Search: “llm”

Mitigating Provenance-Role Collapse in Long-Term Agents via Typed Memory Representation

arXiv:2605.25869v1 Announce Type: new Abstract: Long-term memory is essential for persistent LLM agents, yet prevailing architectures store historical interactions as unstructured, flat text. This unc

researcharxiv-cs-cl

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Agents

More Skills, Worse Agents? Skill Shadowing Degrades Performance When Expanding Skill Libraries

DGX agent

arXiv:2605.24050v1 Announce Type: cross Abstract: Skill libraries allow LLM agents to load task-specific instructions on demand, letting non-expert users solve domain-specific tasks through natural la

agentsarxiv-cs-ai

26 May 2026

Multi-Agent Coordination Adaptation via Structure-Guided Orchestration

arXiv:2605.25746v1 Announce Type: cross Abstract: As large language model (LLM)-based multi-agent systems scale to handle increasingly complex tasks, balancing structural stability and dynamic adaptab

safetyarxiv-cs-ai

Multi-Agent Systems are Mixtures of Experts: Who Becomes an Influencer?

arXiv:2605.25929v1 Announce Type: cross Abstract: The effectiveness of multi-agent LLM deliberation depends not only on the agents' individual predictions, but also on how they communicate and collabo

safetyarxiv-cs-lg

MVR-cache: Optimizing Semantic Caching via Multi-Vector Retrieval and Learned Prompt Segmentation

arXiv:2605.24914v1 Announce Type: cross Abstract: To reduce LLM costs and latency, semantic caching systems must accurately identify when a new prompt matches a cached one. Current methods often rely

Optimizing Token Choice for Code Watermarking: An RL Approach

arXiv:2508.11925v3 Announce Type: replace-cross Abstract: Protecting intellectual property on LLM-generated code necessitates effective watermarking systems that can operate within code's highly struc

safetyarxiv-cs-cl

Prism: Spectral-Aware Block-Sparse Attention

arXiv:2602.08426v2 Announce Type: replace-cross Abstract: Block-sparse attention is promising for accelerating long-context LLM pre-filling, yet identifying relevant blocks efficiently remains a bottl

RL with Learnable Textual Feedback: A Bilevel Approach

model-releasesarxiv-cs-lg

arXiv:2605.24547v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards can improve LLM reasoning, but learning remains sample-inefficient when terminal rewards are sparse. This

Agents

Security of OpenClaw Agents: Fundamentals, Attacks, and Countermeasures

arXiv:2605.25435v1 Announce Type: new Abstract: The rapid evolution of large language model (LLM)-driven autonomous agents has given rise to OpenClaw, a new class of open-source agent frameworks that

agentsarxiv-cs-ai

SkillEvolBench: Benchmarking the Evolution from Episodic Experience to Procedural Skills

arXiv:2605.24117v1 Announce Type: new Abstract: Large language model (LLM) agents accumulate rich episodic trajectories while solving real-world tasks, but it remains unclear whether such experience c

The Age of Curiosity Meets the Age of AI: Benchmarking Child Safety in Large Language Models

arXiv:2605.25510v1 Announce Type: new Abstract: Children increasingly have access to Large Language Models (LLMs), which may expose them to responses that are developmentally inappropriate or require

Voting with the Graph: Stable RLAIF via Topological Consistency Maximization

arXiv:2510.15514v3 Announce Type: replace Abstract: Reinforcement Learning from AI Feedback (RLAIF) relies on LLM judges as preference measurement instruments, yet these instruments are fundamentally

When Does Synthetic Patent Data Help? Volume-Fidelity Trade-offs in Low-Resource Multi-Label Classification

arXiv:2605.24296v1 Announce Type: new Abstract: We study when LLM-generated synthetic data helps low-resource multi-label patent classification, separating true synthetic value from the confound that

When Search Becomes Memory: Turning Robot Design Trials into Transferable Skills

arXiv:2605.25832v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as proposal generators for evolutionary robot design, yet most loops remain memoryless: simulator r

WhenLoss: Diagnosing Write and Retrieval Bottlenecks in Long-Context Memory Systems

arXiv:2605.24579v1 Announce Type: new Abstract: Long-context memory systems often fail under fixed budgets, but end-to-end evaluation does not reveal whether evidence was discarded during compression

XRPO: Pushing the limits of GRPO with Targeted Exploration and Exploitation

arXiv:2510.06672v3 Announce Type: replace Abstract: Reinforcement learning algorithms such as GRPO have driven recent advances in large language model (LLM) reasoning. While scaling the number of roll

safetyarxiv-cs-lg

Adaptive Mass-Segmented KV Compression for Long-Context Reasoning

arXiv:2605.23200v1 Announce Type: cross Abstract: The linear growth of the Key-Value (KV) cache is a critical bottleneck in long-form LLM inference. Existing KV compression methods mitigate this by ev

b9311

local-aillama-cpp-releases

b9311 is a release build version of llama.cpp, an open-source C/C++ framework for large language model inference. The project enables LLM inference in C/C++ , offering optimized performance across var

b9315

local-aillama-cpp-releases

Build b9315 is a release version of llama.cpp, a C/C++ implementation for LLM inference. As an intermediate build in the llama.cpp development sequence, b9315 likely includes bug fixes, performance im

CoSPlay: Cooperative Self-Play at Test-Time with Self-Generated Code and Unit Test

arXiv:2605.23491v1 Announce Type: cross Abstract: Recently, Reinforcement Learning with Verifiable Rewards (RLVR) and Test-Time Scaling (TTS) have advanced LLM code generation through executable verif

Design and Report Benchmarks for Knowledge Work

arXiv:2605.23262v1 Announce Type: new Abstract: The development of LLM agents has led to a growing body of work on knowledge-work AI, including coding, research, and healthcare. However, current knowl

Disentangling Interaction and Bias Effects in Opinion Dynamics of Large Language Models

arXiv:2509.06858v2 Announce Type: replace-cross Abstract: Large Language Models are increasingly used to simulate human opinion dynamics, yet the effect of genuine interaction is often obscured by sys

safetyarxiv-cs-ai

EDGE-OPD: Internalizing Privileged Context with Evidence Guided On-Policy Distillation

arXiv:2605.23493v1 Announce Type: new Abstract: On-Policy Distillation (OPD) has gained wide attraction as an LLM post-training paradigm due to its effectiveness in improving capabilities without intr

safetyarxiv-cs-ai

Agents

Latent Cache Flow: Model-to-Model Communication Without Text

arXiv:2605.22863v1 Announce Type: new Abstract: LLM agents today communicate via text, which incurs considerable latency and information loss due to the need to autoregressively decode the sharer mode

agentsarxiv-cs-lg

MadEvolve: Evolutionary Optimization of Trading Systems with Large Language Models

arXiv:2605.23007v1 Announce Type: cross Abstract: We explore the application of LLM-driven algorithm optimization to several common tasks in quantitative finance. MadEvolve, a general-purpose algorith

ObjectCache: Layerwise Object-Storage Retrieval for KV Cache Reuse

arXiv:2605.22850v1 Announce Type: cross Abstract: Prefix KV caching has become a key mechanism in LLM serving: it reduces time to first token (TTFT) by avoiding redundant computation across requests t

local-aiarxiv-cs-ai

OnePred: Next-Query Prediction via Recursive Intent Memory in Multi-Turn Conversations

arXiv:2605.23668v1 Announce Type: cross Abstract: Although large language model (LLM) conversational systems process millions of multi-turn dialogues daily, they remain fundamentally reactive: they re

R^3L: Reflect-then-Retry Reinforcement Learning with Language-Guided Exploration, Pivotal Credit, and Positive Amplification

arXiv:2601.03715v2 Announce Type: replace-cross Abstract: Reinforcement learning drives recent advances in LLM reasoning and agentic capabilities, yet current approaches struggle with both exploration

What Training Data Teaches RL Memory Agents: An Empirical Study of Curriculum Effects in Memory-Augmented QA

arXiv:2605.23067v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as a viable recipe for training LLM agents to reason over external memory banks in multi-session dialogue. Exist

AutoBaxBuilder: Bootstrapping Code Security Benchmarking

model-releasesarxiv-cs-lg

arXiv:2512.21132v2 Announce Type: replace-cross Abstract: As large language models (LLMs) see wide adoption in software engineering, the reliable assessment of the correctness and security of LLM-gene

Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals

arXiv:2605.22703v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a central paradigm for scaling LLM reasoning, yet its optimization often suffers fr

local-aiarxiv-cs-lg

Decision Potential Surface: A Theoretical and Practical Approximation of Large Language Model Decision Boundary

arXiv:2510.03271v2 Announce Type: replace Abstract: Decision boundary, the subspace of inputs where a machine learning model assigns equal classification probabilities to two classes, is pivotal in re

F-TIS: Harnessing Diverse Models in Collaborative GRPO

arXiv:2605.22537v1 Announce Type: new Abstract: Reinforcement learning methods such as GRPO have seen great popularity in LLM post-training. In GRPO, models produce completions to a set of prompts, wh

local-aiarxiv-cs-lg

Harnesses for Inference-Time Alignment over Execution Trajectories

arXiv:2605.21516v1 Announce Type: new Abstract: Harness engineering has emerged as an important inference-time technique for large language model (LLM) agents, aiming to improve long-term performance

safetyarxiv-cs-lg

Provable Joint Decontamination for Benchmarking Multiple Large Language Models

model-releasesarxiv-cs-lg

arXiv:2605.21543v1 Announce Type: new Abstract: Benchmark data contamination has become a central challenge in LLM evaluation: when evaluation examples appear in the training data of one or more audit

SCI-Defense: Defending Manipulation Attacks from Generative Engine Optimization

arXiv:2605.21948v1 Announce Type: new Abstract: LLM-based ranking systems are vulnerable to Generative Engine Optimization (GEO) attacks, where adversaries inject semantic signals into product descrip

this is even easier

model-releasesyohei-nakajima--x

this is even easier oh even better, since you can test the runtime without an LLM, you can post this into Claude: “look up http://activegraph.ai, install and run a small experiment I would like levera

Total amount raised: 190 billion Total spending pledged: 600 billion Total profits (cumulative): $0

Total amount raised: 190 billion Total spending pledged: 600 billion Total profits (cumulative): 0 @CarinaN818 @GaryMarcus Because you can raise 190 billion in funding by releasing an LLM chatbot that

safetygary-marcus--x

VRPRM: Process Reward Modeling via Visual Reasoning

arXiv:2508.03556v3 Announce Type: replace Abstract: Process Reward Model (PRM) is widely used in the post-training of Large Language Model (LLM) because it can perform fine-grained evaluation of the r

A Comparative Study of Language Models for Khmer Retrieval-Augmented Question Answering

arXiv:2605.22099v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has emerged as a promising paradigm for grounding large language model (LLM) outputs in retrieved evidence, thereby

A realistic fighter vs a fully anime-styled character, generated with Seedance 2.0 using detailed prompting and reference images. This workf…

A realistic fighter vs a fully anime-styled character, generated with Seedance 2.0 using detailed prompting and reference images. This workflow combines Seedance with an LLM-powered prompting setup de

local-aicomfyui--x

Code Researcher: Deep Research Agent for Large Systems Code and Commit History

arXiv:2506.11060v2 Announce Type: replace-cross Abstract: Large Language Model (LLM)-based coding agents have shown promising results on coding benchmarks, but their effectiveness on systems code rema

Declarative Data Services: Structured Agentic Discovery for Composing Data Systems

arXiv:2605.20690v1 Announce Type: new Abstract: Agentic discovery has shown that LLM-driven search can find novel algorithms, designs, and code under benchmark conditions. Translating the paradigm to

DeferMem: Query-Time Evidence Distillation via Reinforcement Learning for Long-Term Memory QA

arXiv:2605.22411v1 Announce Type: new Abstract: Large language model (LLM) agents still struggle with long-term memory question answering, where answer-supporting evidence is often scattered across lo

researcharxiv-cs-cl

Efficient Agentic Reasoning Through Self-Regulated Simulative Planning

arXiv:2605.22138v1 Announce Type: cross Abstract: How should an agent decide when and how to plan? A dominant approach builds agents as reactive policies with adaptive computation (e.g., chain-of-thou

Evaluation of Chunking Strategies for Effective Text Embedding in Low-Resource Language on Agricultural Documents

arXiv:2605.22203v1 Announce Type: new Abstract: In this study, we compare the performance of four text chunking approaches: Recursive, Khmer-Aware, Sentence-Based, and LLM-Based within a Retrieval-Aug

researcharxiv-cs-cl

Hypergraph as Language

arXiv:2605.21858v1 Announce Type: new Abstract: Large language models (LLMs) have recently shown strong potential in modeling relational structures. However, existing approaches remain fundamentally g

local-aiarxiv-cs-cl

Intelligence per Watt: Measuring Intelligence Efficiency of Local AI

arXiv:2511.07885v4 Announce Type: replace-cross Abstract: Large language model (LLM) queries are predominantly processed by frontier models in centralized cloud infrastructure. Demand growth strains t

local-aiarxiv-cs-cl