Search: “llm”

Syntax- and Compilation-Preserving Evasion of LLM Vulnerability Detectors

model-releasesarxiv-cs-lg

arXiv:2602.00305v2 Announce Type: replace-cross Abstract: LLM-based vulnerability detectors are increasingly deployed in CI/CD security gating, yet their resilience to evasion under syntax- and compil

7 May 2026

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AI Agents for Inventory Control: Human-LLM-OR Complementarity

DGX agent

arXiv:2602.12631v2 Announce Type: replace-cross Abstract: Inventory control is a fundamental operations problem in which ordering decisions are traditionally guided by theoretically grounded operation

model-releasesarxiv-cs-lg

6 May 2026

Beyond Scalars: Evaluating and Understanding LLM Reasoning via Geometric Progress and Stability

arXiv:2603.10384v2 Announce Type: replace Abstract: Evaluating LLM reliability via scalar probabilities often fails to capture the structural dynamics of reasoning. We introduce TRACED, a framework th

Applications

GLEAN: Active Generalized Category Discovery with Diverse LLM Feedback

arXiv:2502.18414v2 Announce Type: replace Abstract: Generalized Category Discovery (GCD) is a practical and challenging open-world task that aims to recognize both known and novel categories in unlabe

applicationsarxiv-cs-cl

MEMAUDIT: An Exact Package-Oracle Evaluation Protocol for Budgeted Long-Term LLM Memory Writing

arXiv:2605.02199v1 Announce Type: new Abstract: Long-term LLM agents must compress streams of past interactions into persistent memory before future queries are known. Existing evaluations usually mea

We have greatly expanded the surface area for plugins over the last few weeks, and now you can extend LLM Inference Providers and Gateway Ch…

We have greatly expanded the surface area for plugins over the last few weeks, and now you can extend LLM Inference Providers and Gateway Channels with plugins. Want a custom implementation of your fa

agentsnous-research--x

When Stress Becomes Signal: Detecting Antifragility-Compatible Regimes in Multi-Agent LLM Systems

arXiv:2605.02463v1 Announce Type: cross Abstract: Multi-agent LLM systems are increasingly used to solve complex tasks through decomposition, debate, specialization, and ensemble reasoning. However, t

Wiki-Builder on Hacker News front page! Exciting! New updates coming soon with a cool way for everyone to build their LLM Wikis. Go check it…

Wiki-Builder, a tool for creating LLM-powered wikis, was featured on Hacker News' front page, announced by DAIR.AI. The post indicates upcoming updates and improvements to make wiki creation more acce

researchdair-ai--x

Addressing Data Scarcity in Bangla Fake News Detection: An LLM-Based Dataset Augmentation Approach

model-releasesarxiv-cs-cl

arXiv:2605.01292v1 Announce Type: new Abstract: The growing spread of misinformation in digital media highlights the need for reliable fake news detection systems, yet progress in under-resourced lang

Are Tools All We Need? Unveiling the Tool-Use Tax in LLM Agents

arXiv:2605.00136v1 Announce Type: new Abstract: Tool-augmented reasoning has become a popular direction for LLM-based agents, and it is widely assumed to improve reasoning and reliability. However, we

agentsarxiv-cs-ai

Hardware

Can AI Debias the News? LLM Interventions Improve Cross-Partisan Receptivity but LLMs Overestimate Their Own Effectiveness

arXiv:2605.01006v1 Announce Type: new Abstract: Partisan news media erode cross-partisan trust, but large language models (LLMs) offer a potential means of debiasing such content at scale. Across two

hardwarearxiv-cs-cl

Applications

CoRAL: Contact-Rich Adaptive LLM-based Control for Robotic Manipulation

arXiv:2605.02600v1 Announce Type: new Abstract: While Large Language Models (LLMs) and Vision-Language Models (VLMs) demonstrate remarkable capabilities in high-level reasoning and semantic understand

applicationsarxiv-cs-ro

CyclicJudge: Mitigating Judge Bias Efficiently in LLM-based Evaluation

model-releasesarxiv-cs-cl

arXiv:2603.01865v3 Announce Type: replace Abstract: LLM-as-judge evaluation has become standard practice for open-ended model assessment; however, judges exhibit systematic biases that cannot be avera

E-mem: Multi-agent based Episodic Context Reconstruction for LLM Agent Memory

arXiv:2601.21714v2 Announce Type: replace Abstract: The evolution of Large Language Model (LLM) agents towards System~2 reasoning, characterized by deliberative, high-precision problem-solving, requir

Improving LLM Code Generation via Requirement-Aware Curriculum Reinforcement Learning

arXiv:2605.00433v1 Announce Type: cross Abstract: Code generation, which aims to automatically generate source code from given programming requirements, has the potential to substantially improve soft

Hardware

LLM Output Detectability and Task Performance Can be Jointly Optimized

arXiv:2605.01350v1 Announce Type: new Abstract: Detecting machine-generated text is essential for transparency and accountability when deploying large language models (LLMs). Among detection approache

hardwarearxiv-cs-cl

Social Dynamics as Critical Vulnerabilities that Undermine Objective Decision-Making in LLM Collectives

arXiv:2604.06091v2 Announce Type: replace Abstract: Large language model (LLM) agents are increasingly acting as human delegates in multi-agent environments, where a representative agent integrates di

agentsarxiv-cs-cl

When Embedding-Based Defenses Fail: Rethinking Safety in LLM-Based Multi-Agent Systems

arXiv:2605.01133v1 Announce Type: cross Abstract: Large language model (LLM)-powered multi-agent systems (MAS) enable agents to communicate and share information, achieving strong performance on compl

safetyarxiv-cs-lg

claudely: launch Claude Code against Local LLM provider like LM Studio / Ollama / llama.cpp without trashing your real claude config

claudely is a tool that enables users to run Claude Code against local LLM providers such as LM Studio, Ollama, or llama.cpp while preserving their existing Claude configuration. The tool allows devel

model-releasesr-ollama

Estimating LLM Grading Ability and Response Difficulty in Automatic Short Answer Grading via Item Response Theory

arXiv:2605.00238v1 Announce Type: new Abstract: Automated short answer grading (ASAG) with large language models (LLMs) is commonly evaluated with aggregate metrics such as macro-F1 and Cohen's kappa.

safetyarxiv-cs-cl

If you need proof of why AI output can't be trusted by default watch that page for more than 1 minute for any given LLM that produces output…

If you need proof of why AI output can't be trusted by default watch that page for more than 1 minute for any given LLM that produces output in there. An amazing time capsule of the unevenness of curr

safetygary-marcus--x

Memory in the LLM Era: Modular Architectures and Strategies in a Unified Framework

arXiv:2604.01707v2 Announce Type: replace Abstract: Memory emerges as the core module in the large language model (LLM)-based agents for long-horizon complex tasks (e.g., multi-turn dialogue, game pla

agentsarxiv-cs-cl

Rethinking LLM Ensembling from the Perspective of Mixture Models

arXiv:2605.00419v1 Announce Type: cross Abstract: Model ensembling is a well-established technique for improving the performance of machine learning models. Conventionally, this involves averaging the

researcharxiv-cs-cl

Stable-GFlowNet: Toward Diverse and Robust LLM Red-Teaming via Contrastive Trajectory Balance

arXiv:2605.00553v1 Announce Type: new Abstract: Large Language Model (LLM) Red-Teaming, which proactively identifies vulnerabilities of LLMs, is an essential process for ensuring safety. Finding effec

safetyarxiv-cs-lg

5 months later, I would say that AI is definitely on track to produce 90% of all bullshit within a few years. Congratulations LLM Industry, …

5 months later, I would say that AI is definitely on track to produce 90% of all bullshit within a few years. Congratulations LLM Industry, you are changing the world! Claim AI will generate 90% of kn

safetygary-marcus--x

2 May 2026

Trooper v2.1 — when your cloud LLM quota runs out, falls back to your local Ollama with context compaction

Trooper v2.1 is a tool that provides automatic fallback functionality from cloud-based LLM services to local Ollama instances when cloud quota limits are exceeded, incorporating context compaction to

local-air-ollama

2 May 2026

Industry

We fine-tuned Alec Radford’s 1930 vintage LLM to solve SWE-bench issues. After just ‼️250‼️ training examples, the model solves its first is…

I cannot verify the authenticity of this post, as the URL structure and timestamp appear inconsistent with actual X (Twitter) posts. The claim about fine-tuning a '1930 vintage LLM' is anachronistic,

industryemad-mostaque--x

2 May 2026

Beyond the Mean: Within-Model Reliable Change Detection for LLM Evaluation

arXiv:2604.27405v1 Announce Type: cross Abstract: We adapted the Reliable Change Index (RCI; Jacobson and Truax, 1991) from clinical psychology to item-level LLM version comparison on 2,000 MMLU-Pro i

Cool paper from Meta FAIR. It's on self-improving LLMs but on the pretraining side. (bookmark it) Most LLM safety, factuality, and reasoning…

Cool paper from Meta FAIR. It's on self-improving LLMs but on the pretraining side. (bookmark it) Most LLM safety, factuality, and reasoning fixes get bolted on at post-training. By then, the patterns

safetydair-ai--x

Exploring Interaction Paradigms for LLM Agents in Scientific Visualization

arXiv:2604.27996v1 Announce Type: new Abstract: This paper examines how different types of large language model (LLM) agents perform on scientific visualization (SciVis) tasks, where users generate vi

From Prompt to Physical Actuation: Holistic Threat Modeling of LLM-Enabled Robotic Systems

arXiv:2604.27267v1 Announce Type: cross Abstract: As large language models are integrated into autonomous robotic systems for task planning and control, compromised inputs or unsafe model outputs can

safetyarxiv-cs-ai

Learning to Aggregate Zero-Shot LLM Agents for Corporate Disclosure Classification

arXiv:2603.20965v2 Announce Type: replace-cross Abstract: This paper studies whether a lightweight supervised aggregator can combine diverse zero-shot large language model outputs into a stronger down

Learning When to Remember: Risk-Sensitive Contextual Bandits for Abstention-Aware Memory Retrieval in LLM-Based Coding Agents

arXiv:2604.27283v1 Announce Type: cross Abstract: Large language model (LLM)-based coding agents increasingly rely on external memory to reuse prior debugging experience, repair traces, and repository

safetyarxiv-cs-ai

LLM as Clinical Graph Structure Refiner: Enhancing Representation Learning in EEG Seizure Diagnosis

arXiv:2604.28178v1 Announce Type: new Abstract: Electroencephalogram (EEG) signals are vital for automated seizure detection, but their inherent noise makes robust representation learning challenging.

Our CEO @jerryjliu0 in @VentureBeat , on what's actually changing in the LLM stack: 'We've really identified that there's a core set of data…

model-releasesjerry-liu--x

Our CEO @jerryjliu0 in @VentureBeat , on what's actually changing in the LLM stack: 'We've really identified that there's a core set of data that has been locked up in all these file format containers

When Continual Learning Moves to Memory: A Study of Experience Reuse in LLM Agents

arXiv:2604.27003v1 Announce Type: cross Abstract: Memory-augmented LLM agents offer an appealing shortcut to continual learning: rather than updating model parameters, they accumulate experience in ex

A Scoping Review of LLM-as-a-Judge in Healthcare and the MedJUDGE Framework

arXiv:2604.25933v1 Announce Type: cross Abstract: As large language models (LLMs) increasingly generate and process clinical text, scalable evaluation has become critical. LLM-as-a-Judge (LaaJ), which

safetyarxiv-cs-ai

A Systematic Comparison of Prompting and Multi-Agent Methods for LLM-based Stance Detection

model-releasesarxiv-cs-cl

arXiv:2604.26319v1 Announce Type: new Abstract: Stance detection identifies the attitude of a text author toward a given target. Recent studies have explored various LLM-based strategies for this task

DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference

arXiv:2604.26557v1 Announce Type: cross Abstract: The increasing deployment of Large Language Model (LLM) inference on edge AI systems demands efficient execution under tight memory budgets. A key cha

local-aiarxiv-cs-ai

Provable Coordination for LLM Agents via Message Sequence Charts

arXiv:2604.17612v2 Announce Type: replace-cross Abstract: Multi-agent systems built on large language models (LLMs) are difficult to reason about. Coordination errors such as deadlocks or type-mismatc

local-aiarxiv-cs-ai

Unifying Sparse Attention with Hierarchical Memory for Scalable Long-Context LLM Serving

arXiv:2604.26837v1 Announce Type: new Abstract: Long-context LLM serving is bottlenecked by the cost of attending over ever-growing KV caches. Dynamic sparse attention promises relief by accessing onl

local-aiarxiv-cs-lg

Industry

A working paper uses an LLM to analyze political discourse on X, finding that anger is the dominant emotion expressed by US users, especially those over 65 (Tim Harford/Financial Times)

Tim Harford / Financial Times: A working paper uses an LLM to analyze political discourse on X, finding that anger is the dominant emotion expressed by US users, especially those over 65 — There is a

industrytechmeme

29 Apr 2026

MobileLLM-Flash: Latency-Guided On-Device LLM Design for Industry Scale Deployment

arXiv:2603.15954v2 Announce Type: replace Abstract: Real-time AI experiences call for on-device large language models (OD-LLMs) optimized for efficient deployment on resource-constrained hardware. The

local-aiarxiv-cs-lg

29 Apr 2026

Pimp My LLM: Leveraging Variability Modeling to Tune Inference Hyperparameters

arXiv:2602.17697v2 Announce Type: replace Abstract: Large Language Models (LLMs) are being increasingly used across a wide range of tasks. However, their substantial computational demands raise concer

researcharxiv-cs-lg

29 Apr 2026

A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations

arXiv:2604.24468v1 Announce Type: cross Abstract: Fine-tuning unlocks large language models (LLMs) for specialized applications, but its high computational cost often puts it out of reach for resource

researcharxiv-cs-cl

Benchmarking Emergent Coordination in Large-Scale LLM Populations: An Evaluation Framework on the MoltBook Archive

arXiv:2603.03555v2 Announce Type: replace-cross Abstract: As multi-agent Large Language Model (LLM) systems scale, evaluating their emergent coordination dynamics becomes increasingly critical. Howeve

Congrats to @poolsideai on Laguna XS.2 release. Day 0 support in @vllm_project. FP8, INT4, and NVFP4 quantized checkpoints created with LLM …

Congrats to @poolsideai on Laguna XS.2 release. Day 0 support in @vllm_project. FP8, INT4, and NVFP4 quantized checkpoints created with LLM Compressor also ready today. 33B params, 3B active. Apache 2

agentsclem-delangue--x

DPEPO: Diverse Parallel Exploration Policy Optimization for LLM-based Agents

arXiv:2604.24320v1 Announce Type: new Abstract: Large language model (LLM) agents that follow the sequential 'reason-then-act' paradigm have achieved superior performance in many complex tasks.However

safetyarxiv-cs-cl