Search: “llm”

FormalASR: End-to-End Spoken Chinese to Formal Text

arXiv:2605.19266v1 Announce Type: cross Abstract: Automatic speech recognition (ASR) systems are typically optimized for verbatim transcription, which preserves disfluencies, filler words, and informa

GRASP: Deterministic argument ranking in interaction graphs

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DGX agent

arXiv:2605.19141v1 Announce Type: cross Abstract: Large language models are increasingly deployed as automated judges to evaluate the strength of arguments. As this role expands, their legitimacy depe

local-aiarxiv-cs-ai

20 May 2026

HeadRank: Decoding-Free Passage Reranking via Preference-Aligned Attention Heads

arXiv:2604.17237v2 Announce Type: replace-cross Abstract: Decoding-free reranking methods that read relevance signals directly from LLM attention weights offer significant latency advantages over auto

Improving Retrieval-Augmented Generation without Taxonomy-based Error Categorization

arXiv:2605.18772v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) improves the factual accuracy of large language model (LLM) outputs by grounding generation in external knowledge

MOCHA: Multi-Objective Chebyshev Annealing for Agent Skill Optimization

arXiv:2605.19330v1 Announce Type: new Abstract: LLM agents organize behavior through skills - structured natural-language specifications governing how an agent reasons, retrieves, and responds. Unlike

OpenCompass: A Universal Evaluation Platform for Large Language Models

model-releasesarxiv-cs-cl

arXiv:2605.19276v1 Announce Type: new Abstract: In recent years, the field of artificial intelligence has undergone a paradigm shift from task-specific small-scale models to general-purpose large lang

Phase-Aware Mixture of Experts for Agentic Reinforcement Learning

arXiv:2602.17038v3 Announce Type: replace Abstract: Reinforcement learning (RL) has equipped LLM agents with a strong ability to solve complex tasks. However, existing RL methods normally use a single

Rewarding Beliefs, Not Actions: Consistency-Guided Credit Assignment for Long-Horizon Agents

arXiv:2605.20061v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR) is a promising paradigm for improving large language model (LLM) agents on long-horizon interactiv

safetyarxiv-cs-cl

The World Won't Stay Still: Programmable Evolution for Agent Benchmarks

arXiv:2603.05910v2 Announce Type: replace Abstract: LLM-powered tool-calling agents fulfill user requests by interacting with environments, querying data, and invoking tools in a multi-turn process. Y

Trustworthy Agent Network: Trust in Agent Networks Must Be Baked In, Not Bolted On

arXiv:2605.19035v1 Announce Type: new Abstract: The rapid advancement of Large Language Models has given rise to autonomous LLM-based agents capable of complex reasoning and execution. As these agents

What and When to Distill: Selective Hindsight Distillation for Multi-Turn Agents

arXiv:2605.19447v1 Announce Type: new Abstract: Reinforcement learning can train LLM agents from sparse task rewards, but long-horizon credit assignment remains challenging: a single success-or-failur

When Skills Don't Help: A Negative Result on Procedural Knowledge for Tool-Grounded Agents in Offensive Cybersecurity

arXiv:2605.20023v1 Announce Type: new Abstract: Agent Skills, structured packages of procedural knowledge loaded into an LLM agent at inference time, are widely reported to improve task pass rates by

ADMEDTAGGER: an annotation framework for distillation of expert knowledge for the Polish medical language

arXiv:2601.09722v2 Announce Type: replace-cross Abstract: In this work, we present an annotation framework that demonstrates how a multilingual LLM pretrained on a large corpus can be used as a teache

An Empirical Study of Privacy Leakage Chains via Prompt Injection in Black-Box Chatbot Environments

arXiv:2605.18133v1 Announce Type: cross Abstract: LLM-based chatbot agents increasingly process user requests by combining natural-language reasoning with external tools such as web browsing. These ca

BELIEF: Structured Evidence Modeling and Uncertainty-Aware Fusion for Biomedical Question Answering

arXiv:2605.17435v1 Announce Type: new Abstract: Biomedical question answering often requires decisions from retrieved literature whose relevance, quality, and support for candidate answers are uneven.

CasualSynth: Generating Structurally Sound Synthetic Data

arXiv:2605.17528v1 Announce Type: cross Abstract: Large Language Models (LLMs) generate realistic synthetic data but offer no guarantee that their outputs respect the causal mechanisms governing the t

Context Memorization for Efficient Long Context Generation

arXiv:2605.18226v1 Announce Type: cross Abstract: Modern large language model (LLM) applications increasingly rely on long conditioning prefixes to control model behavior at inference time. While pref

ContraFix: Agentic Vulnerability Repair via Differential Runtime Evidence and Skill Reuse

arXiv:2605.17450v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly used for automated vulnerability repair (AVR), where repository-level reasoning enables them to ins

EXG: Self-Evolving Agents with Experience Graphs

arXiv:2605.17721v1 Announce Type: new Abstract: Large language model (LLM)-based agents have demonstrated strong capabilities in complex reasoning and problem solving through multi-step interactions,

Fidelity Probes for Specification--Code Alignment

arXiv:2605.17246v1 Announce Type: cross Abstract: We introduce fidelity probes: natural-language questions generated from a reference artifact with code-derived ground-truth answers, answered from a c

General Preference Reinforcement Learning

model-releasesarxiv-cs-cl

arXiv:2605.18721v1 Announce Type: cross Abstract: Post-training has split large language model (LLM) alignment into two largely disconnected tracks. Online reinforcement learning (RL) with verifiable

Tutorials

Generating Pretraining Tokens from Organic Data for Data-Bound Scaling

arXiv:2605.17849v1 Announce Type: cross Abstract: LLM pretraining is shifting from a compute-bound to a data-bound regime, where available human (organic) text falls far short of scaling demands. Howe

tutorialsarxiv-cs-ai

GIM: Evaluating models via tasks that integrate multiple cognitive domains

arXiv:2605.18663v1 Announce Type: new Abstract: As LLM benchmarks saturate, the evaluation community has pursued two strategies to increase difficulty: escalating knowledge demands (GPQA, HLE) or remo

GRID: Graph Representation of Intelligence Data for Security Text Knowledge Graph Construction

arXiv:2605.16714v1 Announce Type: new Abstract: Security knowledge graphs can provide computable external memory for security agents, but constructing them from long-form cyber threat intelligence (CT

researcharxiv-cs-ai

HINT-SD: Targeted Hindsight Self-Distillation for Long-Horizon Agents

arXiv:2605.17873v1 Announce Type: cross Abstract: Training long-horizon LLM agents with reinforcement learning is challenging because sparse outcome rewards reveal whether a task succeeds, but not whi

LaDi-RL: Latent Diffusion Reasoning Prevents Entropy Collapse in Reinforcement Learning

arXiv:2602.01705v3 Announce Type: replace-cross Abstract: Reinforcement learning has become a central paradigm for improving LLM reasoning, but most existing methods optimize policies over discrete to

Lying with Truths: Open-Channel Multi-Agent Collusion for Belief Manipulation via Generative Montage

arXiv:2601.01685v2 Announce Type: replace-cross Abstract: As large language models (LLMs) transition to autonomous agents synthesizing real-time information, their reasoning capabilities introduce an

Multi-Paradigm Agent Interaction in Practice:A Systematic Analysis of Generator-Evaluator, ReAct Loop,and Adversarial Evaluation in the buddyMe Framework

arXiv:2605.16821v1 Announce Type: new Abstract: The rapid evolution of Large Language Model (LLM) agents has produced diverse interaction paradigms, yet few production systems integrate multiple parad

OmniCode: A Benchmark for Evaluating Software Engineering Agents

arXiv:2602.02262v3 Announce Type: replace-cross Abstract: LLM-powered coding agents are redefining how real-world software is developed. To drive the research towards better coding agents, we require

PQR: A Framework to Generate Diverse and Realistic User Queries that Elicit QA Agent Failures

arXiv:2605.16551v1 Announce Type: new Abstract: Evaluating LLM-based agents remains challenging because identifying meaningful failure cases often requires substantial human effort to design realistic

safetyarxiv-cs-cl

Reasoning as Compression: Unifying Budget Forcing via the Conditional Information Bottleneck

arXiv:2603.08462v2 Announce Type: replace Abstract: ac{CoT} prompting improves LLM accuracy on complex tasks but often increases token usage and inference cost. Existing ``Budget Forcing'' methods red

researcharxiv-cs-lg

Tutorials

Reasoning Can Be Restored by Correcting a Few Decision Tokens

arXiv:2605.16874v1 Announce Type: new Abstract: Large reasoning models (LRMs) substantially outperform their base LLM counterparts on challenging reasoning benchmarks, yet it remains poorly understood

tutorialsarxiv-cs-ai

Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models

arXiv:2508.06974v2 Announce Type: replace Abstract: 1-bit LLM quantization offers significant advantages in reducing storage and computational costs. However, existing methods typically train 1-bit LL

SKG-Eval: Stateful Evaluation of Multi-Turn Dialogue via Incremental Semantic Knowledge Graphs

arXiv:2605.16650v1 Announce Type: cross Abstract: Evaluating multi-turn dialogue systems remains challenging because response quality depends not only on the current prompt, but also on previously est

SkillJect: Effectively Automating Skill-Based Prompt Injection for Skill-Enabled Agents

arXiv:2602.14211v2 Announce Type: replace-cross Abstract: Agent skills are increasingly used to extend LLM agents with task-specific instructions, executable scripts, and auxiliary resources. While im

SkillsVote: Lifecycle Governance of Agent Skills from Collection, Recommendation to Evolution

arXiv:2605.18401v1 Announce Type: cross Abstract: Long-horizon LLM agents leave traces that could become reusable experience, but raw trajectories are noisy and hard to govern. We treat Agent Skills a

Taming 'Zombie'' Agents: A Markov State-Aware Framework for Resilient Multi-Agent Evolution

arXiv:2605.17348v1 Announce Type: new Abstract: Recent advancements in LLM-based multi-agent systems have demonstrated remarkable collaborative capabilities across complex tasks. To improve overall ef

safetyarxiv-cs-cl

The Alien Space of Science: Sampling Coherent but Cognitively Unavailable Research Directions

arXiv:2603.01092v2 Announce Type: replace Abstract: Scientific discovery is constrained not only by what is true, but by what is cognitively available to the researchers currently exploring a field. M

The Unlearnability Phenomenon in RLVR for Language Models

arXiv:2605.16787v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Reward (RLVR) has proven effective in improving Large Language Model's (LLM) reasoning ability. However, the le

Hardware

TierCheck: Tiered Checkpointing for Fault Tolerance in Large Language Model Training

arXiv:2605.17821v1 Announce Type: cross Abstract: Large Language Model (LLM) training is frequently interrupted by a heterogeneous spectrum of failures, from common GPU crashes to catastrophic cluster

hardwarearxiv-cs-ai

Hardware

TriAxialKV: Toward Extreme Low-Precision KV-Cache Quantization for Agentic Inference Tasks

arXiv:2605.17170v1 Announce Type: new Abstract: Agentic workloads have emerged as a major workload for LLM inference. They differ significantly from chat-only workloads, requiring long-context process

hardwarearxiv-cs-lg

When Personalization Legitimizes Risks: Uncovering Safety Vulnerabilities in Personalized Dialogue Agents

arXiv:2601.17887v2 Announce Type: replace Abstract: Long-term memory enables large language model (LLM) agents to support personalized and sustained interactions. However, most work on personalized ag

Agentic Discovery of Neural Architectures: AIRA-Compose and AIRA-Design

arXiv:2605.15871v1 Announce Type: new Abstract: Toward recursive self-improvement, we investigate LLM agents autonomously designing foundation models beyond standard Transformers. We introduce a dual-

AgentStop: Terminating Local AI Agents Early to Save Energy in Consumer Devices

arXiv:2605.15206v1 Announce Type: cross Abstract: Autonomous agents powered by large language models (LLMs) are increasingly used to automate complex, multi-step tasks such as coding or web-based ques

Beyond Forgetting: Machine Unlearning Elicits Controllable Side Behaviors and Capabilities

arXiv:2601.21702v3 Announce Type: replace-cross Abstract: We consider Representation Misdirection (RM), a class of large language model (LLM) unlearning methods that achieve forgetting by redirecting

Context Pruning for Coding Agents via Multi-Rubric Latent Reasoning

arXiv:2605.15315v1 Announce Type: new Abstract: LLM-powered coding agents spend the majority of their token budget reading repository files, yet much of the retrieved code is irrelevant to the task at

Decouple Searching from Training: Scaling Data Mixing via Model Merging for Large Language Model Pre-training

arXiv:2602.00747v2 Announce Type: replace-cross Abstract: Determining an effective data mixture is a key factor in Large Language Model (LLM) pre-training, where models must balance general competence

H-Mem: A Novel Memory Mechanism for Evolving and Retrieving Agent Memory via a Hybrid Structure

arXiv:2605.15701v1 Announce Type: cross Abstract: Memory data are ubiquitous in Large Language Model (LLM)-based agents (e.g., OpenClaw and Manus). A few recent works have attempted to exploit agents'