Search: “llm”

Ranked by Position: Order Sensitivity as an Exploitable Attack Surface in LLM Listwise Recommenders

arXiv:2607.24869v1 Announce Type: cross Abstract: Large language models (LLMs) used as listwise rerankers in recommendation systems suffer from position bias when serializing candidate sets into promp

safetyarxiv-cs-cl

29 Jul 2026

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Tokens are All You Need: Dual-purpose Semantic IDs for Achieving LLM-Level I/O Efficiency in recommendation systems

DGX agent

arXiv:2607.24865v1 Announce Type: cross Abstract: Large-scale recommendation systems face 'Memory Wall' bottlenecks due to massive, dense embedding tables. While generative retrieval uses discrete tok

applicationsarxiv-cs-ai

29 Jul 2026

Toward an Organizational Science of Multi-Agent LLM Systems: Decoupling Who, How, and Which Algorithm

arXiv:2607.25446v1 Announce Type: new Abstract: Multi-agent frameworks built on large language models (LLMs) routinely entangle three logically distinct concerns: who is on the team (organization), ho

29 Jul 2026

How LLM Task-Adaptation Reshapes Alignment: A Multi-dimensional Study of Behavioral and Representational Drift

arXiv:2607.22676v1 Announce Type: new Abstract: Post-training is a key mechanism for adapting large language models to downstream tasks. While prior work suggests that task adaptation can alter a mode

Keyword Matters: Unveiling the Energy Sensitivity of On-Device LLM Prompting

arXiv:2607.22568v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed on mobile and embedded devices to improve privacy and reduce network latency. Yet on-device infer

LEX-EC: A Lexical Evidence-Channel Audit Framework for Zero-Shot LLM Personality Classification in Black-Box Settings

arXiv:2607.24435v1 Announce Type: cross Abstract: Large language models may easily assign personality labels from text, but model interpretability remains an open problem. To address this gap, we intr

LLM-Assisted Ontology Engineering and Construction of a French Legal Knowledge Graph

arXiv:2607.24551v1 Announce Type: new Abstract: Maintenance regulations are complex legal texts that are difficult to exploit when addressing a specific case and challenging to integrate into operatio

Same Question, Different Answers: Evaluating LLM Reliability Beyond Accuracy

arXiv:2607.22554v1 Announce Type: new Abstract: Large language models (LLMs) often achieve strong accuracy on benchmarks, yet it remains unclear how reliably they apply this knowledge when the same qu

StanceBench: A Benchmark for Audio LLM-Based Interpersonal Stance Evaluation from Speech

arXiv:2607.22658v1 Announce Type: new Abstract: Speech-to-speech dialogue models increasingly depend on prosody and interactional nuance to convey social intent, yet benchmarks for these cues remain l

Efficient Online LLM Watermark Detection via Rao-Blackwellized E-Processes

arXiv:2607.21958v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly deployed, reliable and efficient mechanisms for distinguishing AI-generated text from human-written c

researcharxiv-cs-lg

27 Jul 2026

MEUSLI: a Multilingual Projector for LLM-based ASR and Beyond

arXiv:2607.22100v1 Announce Type: new Abstract: Lightweight projectors are an established way to connect pre-trained speech encoders with large language models (LLMs), mapping acoustic features into t

researcharxiv-cs-cl

27 Jul 2026

Auditing Evidence Use in Medical LLM Diagnosis

arXiv:2607.20848v1 Announce Type: new Abstract: Medical LLMs are often evaluated by whether they select the correct diagnosis, but diagnostic accuracy alone does not show whether the model used the ca

Compile, Then Page: Executable SOP Programs and a Capability-Gated Runtime for Procedural LLM Agents

arXiv:2607.11346v3 Announce Type: replace Abstract: Enterprise agents must follow long-horizon, conditional, safety-critical standard operating procedures (SOPs). We compile machine-readable SOP const

From Resource Flow to Executable Tests: Petri-Net-Guided LLM Test Generation for Concurrent Stateful Rust APIs

arXiv:2607.21530v1 Announce Type: cross Abstract: Concurrent stateful library APIs expose behavior through evolving resource ownership, lifecycle states, and competing interleavings. Large language mo

applicationsarxiv-cs-ai

Marking the Wrong Symptoms: Evaluating LLM Watermarks in Medical Texts

arXiv:2607.20462v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into clinical workflows, stressing the need for reliable traceability of model-generated output

Overcoming the Communication-Performance Tradeoff in LLM Pretraining

arXiv:2508.15706v3 Announce Type: replace Abstract: Communication-efficient distributed training algorithms (e.g., DiLoCo) have received considerable interest due to their benefits for training large

local-aiarxiv-cs-lg

Reliability-Aware LLM Alignment from Inconsistent Human Feedback

arXiv:2607.20515v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) is critical for aligning Large Language Models (LLMs) with human preferences. However, its efficacy is

Routing Without Training: Controllable-Ratio LLM Offloading via Reliability Gating

arXiv:2607.20481v1 Announce Type: new Abstract: Local-cloud collaboration is a practical way to deploy large language models under resource constraints, but existing methods often rely on trained rout

Self-Evolving Recommendation System: End-To-End Autonomous Model Optimization With LLM Agents

arXiv:2602.10226v2 Announce Type: replace-cross Abstract: Optimizing large-scale machine learning systems, such as recommendation models for global video platforms, requires navigating a massive hyper

SOAP, Muon, and Beyond: Pushing LLM Pretraining Scales

arXiv:2607.20548v1 Announce Type: cross Abstract: Higher-order optimizers such as Muon and SOAP offer faster convergence than AdamW, but their computational cost and numerical stability challenges hav

What is Good? Extracting and Testing Implicit Theories of Literary Quality from LLM Reasoning Traces

model-releasesarxiv-cs-cl

arXiv:2607.20425v1 Announce Type: new Abstract: What makes writing 'good' remains a persistent question in literary studies and computational linguistics. We present a two-study investigation of how r

Wisdom of LLM Crowds: Aggregation and Contamination in Language Model Ensembles

arXiv:2607.18269v2 Announce Type: replace Abstract: The wisdom of crowds -- the finding that aggregating judgments across individuals often outperforms the best individual -- has been extensively stud

Tutorials

Auditing Retrieval-Augmented LLM Hypotheses for Longitudinal Cell Painting Morphology

arXiv:2607.19415v1 Announce Type: cross Abstract: High-content morphological profiling (Cell Painting) yields sensitive, high-dimensional signatures of cellular state, but translating longitudinal mor

tutorialsarxiv-cs-ai

BaseRT: Advancing Best-in-Class LLM Inference with Apple M5 Neural Accelerators

arXiv:2607.19438v1 Announce Type: cross Abstract: Apple's M5 generation introduces a redesigned GPU architecture in which every core carries a dedicated Neural Accelerator: on-die matrix units exposed

FedLSG: LLM-Enhanced Semantic Calibration for Federated Graph Backdoor Defense

arXiv:2607.19674v1 Announce Type: cross Abstract: Federated Graph Neural Networks (FedGNNs) are highly vulnerable to backdoor poisoning, yet existing defenses typically rely on rule-based approaches t

Fidelity Before Structure: Verbatim Chunks Beat Lossy Artifact Extraction in Long-Conversation LLM Memory

arXiv:2601.00821v4 Announce Type: replace Abstract: A growing class of conversational-memory systems compresses dialogue history into structured artifacts (extracted facts, decisions, or events) on th

LENS: LLM-guided Environment Simplification for Planning and Control in Clutter

arXiv:2607.19633v1 Announce Type: new Abstract: Despite recent advances in general-purpose robotic manipulation, real-world multi-object clutter remains challenging to handle for today's prevalent app

applicationsarxiv-cs-ro

Small, Free, and Effective: Orchestrating Open-Weight Small Language Models to Outperform Single LLM for Malware Analysis

arXiv:2607.20216v1 Announce Type: cross Abstract: Malware analysis demands rapid interpretation of complex detonation reports spanning filesystem, network, and process behaviours. While large language

Stateful Guardrails for Multi-Turn LLM Systems: A Conversational Risk Accumulation Framework

arXiv:2607.19361v1 Announce Type: cross Abstract: Most safety guardrails for large language models (LLMs) evaluate each prompt-response pair in isolation, which misses failures that arise only over a

Understanding the Impact of Linguistic Realization Choices on LLM Stance with Causal Tracing

arXiv:2607.20115v1 Announce Type: new Abstract: Large language models (LLMs) are known to be sensitive to prompt and input formulations. However, existing studies have focused on lexical realization a

local-aiarxiv-cs-cl

When Reasoning Narrows the Move: Diversity Collapse in LLM Game Play

arXiv:2607.19523v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) is widely used to adapt large language models to downstream tasks, but its effect on behavioral diversity in sequential dec

safetyarxiv-cs-cl

Agents

Interpretable and Verifiable Hardware Generation with LLM-Driven Stepwise Refinement

arXiv:2606.19387v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have achieved remarkable success in software development. However, they are susceptible to hallucinations, meanin

agentsarxiv-cs-ai

15 Jul 2026

Mobility-Aware Cache Framework for Scalable LLM-Based Human Mobility Simulation

arXiv:2602.16727v2 Announce Type: replace Abstract: Simulating large-scale human mobility is fundamental to understanding population movement patterns and supporting real-world geospatial applications

applicationsarxiv-cs-ai

15 Jul 2026

SheetMind: An End-to-End LLM-Powered Multi-Agent Framework for Spreadsheet Automation

arXiv:2506.12339v2 Announce Type: replace-cross Abstract: We present SheetMind, a modular multi-agent framework powered by large language models (LLMs) for spreadsheet automation via natural language

15 Jul 2026

Can We Trust LLM's Logic? Quantifying Uncertainty, Coherence, and Robustness via a Graph-Based Framework

arXiv:2607.08017v1 Announce Type: cross Abstract: Large-Language Models (LLMs) can be prone to flawed and unfaithful reasoning that decoding strategies like Self-Consistency (SC) fail to detect as the

PhyMAGIC: Physical Motion-Aware Generative Inference with Confidence-guided LLM

arXiv:2505.16456v3 Announce Type: replace Abstract: Recent advances in 3D content generation have amplified demand for dynamic models that are both visually realistic and physically consistent. Howeve

safetyarxiv-cs-cv

Scalable and Culturally Specific Stereotype Dataset Construction via Human-LLM Collaboration

arXiv:2607.07895v1 Announce Type: new Abstract: Research on stereotypes in large language models (LLMs) has largely focused on English-speaking contexts, due to the lack of datasets in other languages

safetyarxiv-cs-cl

SQuaD-SQL: Efficient Text-to-SQL with Small Language Models via LLM-Guided Knowledge Distillation

model-releasesarxiv-cs-cl

arXiv:2607.08161v1 Announce Type: new Abstract: Text-to-SQL is a fundamental task in natural language processing that enables users to interact with structured databases using natural language. While

Framing Instability in LLM Ethical Stance: Auditing Negation Sensitivity in Moral Dilemmas

arXiv:2601.21433v2 Announce Type: replace Abstract: Language models are increasingly consulted on ethically consequential questions, yet the stance a model expresses may not survive a change in framin

Immersive Social Interaction with VR and LLM-Assisted Humanoids

arXiv:2607.07430v1 Announce Type: new Abstract: Humanoid robots can extend human presence to remote, constrained, or hazardous environments, but existing teleoperation interfaces often require physica

researcharxiv-cs-ro

Tutorials

MILES: Modular Instruction Memory with Learnable Selection for Self-Improving LLM Reasoning

arXiv:2607.06974v1 Announce Type: new Abstract: Large language models (LLMs) increasingly improve their reasoning at test time via additional computation, yet most existing works treat each problem in

tutorialsarxiv-cs-cl

Agents

SpaCellAgent: A Self-Evolving LLM-Based Multi-Agent Framework for Trajectory Analysis

arXiv:2607.07467v1 Announce Type: new Abstract: Spatial and Single-cell transcriptomics are transformative in deciphering cellular dynamics. As the fundamental paradigm for reconstructing cell develop

agentsarxiv-cs-ai

SynthAVE: Scalable Synthetic Labeling for E-Commerce with LLM-Arena Validation

arXiv:2607.07469v1 Announce Type: cross Abstract: Fine-tuning large language models (LLMs) for e-commerce attribute extraction requires labeled data representative across thousands of product types, a

Agents

From Voting to Agent Collaboration: Answer-Type-Aware LLM Pipelines for BioASQ 14b

arXiv:2607.06452v1 Announce Type: cross Abstract: Biomedical question answering requires not only accurate extraction of information from scientific literature but also reliable integration of evidenc

agentsarxiv-cs-ai

Improving LLM-Generated Process Model Quality Through Reinforcement Learning: The Role of Reward Function Design

arXiv:2607.06175v1 Announce Type: cross Abstract: Large language models (LLMs) can generate BPMN process models from natural-language descriptions, yet supervised fine-tuning (SFT) limits their output

Information Limits and Attractor Dynamics in Economies of Frontier LLM Agents: A Pre-Registered Test

arXiv:2607.06001v1 Announce Type: new Abstract: We report a pre-registered, two-part experiment on small economies of frontier language-model agents (Claude Opus 4.8), testing two quantitative predict

LLM-Guided Measurement Credibility Correction for Trustworthy Industrial Process Inference

arXiv:2607.06111v1 Announce Type: cross Abstract: Industrial prediction and soft sensing depend on credible input measurements. In field deployment, a predictor may receive biased, delayed, stale, or

Mitigating Errors in LLM-Generated Web API Invocations via Retrieval-Augmented Generation and Constrained Decoding

arXiv:2607.05936v1 Announce Type: cross Abstract: Integration of web APIs is a cornerstone of modern software systems, yet writing correct web API invocation code remains challenging due to complex an

applicationsarxiv-cs-lg