Search: “llm”

NetKV: Network-Aware Decode Instance Selection for Disaggregated LLM Inference

arXiv:2606.03910v1 Announce Type: cross Abstract: Disaggregated LLM inference forces the KV cache to traverse the datacenter network before decoding begins, so transfer time enters directly into the T

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Agents

New research from Google. Just shows the impressive results you can get from custom agent harnesses. LEAP wraps a general-purpose LLM in an …

DGX agent

New research from Google. Just shows the impressive results you can get from custom agent harnesses. LEAP wraps a general-purpose LLM in an agentic scaffold that grounds every step in the Lean compile

agentsdair-ai--x

3 Jun 2026

Qift: Shift-Friendly No-Zero W2 Post-Training Quantization for Rotated W2A4/KV4 LLM Inference

arXiv:2606.02823v1 Announce Type: new Abstract: Two-bit weight quantization is attractive for memory-efficient LLM inference, but the standard W2 level set {-2,-1,0,+1} often collapses under aggressiv

Say goodbye to month-end surprise invoices. LangSmith LLM Gateway lets you see your spend. Roll up your costs in real time by workspace, use…

LangSmith's LLM Gateway provides real-time cost visibility and spend tracking capabilities, allowing users to monitor their language model usage costs aggregated by workspace. This feature helps elimi

agentsharrison-chase--x

The Ringelmann Effect in Multi-Agent LLM Systems: A Scaling Law for Effective Team Size

arXiv:2606.02646v1 Announce Type: cross Abstract: Inference-time multi-agent LLM scaling lacks a shared unit: counting nominal agents conflates cost with independent evidence. We derive a two-paramete

A Finite-Calibration Regime Map for LLM Judge Panels

arXiv:2606.01034v1 Announce Type: new Abstract: We study when LLM judge panels should be calibrated with low-dimensional stackers versus joint output tables under finite human-label budgets. Low-dimen

AlphaToken: Decoupling Adaptation and Stability for Path-Aware Response Token Valuation in LLM Post-Training

arXiv:2606.01635v1 Announce Type: cross Abstract: Token selection is pivotal for effective LLM post-training. However, existing methods mostly rely on local heuristics and rarely formulate token selec

Better with Experience: Self-Evolving LLM Agents for Evidence-Grounded Health Community Notes

arXiv:2606.02215v1 Announce Type: new Abstract: Large Language Model (LLM)-augmented Community Notes offer a scalable path for timely, evidence-grounded correction of health misinformation on social p

Can we design legal agent verifiers that are up to 1,000x cheaper? Verifiers are LLM judges that check an agent’s work against rubric criter…

model-releasesharrison-chase--x

Can we design legal agent verifiers that are up to 1,000x cheaper? Verifiers are LLM judges that check an agent’s work against rubric criteria: they're used both in agent benchmarking and as reward si

Doing What They Say, Not What They Reason: Locating the Faithfulness Gap in LLM Agents

arXiv:2606.00476v1 Announce Type: new Abstract: Do LLM agents act on the reasoning they state? This question of process fidelity is central to using LLMs in social simulation, yet it is hard to measur

researcharxiv-cs-ai

Enhancing LLM Metacognition via Cognitive Pairwise Training

arXiv:2606.00869v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become central to LLM reasoning, but its outcome-level rewards can make models more willing to

Evolutionary Discovery of Bivariate Bicycle Codes with LLM-Guided Search

arXiv:2606.02418v1 Announce Type: cross Abstract: Quantum LDPC code discovery requires searching large algebraic design spaces while reliably certifying the parameters and equivalence classes of any c

How to Correctly Report LLM-as-a-Judge Evaluations

arXiv:2511.21140v4 Announce Type: replace-cross Abstract: Large language models (LLMs) are widely used as scalable evaluators of model responses in lieu of human annotators. However, imperfect sensiti

LLM agents patch security bugs, pass all tests, but still leave the vulnerability open [R]

researchr-machinelearning

Research demonstrates that LLM-based agents can generate functionally correct patches that pass all tests while still containing security vulnerabilities, challenging the assumption that test-passing

LLM Consortium for Software Design Refinement: A Controlled Experiment on Multi-Agent Collaboration Topologies

arXiv:2606.01490v1 Announce Type: cross Abstract: We present a controlled experiment evaluating 12 multi-agent LLM collaboration topologies for software architecture design. Using a 2imes2imes2 factor

LRAgent: Efficient KV Cache Sharing for Multi-LoRA LLM Agents

arXiv:2602.01053v2 Announce Type: replace Abstract: Role specialization in multi-LLM agent systems is often realized via multi-LoRA, where agents share a pretrained backbone and differ only by lightwe

agentsarxiv-cs-lg

Make a Video Call with LLM: A Measurement Campaign over Six Mainstream Apps

arXiv:2510.00481v2 Announce Type: replace-cross Abstract: In 2025, Large Language Model (LLM) services have launched a new feature -- AI video chat -- allowing users to interact with AI agents via rea

Not All Flips Are Conformity: Decomposing Stance Convergence in Multi-Agent LLM Debate

arXiv:2606.00820v1 Announce Type: new Abstract: Multi-agent debate (MAD) is a promising strategy for improving LLM reasoning, but when agents converge on a shared answer, it is unclear whether that co

agentsarxiv-cs-cl

On Information Self-Locking in Reinforcement Learning for Active Reasoning of LLM agents

arXiv:2603.12109v2 Announce Type: replace Abstract: Reinforcement learning (RL) has become a de facto paradigm for building LLM-based agents that act, interact, and reason over extended task horizons.

agentsarxiv-cs-ai

Plausibility Is Not Prediction: Contrastive Evidence for LLM-Based Cellular Perturbation Reasoning

arXiv:2606.01042v1 Announce Type: cross Abstract: Perturbation experiments are central to understanding cellular mechanisms, but remain costly and sparse, motivating prediction of gene expression resp

researcharxiv-cs-ai

ProjQ: Project-and-Quantize for Adapter-Aware LLM Compression

arXiv:2606.00494v1 Announce Type: new Abstract: Post-Training Quantization (PTQ) and Low-Rank Adaptation (LoRA) constitute the standard pipeline for efficient Large Language Model (LLM) deployment. Ho

ReasonBENCH: Benchmarking the (In)Stability of LLM Reasoning

arXiv:2512.07795v2 Announce Type: replace Abstract: Benchmark scores for LLM reasoning systems are reported as single numbers, yet the same model, strategy, and task can produce meaningfully different

Safety

SafeMCP: Proactive Power Regulation for LLM Agent Defense via Environment-Grounded Look-Ahead Reasoning

arXiv:2606.01991v1 Announce Type: new Abstract: As Large Language Model (LLM) agents increasingly leverage the Model Context Protocol (MCP) to operate in complex environments, the expansion of their a

safetyarxiv-cs-ai

// Scaling Behavior of Single LLM-Driven Multi-Agent Systems // Does adding more agents actually make a multi-agent system better? It's poss…

// Scaling Behavior of Single LLM-Driven Multi-Agent Systems // Does adding more agents actually make a multi-agent system better? It's possible that collective intelligence emerges from interaction d

agentsdair-ai--x

Safety

SIRI: Self-Internalizing Reinforcement Learning with Intrinsic Skills for LLM Agent Training

arXiv:2606.02355v1 Announce Type: new Abstract: Long-horizon LLM agents can benefit from reusable skills, yet existing skill-based methods often rely on external skill generators during training or pe

safetyarxiv-cs-ai

SkillAdaptor: Self-Adapting Skills for LLM Agents from Trajectories

arXiv:2606.01311v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly rely on reusable external skills to solve long-horizon interactive tasks. Existing training-free skill

🌞This is big Local AI news! A new open-source Computer-Use LLM has just launched. Holo 3.1 is H Company’s (🇫🇷) new local computer-use age…

model-releasesclem-delangue--x

🌞This is big Local AI news! A new open-source Computer-Use LLM has just launched. Holo 3.1 is H Company’s (🇫🇷) new local computer-use agent model that beats Qwen3.5-397B, Kimi-K2.5, and Sonnet 4.6! Si

Hardware

Threshold-Based Exclusive Batching for LLM Inference

arXiv:2606.00516v1 Announce Type: new Abstract: Mixed batching (MB)--interleaving prefill and decode in a single batch--has become the standard scheduling strategy for large language model (LLM) infer

hardwarearxiv-cs-ai

Tutorials

ToMAP: Training Opponent-Aware LLM Persuaders with Theory of Mind

arXiv:2505.22961v3 Announce Type: replace Abstract: Large language models (LLMs) have shown promising potential in persuasion, but existing works on training LLM persuaders are still preliminary. Nota

tutorialsarxiv-cs-cl

Unified Context Evolution for LLM Agents

arXiv:2606.02304v1 Announce Type: new Abstract: LLM-based agents can solve multi-step interactive tasks by combining reasoning with environment feedback, yet each episode starts from the same fixed co

agentsarxiv-cs-cl

WUSH: Near-Optimal Adaptive Transforms for LLM Quantization

arXiv:2512.00956v3 Announce Type: replace Abstract: Quantizing LLM weights and activations is a standard approach for efficient deployment, but a few extreme outliers can stretch the dynamic range and

Auditing LLM Benchmarks with Item Response Theory

arXiv:2605.30504v1 Announce Type: new Abstract: LLM benchmark labels are frozen at release and silently propagated into downstream benchmarks, errors and all. We introduce an Item Response Theory-base

Counterfactual Graph for Multi-Agent LLM Calibration

arXiv:2605.30653v1 Announce Type: new Abstract: Multi-agent LLM systems often treat agreement as evidence: when many agents in a panel give the same answer, that answer is assumed to be more reliable.

agentsarxiv-cs-cl

Generating and Refining Dynamic Evaluation Rubrics for LLM-as-a-Judge

arXiv:2605.30568v1 Announce Type: new Abstract: LLM-as-a-Judge is a scalable alternative to human evaluation, yet existing rubric-based methods rely on human-annotated data such as reference answers o

researcharxiv-cs-cl

Harness Updating Is Not Harness Benefit: Disentangling Evolution Capabilities in Self-Evolving LLM Agents

arXiv:2605.30621v1 Announce Type: new Abstract: LLM agents are increasingly deployed as systems built around editable external harnesses, including prompts, skills, memories and tools, that shape task

LLM Bias Evaluation: Gender, Racial, and Age Disparities in Occupational and Crime Scenarios

arXiv:2409.14583v4 Announce Type: replace Abstract: LLM bias evaluation is critical as large language models (LLMs) increasingly influence high-stakes decisions. This paper provides a comprehensive as

Memory-Bound but Not Bandwidth-Limited: The Physical AI Inference Gap in Batch-1 LLM Decode

arXiv:2605.30571v1 Announce Type: cross Abstract: Physical AI systems, including robots, autonomous vehicles, embodied agents and edge copilots, often run a different inference workload from cloud LLM

Applications

Neither Replacement nor Panacea: Comparing LLM-Based Conversational and Graphical Decision Support in Industrial Tasks

arXiv:2605.31287v1 Announce Type: cross Abstract: Managers in manufacturing settings rely on digital interfaces to interpret operational data for decision-making, but growing data volume and complexit

applicationsarxiv-cs-ai

slowly we're all realizing that tools should be called from code, not from within the llm api

slowly we're all realizing that tools should be called from code, not from within the llm api Introducing Search as Code, our new search architecture for AI agents. It writes Python that calls our sea

agentsyohei-nakajima--x

The Architecture of Errors: From Universal Impossibility to Patch-Local LLM Reliability

arXiv:2605.30628v1 Announce Type: cross Abstract: Universal LLM reliability is not a finite-library problem: across all possible tasks, tools, schemas, knowledge sources, and evaluator expectations, n

These are some of my LLM assisted contributions from the past month. Nothing amazing, but I'm slowly getting better at it. Atm, using Qwen3.…

local-aigeorgi-gerganov--x

These are some of my LLM assisted contributions from the past month. Nothing amazing, but I'm slowly getting better at it. Atm, using Qwen3.6 27B exclusively. For hardware - switching between M2 Ultra

31 May 2026

Tutorials

I did a talk on LLM Wikis and HTML artifacts recently, if you are curious to learn more on the topic: https://academy.dair.ai/events/cmovobp…

I did a talk on LLM Wikis and HTML artifacts recently, if you are curious to learn more on the topic: https://academy.dair.ai/events/cmovobp97000904l5h0n9a2yz Doing a second session and a few releases

tutorialsdair-ai--x

30 May 2026

Safety

Agora: Toward Autonomous Bug Detection in Production-Level Consensus Protocols with LLM Agents

arXiv:2605.29910v1 Announce Type: cross Abstract: Consensus protocols form the backbone of distributed systems and blockchains, where implementation bugs can cause data corruption and financial losses

safetyarxiv-cs-ai

ComfyUI just added @OpenRouter support. Instead of being locked into a single LLM, you can now access 20+ models directly inside Comfy. More…

ComfyUI just added @OpenRouter support. Instead of being locked into a single LLM, you can now access 20+ models directly inside Comfy. More flexibility, less friction, same workflow. Links to the wor

local-aicomfyui--x

Applications

Double-Edged Sword or Sharp Tool? Designing and Evaluating Triadic LLM-Teacher Collaboration for K-12 Writing at Scale

arXiv:2605.30200v1 Announce Type: new Abstract: The double-edged sword of integrating Large Language Models (LLMs) requires an effective triadic collaboration mechanism among LLMs, teachers and studen

applicationsarxiv-cs-ai

EVADE: LLM-Based Explanation Generation and Validation for Error Detection in NLI

arXiv:2511.08949v2 Announce Type: replace Abstract: High-quality datasets are critical for training and evaluating reliable NLP models. In tasks like natural language inference (NLI), human label vari

researcharxiv-cs-cl

Evolutionary Dynamics of Cooperation in Next-Generation LLM Agent Systems: A Cross-Provider Empirical Extension

arXiv:2605.29874v1 Announce Type: cross Abstract: Do next-generation LLM agents inherit the cooperative biases documented in their predecessors, or does scale and provider diversity reshape equilibriu

Evolve as a Team: Collaborative Self-Evolution for LLM-based Multi-Agent Systems

arXiv:2605.29790v1 Announce Type: cross Abstract: LLM-based multi-agent systems (MAS) have emerged as an effective paradigm for complex and long-horizon tasks. However, in real-world tasks, MAS often

agentsarxiv-cs-ai