Search: “models”

An Enhanced Large Neighborhood Search Approach for the Capacitated Facility Location Problem with Incompatible Customers

arXiv:2605.28337v1 Announce Type: new Abstract: A new variant of the classic capacitated facility location problem, which considers incompatibilities between customers, has recently been introduced in

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Model Releases

Analyzing Quality-Latency-Resource Trade-offs in a Technical Documentation RAG Assistant Using LoRA Adaptation

DGX agent

arXiv:2605.28222v1 Announce Type: new Abstract: We study quality-latency-resource trade-offs in a documentation-grounded retrieval-augmented generation (RAG) system that uses Low-Rank Adaptation (LoRA

model-releasesarxiv-cs-cl

28 May 2026

AOE: Exhaustive Out-of-Distribution Detection via Recalibrating Outlier Labels

arXiv:2605.28021v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection is essential for deploying machine learning models in open-world and safety-critical scenarios, where test inputs ma

safetyarxiv-cs-lg

Ask Now, Use Later: Benchmarking the Proactivity Gap in Long-Lived LLM Agents

arXiv:2605.28108v1 Announce Type: new Abstract: A long-lived LLM agent, such as OpenClaw, earns its value by acting on a user's preferences and constraints across sessions, not just the current reques

Applications

BEAR: Budgeted Evidence Allocation for Multi-Document Reasoning

arXiv:2601.18116v2 Announce Type: replace Abstract: We argue that multi-document reasoning is constrained not only by how much text a model can read, but also by how limited query-time evidence budget

applicationsarxiv-cs-cl

Benchmarking Inductive Biases for Multivariate Time-Series Anomaly Detection with a Robust Multi-View Channel-Graph Detector

arXiv:2605.28103v1 Announce Type: new Abstract: We present a unified experiment, analysis, and benchmark study of multivariate time-series (MTS) anomaly detection. Ten family-representative detectors

Beyond Input Understanding: Diagnosing Multilingual Mathematical Reasoning with Directed Acyclic Trace Graphs

arXiv:2605.27715v1 Announce Type: new Abstract: Large reasoning models (LRMs) achieve strong mathematical reasoning performance in English, but remain much less reliable in many low- and medium-resour

BioELX: Cross-lingual Biomedical Entity Linking via Alias-based Retrieval and LLM Ranking

arXiv:2605.27380v1 Announce Type: cross Abstract: Cross-lingual biomedical entity linking (BEL) maps mentions in any language to unique identifiers in a biomedical knowledge base (KB), supporting clin

Blind PRNG Hijacking: An Undetectable Integrity-Preserving Attack Against LLM Watermarking

arXiv:2605.28632v1 Announce Type: cross Abstract: Cryptographic watermarking is a leading defense for attributing text generated by large language models (LLMs). Existing schemes, including KGW, Unigr

Boundary Suppression Asymmetry in Post-trained Assistants: Over-expansion as a Controllability Cost

arXiv:2605.27969v1 Announce Type: new Abstract: Post-trained language-model assistants are often optimized to avoid under-answering, encouraging complete, helpful, cautious, and proactive responses. W

safetyarxiv-cs-cl

Bridging the Generalization Gap in Adverse Weather Segmentation: A Training Recipe Perspective

arXiv:2605.27962v1 Announce Type: new Abstract: This paper describes our approach for the 8th UG2+ Workshop (CVPR 2026) Track~2, which targets semantic segmentation of outdoor scenes degraded by five

researcharxiv-cs-cv

BuddyBench: A Privacy-Constrained Multi-Task Benchmark for Pediatric Social-Communication Personalization

arXiv:2605.28089v1 Announce Type: new Abstract: BuddyBench introduces a privacy-constrained multi-task benchmark for pediatric social-communication personalization. Unlike existing neurodevelopmental

Tutorials

C-MIG: Multi-view Information Gain-based Retrieval-Augmented Generation for Clinical Diagnosis Reasoning

arXiv:2605.27860v1 Announce Type: new Abstract: Retrieval-augmented generation combined with reinforcement learning has shown promise for grounding large language models in trustworthy medical evidenc

tutorialsarxiv-cs-ai

Can LLMs Use Linguistic Uncertainty Markers to Reliably Reflect Intrinsic Confidence?

arXiv:2605.28778v1 Announce Type: new Abstract: LLMs' linguistically expressed confidence should faithfully reflect their intrinsic uncertainty. While recent work shows LLMs struggle to use epistemic

CAREF: Calibration-Aware Regularization for Explanation Faithfulness Without Rationale Supervision

arXiv:2605.27835v1 Announce Type: cross Abstract: We introduce CAREF, a parameter-efficient fine-tuning framework that jointly optimizes predictive accuracy and explanation faithfulness via calibratio

Chance-Constrained MPPI under State and Dynamic Object Prediction Uncertainty and the Evaluation of Collision Risk Calibration

arXiv:2605.28330v1 Announce Type: new Abstract: Chance-constrained Model Predictive Path Integral (MPPI) control is increasingly adopted for navigation in dynamic environments to explicitly bound coll

safetyarxiv-cs-ro

Tutorials

Checking Fact with Better Retrieval: Dynamic Contrastive Learning for Evidence Retrieval

arXiv:2605.27449v1 Announce Type: cross Abstract: In the field of multimodal fact checking, the accuracy of retrieving evidence from different modalities has a significant impact on the downstream cla

tutorialsarxiv-cs-ai

Chinese Word Boundary Recovery through Character Alignment Projection

arXiv:2605.28128v1 Announce Type: new Abstract: Chinese word segmentation is especially fragile in non-standard text, where language learner errors and other character-level divergences disrupt the wo

Conservative neural posterior estimation via distributionally robust training

arXiv:2605.28516v1 Announce Type: cross Abstract: Simulation-based inference with neural posterior estimation (NPE) often yields overconfident and unreliable posteriors under limited simulation budget

ConvMemory: A Lightweight Learned Memory Reranker, a Negative Attribution Result, and a Research-Preview Conflict Editor

arXiv:2605.28062v1 Announce Type: new Abstract: We describe ConvMemory, a small 3.6M-parameter learned reranker for conversational long-term memory retrieval, trained with cross-encoder teacher superv

Cultural Fidelity in English-to-Hindi Translation: A Preservation-Fluency Frontier for Gender Recoverability

arXiv:2605.27654v1 Announce Type: cross Abstract: Generative translation systems are cultural technologies because they decide how socially meaningful cues are rendered within culturally specific gram

CyberJurors: A Multi-Agent Simulation Task for E-Commerce Disputes Verdict

arXiv:2605.28369v1 Announce Type: new Abstract: E-commerce platforms have begun recruiting crowdsourced jurors to adjudicate massive volumes of transaction disputes. Unlike formal legal judgment, E-co

Agents

Cyclical Entropy Eruption: Entropy Dynamics in Agent Reinforcement Learning

arXiv:2605.27954v1 Announce Type: new Abstract: Agentic large language models are increasingly used to solve real-world tasks by reasoning over goals, invoking tools, and interacting with external env

agentsarxiv-cs-lg

Decentralized Parameter-Free Online Learning with Compressed Gossip

arXiv:2605.27831v1 Announce Type: new Abstract: We study decentralized online convex optimization when agents communicate over a graph and messages may be compressed. Classical decentralized online me

Diffusion-Augmented Markov Decision Processes for Maximum Entropy Reinforcement Learning

arXiv:2512.02019v3 Announce Type: replace-cross Abstract: Diffusion models excel at sampling from complex, unnormalized distributions. In this work, we extend Maximum Entropy Reinforcement Learning (M

Disentangling Adversarial Prompts: A Semantic-Graph Defense for Robust LLM Security

arXiv:2605.27823v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly vulnerable to adversarial prompts that exploit semantic ambiguities to bypass safety mechanisms, resulti

Dr-CiK: A Testbed for Foresight-Driven Agents

arXiv:2605.27904v1 Announce Type: new Abstract: Time series forecasting in real-world settings often depends not only on historical observations, but also on external context that must be actively dis

DynaSchedBench: Calibrated Dynamic Scheduling Benchmarks and Observability Paradox in LLM-based Scheduling Agents

arXiv:2605.27566v1 Announce Type: new Abstract: Progress in neural combinatorial optimization for Dynamic Flexible Job Shop Scheduling Problem (DFJSP) is currently hindered by a methodological tension

Efficient Post-training of LLMs for Code Generation With Offline Reinforcement Learning

arXiv:2605.28409v1 Announce Type: new Abstract: Post-training using online reinforcement learning (RL) is an important training step for LLMs, including code-generating models. However, online RL for

researcharxiv-cs-ai

EventShiftFlow: Towards Hardware-efficient FPGA-based Flow Estimation

model-releasesarxiv-cs-cv

arXiv:2605.28312v1 Announce Type: cross Abstract: Event-based vision sensors offer asynchronous, high-temporal-resolution measurements that are attractive for low-latency robotic perception, but many

Evolving and Detecting Multi-Turn Deception using Geometric Signatures

arXiv:2605.27671v1 Announce Type: cross Abstract: Safety defenses for large language models (LLMs) are typically trained and evaluated on single-turn prompts, yet real attacks often unfold as indirect

safetyarxiv-cs-lg

Explanation Generation for Contradiction Reconciliation with LLMs

arXiv:2603.22735v2 Announce Type: replace Abstract: Existing NLP work commonly treats contradictions as errors to be resolved by choosing which statements to accept or discard. Yet a key aspect of hum

FactReview: Evidence-Grounded Peer Review with Execution-Based Claim Verification

arXiv:2604.04074v3 Announce Type: replace Abstract: LLM-based reviewing systems typically take only the manuscript as input, leaving literature and code-based claims hard to verify. We present FactRev

Federated Learning for Multivariate Time Series Anomaly Detection in Industrial Automation

arXiv:2605.27486v1 Announce Type: new Abstract: Federated learning (FL) has broadened the horizon for multivariate time series anomaly detection (MTSAD). However, benchmarking such anomaly detection m

ForestHG-Trace: Traceable Long-Horizon Ecological Reasoning over Large-Scale Forest Scenes

model-releasesarxiv-cs-cv

arXiv:2605.27590v1 Announce Type: new Abstract: Remote sensing question answering (RS-QA) often requires more than direct semantic prediction, especially in large-scale forest scenes where ecological

From Affect to Complex Behavior: Advancing Multimodal Human-Centered AI at the 10th ABAW Workshop & Competition

arXiv:2605.27451v1 Announce Type: new Abstract: The 10th Affective & Behavior Analysis in-the-Wild (ABAW) Workshop and Competition, held at CVPR 2026, continues to advance research on modelling, analy

safetyarxiv-cs-cv

From Detection to Mechanism: Cross-Attention Graph Neural Networks Enable Drug-Drug Interaction Type Prediction An Ablation Study with Acetylsalicylic Acid Validation

arXiv:2605.27861v1 Announce Type: cross Abstract: Predicting whether two drugs interact (binary detection) is a substantially dif- ferent task from predicting the mechanism type of that interaction (m

Tutorials

From Talking to Singing: A New Challenge for Audio-Visual Deepfake Detection

arXiv:2605.27944v1 Announce Type: new Abstract: With rapid advances in audio-visual generative models, reliable forgery detection becomes increasingly critical. Existing methods for audio-visual deepf

tutorialsarxiv-cs-ai

Agents

FundaPod: A Multi-Persona Agent Pod Platform with Knowledge Graph Memory for AI-Assisted Fundamental Investment Research

arXiv:2605.27864v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly applied in finance, yet most existing work emphasizes trading signals or financial NLP tasks centered on p

agentsarxiv-cs-ai

GenSBI: Generative Methods for Simulation-Based Inference in JAX

arXiv:2605.27499v1 Announce Type: new Abstract: Flow and diffusion generative models have established themselves as widely adopted density estimators for simulation-based inference (SBI), extending na

researcharxiv-cs-lg

Global Policy-Space Response Oracles for Two-Player Zero-Sum Games

arXiv:2605.28273v1 Announce Type: new Abstract: The Policy-Space Response Oracles (PSRO) framework scales equilibrium computation to large zero-sum games by iteratively expanding a restricted strategy

Got a Secret? LLM Agents Can't Keep It: Evaluating Privacy in Multi-Agent Systems

arXiv:2605.27766v1 Announce Type: new Abstract: LLM safety evaluations predominantly test models in isolation, yet deployed AI agents increasingly operate within persistent social environments alongsi

GRADE: Generalizable Reasoning-Aware Dialogue Evaluation for AI Tutors

arXiv:2605.27866v1 Announce Type: new Abstract: Evaluating AI tutor responses requires more than factual correctness: tutors must identify mistakes, locate errors, provide guidance, and offer actionab

GradientStabilizer:Fix the Norm, Not the Gradient

arXiv:2502.17055v4 Announce Type: replace-cross Abstract: Training instability in modern deep learning systems is frequently triggered by rare but extreme gradient-norm spikes, which can induce oversi

Graph Neural Networks for Source Detection: A Review and Benchmark Study

arXiv:2512.20657v2 Announce Type: replace-cross Abstract: The source detection problem arises when an epidemic process unfolds over a contact network, and the objective is to identify its point of ori

Local Ai

HarmoVid: Relightful Video Portrait Harmonization

arXiv:2605.28811v1 Announce Type: new Abstract: We present a method for harmonizing the lighting of a foreground video to match a target background scene, adjusting shadows, color tone, and illuminati

local-aiarxiv-cs-cv

HO-SFL: Hybrid-Order Split Federated Learning with Backprop-Free Clients and Dimension-Free Aggregation

arXiv:2603.14773v2 Announce Type: replace-cross Abstract: Fine-tuning large models on edge devices is severely hindered by the memory-intensive backpropagation (BP) in standard frameworks like federat

researcharxiv-cs-ai

Local Ai

Integrating Inductive Biases in Transformers via Distillation for Financial Time Series Forecasting

arXiv:2603.16985v2 Announce Type: replace Abstract: Transformer-based models have been widely adopted for time-series forecasting due to their high representational capacity and architectural flexibil

local-aiarxiv-cs-lg