Search: “llm”

Bridging LLM Agents and Data Spaces: An Architectural Mediation Approach using the Model Context Protocol

arXiv:2609.30341v1 Announce Type: new Abstract: Data Spaces enable sovereign and governed data sharing across organizational boundaries, but their integration with AI agents remains challenging due to

28 Sept 2026

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Research

Enhancing Assessment of Self-Consistency in LLM Explanations using Perturbation Strength

DGX agent

arXiv:2609.30849v1 Announce Type: new Abstract: Prior work has examined the self-consistency of LLM-generated explanations using surface-level perturbation methods. However, the strength of these pert

researcharxiv-cs-cl

28 Sept 2026

Same Text, Different Numbers: The Divergence of LLM-Based Measures

arXiv:2609.31013v1 Announce Type: new Abstract: Researchers increasingly use generative large language models (LLMs) to convert corporate text into empirical variables. We examine the extent to which

researcharxiv-cs-ai

28 Sept 2026

Applications

How Reproducible Are Evaluation Conclusions? A Self-Audit of LLM-Inferred Prompt Structure

arXiv:2609.30074v1 Announce Type: cross Abstract: Evaluations of LLM systems routinely average over small prompt sets and report models as a ranked table. We ask how much confidence such a table deser

applicationsarxiv-cs-ai

25 Sept 2026

The Recall Ceiling of LLM Recommendation Reranking

model-releasesarxiv-cs-lg

arXiv:2609.27953v1 Announce Type: cross Abstract: Some LLM-based recommendation rerankers are evaluated under an oracle protocol that guarantees the ground-truth item is present in the scored set, eit

24 Sept 2026

ActGov: Governing LLM Agent Actions via Policy-Constrained Validation

arXiv:2609.24446v2 Announce Type: cross Abstract: Large language model (LLM) agents increasingly execute long-horizon workflows through external tools, allowing untrusted outputs to influence subseque

Agreement Overstates Evidence: Error Dependence in LLM Judge Consensus

arXiv:2609.22512v1 Announce Type: new Abstract: Consensus among LLM judges is often taken as strong evidence that a decision is correct. This assumes that judges make their errors independently. In pr

researcharxiv-cs-ai

RoofLang: Enabling AI-Driven Architecting of LLM Inference Systems

arXiv:2609.12551v2 Announce Type: replace-cross Abstract: AI is beginning to make substantive contributions to LLM inference optimization. Existing AI optimizations are predominantly profiling-based.

Structured Decomposition for Reliable LLM-Generated Access Control Policies

arXiv:2609.24036v1 Announce Type: new Abstract: This paper presents an LLM-based system that translates natural-language access control policies (NLACPs) into executable Rego code for Open Policy Agen

Text-only adaptation in LLM-based ASR through text denoising

arXiv:2601.20900v4 Announce Type: replace-cross Abstract: Adapting large language model (LLM)-based automatic speech recognition (ASR) systems to new domains using text-only data is a significant yet

safetyarxiv-cs-cl

The Price of Safety: Benign-Case Utility and Token Overhead of Memory-Poisoning Defenses in LLM Agents

arXiv:2609.22818v1 Announce Type: cross Abstract: Memory-poisoning defenses for LLM agents are typically evaluated by their ability to prevent attacks. However, the traffic they process is rarely adve

From Semantic Decisions to Feasible Trajectories: Self-Evolving LLM-Guided Optimal Control for Narrow-Space Parking

arXiv:2609.24631v1 Announce Type: new Abstract: Autonomous parking in nonconvex and narrow environments remains challenging. Although optimal-control methods can explicitly enforce vehicle dynamics an

agentsarxiv-cs-ro

Human-LLM Deliberation as Interactive Proof: Conditions for Verifiability Without Transparency

arXiv:2609.24895v1 Announce Type: new Abstract: When an LLM supplies an argument that a user could not readily construct, how can the user decide whether to accept its claim? Inspired by interactive p

researcharxiv-cs-cl

Judging a Review by its Cover: A Reliability Analysis of LLM-based Peer Review Evaluation Metrics

arXiv:2609.23264v1 Announce Type: new Abstract: Peer-review evaluation is increasingly being automated with LLM-as-a-judge metrics, but this creates a measurement risk. A review may receive a high sco

researcharxiv-cs-cl

LoRA-generating hypernetworks for efficient on-device LLM generative personalization

model-releasesarxiv-cs-lg

arXiv:2609.24979v1 Announce Type: new Abstract: On-device large language models (`LLMs'), e.g. running on mobile phones, are ripe for improvement via personalization. The limited compute resources of

RADAR: Retrieval-Augmented Detector with Adversarial Refinement for Adaptive LLM-Generated Fake News Detection

arXiv:2601.03981v3 Announce Type: replace Abstract: To efficiently combat the spread of LLM-generated misinformation in the news domain, we present RADAR, a Retrieval-Augmented Detector with Adversari

Applications

RAILS: Retrieval-Augmented Incremental LLM Clustering at Scale

arXiv:2609.24464v1 Announce Type: new Abstract: Using a Large Language Model (LLM) as the clusterer at production scale is hard: prompts cannot hold the entire label space, and per-document serial pro

applicationsarxiv-cs-lg

Architectural Design, Not Only Model Intelligence, Governs Multi-Agent LLM Performance

arXiv:2602.03128v2 Announce Type: replace Abstract: Multi-agent LLM frameworks are data-intensive systems that govern how agents orchestrate tasks, manage state, and coordinate decisions. These archit

18 Sept 2026

PersonalAI 2.0: Enhancing knowledge graph traversal/retrieval with planning mechanism for Personalized LLM Agents

arXiv:2605.13481v2 Announce Type: replace Abstract: We introduce PersonalAI 2.0 (PAI-2), a novel framework designed to enhance LLM-based systems through integration of external knowledge graphs (KGs).

18 Sept 2026

PetriBench: Benchmarking LLM Reasoning over Dynamic State Spaces

arXiv:2609.19883v1 Announce Type: cross Abstract: Characterizing LLM reasoning remains an open challenge, as many existing benchmarks isolate specific reasoning skills, rely on external knowledge, or

18 Sept 2026

AutoTuneBench: Trustworthy Measurement for Agent Auto-Tuning of LLM Serving Engines

arXiv:2609.18123v1 Announce Type: new Abstract: Large language model agents tune GPU kernels and serving engines through a closed loop of propose, measure, and keep, but the measurements behind this l

17 Sept 2026

GraphEcho: Structural Redundancy and Evidence Provenance in LLM Graph Agents

arXiv:2609.17695v1 Announce Type: new Abstract: A large language model (LLM) agent can follow more graph paths without acquiring more independent evidence. GraphEcho tests whether agents mistake these

17 Sept 2026

Calibrate, Then Route: A Measured Study of Learned Request Routing for Disaggregated LLM Serving

arXiv:2609.16206v1 Announce Type: new Abstract: Disaggregated LLM serving places compute heavy prefill and memory heavy decode on separate GPU pools. Systems such as DistServe, Splitwise, and Mooncake

Coupled Calibration and Learning: Mitigating Teacher Bias in LLM Distillation without Target-Domain Reward Feedback

arXiv:2609.17474v1 Announce Type: cross Abstract: Large language model (LLM) distillation aims to transfer the capabilities of a powerful teacher to a smaller student. Direct imitation, however, can a

LLM Inference in a Flash!

model-releasesarxiv-cs-lg

arXiv:2609.16161v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown impressive capabilities across a range of natural language processing tasks, and LLM inference has emerged as a

Shared Selective Persistent Memory for Agentic LLM Systems

arXiv:2607.09493v2 Announce Type: replace Abstract: Agentic LLM systems that generate code through multi-turn tool use face a fundamental context problem: each session starts from zero, discarding the

Style-Debiased DPO: Updating LLM Knowledge with Factuality-Aware Synthetic Preference Data

arXiv:2609.16532v1 Announce Type: new Abstract: Continued pretraining (CPT) with data augmentation such as paraphrasing can store inside a large language model (LLM) the knowledge of a small source co

Efficiency Hallucination: Formalizing and Measuring Behavioral Calibration in LLM-Based Code Optimization

arXiv:2609.14839v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) into automated code optimization introduces a critical reliability risk we term the Efficiency Halluci

Loop-Back Authority in LLM Agent Teams: A Paired Experiment on Flat and Hierarchical Coordination

arXiv:2609.14767v1 Announce Type: cross Abstract: Hierarchical orchestration, in which a Manager agent reviews worker output and can send it back for revision, is the default coordination pattern in p

Hardware

OpWeave: Flexible Operator Disaggregation for Heterogeneous LLM Serving

arXiv:2609.14237v1 Announce Type: cross Abstract: LLM serving systems increasingly disaggregate inference into finer-grained stages, with recent approaches separating attention from FFN or MoE executi

hardwarearxiv-cs-ai

The Stochastic Deputy: Structural Tenant Isolation for Tool-Using LLM Agents

arXiv:2609.14780v1 Announce Type: cross Abstract: Multi-tenant tools commonly accept a tenant identifier and validate it against the caller's entitlement. For a large language model (LLM) agent, that

Vibe Patenting: Evaluating LLM Judges for Professional Patent-Drafting Agents

arXiv:2609.13422v1 Announce Type: new Abstract: LLM judges are increasingly used to evaluate and improve AI-generated outputs, yet their reliability for complex professional work remains unclear. We s

BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines

model-releasesarxiv-cs-cv

arXiv:2604.05793v2 Announce Type: replace-cross Abstract: In LLM agent pipelines, prompt privacy risk propagates beyond a single model call: raw user content enters retrieval queries, memory writes, t

14 Sept 2026

Hardware

Dissecting GPU Utilization for LLM Inference on Nvidia Hopper

arXiv:2609.12923v1 Announce Type: cross Abstract: A single SM utilization percentage can make an LLM inference workload look compute-saturated while hiding how much useful work is being done. The prob

hardwarearxiv-cs-lg

14 Sept 2026

Decoupling Readiness from Release for Tail-Aware Scheduling of Agentic LLM Workflows

arXiv:2609.10964v1 Announce Type: new Abstract: Agentic LLM workflows consist of sequences of model turns interleaved with tool interactions, so their end-to-end completion time depends not only on in

12 Sept 2026

How AI Coders Discuss, Disagree, and Reach Consensus: Challenges and Opportunities for LLM-Based Qualitative Coding

arXiv:2609.11109v1 Announce Type: cross Abstract: The utility of AI in multi-coder qualitative coding has been widely discussed, yet little empirical evidence exists to delineate the contexts in which

12 Sept 2026

Characterizing Narrative Content in Web-scale LLM Pretraining Data

arXiv:2606.19468v2 Announce Type: replace Abstract: The narrative composition of web-scale LLM pretraining corpora remains largely unexplored, even though narrative is a fundamental mode of human comm

researcharxiv-cs-cl

11 Sept 2026

LLM-Anchored Paralinguistic Enrichment for Alzheimer's Disease Detection

arXiv:2609.10896v1 Announce Type: new Abstract: Speech-based automatic detection of Alzheimer's disease (AD) provides a non-invasive and scalable approach to early cognitive screening. AD affects both

11 Sept 2026

Agent-Based ML-LLM Fusion with Self-Optimizing Prompts for Plateau Weather Alerts

arXiv:2609.10135v1 Announce Type: cross Abstract: To address insufficient contextualization, weak generalization, and poor scenario adaptation in tourism meteorological services, we propose SmartWeath

agentsarxiv-cs-lg

AURA-Eval: Evaluation Framework for Acting Under Risk Awareness in LLM Agent Trajectories

arXiv:2609.06783v1 Announce Type: cross Abstract: LLM agents operate in workflows where unsafe actions can have real consequences. Existing safety evaluations often reduce behavior to a single score,

Beyond Cross-Lingual Transfer: Benchmarking Propagation Boundaries in Multilingual LLM Unlearning

arXiv:2609.05976v1 Announce Type: cross Abstract: Large Language Model (LLM) unlearning aims to suppress target knowledge while preserving general capabilities. In multilingual settings, unlearning mu

Beyond Single-Negative Preference: Multi-Negative DPO for LLM-Centric Historical Entity Linking

arXiv:2609.07379v1 Announce Type: cross Abstract: Large language models (LLMs) have recently shown promise for historical entity linking, but preference optimization for this task is often formulated

researcharxiv-cs-ai

Beyond Top-k Skill Retrieval: Diversity-Aware Skill Routing for LLM Agents

arXiv:2609.05824v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on external skills, but routing user requests over large skill registries is difficult because many

Characterizing Contention-Induced Reliability Collapse in KV-Cache Timing Side Channels for Multi-Tenant LLM Serving

arXiv:2609.06853v1 Announce Type: cross Abstract: Shared key--value (KV) cache reuse improves large language model (LLM) serving, but it can also create a timing side channel that reveals whether a pr

Local Ai

Detokenization Leaks: Reconstructing Local LLM Outputs From Cache Traces

arXiv:2609.06674v1 Announce Type: cross Abstract: We present a new attack that reconstructs the text generated by locally hosted LLMs by observing CPU cache activity during detokenization. Unlike prio

local-aiarxiv-cs-ai

Forward-Free LLM Depth Pruning via Weight Redundancy

arXiv:2609.09883v1 Announce Type: new Abstract: Depth pruning reduces large language model (LLM) inference cost by removing complete Transformer blocks. Activation-based methods collect hidden states

researcharxiv-cs-lg

From Review to Authorization: Key-Isolated Threshold Signing for LLM Agents

arXiv:2609.05901v1 Announce Type: cross Abstract: Autonomous LLM agents can turn untrusted content into effectful actions such as payments and permission changes. If the same process interprets this c

HyperTrace: Hypothesis-Based Preference Tracing for Online LLM Personalization

arXiv:2609.09835v1 Announce Type: new Abstract: Personalized language models aim to adapt responses to individual users, whose preferences are often latent and revealed gradually through interaction.