Search: “llm”

curious how people here approach buying high-end/workstation cards (RTX 6000 Ada, 5000 Ada, etc) for local LLM work, do you actively watch pricing/timing on these specifically, or is the consumer 5090

local-air-localllama

26 Jul 2026

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Model Releases

Local-first LLM pipeline tracer — @trace on any function, dashboard at localhost. Feedback welcome.

DGX agent

Hey r/LocalLLaMA — maintainer here, obviously biased. OpenSmith is an open-source Python tracing tool for LLM pipelines. The idea: drop u/trace on any function, run opensmith ui, get a full local dash

model-releasesr-localllama

26 Jul 2026

“Pelican on a bicycle” LLM benchmark by @simonw 2024 (https://simonwillison.net/2024/Oct/25/pelicans-on-a-bicycle/) is now “Call of Duty” 20…

model-releasesitamar-friedman--x

“Pelican on a bicycle” LLM benchmark by @simonw 2024 (https://simonwillison.net/2024/Oct/25/pelicans-on-a-bicycle/) is now “Call of Duty” 2026… Claude Opus 5 one-shotted this game. EVERYTHING you see

26 Jul 2026

DKV: Open-source KV-cache compression framework for local LLM inference (CLI + technical report)

model-releasesr-localllama

Hi everyone! Over the past five months I've been working on DKV (DifferentialKV), an open-source project exploring KV-cache compression for long-context local LLM inference. The goal is to reduce KV-c

25 Jul 2026

ARCO: Adaptive Rubrics with Co-Evolution for Multi-Step LLM-Based Agents

arXiv:2606.21262v2 Announce Type: replace Abstract: Reinforcement learning for multi-step LLM agents often relies on scalar rewards that indicate success but cannot explain why a trajectory is good or

DynamicMCPBench: A Trace-Grounded, Effect-Scored Benchmark for LLM Agents over Live MCP Servers

arXiv:2607.20531v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed over Model Context Protocol (MCP) servers, yet the benchmarks used to evaluate them score th

Local Ai

From Dependency to Compositionality: A Neurosymbolic Lifting of LLM Outputs via Combinatory Categorial Grammar

arXiv:2607.18961v2 Announce Type: replace Abstract: Large language models (LLMs) generate fluent text by incrementally predicting the next token from a prefix. Critics in the generative tradition argu

local-aiarxiv-cs-ai

From Noise to Diversity: Random Embedding Injection in LLM Reasoning

arXiv:2605.11936v2 Announce Type: replace Abstract: Recent soft prompt research has tried to improve reasoning by inserting trained vectors into LLM inputs, yet whether the gain comes from the learned

Position Bias is Hidden Behind Ceiling Effects: A Permutation Diagnostic for LLM Benchmarks

model-releasesarxiv-cs-cl

arXiv:2607.20864v1 Announce Type: cross Abstract: Position bias in multiple-choice LLM evaluation is widely cited as a confound in capability comparisons, but published measurements rely on single ans

Probably no LLM will ever achieve that, no matter how many data centers they build.

Probably no LLM will ever achieve that, no matter how many data centers they build. Lets compare Amazon Prime to AI: According to market research from Consumer Intelligence Research Partners (CIRP), t

safetygary-marcus--x

PromptPack: Scaling LLM Annotation Agents for Online Recommendation

arXiv:2607.20528v1 Announce Type: new Abstract: Online recommendation platforms increasingly use Large Language Models (LLMs) to extract structured features from ad creatives. While deploying a single

SkillCorpus: Consolidating and Evaluating the Open Skill Ecosystem for Real-World LLM Agents

model-releasesarxiv-cs-cl

arXiv:2607.15557v4 Announce Type: replace Abstract: Agent skills, SKILL files that package reusable procedural knowledge for an LLM agent, are a popular mechanism for extending agent capabilities. Pub

The Dark Room in the Reward Channel: Dense Prediction Rewards Collapse GRPO-Trained LLM Agents -- and What Actually Works

arXiv:2607.21273v1 Announce Type: new Abstract: Dense per-step supervision is an appealing remedy for sparse-reward, long-horizon LLM agents: reward the agent for predicting its next observation, and

safetyarxiv-cs-lg

The Hidden Footprint: Making Storage a First-Class Metric for LLM Agent Evaluation

arXiv:2607.11149v3 Announce Type: replace Abstract: LLM agent benchmarks measure task completion, reliability, and inference cost, but not the persistent data an agent run leaves on disk, including lo

Geometry-Guided Constraint Learning for LLM Safety Classification

arXiv:2607.19366v1 Announce Type: new Abstract: Safety as Polytope (SaP) learns linear half-space constraints in LLM hidden space but requires per-category tuning of the constraint count K. We show th

Guardrails as Scapegoats: Auditing Unfaithful Safety Refusals in Tool-Augmented LLM Agents

arXiv:2607.19449v1 Announce Type: cross Abstract: Evaluation frameworks for tool-augmented LLM agents focus overwhelmingly on capability metrics or explicit tool crashes, leaving silent infrastructure

Integrity of peer-to-peer distributed LLM inference under malicious nodes

arXiv:2607.19490v1 Announce Type: cross Abstract: Peer-to-peer distributed inference executes a Large Language Model (LLM) on pooled consumer hardware by spreading its layers across many nodes. Every

Applications

Measuring LLM Trust Allocation Across Conflicting Software Artifacts

arXiv:2604.03447v2 Announce Type: replace-cross Abstract: LLM-based software engineering assistants often reason over multiple artifacts, including code, documentation, signatures, and tests, even whe

applicationsarxiv-cs-ai

NEXUS: Structured Runtime Safety for Tool-Using LLM Agents

arXiv:2607.19356v1 Announce Type: new Abstract: Tool-using LLM agents increasingly execute high-impact actions, making runtime safety monitoring essential. We present NEXUS (Neural EXecution Utility a

Rewarding Better Thinking for LLM Preference Alignment

arXiv:2607.19824v1 Announce Type: new Abstract: LLM preference alignment aims to optimize models toward human preferences across diverse user instructions. Reinforcement learning has become a major po

When Shippers Become Algorithms: Candidate Exposure, Information Design, and the Concentration of LLM-Mediated Freight Markets

arXiv:2607.19967v1 Announce Type: cross Abstract: Shippers are beginning to delegate carrier selection to large language model (LLM) agents. We ask what such delegation does to a freight matching mark

Applications

“Geologists communicated in English; and they could name things in a manner that sent shivers through the bones' Not a whiff of LLM feel, de…

applicationsethan-mollick--x

“Geologists communicated in English; and they could name things in a manner that sent shivers through the bones' Not a whiff of LLM feel, despite the em-dashes & period-separated lists. We focus too m

21 Jul 2026

Evaluation Ability Does Not Imply Optimization Utility: LLM-as-a-Judge Signals in Closed-Loop Table Recognition

arXiv:2607.13347v1 Announce Type: cross Abstract: LLM-as-a-judge is widely used to provide feedback and selection signals in closedloop regeneration, but this use remains insufficiently validated. We

ExTernD: Expanded-Rank Ternary Decomposition Ternary LLM PTQ with Accuracy Approaching Any Quantization Level

arXiv:2607.13511v1 Announce Type: cross Abstract: We introduce ExTernD (Expanded-rank Ternary Decomposition), a post-training factorization of each LLM weight matrix A in R^{m imes n} into A approx B

LLM-Guided Reinforcement Learning for Audio-Visual Speech Enhancement

arXiv:2603.13952v3 Announce Type: replace-cross Abstract: In existing Audio-Visual Speech Enhancement (AVSE) methods, objectives such as Scale-Invariant Signal-to-Noise Ratio (SI-SNR) and Mean Squared

Partially Correlated Verifier Cascades in LLM Harnesses: Concave Log-Odds, Polynomial Reliability, and Blind-Spot Ceilings

arXiv:2607.13918v1 Announce Type: cross Abstract: Serial verification gates are a core reliability primitive in LLM harnesses: a candidate answer is returned only if k verifier calls all accept it. Un

STOCKTAKE: Measuring the Gap Between Perception and Action in LLM Agents with a Fair Oracle

arXiv:2607.13618v1 Announce Type: new Abstract: LLM agents are increasingly evaluated on multi-week decision tasks in which the state that drives cost is never directly observed. On such tasks the fin

Uncertainty-Aware Sequential Decision Rules for Event-Triggered LLM Invocation in Streaming Systems

arXiv:2607.13048v1 Announce Type: cross Abstract: Streaming inference pipelines increasingly pair lightweight fast models with Large Language Models (LLMs) that provide rich semantic understanding at

When Agents Disagree With Themselves: Behavioral Consistency as an Uncertainty Signal for LLM Agents

arXiv:2602.11619v2 Announce Type: replace Abstract: Running the same LLM agent on identical inputs yields 2.3-4.2 distinct action sequences per 10 runs; this behavioral variance constitutes a training

An Omnilingual-ASR-Based Speech-LLM System for the 2nd MLC-SLM Challenge

arXiv:2607.12468v1 Announce Type: cross Abstract: We describe our submission to Task 1 of the 2nd MLCSLM Challenge: a cascaded diarization-then-recognition system that combines DiariZen-Large-s80 (Wav

Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning

arXiv:2509.24372v3 Announce Type: replace-cross Abstract: Fine-tuning large language models (LLMs) for downstream tasks is an essential stage of modern AI deployment. Reinforcement learning (RL) has e

From Words to Widgets for Controllable LLM Generation

arXiv:2604.10925v2 Announce Type: cross Abstract: Natural language remains the predominant way people interact with large language models (LLMs). However, users often struggle to precisely express and

researcharxiv-cs-cl

Towards Vision-Free CIR: Attribute-Augmented Scoring and LLM-Based Reranking for Zero-Shot Composed Image Retrieval

arXiv:2607.12621v1 Announce Type: new Abstract: Recent work has shown that 'Vision-Free'' approaches (representing images as text) can be effective for standard image retrieval tasks. However, it rema

researcharxiv-cs-cv

Applications

Grok is the Flow LLM. Grok 4.5’s biggest advantage is its speed. It’s smart enough to be comparable to the other models on most things. But …

Grok is the Flow LLM. Grok 4.5’s biggest advantage is its speed. It’s smart enough to be comparable to the other models on most things. But that speed allows you to make little tweaks to your system s

applicationselon-musk--x

14 Jul 2026

Tutorials

How to measure AI productivity: From LLM token costs to business value with Arize AX

AI productivity is best measured by connecting AI usage to validated downstream outcomes. Tokens, prompts, and generated lines show activity, but they do not prove value. A better measurement model tr

tutorialsarize-ai

14 Jul 2026

🆕 In Code They Act, In Proof We Trust — Erik Meijer last year, @solomonstre defined agents as 'an LLM that's wrecking its environment in a …

🆕 In Code They Act, In Proof We Trust — Erik Meijer last year, @solomonstre defined agents as 'an LLM that's wrecking its environment in a loop', and @simonw coined the Lethal Trifecta for agents, tha

model-releasesswyx--x

13 Jul 2026

Language models and coding agents are great, but there is more to life, and more to AI, than just LLM agents.

Hardmaru posted a tweet on July 13, 2026 stating that while language models and coding agents are valuable, “there is more to life, and more to AI, than just LLM agents.” The tweet received 61.1 k vie

researchdavid-ha--x

13 Jul 2026

1/ I watched LangChain’s webinar on LLM wikis and agent memory and wrote up a working paper from my own small agent-fleet experiments: Soft-…

1/ I watched LangChain’s webinar on LLM wikis and agent memory and wrote up a working paper from my own small agent-fleet experiments: Soft-Cache: A Human-Supervisable Coherence Protocol for Persisten

agentsharrison-chase--x

10 Jul 2026

Fair Document Valuation in LLM Summaries via Shapley Values

arXiv:2505.23842v5 Announce Type: replace Abstract: Large Language Models (LLMs) increasingly power search engines and AI assistants that retrieve and summarize content from many sources. By serving a

researcharxiv-cs-cl

10 Jul 2026

From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents

arXiv:2607.07321v1 Announce Type: new Abstract: Tool utilization enables Large Language Model (LLM) agents to interact with the real world and resolve complex tasks. However, existing agent frameworks

agentsarxiv-cs-ai

GIFT: Geometry-Informed Low-precision Gradient Communication for LLM Pretraining

model-releasesarxiv-cs-lg

arXiv:2607.07494v1 Announce Type: cross Abstract: Gradient communication is a primary scaling bottleneck in large language model (LLM) pretraining. Communicating gradients in low-precision formats, su

Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode in Tool-Using LLM Agents

arXiv:2607.07405v1 Announce Type: new Abstract: Tool-using LLM agents can violate the very policies they are deployed to enforce while appearing to complete the task successfully. In policy-permissive

Thank you @hwchase17 @BraceSproul @devstein64 @jeffreyhuber for the LLM Wikis session today, I absolutely loved it! Really grateful you keep…

Thank you @hwchase17 @BraceSproul @devstein64 @jeffreyhuber for the LLM Wikis session today, I absolutely loved it! Really grateful you keep these sessions open and honest about what actually works in

agentsharrison-chase--x

Zoom In Disparities in Healthcare LLM Q&A

arXiv:2510.17476v2 Announce Type: replace Abstract: Equitable access to reliable health information is vital when integrating AI into healthcare. Yet, information quality varies across languages, rais

safetyarxiv-cs-cl

Akashic: A Low-Overhead LLM Inference Service with MemAttention

arXiv:2607.05708v1 Announce Type: new Abstract: Recent LLM-based agent systems continuously accumulate context across multi-turn interactions, tool invocations, and cross-session workflows. Replaying

agentsarxiv-cs-ai

Beyond Heuristic Tuning: Power-Calibrated LLM Watermarking

model-releasesarxiv-cs-lg

arXiv:2607.05694v1 Announce Type: cross Abstract: Logit-based watermarking is a widely used mechanism for identifying LLM generated content, yet its effectiveness is governed by a fundamental trade-of

Beyond Reactivity: Measuring Proactive Problem Solving in LLM Agents

arXiv:2510.19771v4 Announce Type: replace Abstract: LLM-based agents are increasingly moving towards proactivity: rather than awaiting instruction, they exercise agency to anticipate user needs and so

Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade

arXiv:2607.06503v1 Announce Type: new Abstract: Large language model (LLM) agents solving multi-step tasks frequently commit to trajectories that are doomed to fail, yet continue to consume substantia