Search: “agents”

MedFact: Benchmarking the Fact-Checking Capabilities of Large Language Models on Chinese Medical Texts

arXiv:2509.12440v3 Announce Type: replace-cross Abstract: Deploying Large Language Models (LLMs) in medical applications requires fact-checking capabilities to ensure patient safety and regulatory com

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Mellum2 Technical Report

DGX agent

arXiv:2605.31268v1 Announce Type: new Abstract: We present Mellum 2, an open-weight 12B-parameter Mixture-of-Experts (MoE) language model with 2.5B active parameters per token. Mellum 2 is a general-p

model-releasesarxiv-cs-cl

1 Jun 2026

Applications

Neither Replacement nor Panacea: Comparing LLM-Based Conversational and Graphical Decision Support in Industrial Tasks

arXiv:2605.31287v1 Announce Type: cross Abstract: Managers in manufacturing settings rely on digital interfaces to interpret operational data for decision-making, but growing data volume and complexit

applicationsarxiv-cs-ai

nuReasoning: A Reasoning-Centric Dataset and Benchmark for Long-Tail Autonomous Driving

arXiv:2605.31572v1 Announce Type: new Abstract: Reasoning is essential for autonomous driving (AD) in long-tail scenarios, where vehicles must apply commonsense knowledge, understand spatial relations

SAW-Bench: Learning Situated Awareness in the Real World

arXiv:2602.16682v2 Announce Type: replace Abstract: A core aspect of human perception is situated awareness, the ability to relate ourselves to the surrounding physical environment and reason over pos

Send a SCOUT First: Pre-hoc Reasoning for Adaptive Detector Allocation in Prompt-Injection Defense

model-releasesarxiv-cs-lg

arXiv:2605.30837v1 Announce Type: cross Abstract: Prompt-injection detectors are heterogeneous: each is strong on a different slice of attacks, and none is always reliable. Yet existing systems still

TARIC: Memory-Augmented Traversability-Aware Outdoor VLN under Interrupted Semantic Cues

arXiv:2605.31121v1 Announce Type: cross Abstract: Outdoor vision-language navigation (VLN) in long-range, open-world environments is frequently disrupted by semantic-cue interruptions, where informati

Local Ai

The Architecture of Errors: From Universal Impossibility to Patch-Local LLM Reliability

arXiv:2605.30628v1 Announce Type: cross Abstract: Universal LLM reliability is not a finite-library problem: across all possible tasks, tools, schemas, knowledge sources, and evaluator expectations, n

local-aiarxiv-cs-ai

Zero Collapse: A Failure Mode of Policy Gradient Methods in Discontinuous Reward Environments

arXiv:2605.30896v1 Announce Type: new Abstract: Bidding in repeated auctions is a central challenge for reinforcement learning (RL), combining continuous control with the strategic complexities of dig

safetyarxiv-cs-lg

Automating Low-Risk Code Review at Meta: RADAR, Risk Calibration, and Review Efficiency

arXiv:2605.30208v1 Announce Type: cross Abstract: AI-assisted coding tools have altered software production. At Meta, significant lines of code per human-landed diff grew by 105.9% year over year and

Benchmarking LLM-Assisted Blue Teaming via Standardized Threat Hunting

arXiv:2509.23571v3 Announce Type: replace-cross Abstract: As cyber threats continue to grow in scale and sophistication, blue team defenders increasingly require advanced tools to proactively detect a

Causal-JEPA: Learning World Models through Object-Level Latent Masking

arXiv:2602.11389v2 Announce Type: replace Abstract: World models require robust relational understanding to support prediction, reasoning, and control. While object-centric representations provide a u

Certified Policy Optimisation for Nested Causal Bandits via PAC-Bayes Risk

arXiv:2605.29788v1 Announce Type: new Abstract: Critical sequential decisions are rarely single-timescale: a strategic decision causally shapes the context in which every subsequent tactical choice is

Conf-Gen: Conformal Uncertainty Quantification for Generative Models

arXiv:2605.28920v1 Announce Type: cross Abstract: Conformal prediction (CP) and its extension, conformal risk control (CRC), are established frameworks for quantifying uncertainty in supervised machin

researcharxiv-cs-ai

Cookie-Bench: Continuous On-screen Key Interaction Evaluation for Web Generation

arXiv:2605.30000v1 Announce Type: new Abstract: Front-end web code has become a core product surface for every frontier LLM release, yet evaluating these interactive applications at development speed

DiffSpot: Can VLMs Spot Fine-Grained Visual Differences in Web Interfaces?

arXiv:2605.29615v1 Announce Type: cross Abstract: Vision-language models (VLMs) have made strong progress on high-level image-text alignment, yet their ability to perceive subtle visual differences re

Emergent Semantic Representations in World Models through Physical Interaction without Linguistic Supervision

arXiv:2605.28865v1 Announce Type: cross Abstract: What does a world model learn from physical exploration, without any linguistic supervision? We argue the answer is organized by a single principle: t

GrowLoop: Self-Evolving Conversation Evaluation Seeded by Human

arXiv:2605.28882v1 Announce Type: cross Abstract: With the rapid advancement of large language models, evaluating human-likeness in open-ended conversation has become increasingly important. However,

Jailbreaking and Mitigation of Vulnerabilities in Large Language Models

arXiv:2410.15236v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have transformed artificial intelligence by advancing natural language understanding and generation, enabling app

Tutorials

MEMENTO: Leveraging Web as a Learning Signal for Low-Data Domains

arXiv:2605.29795v1 Announce Type: new Abstract: Real-world tasks often lack large labeled datasets, motivating extensive work on learning in low-data regimes. However, existing approaches such as few-

tutorialsarxiv-cs-ai

Minimal Prompt Perturbations Lead to Code Vulnerabilities: Prompt Fragility and Hidden-State Signals in Coding LLMs

arXiv:2605.29737v1 Announce Type: cross Abstract: LLM-based coding assistants are seeing rapid adoption, offering substantial gains in developer productivity. As organizations increasingly ship code t

OptSkills: Learning Generalizable Optimization Skills from Problem Archetypes via Cluster-Based Distillation

arXiv:2605.29829v1 Announce Type: new Abstract: Leveraging Large Language Models (LLMs) to automatically formulate and solve optimization problems from natural language has emerged as an efficient par

PEARL: Training Socratic Tutors with Pedagogically Aligned Reinforcement Learning

arXiv:2605.29582v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown promise as educational tutors, yet effective tutoring requires more than solving problems: it must provide pro

safetyarxiv-cs-cl

REST3D: Reconstructing Physically Stable 3D Scenes from a Single Image

arXiv:2605.30338v1 Announce Type: new Abstract: Reconstructing physically stable 3D scenes from a single RGB image enables casual images to be converted into simulation-ready digital assets for applic

safetyarxiv-cs-cv

SalsaAgent: A multimodal embodied language model for interactive dance generation

arXiv:2605.29219v1 Announce Type: new Abstract: Interaction between humanoids involves bidirectional and nonverbal reactivity, coordination and synchrony. Toward socially aware robots and interactive

researcharxiv-cs-cv

The Price Reversal Phenomenon: When Cheaper Reasoning Models Cost More

arXiv:2603.23971v2 Announce Type: replace-cross Abstract: Developers and consumers increasingly choose reasoning models (RMs) based on their listed API prices. However, how accurately do these prices

When LLM Reward Design Fails: Diagnostic-Driven Refinement for Sparse Structured RL

model-releasesarxiv-cs-lg

arXiv:2605.28918v1 Announce Type: new Abstract: For sparse, structured reinforcement-learning tasks with semantic reward-function interfaces, LLM-generated reward shaping is better framed as debugging

Are We Truly Innovating? A Qualitative and Quantitative Study of Originality in AI Research Papers

arXiv:2602.06054v3 Announce Type: replace Abstract: Assessing originality in AI research is arguably the most consequential yet least reliable step in peer review. Reviewer judgments of originality re

researcharxiv-cs-cl

Commit to the Bit: Reactive Reinforcement Learning Done Right

arXiv:2605.28276v1 Announce Type: new Abstract: Reinforcement learning algorithms are commonly analyzed (and designed) under the Markov assumption. This is unrealistic, as most environments encountere

safetyarxiv-cs-lg

Debate with Images: Detecting Deceptive Behaviors in Multimodal Large Language Models

arXiv:2512.00349v2 Announce Type: replace Abstract: Are frontier AI systems becoming more capable? Certainly. Yet such progress is not an unalloyed blessing but rather a Trojan horse: behind their per

Delay-Aware Reinforcement Learning for Highway On-Ramp Merging under Stochastic Communication Latency

arXiv:2403.11852v5 Announce Type: replace-cross Abstract: Delayed and partially observable state information poses significant challenges for reinforcement learning (RL)-based control in real-world au

DisasterBench: Benchmarking LLM Planning under Typed Tool Interface Constraints

arXiv:2605.27957v1 Announce Type: new Abstract: Disasters cause severe societal impacts, demanding rapid coordination of heterogeneous AI tools, from satellite analysis to flood prediction and damage

EchoAvatar: Real-time Generative Avatar Animation from Audio Streams

arXiv:2605.28272v1 Announce Type: new Abstract: Real-time synthesis of high-fidelity 3D character motion from audio is a pivotal component for next-generation interactive avatars and virtual assistant

researcharxiv-cs-cv

ESC-Skills: Discovering and Self-Evolving Skills for Emotional Support Conversations

arXiv:2605.27908v1 Announce Type: cross Abstract: Existing emotional support conversation (ESC) systems mainly rely on end-to-end response generation or coarse strategy supervision, offering limited i

How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving

arXiv:2605.28302v1 Announce Type: cross Abstract: Modern large language model (LLM) inference has progressively disaggregated to keep pace with growing model sizes and tight TTFT and TPOT service-leve

Local Ai

OmniVerifier-M1: Multimodal Meta-Verifier with Explicit Structured Recalibration

arXiv:2605.28805v1 Announce Type: cross Abstract: Visual outcomes are increasingly central to multimodal large language models, making reliable and fine-grained verification essential for scaling gene

local-aiarxiv-cs-ai

Applications

OphIn-500K: Curating Web-Scale Visual Instructions for Scaling Ophthalmic Multimodal Large Language Models

arXiv:2605.27916v1 Announce Type: cross Abstract: The advancement of general medical Multimodal Large Language Models (MLLMs) has shown great potential for building conversational assistants to suppor

applicationsarxiv-cs-cl

OralAgent: Integrating Reasoning, Tools, and Knowledge for Interactive Dental Image Analysis

arXiv:2605.27378v1 Announce Type: new Abstract: Dental image analysis plays a pivotal role in supporting accurate diagnosis and treatment planning in oral healthcare. Although recent advances have pro

Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control

model-releasesarxiv-cs-lg

arXiv:2601.15015v2 Announce Type: replace Abstract: Reinforcement learning (RL) has shown promising results in active flow control (AFC), yet progress in the field remains difficult to assess as exist

POINav: Benchmarking and Enhancing Final-Meters Arrival in Real-World Vision-Language Navigation

arXiv:2605.28237v1 Announce Type: cross Abstract: Real-world navigation is fundamentally driven by Points of Interest (POIs), yet reaching a precise POI remains a critical 'final-meters' challenge. Ex

Snowveil: A Framework for Decentralised Preference Discovery

arXiv:2512.18444v2 Announce Type: replace-cross Abstract: Aggregating subjective preferences in social choice traditionally assumes a trusted central authority. In contrast, this paper formalises Dece

Teacher-Student Representational Alignment for Reinforcement Learning-Driven Imitation Learning

arXiv:2605.28372v1 Announce Type: new Abstract: Imitation learning (IL) from a state-based reinforcement learning (RL) policy is a common approach to overcome the curse of dimensionality in complex an

safetyarxiv-cs-lg

The Illusion of Opting in AI-Mediated Consequential Decisions

arXiv:2605.28210v1 Announce Type: new Abstract: Drawing on Ullmann-Margalit's concept of opting (transformative, irrevocable, and shadowed by foreclosed alternatives), we show that current AI systems

VLA-Hijack: A Transferable Patch Attack against Vision-Language-Action Models via Visual Proprioception Hijacking

arXiv:2605.28083v1 Announce Type: new Abstract: While Vision-Language-Action (VLA) models have emerged as powerful generalist policies, their severe vulnerability to adversarial patches significantly

safetyarxiv-cs-cv

Alignment Tuning for Large Language Models: A Data-Centric Lens on Alignment Data Pipelines

arXiv:2605.26442v1 Announce Type: cross Abstract: Much of the alignment tuning literature is organized around optimization objectives, while the construction of alignment data is often treated implici

Hardware

AssetGen: Deployable 3D Asset Generation at Interactive Speed

arXiv:2605.26137v1 Announce Type: cross Abstract: While 3D generation is progressing rapidly, recent work has often focused on obtaining high-resolution assets, leaving user experience and deployabili

hardwarearxiv-cs-ai

Breaking the Epistemic Trap: Active Perception Under Compound Uncertainty

arXiv:2605.26627v1 Announce Type: cross Abstract: Deploying reinforcement learning in safety critical domains, from autonomous vehicles to medical decision support, is constrained by failures arising

safetyarxiv-cs-ro

ChartAct: A Benchmark for Dynamic Chart Understanding

arXiv:2605.26994v1 Announce Type: new Abstract: Charts are widely used to present complex data for analysis and decision making. Existing chart understanding benchmarks mainly focus on static charts,