Search: “llm”

Whom Do AI Agents Work For? Role Assignment Induces Sponsorship Bias in LLM Recommenders

arXiv:2609.17989v1 Announce Type: cross Abstract: Large language models (LLMs) now serve as conversational shopping assistants on platforms that also sell advertising. These AI agents face a conflict

17 Sept 2026

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Research

Comparative Characterization of KV Cache Management Strategies for LLM Inference

DGX agent

arXiv:2604.05012v2 Announce Type: replace-cross Abstract: Efficient inference with Large Language Models (LLMs) increasingly relies on Key-Value (KV) caches to store previously computed key and value

researcharxiv-cs-ai

16 Sept 2026

Local Ai

Retrieval-Driven Memory Reconsolidation for Long-Term LLM Agents

arXiv:2609.16053v1 Announce Type: cross Abstract: Long-term memory is essential for LLM-based agents operating over extended interactions. Existing memory systems primarily update memory when new info

local-aiarxiv-cs-ai

16 Sept 2026

Toward Governance-Aware Autonomous GIS: A Narrative Review of Ethical and Privacy Risks in LLM-Enabled GeoAI

arXiv:2609.16232v1 Announce Type: new Abstract: Geospatial artificial intelligence (GeoAI) powered by large language models (LLMs) is expanding the capacity to query, generate, and interpret spatial i

16 Sept 2026

Hardware

BOOST: Concurrent Access to Host Memory and HBM to Accelerate LLM Inference

arXiv:2609.13592v1 Announce Type: cross Abstract: GPU memory bandwidth and capacity limit throughput in large language model (LLM) inference. The GPU memory system consists of a primary tier of high-b

hardwarearxiv-cs-lg

LIMBO: Lifelong Inference-Time Memory and Budget Optimization for LLM Agents

arXiv:2609.14138v1 Announce Type: cross Abstract: As LLM agents become integrated into increasingly complex workflows, they must continually acquire new capabilities while retaining competence on prev

LLM-Based Schema-Aware Split Learning for Privacy-Preserving Mental Distress Prediction Across Heterogeneous Surveys

arXiv:2609.15871v1 Announce Type: cross Abstract: Rising societal and lifestyle complexity has been linked to a growing prevalence of mental distress worldwide. Educational institutions, workplaces, c

LLM-Enhanced Multi-Agent Reinforcement Learning for Unified Electric Vehicles-Charging Station-Grid Optimization in Public Charging Systems

arXiv:2609.13805v1 Announce Type: new Abstract: In the era of the Internet of Things (IoT), coordinating connected electric vehicle (EV) charging scheduling to balance EV charging satisfaction, statio

LLM-Microscope: Uncovering the Hidden Role of Punctuation in Context Memory of Transformers

arXiv:2502.15007v2 Announce Type: replace-cross Abstract: We introduce methods to quantify how Large Language Models (LLMs) encode and store contextual information, revealing that tokens often seen as

researcharxiv-cs-ai

Natural Language Knowledge Graph Query Execution: Leveraging Controlled Semantics in the LLM Context Window

arXiv:2609.14652v1 Announce Type: cross Abstract: Large Language Model (LLM) applications often transfer domain concepts into the model's context informally, through prompt prose, schema dumps, and ex

On-Demand Human Assistance for Task Continuation under Physical Action Failures in LLM-based Planning

arXiv:2603.28156v2 Announce Type: replace Abstract: While robot action planning based on large language models (LLMs) has advanced remarkably, continuing a task after a physical action failure during

agentsarxiv-cs-ro

Rubrics as an Attack Surface: Stealthy Preference Drift in LLM Judges

arXiv:2602.13576v2 Announce Type: replace-cross Abstract: Evaluation and alignment pipelines for large language models increasingly rely on LLM-based judges, whose behavior is guided by natural-langua

Towards Optimizing SQL Generation via LLM Routing

arXiv:2411.04319v1 Announce Type: cross Abstract: Text-to-SQL enables users to interact with databases through natural language, simplifying access to structured data. Although highly capable large la

researcharxiv-cs-ai

Utility-Guided Agent Orchestration for Efficient LLM Tool Use

arXiv:2603.19896v2 Announce Type: replace Abstract: Tool-using large language model (LLM) agents often face a fundamental tension between answer quality and execution cost. Fixed workflows are stable

Why LLM Agents Collapse Without Oversight: The Enforcement Gap as the Mechanism Behind Emergence World Failures

arXiv:2609.15293v1 Announce Type: new Abstract: When Emergence World placed frontier LLM agents in an unsupervised multi-agent simulation, the results were alarming: agents committed crimes, starved,

ORQA: An Occupation-Realistic Question and Answer Framework for LLM Professional Knowledge

model-releasesarxiv-cs-cl

arXiv:2609.12366v1 Announce Type: new Abstract: We present ORQA, a method for testing occupation-level knowledge in large language models. Prior methods either map abstract LLM skills to occupations v

14 Sept 2026

Tutorials

SAGE-Loop: Reliable Closed-Loop LLM-Driven AutoML with Trial-and-Correction and Adaptive Ensembling

arXiv:2609.12455v1 Announce Type: new Abstract: Automated machine learning (AutoML) is reshaping data-driven science and industrial practice, and as large language models are introduced into AutoML, p

tutorialsarxiv-cs-lg

14 Sept 2026

CS-Guard: Benchmarking LLM Guardrails for Code Generation Security

arXiv:2609.09798v1 Announce Type: cross Abstract: Large language models (LLMs) have been ex- ploited to generate malware, but the effective- ness of guardrails for code generation secu- rity remains u

R4Tun: LLM-guided adaptive segmental tunnel lining segmentation in point clouds

model-releasesarxiv-cs-cv

arXiv:2609.11360v1 Announce Type: new Abstract: Automated inspection of segmental tunnel linings requires adaptive segmentation from 3D point clouds, yet expert-tuned pipelines often degrade when tunn

Spec-Harness: Measuring and Improving Behavioral Adequacy of LLM-Synthesized Formal Specifications

arXiv:2604.00280v2 Announce Type: replace-cross Abstract: Formal specifications play a central role in ensuring software reliability, yet automatically synthesizing high-quality specifications remains

The Era by Eon Benchmark: A Generated Enterprise Estate with Exact Ground Truth for Benchmarking LLM Agents

arXiv:2609.09853v1 Announce Type: new Abstract: LLM agents for enterprise systems of record cannot be evaluated on customer production data, and no existing substitute provides ground truth. We presen

When Synthetic Data Hurts: On Catastrophic Forgetting in Skill Retrieval for LLM Agents

model-releasesarxiv-cs-lg

arXiv:2609.10750v1 Announce Type: cross Abstract: LLM agents increasingly rely on external skills retrieved at runtime, making skill selection from large repositories a critical challenge. We present

Beyond Prompts: Measuring and Optimizing LLM Tool-Agent Harnesses

arXiv:2609.05736v2 Announce Type: new Abstract: LLM tool agents can be improved without retraining by modifying the runtime harness around a fixed model: prompts, tool interfaces, middleware, state ha

DART: A DAG-Based Reputation and Incentive Framework via Blockchain-Enabled Governance for Trustworthy LLM Multi-Agent Collaboration

arXiv:2609.05529v1 Announce Type: cross Abstract: Large language model (LLM)-based multi-agent systems (MAS) predominantly rely on centralized orchestration and lack formal verification mechanisms for

Knowing Your Uncertainty -- On the application of LLM in social sciences

arXiv:2512.05461v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are rapidly being integrated into computational social science research, yet their blackboxed training and design

researcharxiv-cs-ai

MVFA: A Multi-View Text-Guided Multimodal Fusion LLM Adapter for Sentiment Analysis and Emotion Recognition

arXiv:2609.06188v1 Announce Type: new Abstract: Multimodal sentiment analysis and emotion recognition in conversations demand effective modeling of heterogeneous interactions across textual, acoustic,

Local Ai

PELM: Power Efficient On-Device LLM Inference with Speculative Decoding and Dynamic Voltage Frequency Scaling

arXiv:2609.09662v1 Announce Type: cross Abstract: Deploying Large Language Models (LLMs) directly on mobile platforms at the edge is gaining traction due to a myriad of benefits, such as increased pri

local-aiarxiv-cs-cl

ProbPlug: A Plugin Uncertainty Network for Reliable Confidence in LLM Binary Classification

arXiv:2609.10122v1 Announce Type: new Abstract: Large language models (LLMs) have achieved strong performance across a broad range of classification settings, yet the reliability of their predictions

researcharxiv-cs-cl

Reasoning-Aware Compression: Identifying and Protecting Vulnerable Reasoning Circuits for Energy-Efficient LLM Deployment

arXiv:2609.05512v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) impose substantial energy costs during deployment, yet current compression methods apply uniform quantization across all c

SchemeArena: Factorized Stress Testing of Scheming in LLM Agents

arXiv:2609.08126v1 Announce Type: new Abstract: We study scheming in LLM agents, in which agents covertly pursue misaligned goals. Our focus is to understand how scheming arises from the interaction o

Style Over Substance: Content-Invariant Wrappers Flip LLM Safety-Judge Verdicts

arXiv:2609.08236v1 Announce Type: new Abstract: Automatic safety judges -- systems such as Llama Guard or a GPT-4o grading prompt that decide whether a model's reply is harmful -- produce the numbers

Substrate-Portable Execution for Production LLM Workflows

arXiv:2609.06128v1 Announce Type: new Abstract: Production LLM agents execute tool-calling loops, retrieval chains, and compositional workflows in multiple modes, yet execution semantics are often cou

What Does an LLM-Agent Leaderboard Rank Actually Compare?

arXiv:2609.07785v1 Announce Type: new Abstract: An LLM-agent leaderboard invites a familiar inference: an agent ranked above another is the better agent. Public evaluation logs may not support that co

Tutorials

Decomposing LLM-Judge Uncertainty to Target Expert Labels

arXiv:2609.06444v1 Announce Type: cross Abstract: An LLM judge evaluates outputs at scale. Experts should label only where it is least sure. Its natural escalation signal conflates two uncertainties:

tutorialsarxiv-cs-lg

9 Sept 2026

Deposon: An Auditable, Conservation-Guaranteed, Game-Theoretically Tested Scattering Layer over LLM Reasoning Paths

model-releasesarxiv-cs-lg

arXiv:2609.09001v1 Announce Type: cross Abstract: Multi-step LLM reasoning lacks a machine-recheckable ledger: discarded reasoning paths leave no auditable record. We propose the Deposon scattering la

9 Sept 2026

PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving

arXiv:2609.08965v1 Announce Type: cross Abstract: Ensuring the safety of autonomous driving is a critical challenge. Scenario-based testing is a systematic process used to validate Autonomous Driving

safetyarxiv-cs-ro

9 Sept 2026

CoLMIN: LLM-based Multi-Decision Path Negotiation for Cooperative Autonomous Driving

arXiv:2609.04807v1 Announce Type: cross Abstract: Multi-vehicle cooperative autonomous driving enhances the safety and reliability of autonomous driving systems through information sharing among conne

safetyarxiv-cs-cv

7 Sept 2026

Necessary or Sufficient? Evaluating LLM Explanations With Behavioural Evidence

arXiv:2609.05385v1 Announce Type: new Abstract: LLM decision components that can operate within agent workflows often produce action-relevant recommendations or judgements together with explanations.

7 Sept 2026

SiLR: Structure-Preserving Admission and Process Reward for LLM Tool Agents

arXiv:2609.04629v1 Announce Type: new Abstract: A runtime gate for an LLM tool agent is usually cast as a filter. In a ReAct loop a rejected proposal is followed by another at the same state, so the g

7 Sept 2026

EasySteer: A Unified Framework for High-Performance and Extensible LLM Steering

arXiv:2509.25175v3 Announce Type: replace-cross Abstract: Large language model (LLM) steering has emerged as a promising paradigm for controlling model behavior at inference time through targeted mani

4 Sept 2026

Privacy-Preserving Heterogeneous Multi-LLM Federated Inference for Cognitive Diagnosis

arXiv:2609.02947v1 Announce Type: cross Abstract: Significant challenges remain in AI-driven educational systems in balancing privacy preservation with accurate cognitive diagnosis. To overcome this,

4 Sept 2026

Applications

Architecting Conversational Data Systems for Stateless LLM APIs: The Hydration Proxy Pattern

arXiv:2609.01834v1 Announce Type: new Abstract: As enterprise platforms transition to conversational reasoning interfaces, the stateless nature of LLM APIs creates an architectural gap. While stateles

applicationsarxiv-cs-ai

Bilevel Coordinated Reflection: A Game-Theoretic Approach to Multi-Agent LLM Systems

arXiv:2609.02750v1 Announce Type: new Abstract: Multi-agent LLM systems commonly use an orchestrator to decompose a task for a team of workers and then improve through textual reflection. Despite stro

Evaluating the Evaluator: Summarization Metrics and LLM-Judges beyond English

arXiv:2503.17039v3 Announce Type: replace-cross Abstract: Automatic text summarization relies on automatic evaluation to quickly determine the quality of summarization models via automatic metrics and

Local Ai

Grounded, Compute-Efficient LLM Policy Agents for Energy-Poverty Equity in Physically-Constrained Peer-to-Peer Energy Markets

arXiv:2609.01918v1 Announce Type: new Abstract: Energy poverty is nearly absent from NLP-for-social-good, and the little existing work is either static retrieval/QA or relies on carbon-intensive cloud

local-aiarxiv-cs-cl

Incremental Pooled LLM Evaluation for Cost-Effective Retrieval Model Selection

model-releasesarxiv-cs-cl

arXiv:2609.02745v1 Announce Type: cross Abstract: Selecting a retrieval model for a production RAG system requires reliable comparative evaluation, but obtaining relevance judgments at scale is expens

LLM-as-a-Judge Is Not an Oracle: Why Self-Improving Agents Need Deterministic Guardrails

arXiv:2609.02246v1 Announce Type: new Abstract: Self-improving agent pipelines have a problem at their center. An optimizer rewrites prompts to score higher, and the score comes from a judge that is i

Rating the Raters: Rasch Measurement Theory for LLM Evaluation

arXiv:2608.27463v2 Announce Type: replace Abstract: LLMs now sit on every side of evaluation: as examinees scored on benchmarks, judges of other models' outputs, and raters of human-generated content.