Search: “llm”

Reformulate LLM Reinforcement Learning for Efficient Training under Black-box Discrepancy

model-releasesarxiv-cs-lg

arXiv:2606.08779v1 Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a pivotal post-training paradigm, yet it frequently suffers from unpredictable sub-optimum performance or eve

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Research

RetroReasoner: A Reasoning LLM for Strategic Retrosynthesis Prediction

DGX agent

arXiv:2603.12666v2 Announce Type: replace-cross Abstract: Retrosynthesis prediction aims to identify reactants that can synthesize a given product molecule. Although molecular large language models (L

researcharxiv-cs-ai

9 Jun 2026

Tutorials

Sample-Efficient LLM-Based Detection of Malicious Web Server Logs with Forensically Explainable Reasoning

arXiv:2606.08649v1 Announce Type: cross Abstract: Forensic analysis of web server logs demands both accurate detection and human-readable explanations that can satisfy legal requirements. We present C

tutorialsarxiv-cs-ai

SpectrumKV: Per-Token Mixed-Precision KV Cache Transfer for Prefill-Decode Disaggregated LLM Serving

model-releasesarxiv-cs-lg

arXiv:2606.08635v1 Announce Type: new Abstract: Prefill-decode (PD) disaggregation decouples prompt processing from token generation, but it also turns the key-value (KV) cache into a network payload.

Symbolic Reasoning Frameworks Modulate LLM Risk Aversion in Multi-Agent Strategic Settings

arXiv:2606.07552v1 Announce Type: cross Abstract: Large language models exhibit innate behavioral tendencies when deployed as strategic agents -- notably a risk-averse 'turtle' bias toward defensive p

Think Before You Act: Intention-Guided Reasoning for LLM-Based Location Prediction

arXiv:2606.08122v1 Announce Type: new Abstract: Predicting a user's next Point-of-Interest (POI) based on their historical check-in records is a fundamental task in location-based services. While rece

Your Self-Play Algorithm is Secretly an Adversarial Imitator: Understanding LLM Self-Play through the Lens of Imitation Learning

arXiv:2602.01357v2 Announce Type: replace Abstract: Self-play post-training methods has emerged as an effective approach for finetuning large language models and turn the weak language model into stro

safetyarxiv-cs-lg

A Comprehensive Anatomy of Human and DeepSeek-R1 LLM Mathematical Reasoning

arXiv:2606.07410v1 Announce Type: cross Abstract: The emergence of 'Aha moments' in large language models, particularly DeepSeek-R1-0120, has raised the question of whether these systems genuinely rea

Contrastive Training with LLM-generated Near-Misses for Robust Code-Switching Speech Recognition

arXiv:2606.06985v1 Announce Type: new Abstract: Code-switching (CS), the alternation between multiple languages within a single utterance, remains challenging for Automatic Speech Recognition (ASR). T

Tutorials

Database Normalization via Dual-LLM Self-Refinement

arXiv:2508.17693v2 Announce Type: replace-cross Abstract: Database normalization is crucial to preserving data integrity. However, it is time-consuming and error-prone, as it is typically performed ma

tutorialsarxiv-cs-ai

MAGE: All-[MASK] Block Already Knows Where to Look in Block Diffusion LLM

arXiv:2602.14209v2 Announce Type: replace-cross Abstract: Block diffusion LLMs are an emerging paradigm for parallel language generation, but their KV caching makes memory access the dominant bottlene

Agents

OpenSkill: Open-World Self-Evolution for LLM Agents

arXiv:2606.06741v1 Announce Type: new Abstract: Self-evolving agents requires adaptation after deployment, but existing approaches assume a usable learning loop, such as curated skills, successful tra

agentsarxiv-cs-ai

When to Think Deeply: Inhibitory Deliberation for LLM Reasoning

arXiv:2606.06745v1 Announce Type: new Abstract: Reasoning Large Language Models can improve problem-solving performance through deliberative inference, but invoking slow reasoning for every input is c

Applications

Binary Gaussian Copula Synthesis: an LLM-powered data augmentation framework for early dialysis prediction in chronic kidney disease

arXiv:2403.00965v2 Announce Type: replace-cross Abstract: Only a small fraction of patients with chronic kidney disease (CKD) progress to dialysis, creating severe class imbalance that limits the perf

applicationsarxiv-cs-ai

Breaking the Chain: A Causal Analysis of LLM Faithfulness to Intermediate Structures

arXiv:2603.16475v2 Announce Type: replace Abstract: In schema-guided reasoning (SGR) pipelines, LLMs produce explicit intermediate structures -- rubrics, checklists, or verification queries -- before

researcharxiv-cs-ai

Hardware

CuTeGen: An LLM-Based Agentic Framework for Generation and Optimization of High-Performance GPU Kernels using CuTe

arXiv:2604.01489v2 Announce Type: replace-cross Abstract: High-performance GPU kernels are critical to modern machine learning systems, yet developing them remains a manual, expert-driven process. Rec

hardwarearxiv-cs-ai

DPBench: Structural Determinants of Multi-Agent LLM Coordination Under Simultaneous Resource Contention

arXiv:2602.13255v2 Announce Type: replace Abstract: We present DPBench, a benchmark for evaluating coordination in multi-agent systems built from large language models. Existing benchmarks measure tas

From Reward-Hack Activations to Agentic Risk States: Context-Calibrated Mechanistic Monitoring in LLM Agents

arXiv:2606.06223v1 Announce Type: new Abstract: Language-model agents act through repeated cycles of observation, reasoning, and action selection, making safety monitoring depend on both internal mode

How Far Did They Go? The Persuasive Tactics of Covert LLM Agents in a Discontinued Field Experiment

arXiv:2606.05256v1 Announce Type: new Abstract: This study analyzes a publicly released dataset from a discontinued field experiment on Reddit's r/ChangeMyView. The intervention, conducted by unknown,

PerceptUI: LLM Agents as Human-Aligned Synthetic Users for UI/UX Evaluation

arXiv:2606.05697v1 Announce Type: new Abstract: User interface (UI) and user experience (UX) evaluation is central to product development, yet reliable feedback still relies on recruiting human partic

researcharxiv-cs-ai

Towards the Readability of LLM-Generated Codes through Multitask Representation Engineering

arXiv:2606.06214v1 Announce Type: cross Abstract: Correctness and readability are key measures of code quality, respectively ensuring functional fidelity and ease of comprehension. While most existing

researcharxiv-cs-ai

Trust, but Don't Verify: Epistemic Blind Spots in LLM Source Evaluation

arXiv:2606.05403v1 Announce Type: cross Abstract: Language models increasingly act as epistemic proxies, synthesizing evidence from multiple sources to inform decisions. Whether they evaluate the qual

When Tools Fail: Benchmarking Dynamic Replanning and Anomaly Recovery in LLM Agents

arXiv:2606.05806v1 Announce Type: new Abstract: Existing benchmarks evaluate Tool-Integrated Reasoning (TIR) in LLMs on idealized ''happy paths'', largely overlooking real-world tool failures. We intr

AURA: Intent-Directed Probing for Implicit-Need Surfacing in Situated LLM Agents

arXiv:2606.05557v1 Announce Type: new Abstract: A situated query like 'where is Lin Wei?' often encodes more than its literal content: the user may also want to know whether Lin Wei is free, in a good

Beyond tokens: a unified framework for latent communication in LLM-based multi-agent systems

arXiv:2606.05711v1 Announce Type: new Abstract: Multi-agent systems built on large language models (LLMs) have become a prevailing paradigm for tackling complex reasoning, planning, and tool-use tasks

Agents

CollabSim: A CSCW-Grounded Methodology for Investigating Collaborative Competence of LLM Agents through Controlled Multi-Agent Experiments

arXiv:2606.06399v1 Announce Type: new Abstract: Multi-agent systems (MAS) built on large language models have shown growing promise, with their effectiveness resting on agents' ability to coordinate t

agentsarxiv-cs-cl

Emotion-Aware Image Generation from Korean Diary Text via LLM-based Prompt Translation and LoRA Fine-Tuning

arXiv:2606.05816v1 Announce Type: new Abstract: T2I models cannot effectively capture sentiment from various types of text, including diaries, as they primarily focus on visual object-related patterns

researcharxiv-cs-cv

From Self to Other: Evaluating Demographic Perspective-Taking in LLM Hate Speech Annotation

arXiv:2606.06266v1 Announce Type: new Abstract: Hate speech detection is inherently subjective: people from different demographic groups perceive the same content very differently. Collecting enough a

Learning What to Forget: Improving LLM Unlearning via Learned Token-Level Importance

arXiv:2606.06320v1 Announce Type: cross Abstract: Machine unlearning aims to remove targeted knowledge from a trained model while preserving its general capabilities. For autoregressive language model

Measuring the sensitivity of LLM-based structured extraction to prompt, model, and schema choices in clinical discharge summaries

arXiv:2606.05970v1 Announce Type: new Abstract: Large language models are increasingly used for structured extraction from clinical free-text notes, but the sensitivity of their output to upstream con

Membrane: A Self-Evolving Contrastive Safety Memory for LLM Agent Defense

arXiv:2606.05743v1 Announce Type: cross Abstract: Despite advances in safety alignment, large language models remain vulnerable to continuously evolving jailbreaks. Existing fine-tuned safety classifi

Predictable Scaling Laws of Optimal Hyperparameters for LLM Continued Pre-training

arXiv:2606.05610v1 Announce Type: new Abstract: The efficacy of continued pre-training for Large Language Models (LLMs) hinges upon hyperparameter configurations, such as learning rate and batch size.

ReasoningFlow: Discourse Structures for Understanding LLM Reasoning Traces

arXiv:2606.05402v1 Announce Type: new Abstract: Large reasoning models (LRMs) produce reasoning traces with non-linear structures, such as backtracking and self-correction, that complicate the evaluat

RedditPersona: A Modular Framework for Community-Conditioned LLM Adaptation from Reddit

arXiv:2606.06027v1 Announce Type: cross Abstract: Community-conditioned language model adaptation requires choices about data collection, community definition, and evaluation that are currently made i

Agents

Retrospective Harness Optimization: Improving LLM Agents via Self-Preference over Trajectory Rollouts

arXiv:2606.05922v1 Announce Type: cross Abstract: AI agents rely on a harness of skills, tools, and workflows to solve complex problems. Continually improving this harness is essential for adapting to

agentsarxiv-cs-cl

When Evidence is Sparse: Weakly Supervised Early Failure Alerting in Dialogs and LLM-Agent Trajectories

arXiv:2606.05414v1 Announce Type: new Abstract: Early failure alerting requires deciding, while a dialog or agent trajectory is still unfolding, whether to flag it as likely to fail. This is challengi

Adaptive Information Control for Search-Augmented LLM Reasoning

arXiv:2602.01672v2 Announce Type: replace Abstract: Search-augmented reasoning agents interleave multi-step reasoning with external retrieval, but uncontrolled retrieval can introduce redundant eviden

Beyond Objective Equivalence: Constraint Injection for LLM-Based Optimization Modeling on Vehicle Routing Problems

arXiv:2606.04816v1 Announce Type: new Abstract: Large language models (LLMs) increasingly translate natural-language optimization problems into executable solver code. Yet for constraint-dense operati

Can I Take Another Dose? Evaluating LLM Decision-Making Under Temporal Uncertainty in OTC Dosing QA

arXiv:2606.04262v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for everyday health questions, including whether a user can safely take another dose of an over-the

Confidence Before Answering: A Paradigm Shift for Efficient LLM Uncertainty Estimation

arXiv:2603.05881v2 Announce Type: replace Abstract: Reliable deployment of large language models (LLMs) requires accurate uncertainty estimation. Existing methods are predominantly answer-first, produ

DLLG: Dynamic Logit-Level Gating of LLM Experts

arXiv:2606.04378v1 Announce Type: new Abstract: Leveraging multiple specialized LLMs can combine complementary strengths, but existing approaches trade adaptability for stability: routing commits prem

Entity Binding Failures in Speech LLM Reasoning: Diagnosis and Chain-of-Thought Intervention

arXiv:2606.04474v1 Announce Type: new Abstract: Speech Large Language Models (SLLMs) underperform their text counterparts on complex reasoning. We reveal that this modality gap is not a uniform cognit

GPT-5.5 dominates $1,500 LLM hacking test while Gemini refuses to even try

A security researcher spent 1,500 running 13+ AI models against a deliberately vulnerable app, with GPT-5.5 achieving a 70% solve rate while Gemini refused to engage almost entirely. The test app cont

model-releasesr-chatgpt

Hardware

LLM Compression with Jointly Optimizing Architectural and Quantization choices

arXiv:2606.04063v1 Announce Type: cross Abstract: Deploying large language models (LLMs) is challenging due to their significant memory and computational requirements. While some methods address this

hardwarearxiv-cs-ai

Need to Know: Contextual-Integrity-Grounded Query Rewriting for Privacy-Conscious LLM Delegation

arXiv:2606.04067v1 Announce Type: cross Abstract: As LLMs become increasingly woven into everyday workflows, user queries sent to cloud hosted LLMs routinely mix task-essential content with task non-e

OckBench: Measuring the Efficiency of LLM Reasoning

arXiv:2511.05722v3 Announce Type: replace-cross Abstract: Large language models (LLMs) such as GPT-5 and Gemini 3 have pushed the frontier of automated reasoning and code generation. Yet current bench

Rethinking Continual Experience Internalization for Self-Evolving LLM Agents

arXiv:2606.04703v1 Announce Type: new Abstract: Experience internalization converts contextual experience from past interactions into reusable parametric capability, offering a promising path toward c

TamperBench: Systematically Stress-Testing LLM Safety Under Fine-Tuning and Tampering

arXiv:2602.06911v2 Announce Type: replace-cross Abstract: As increasingly capable open-weight large language models (LLMs) are deployed, improving their tamper resistance against unsafe modifications,