Search: “llm”

RolloutPipe: Overlapping Pipelined Rollout and Training in Disaggregated On-Policy LLM Reinforcement Learning

arXiv:2606.26997v1 Announce Type: cross Abstract: Large language model (LLM) post-training for reasoning increasingly relies on reinforcement learning with verifiable rewards (RLVR), where models lear

safetyarxiv-cs-lg

26 Jun 2026

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SharQ: Bridging Activation Sparsity and FP4 Quantization for LLM Inference

DGX agent

arXiv:2606.26587v1 Announce Type: cross Abstract: Low-bit floating-point formats and semi-structured sparsity are increasingly supported by modern accelerators, yet combining them for LLM activation c

model-releasesarxiv-cs-ai

26 Jun 2026

A Probabilistic Framework for LLM-Based Model Discovery

arXiv:2602.18266v2 Announce Type: replace Abstract: Automated methods for discovering mechanistic simulator models from observational data offer a promising path toward accelerating scientific progres

agentsarxiv-cs-lg

Local Ai

BiPACE: Bisimulation-Guided Policy Optimization with Action Counterfactual Estimation for LLM Agents

arXiv:2606.25556v1 Announce Type: new Abstract: Stepwise group-based RL is an attractive way to train long-horizon LLM agents without a learned critic: it reuses multiple sampled rollouts to estimate

local-aiarxiv-cs-cl

Geometry-Aware Online Scheduling for LLM Serving: From Theoretical Bound to System Practice

arXiv:2606.22327v2 Announce Type: replace Abstract: The explosive demand for interactive Large Language Model serving has highlighted the management of the Key-Value cache's dynamic memory footprint a

Semantic Consistency Policy Optimization for Reinforcement Learning of LLM Agents

arXiv:2606.25852v1 Announce Type: new Abstract: Group-based reinforcement learning effectively post-trains LLM agents for long-horizon, sparse-reward tasks by deriving step-level credit from trajector

safetyarxiv-cs-lg

The Interplay of Harness Design and Post-Training in LLM Agents

arXiv:2606.25447v1 Announce Type: cross Abstract: Tool-integrated LLM agents are often wrapped within a harness: the scaffolding that determines which tools are exposed, how they are described, and wh

researcharxiv-cs-cl

TRUSTMEM: Learning Trustworthy Memory Consolidation for LLM Agents with Long-Term Memory

arXiv:2606.25161v1 Announce Type: new Abstract: Large language model (LLM) agents rely on long-term memory to support extended interactions and personalized assistance beyond finite context windows. E

researcharxiv-cs-ai

Verifiable Manifest Signing and Transparency Enforcement for Secure MCP-Based LLM Pipelines

arXiv:2601.23132v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly deployed in tool-driven environments such as healthcare analytics, financial systems, retrieval-

Age of LLM: A Strategic 1v1 Benchmark for Reasoning, Diplomacy and Reliability of Large Language Models under Fog of War

arXiv:2606.24391v1 Announce Type: new Abstract: We introduce Age of LLM, a turn-based 1v1 benchmark in which two LLMs face off on a 13x7 grid to destroy the enemy base. Three stressors are deliberate:

AutoSpec: Safety Rule Evolution for LLM Agents via Inductive Logic Programming

arXiv:2606.24245v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly automate complex tasks by integrating language models with external tools and environments. However, th

safetyarxiv-cs-ai

Governed Shared Memory for Multi-Agent LLM Systems

arXiv:2606.24535v1 Announce Type: new Abstract: Multi-agent LLM environments require robust mechanisms for shared knowledge management. This paper formalizes the fleet-memory problem and identifies fo

safetyarxiv-cs-ai

Hardware

OpenAI and Broadcom announce chip designed for LLM inference at scale

OpenAI and Broadcom unveiled Jalapeño, OpenAI's first Intelligence Processor designed specifically for LLM inference , with early testing showing substantially better performance per watt than current

hardwarears-technica

Hardware

OpenAI and Broadcom unveil Jalapeño, an LLM-optimized inference chip developed from design to manufacturing tape-out in nine months, aided by OpenAI's models (OpenAI)

OpenAI: OpenAI and Broadcom unveil Jalapeño, an LLM-optimized inference chip developed from design to manufacturing tape-out in nine months, aided by OpenAI's models — Loading... - Early testing shows

hardwaretechmeme

RASC+: Retrieval-Constrained LLM Adjudication for Clinical Value Set Authoring

arXiv:2606.23992v1 Announce Type: cross Abstract: Clinical value sets define the standardized terminology codes used in quality measurement, phenotyping, cohort construction, and clinical decision sup

Local Ai

Subjective-Graph LLM Agents for Simulating Uncertainty in Classroom Social Perception

arXiv:2603.20750v2 Announce Type: replace Abstract: Social actors do not observe a common social world: each individual forms judgments from a partial and potentially distorted view of the surrounding

local-aiarxiv-cs-ai

Toward Self-Evolution-Ready Workflow Harnesses: A Reversible Migration Path and Convertibility Taxonomy for Expert LLM Pipelines

arXiv:2606.24598v1 Announce Type: cross Abstract: While expert-validated 'LLM + script' workflows deliver significant value, they remain static: they encode hard-won domain knowledge yet fail to adapt

TruncProof: A Guardrail for LLM-based JSON Generation under Token-Length Constraints

arXiv:2605.13076v2 Announce Type: replace Abstract: The LLM-based generation of machine-readable outputs such as JSON has attracted significant attention for integration with external systems. However

safetyarxiv-cs-cl

VeriPilot: An LLM-Powered Verilog Debugging Framework

arXiv:2606.23759v1 Announce Type: cross Abstract: Verilog debugging remains one of the most time-consuming stages in digital circuit design. Recent advances in Large Language Models (LLMs) have enable

AD-Bench: A Real-World, Trajectory-Aware Advertising Analytics Benchmark for LLM Agents

arXiv:2602.14257v2 Announce Type: replace-cross Abstract: While Large Language Model (LLM) agents have made remarkable progress on complex reasoning, evaluating them in real-world environments remains

EasyUUV: An LLM-Enhanced Universal and Lightweight Sim-to-Real Reinforcement Learning Framework for UUV Attitude Control

arXiv:2510.22126v3 Announce Type: replace Abstract: Despite recent advances in Unmanned Underwater Vehicle (UUV) attitude control, existing methods still struggle with generalizability, robustness to

safetyarxiv-cs-ro

From Knowing to Acting: Benchmarking Self-Awareness Capability of LLM Agents

arXiv:2606.20661v1 Announce Type: cross Abstract: The integration of external tools has transitioned LLM agents from passive responders to autonomous systems. However, current benchmarks prioritize ex

In LLM Reasoning, there is Irrationality on top of Value Misalignment

arXiv:2606.20624v1 Announce Type: cross Abstract: Significant progress has been made in aligning LLMs with target value functions. We argue that, even when an LLM has been well aligned in (post-)train

LLM-Based Generalizable Hierarchical Task Planning and Execution for Heterogeneous Robot Teams with Event-Driven Replanning

model-releasesarxiv-cs-ro

arXiv:2511.22354v2 Announce Type: replace Abstract: This paper introduces CoMuRoS (Collaborative Multi-Robot System), a generalizable hierarchical architecture for heterogeneous robot teams that unifi

MAS-PromptBench: When Does Prompt Optimization Improve Multi-Agent LLM Systems?

arXiv:2606.23664v1 Announce Type: new Abstract: Multi-agent systems (MAS) offer a scalable path forward for agentic AI, comprising multiple LLM-based agents, each assigned a system prompt and a positi

agentsarxiv-cs-lg

Quantifying the Agreement Between Data-Influence and Data-Similarity to Understand LLM Behavior

arXiv:2606.23591v1 Announce Type: new Abstract: One way to understand LLM behavior is to trace its output back to the training data. Two types of measures are commonly used for output tracing: data-si

researcharxiv-cs-lg

RLM-Cascade: Response-Level Speculative Decoding for Cost-Efficient LLM API Serving

arXiv:2606.22840v1 Announce Type: new Abstract: We present RLM-Cascade, a proxy-layer system that applies speculative decoding at the response level to reduce LLM API costs without requiring model arc

Great report on LLM agent communication protocols. Communication is a huge bottleneck in multi-agent systems. (worth bookmarking) The report…

Great report on LLM agent communication protocols. Communication is a huge bottleneck in multi-agent systems. (worth bookmarking) The report builds a five-dimensional taxonomy (counterparty, payload,

safetydair-ai--x

22 Jun 2026

How does it work? Sakana Fugu is itself an LLM, trained to call various LLMs in an agent pool, including instances of itself recursively. Fu…

How does it work? Sakana Fugu is itself an LLM, trained to call various LLMs in an agent pool, including instances of itself recursively. Fugu dynamically orchestrates the world's best models to tackl

agentsdavid-ha--x

22 Jun 2026

The largest LLM-as-a-Judge reliability audit yet. Researchers ran 21 judges from nine providers over roughly 541,000 judgments on MT-Bench, …

The largest LLM-as-a-Judge reliability audit yet. Researchers ran 21 judges from nine providers over roughly 541,000 judgments on MT-Bench, JudgeBench, and RewardBench. Findings: Validating a judge wi

safetydair-ai--x

22 Jun 2026

Tutorials

'When an LLM outputs a step-by-step plan, it creates a powerful illusion that you are watching a machine reason its way to a conclusion. A p…

'When an LLM outputs a step-by-step plan, it creates a powerful illusion that you are watching a machine reason its way to a conclusion. A position paper by professor Subbarao Kambhampati and research

tutorialsyann-lecun--x

21 Jun 2026

Automated Mediator for Human Negotiation: Pre-Mediation via a Structured LLM Pipeline

arXiv:2606.11379v1 Announce Type: new Abstract: Pre-mediation, the preparatory phase preceding direct human negotiation, plays a critical role in achieving mutually beneficial agreements, yet is often

Called the LLM price wars – which are about to heat up — over two years ago. See especially predictions 3, 4, and 7, which laid out the no m…

Called the LLM price wars – which are about to heat up — over two years ago. See especially predictions 3, 4, and 7, which laid out the no moat, small profit, price war regime we have seen ever since.

safetygary-marcus--x

Tools

Don't miss the exact text though: 'We’re changing Fable 5’s safeguards for frontier LLM development to make them visible' - make them visibl…

Don't miss the exact text though: 'We’re changing Fable 5’s safeguards for frontier LLM development to make them visible' - make them visible means they're undoing the truly egregious (dare I say 'una

toolssimon-willison--x

External Experience Serving in Production LLM Systems: A Deployment-Oriented Study of Quality-Cost Trade-offs

arXiv:2606.11806v1 Announce Type: new Abstract: Production LLM systems accumulate reusable operational experience, but the practical deployment issue is not merely whether such experience can help. It

safetyarxiv-cs-cl

GraphInfer-Bench: Benchmarking LLM's Inference Capability on Graphs

model-releasesarxiv-cs-cl

arXiv:2606.11562v1 Announce Type: cross Abstract: Graph analysis underlies many applications whose answers cannot be looked up in a single record or retrieved along a path: laundering rings, drug repu

PoQ-Judge: A Multi-Architecture Evaluation Framework for Cost-Aware Proof-of-Quality in Decentralized LLM Inference

arXiv:2606.11196v1 Announce Type: cross Abstract: Decentralized LLM inference networks need lightweight, reference-free quality evaluation for Proof of Quality (PoQ). We present PoQ-Judge, a framework

researcharxiv-cs-ai

Resource-Aware LLM Reasoning for Mobile Edge General Intelligence

arXiv:2509.23248v3 Announce Type: replace Abstract: The rapid advancement of large language models (LLMs) has enabled an emergence of agentic artificial intelligence (AI) with powerful reasoning and a

100%, hallucinations are baked in (as I have been saying since 2001) and that is why basically no LLM company afford to operate in Germany n…

100%, hallucinations are baked in (as I have been saying since 2001) and that is why basically no LLM company afford to operate in Germany now. We need a better technology. @GaryMarcus Lol, and Google

safetygary-marcus--x

ActiveMem: Distributed Active Memory for Long-Horizon LLM Reasoning

arXiv:2606.10532v1 Announce Type: new Abstract: Memory is essential for enabling large language model (LLM) agents to handle long-horizon reasoning tasks. Existing memory mechanisms are largely centra

Anthropic secretly limiting Claude's usefulness for LLM development strengthens the argument that Anthropic is using AI safety to justify monopolistic behavior (Dean W. Ball/@deanwball)

Dean W. Ball / @deanwball: Anthropic secretly limiting Claude's usefulness for LLM development strengthens the argument that Anthropic is using AI safety to justify monopolistic behavior — My last obs

model-releasestechmeme

Beyond Uniform Token-Level Trust Region in LLM Reinforcement Learning

arXiv:2606.10968v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become standard for improving LLM reasoning. However, existing PPO-style trust-region mechan

safetyarxiv-cs-ai

Fact-Augmented Lookahead Planning for LLM Agents

arXiv:2506.09171v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly capable, but LLM agents still struggle to plan effectively in interactive, partially observable,

first in a series of technical blogs of how we build llm infra

first in a series of technical blogs of how we build llm infra How do you support full-text search JSON filtering over agent traces that span up to hundreds of MBs, while keeping a median (P50) latenc

agentsharrison-chase--x

Tutorials

I believe what Anthropic is doing, gating the ability to do certain harmless things like LLM research, and with incredibly sensitive filters…

tutorialsjeremy-howard--x

I believe what Anthropic is doing, gating the ability to do certain harmless things like LLM research, and with incredibly sensitive filters that even medical questions are often blocked, is *deeply*

Local Ai

I took Andrej Karpathy's LLM Council concept to the next level (Docker, MCP, Skill, Search, local (Ollama)/cloud model support and much more)

A developer expanded on Andrej Karpathy's LLM Council concept by implementing an enhanced system with Docker containerization, Model Context Protocol (MCP) integration, skill modules, web search capab

local-air-ollama

If the new German court decision — to hold LLM companies liable for the false claims their LLMs made — become universal, effectively sidelin…

If the new German court decision — to hold LLM companies liable for the false claims their LLMs made — become universal, effectively sidelining generative AI until better techniques came along, would

safetygary-marcus--x

LLM-as-a-Discriminator: When Synthetic Tables Still Look Real

arXiv:2606.09865v1 Announce Type: new Abstract: Privacy and data sharing are often in tension. Many organizations use synthetic data to reduce privacy risk and still share useful data. For tabular dat