile Manipulation with Teleoperation

arXiv:2610.19594v1 Announce Type: new Abstract: Whole-body mobile manipulation suffers from two fundamental challenges: inherent motion heterogeneity across locomotion, arm, and hand modules, and prog

researcharxiv-cs-ro

Robust Explanatory Analysis for Structured Optimization Networks (REASON) in Learning

arXiv:2610.19378v1 Announce Type: cross Abstract: We seek a hierarchical resolution of residual error under a composite learning objective as a principled mechanism for identifying and exploiting addi

researcharxiv-cs-cv

Robust Real-Time Monocular Human Pose Estimation with Insole-Based Sensor Fusion

arXiv:2610.19494v1 Announce Type: new Abstract: Real-time monocular human pose estimation is an important sensing component for human-centered intelligent systems. While recent camera-based methods ha

researcharxiv-cs-cv

Rule-based and reinforcement-learning policies for resource allocation in a pediatric echo lab: a simulation study

arXiv:2506.06297v2 Announce Type: replace Abstract: Scheduling echocardiographic examinations in a hospital setting presents significant challenges due to non-deterministic factors such as patient no-

Scaling Discovery Requires Forgetting

arXiv:2610.20563v1 Announce Type: cross Abstract: Automated research with LLM agents has shown promising results in various domains. Through iterative refinement and scored verifiers, LLM agents are c

Applications

Schrodinger's Navigator: Imagining an Ensemble of Futures for Zero-Shot Object Navigation

arXiv:2512.21201v4 Announce Type: replace-cross Abstract: Zero-shot object navigation (ZSON) requires robots to find target objects in unseen environments without task-specific fine-tuning or pre-buil

applicationsarxiv-cs-cv

SimInter: Evidence-Grounded User Simulation with Interview-Based Profile Enrichment for Conversational Recommender Systems

arXiv:2610.18308v1 Announce Type: cross Abstract: Conversational Recommender Systems (CRSs) require reliable user simulators for cost-effective evaluation. LLM-based simulators have made realistic sim

SkillForgeBench: Evaluating Agent Reliability Grounded in Agent Skill Specifications

arXiv:2610.14918v1 Announce Type: new Abstract: Evaluating an agent's ability to strictly satisfy diverse and procedurally complex requirements is essential to determining its practical reliability. C

Socratic Skill Learning: Interleaving Teaching and Practice for Transferable Agent Skills

arXiv:2610.17860v1 Announce Type: new Abstract: Agent Skills improve LLM agents by making task knowledge reusable across sessions. Learning transferable Skills requires evidence about a procedure and

Spacecraft Pose Tracking via Kinematic Prediction and Selective Event Correction

arXiv:2610.19465v1 Announce Type: new Abstract: In initialized spacecraft tracking, a short-horizon motion prediction remains available as event support changes. Observation-Aligned Event Innovation (

researcharxiv-cs-ro

Tutorials

Spectral structure learning for clinical time series

arXiv:2502.11680v2 Announce Type: replace Abstract: We develop and evaluate a structure learning algorithm for clinical time series. Clinical time series are multivariate time series observed in multi

tutorialsarxiv-cs-lg

Synthetic reaction data from transformation rules for graph-to-sequence pretraining in biodegradation pathway prediction

arXiv:2610.19640v1 Announce Type: new Abstract: The scarcity of observed biodegradation reactions constrains the training of product and pathway predictors. We present enviGraph, a framework that gene

researcharxiv-cs-lg

The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery

arXiv:2610.18933v1 Announce Type: cross Abstract: Modern coding agents are fundamentally reshaping the role of human input for computational research: they write code, launch in silico experiments, an

The Compiler May Read It, the Agent May Not: Keeping Part of a Research Code Away from a Coding Agent

arXiv:2609.35557v2 Announce Type: replace-cross Abstract: The compiler must read modules a physics-based solver cannot build without; the coding agent must not read that intellectual property. The har

The Many Morals of AI: The cautionary bias of North American news narratives in global perspective

arXiv:2610.18573v1 Announce Type: cross Abstract: The stories told about artificial intelligence (AI) play a central role in shaping public understanding, policy debates, and expectations about techno

Local Ai

Towards Certifiable State Estimation under Observability Degeneracy

arXiv:2610.20490v1 Announce Type: new Abstract: Certifiable optimization provides global optimality guarantees for non-convex state estimation in robotics. However, under severe physical sensing degen

local-aiarxiv-cs-ro