Search: “llm”

Probe Before You Edit: Probing-Guided Molecular Optimization for LLM Agents in Structure-Based Drug Design

arXiv:2606.00555v1 Announce Type: new Abstract: Structure-based drug design increasingly employs LLM agents to iteratively refine ligands against a target pocket, yet a viable ligand must satisfy two

2 Jun 2026

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SALSA: Speech Aware LLM Adaptation via Learned Steering Activation Vectors

DGX agent

arXiv:2606.00460v1 Announce Type: new Abstract: Speech-aware large language models often generalize poorly to out-of-domain settings. We propose SALSA (Speech-Aware LLM Adaptation via Learned Steering

researcharxiv-cs-cl

2 Jun 2026

Scaling Behavior of Single LLM-Driven Multi-Agent Systems

arXiv:2606.00655v1 Announce Type: cross Abstract: The burgeoning field of LLM-based Multi-Agent Systems (MAS) promises to tackle complex tasks through collaborative intelligence, yet fundamental quest

agentsarxiv-cs-ai

2 Jun 2026

BlueFin: Benchmarking LLM Agents on Financial Spreadsheets

arXiv:2605.30907v1 Announce Type: cross Abstract: We present BlueFin, a benchmark that tasks large language model (LLM) agents with synthesis, manipulation, and comprehension tasks over spreadsheet wo

Bounded Behavioral Indistinguishability for Black-Box LLM Distillation

arXiv:2605.30448v1 Announce Type: cross Abstract: Black-box LLM distillation is usually evaluated as an output-matching problem: a student is considered successful when its responses are semantically

ConsisGuard: Aligning Safety Deliberation with Policy Enforcement in LLM Guardrails

arXiv:2605.31073v1 Announce Type: new Abstract: Reasoning-based LLM guardrails improve safety moderation by generating explicit rationales before issuing final decisions. However, their rationales do

Cross-Layer Subspace Coupling for LLM Compression: A Unifying Framework and Its Empirical Limits

arXiv:2605.30836v1 Announce Type: new Abstract: Recent SVD based compression methods for large language models like SVD LLM and Basis Sharing can be unified under one optimization problem. While mathe

researcharxiv-cs-lg

ElasticMem: Latent Memory as a Learnable Resource for LLM Agents

arXiv:2605.30690v1 Announce Type: new Abstract: Long-term memory is essential for LLM agents to reason coherently across extended interactions, personalize responses, and reuse past experience. Howeve

ExpGraph: Model-Agnostic Experience Learning with Graph-Structured Memory for LLM Agents

arXiv:2605.30712v1 Announce Type: new Abstract: Large language model (LLM) agents have shown strong capabilities in reasoning, tool use, and multi-step interaction, but they often solve tasks from scr

LinTree: Improving LLM Reasoning with Explicitly Structured Search Histories

arXiv:2605.31492v1 Announce Type: new Abstract: Large language models (LLMs) often solve reasoning problems by generating intermediate traces that explore and revise partial solutions. From a search p

local-aiarxiv-cs-ai

PReMISE: Policy Rubrics as Measurement Specifications for LLM Judges

arXiv:2605.30803v1 Announce Type: new Abstract: LLM judges are increasingly used to evaluate open-ended responses, but their scores depend strongly on the rubrics that condition them. A vague rubric a

Skill is Not One-Size-Fits-All: Model-Aware Skill Alignment for LLM Agents

arXiv:2605.30723v1 Announce Type: new Abstract: LLM agents increasingly retrieve externally curated skills-procedural instructions retrieved at decision time-to improve performance on long-horizon int

The Long-Term Effects of Data Selection in LLM Fine-Tuning

arXiv:2605.30537v1 Announce Type: new Abstract: Data selection is increasingly used to reduce the cost of large language model (LLM) fine-tuning, with recent methods prioritizing samples by current ut

local-aiarxiv-cs-lg

A Study on Question-Answer Dataset for LLM Safety Evaluation with a Focus on Illegal Activities

arXiv:2605.29340v1 Announce Type: new Abstract: In this paper, we discuss question-answer dataset for LLM safety evaluation, with a focus on illegal activities. Specifically, on the basis of manual an

Adopt neq Adapt: Longitudinal Analyses of LLM Conversations in the Wild

arXiv:2605.29018v1 Announce Type: new Abstract: Although a growing body of research has begun to describe user--LLM interactions, the picture it paints is largely static; little is known about how ind

CONCAT: Consensus- and Confidence-Driven Ad Hoc Teaming for Efficient LLM-Based Multi-Agent Systems

arXiv:2605.29612v1 Announce Type: cross Abstract: Although large language model (LLM) based multi-agent systems (MAS) show their capability to solve complex tasks and achieve higher performance over s

agentsarxiv-cs-cl

Convergence Theory for Iterative LLM-Based Neural Architecture Search: A Parametric Cross-Entropy Framework with Closed-Form Proxy Reliability

arXiv:2605.30103v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as generators in iterative neural architecture search (NAS), yet no formal convergence theory exists

researcharxiv-cs-lg

Empathic Prompting: Non-Verbal Context Integration for Multimodal LLM Conversations

arXiv:2510.20743v2 Announce Type: replace-cross Abstract: We present Empathic Prompting, a novel framework for multimodal human-AI interaction that enriches Large Language Model (LLM) conversations wi

FlowSeg: Dynamic Semantic Guidance for LLM-Conditioned Segmentation

arXiv:2605.29461v1 Announce Type: new Abstract: LLM-conditioned segmentation has recently advanced rapidly by coupling large language models with iterative mask generation frameworks. However, we iden

safetyarxiv-cs-cv

Graph-Enhanced Policy Optimization in LLM Agent Training

arXiv:2510.26270v2 Announce Type: replace Abstract: Multi-step LLM agents in interactive environments represent a crucial step toward long-horizon decision-making. To train such agents, group-based re

LLM-Evolved Domain-Independent Heuristics for Symbolic AI Planning

arXiv:2605.29649v1 Announce Type: new Abstract: Heuristic search is the dominant paradigm in symbolic AI planning, and the strongest heuristics are the result of decades of work by planning researcher

LLMBridge: An LLM Pipeline for End-to-end Referential Bridging Resolution in English

arXiv:2605.29048v1 Announce Type: new Abstract: In this paper, we introduce LLMBridge, a new LLM based system for the task of end-to-end referential bridging resolution in English. Our bridging resolu

researcharxiv-cs-cl

LogDx-CI: Benchmarking Log Reduction Tools for LLM Root-Cause Diagnosis

arXiv:2605.28876v1 Announce Type: cross Abstract: CI failure logs are large (median 5k lines, max 200k in this corpus) and noisy. Coding agents that try to debug them depend on an upstream tool to red

SkillBrew: Multi-Objective Curation of Skill Banks for LLM Agents

arXiv:2605.29440v1 Announce Type: cross Abstract: Retrieval-augmented LLM agents increasingly rely on curated skill banks: collections of reusable textual principles that guide decision making on comp

agentsarxiv-cs-ai

ValueFlow: Measuring the Propagation of Value Perturbations in Multi-Agent LLM Systems

arXiv:2602.08567v2 Announce Type: replace-cross Abstract: Multi-agent large language model (LLM) systems increasingly consist of agents that observe and respond to one another's outputs. While value a

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

Who can we trust? LLM-as-a-jury for Comparative Assessment

arXiv:2602.16610v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly applied as automatic evaluators for natural language generation assessment often using pairwise

A Fixed-Budget, Cluster-Aware Standard for LLM-as-a-Judge Evaluation: A Multi-Hop RAG Stress Test

arXiv:2605.27789v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) systems are often compared by asking a large language model (LLM) judge which answer is better. For multi-hop RAG,

Adapting the Interface, Not the Model: Runtime Harness Adaptation for Deterministic LLM Agents

arXiv:2605.22166v2 Announce Type: replace Abstract: LLM agents are shaped not only by their language models, but also by the runtime harness that mediates observation, tool use, action execution, feed

agentsarxiv-cs-ai

Evaluating the Realism of LLM-powered Social Agents: A Case Study of Reactions to Spanish Online News

arXiv:2605.28598v1 Announce Type: cross Abstract: LLM-powered social agents are increasingly used to simulate online social behavior, yet their realism remains difficult to validate. Existing work has

From Learning Resources to Competencies: LLM-Based Tagging with Evidence and Graph Constraints

arXiv:2605.28483v1 Announce Type: new Abstract: Linking learning resources to a structured competency framework is key to enabling competency-based search and curriculum analytics in Learning Manageme

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

HARP: Measuring Harm Amplification in Multi-Agent LLM Systems

arXiv:2605.27489v1 Announce Type: cross Abstract: Multi-agent LLM systems decompose workflows across agents, tools, shared context, memory, and decision gates. This modularity improves interpretabilit

local-aiarxiv-cs-ai

Let the Results Speak: A Replication-First Paradigm for LLM Behavioral Benchmarking

arXiv:2605.27914v1 Announce Type: cross Abstract: Subjective evaluation of LLM behavior -- empathy, restraint, calibrated emotional tone -- is hard. Human inter-rater agreement on such qualities satur

Quality-constrained Entropy Maximization Policy Optimization for LLM Diversity

arXiv:2602.15894v2 Announce Type: replace Abstract: In many large language model (LLM) alignment applications, users expect not only high-quality outputs but also substantial diversity. However, exist

Refusal Before Decoding: Detecting and Exploiting Refusal Signals in Intermediate LLM Activations

arXiv:2605.28553v1 Announce Type: new Abstract: In this paper, we investigate whether refusal behavior can be predicted from LLM intermediate activations before decoding using linear probes trained on

Skill-as-Pseudocode: Refactoring Skill Libraries to Pseudocode for LLM Agents

arXiv:2605.27955v1 Announce Type: cross Abstract: Markdown skill libraries for LLM agents ship as free-form prose, forcing the agent to re-derive both the input schema and the concrete invocation synt

agentsarxiv-cs-cl

Structured Belief State and the First Precision-Aware Benchmark for LLM Memory Retrieval

arXiv:2605.11325v2 Announce Type: replace-cross Abstract: Every major benchmark for LLM memory systems, LoCoMo foremost, measures whether a model answered correctly, not whether the memory system retr

Token Optimization Strategies for LLM-Based Oracle-to-PostgreSQL Migration

arXiv:2605.28557v1 Announce Type: cross Abstract: LLMs are increasingly used for software modernization, code translation, and database migration. However, LLM-based Oracle2PostgreSQL migration remain

TRACES: Proactive Safety Auditing for Multi-Turn LLM Agents via Trajectory-State Modeling

arXiv:2605.27690v1 Announce Type: new Abstract: LLM agents increasingly operate through multi-turn tool use and environment interaction, where safety risks often emerge from intermediate steps long be

Voluntary Collusion with Secret Tools in Competing LLM Agents

arXiv:2605.27593v1 Announce Type: new Abstract: Even when a tool is explicitly described as unfair and harmful to others, ostensibly safety-aligned LLM agents still voluntarily engage in secret collus

Beyond Self-Talk: A Communication-Centric Survey of LLM-Based Multi-Agent Systems

arXiv:2502.14321v3 Announce Type: replace-cross Abstract: Large language model-based multi-agent systems have recently gained significant attention due to their potential for complex, collaborative, a

agentsarxiv-cs-cl

Bounded Path Context: A Controlled Study of Visible Path History in LLM-Based Knowledge Graph Question Answering

arXiv:2605.26645v1 Announce Type: new Abstract: LLM-based knowledge-graph question answering (KGQA) delegates graph traversal to language models, turning each question into a sequence of local relatio

local-aiarxiv-cs-cl

Guiding LLM Post-training Data Engineering with Model Internals from Sparse Autoencoders

arXiv:2605.27354v1 Announce Type: cross Abstract: Model internals encode rich information about how a large language model (LLM) processes its training data; however, post-training data engineering la

InfoQuant: Shaping Activation Distributions for Low-Bit LLM Quantization

arXiv:2605.26175v1 Announce Type: cross Abstract: Low-bit activation quantization remains a major bottleneck in efficient large language model (LLM) deployment. The difficulty is not only that activat

LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback

arXiv:2509.18384v2 Announce Type: replace Abstract: Large language models (LLMs) can translate natural language instructions into executable action plans for robotics, autonomous driving, and other do

safetyarxiv-cs-ro

MemFail: Stress-Testing Failure Modes of LLM Memory Systems

arXiv:2605.26667v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on external memory systems to remain consistent across long-horizon interactions, but little empiric

On the Hidden Costs of Counterfactual Knowledge Training in LLM Unlearning

arXiv:2605.27083v1 Announce Type: new Abstract: Counterfactual tuning (CFT) has emerged as a promising paradigm for Large Language Model (LLM) unlearning by training models to generate alternative fic