Search: “llm”

Correct Verdicts, Flawed Reasoning: Structured Auditing of LLM-based Vulnerability Reasoning

arXiv:2610.06366v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed for automated software vulnerability analysis. Binary classification alone is insufficient; pra

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Evolving LLM-Generated Features for Interpretable Classification

DGX agent

arXiv:2610.03951v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as classifiers, yet they operate as opaque systems whose decisions are difficult to interpret, which

safetyarxiv-cs-lg

6 Oct 2026

How RL Reshapes LLM Reasoning: Transferability, Coverage, and Scaling Laws

arXiv:2610.04158v1 Announce Type: new Abstract: Recent studies on reinforcement learning (RL) report seemingly conflicting evidence about large language model (LLM) reasoning. Training on mathematics

PACMI: Provenance-Aware Cascading Memory Invalidation for Long-Term LLM Agents

arXiv:2610.05732v1 Announce Type: new Abstract: LLM agents rely on long-term memory to retain and reuse information when performing tasks over long horizons. Existing methods provide limited support f

What agent traces can tell you without an LLM judge

Some agent failures are already in the trace. tracelint proves them on Phoenix OpenInference spans and fails CI, without an LLM judge. The post What agent traces can tell you without an LLM judge appe

agentsarize-ai

Ask, Relax, or Act? Evaluating Actionable Indeterminacy in LLM Preference Reasoning

arXiv:2610.03102v1 Announce Type: cross Abstract: An LLM agent can recognize uncertainty yet still choose the wrong next step: asking when action is already justified, or seeking clarification when th

FSPO: Policy-Consistent Risk and Pareto-Feasible Control for Budgeted LLM RL Post-Training

arXiv:2610.02828v1 Announce Type: new Abstract: Adaptive LLM reinforcement-learning post-training changes multiple training actuators online, including rollout temperature, group size, clipping, KL re

safetyarxiv-cs-ai

Passing the Test You Trained On: Re-evaluating Prompt-Injection Detectors for LLM Agents

arXiv:2610.03448v1 Announce Type: cross Abstract: LLM agents increasingly screen tool outputs with small prompt-injection detectors, and teams choose among detectors by their scores on public benchmar

Silent Dissent: LLM Agents That Yield to the Majority Still Represent Their Original Premise

arXiv:2610.02702v1 Announce Type: new Abstract: Multi-agent debate is increasingly used to reach consensus among LLM agents, yet agents often yield to a unanimous majority. When an agent changes its a

Semantic Cooperative Games for Contribution Attribution in LLM-Based Multi-Agent Systems

arXiv:2607.18255v2 Announce Type: replace Abstract: Contribution attribution has become a central problem in LLM-based multi-agent systems, where final outputs are produced through multiple agents, me

2 Oct 2026

The Innocent Courier: Covert Exfiltration Through Legitimate LLM Web Fetching

arXiv:2610.01768v1 Announce Type: cross Abstract: With the increasing capabilities of Large-Language-Models (LLMs) and LLM-based agents, users are increasingly using them to solve everyday problems, s

2 Oct 2026

TopK-Guided: Adaptive, Budget-Aware Activation Sparsity for Efficient LLM Inference

arXiv:2610.01763v1 Announce Type: new Abstract: Activation sparsity speeds up large language model (LLM) inference by setting unimportant activations to zero so that the corresponding computations can

2 Oct 2026

Anchor-ECC: Local Integrity Checking for Watermarked LLM Outputs via Error-Correcting Codes

arXiv:2609.38722v1 Announce Type: cross Abstract: LLM watermarking has become an effective approach to distinguishing AI-generated text from human-written text by embedding detectable patterns during

Can Agents Trust Their Skills? Uncovering Unsafe Chains of Trust in Skill-Based LLM Agents

arXiv:2609.39065v1 Announce Type: cross Abstract: LLM agents increasingly rely on installable skills, which are packages of instructions, code, and resources that equip them with task-specific capabil

Characterizing High Bandwidth Flash for LLM Serving

arXiv:2609.39131v1 Announce Type: new Abstract: Large language model (LLM) serving requires substantial memory to store model weights and KV caches. As models grow larger and contexts become longer, m

agentsarxiv-cs-lg

PhantomEnvironments: Training LLM Agents in Fictional Worlds

arXiv:2609.40221v1 Announce Type: cross Abstract: Training LLM agents with reinforcement learning (RL) is bottlenecked by environments, which must provide verifiable rewards, support long-horizon inte

SEPAL: Separated Expert Pairs with Answer-Level Fusion for Reliable LLM Collaboration

arXiv:2609.39645v1 Announce Type: cross Abstract: Multi-agent collaboration lets large language models (LLMs) improve question answering through deliberation and feedback. Yet shared discussion couple

Taming Speculative Search for Test-Time Scaling in LLM Serving

arXiv:2609.39334v1 Announce Type: cross Abstract: Test-time scaling has recently emerged as a powerful approach for improving LLM reasoning by allocating additional computation during inference, subst

researcharxiv-cs-cl

AutoMark: Enabling Autoresearch to Discover Better LLM Watermarks

arXiv:2609.37310v1 Announce Type: cross Abstract: With LLM watermarking being deployed commercially and now required by regulations, improving its reliability and effectiveness has become crucial. Yet

Evaluating Test-Time Scaling of General LLM Agents

arXiv:2602.18998v2 Announce Type: replace Abstract: LLM agents are increasingly expected to operate as general-purpose systems that resolve real-world user requests, yet their dynamic scaling behavior

Tutorials

JudgeProfile: Understanding and Steering Subjectivity in LLM Judges

arXiv:2609.36705v1 Announce Type: new Abstract: LLM judges are inherently subjective, often favoring different responses in pairwise comparison when neither option is objectively wrong. To study this

tutorialsarxiv-cs-ai

MERGE: Multi-LLM Ensemble for Retrieval via Generative Enrichment

arXiv:2609.37574v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used to enrich user queries in information retrieval (IR) so that a standard retriever such as BM25 can

Pretrain Once, Route Anywhere: Towards a Foundation Model for LLM Routing

arXiv:2609.37362v1 Announce Type: new Abstract: Large language model (LLM) routing aims to assign each query to the most suitable model from a heterogeneous candidate pool, improving the quality--effi

local-aiarxiv-cs-ai

vSkipper: Translating Dynamic Layer Skipping into LLM Serving Gains

arXiv:2609.37062v1 Announce Type: cross Abstract: Dynamic layer skipping reduces LLM computation by allowing each token to execute only a subset of the model's layers. However, existing skippers rely

A Large-Scale Benchmark and Risk Assessment of Traffic Analysis Attacks on Cloud LLM Services

arXiv:2609.31877v1 Announce Type: cross Abstract: Cloud-based Large language model (LLM) services create a network-level traffic side channel that can expose model, prompt, and task behavior despite e

29 Sept 2026

From Preference to Reciprocity: Decentralized Matching with Empirically Grounded LLM-agent Based Modeling

arXiv:2609.34679v1 Announce Type: new Abstract: Bipartite matching is a fundamental problem in game theory and market design. Classical approaches such as Gale--Shapley assume complete preferences and

local-aiarxiv-cs-lg

29 Sept 2026

ActKV: Efficient LLM Agents through Action-Guided KV Cache Management

arXiv:2609.31395v1 Announce Type: cross Abstract: Agentic LLM inference accumulates long KV caches across iterative observation-reasoning-action loops, imposing substantial memory overhead and limitin

Can You Check That? The Checkability Boundary for Local LLM Network Automation

arXiv:2609.31540v1 Announce Type: cross Abstract: Sending every network-automation input to a third-party frontier LLM exports sensitive artifacts such as production configurations, topologies, and lo

local-aiarxiv-cs-ai

LLM Parkinsonism: Executive-Control Failure, Token-Inefficient Persistence, and an Uncertainty-Aware Global Executive Control Architecture for Autonomous Language-Model Agents

arXiv:2609.30662v1 Announce Type: new Abstract: Large language models (LLMs) can plan, use tools, write code, and execute long-horizon workflows, yet strong local competence does not guarantee project

macOS 27 ships a free local LLM on Apple Silicon Macs. I made it easy to use from Node and Python

Apple Silicon Macs on macOS 26+ come with a small LLM built in. No download, no API key, and nothing leaves your Mac. Why I built it I was making a tool that writes API docs from code, and I didn't wa

local-air-localllama

PriceBench: A Diagnostic Benchmark for Price, Quality, and Brand Preferences in LLM Booking Agents

arXiv:2609.31468v1 Announce Type: cross Abstract: LLMs increasingly act as purchasing agents, which makes the LLM, not the user, the one choosing among the options that satisfy a request; its preferen

Tools

What Jev’s probabilities reveal that repeated LLM judgments miss

I tested Jev and five LLM judges across ten Arize Phoenix evaluators to measure how often their answers changed alongside accuracy, cost, and latency. The post What Jev’s probabilities reveal that rep

toolsarize-ai

Experts Rise Where LLMs Disagree: Using Cross-Model Disagreement to Target Expert Effort in LLM Codebook Revision for Large-Scale Annotation

arXiv:2609.26926v1 Announce Type: cross Abstract: Large-scale text annotation brings expert insight to millions of documents, often through a codebook that AI annotators follow. Developing a robust co

24 Sept 2026

State-Grounded Conditioning: Wrapping User-Facing LLM Agents Where Direction Depends on Live State

arXiv:2609.27606v1 Announce Type: new Abstract: We introduce State-Grounded Conditioning (SGC), a design principle for user-facing LLM agents that must condition on live user state (game state, sessio

24 Sept 2026

Real-time LLM guardrails with Jev: comparing latency and cost

Compare Jev and GPT-5.4 nano for real-time LLM guardrails, with demo results on latency, cost, and checks on agent inputs, replies, and tool calls. The post Real-time LLM guardrails with Jev: comparin

model-releasesarize-ai

23 Sept 2026

Toollery: Scaling LLM Agents to Thousands of Skills and Tools

arXiv:2609.22218v1 Announce Type: cross Abstract: As LLM agents are exposed to hundreds to tens of thousands of skills, tools, and API functions, full-library prompting becomes costly, slow, and less

22 Sept 2026

Communication and Verification in LLM Agents towards Collaboration under Information Asymmetry

arXiv:2510.25595v2 Announce Type: replace-cross Abstract: While Large Language Model (LLM) agents are often approached from the angle of action planning/generation to accomplish a goal (e.g., given by

18 Sept 2026

PrefixBench-H100: Characterizing Prefix Reuse and Time-to-First-Token in H100 LLM Serving

arXiv:2609.19657v1 Announce Type: cross Abstract: Repeated prompt prefixes are increasingly common in LLM serving workloads, appearing in system prompts, templated retrieval-augmented generation pipel

18 Sept 2026

Reproducibility is not construct validity: LLM measurement of institutionally situated communication

arXiv:2609.19866v1 Announce Type: new Abstract: High annotation reproducibility does not necessarily imply that an LLM-inferred measure captures the construct it is intended to measure. We test this d

safetyarxiv-cs-ai

18 Sept 2026

Beyond Accuracy: How Procedural Traces Shift the Decision Criterion of LLM Overseers

arXiv:2609.18204v1 Announce Type: cross Abstract: Organizations increasingly use oversight loops where one large language model (LLM) audits another's outputs alongside procedural traces of claimed st

Do Social Patterns Hold in Synthetic Data? Analyzing Cyberbullying Dynamics in LLM-Generated and Authentic Dialogues

arXiv:2609.17549v1 Announce Type: new Abstract: Cyberbullying (CB) is a complex social phenomenon characterized by repeated aggression, power imbalance, and multi-party interaction. Although large lan

EvoUndo: Recoverability-Constrained Self-Evolution for LLM Agent Harnesses

arXiv:2608.28363v2 Announce Type: replace Abstract: LLM agents increasingly modify their own prompts, tools, middleware, resources, and execution harnesses at runtime. Such self-evolution can improve

Faithful yet Collusive: Why Chain-of-Thought Monitoring Cannot Detect Collusion in LLM Pricing Agents under Oligopolistic Competition

arXiv:2609.18346v1 Announce Type: new Abstract: Large language models (LLM) deployed as autonomous pricing agents may sustain supracompetitive prices through tacit coordination. We develop a causal gr

Token Latency Fairness: Performance Isolation for Multi-Tenant LLM Serving

arXiv:2609.18112v1 Announce Type: cross Abstract: LLM serving is typically offered as a shared, multi-tenant service, where high-demand workloads from one client can cause latency SLO violations for o

safetyarxiv-cs-lg

FlexEE: Self-Speculative and KV-Compatible Early Exiting for Offloading-Aware LLM Inference

arXiv:2609.17008v1 Announce Type: new Abstract: Large language model (LLM) inference is often constrained by both computation and memory, especially in offloading-based deployments where model weights

16 Sept 2026

ActGuard: Pre-execution Action Auditing against Indirect Prompt Injection in LLM Agents

arXiv:2609.14987v1 Announce Type: cross Abstract: Large language model (LLM) agents interact with external environments through tool invocation, but tool outputs can also expose them to indirect promp

15 Sept 2026

AGENTQ: Quantization-Conditioned Backdoor Attacks on LLM Agents

arXiv:2609.14060v1 Announce Type: cross Abstract: Quantization is one of the default deployment paths for open-weight LLM agents, but it is not behavior-preserving: an adversary can release a full-pre

safetyarxiv-cs-ai

15 Sept 2026

MOSCOPT: Mixture-of-Skills Collective Optimization for LLM Agents

arXiv:2609.14399v1 Announce Type: new Abstract: Natural language prompts and skills serve as the strategic backbone of LLM-based agents. Recent advances in prompt and skill optimization have achieved