Search: “llm”

Competence-Preserving Resume Perturbations Expose Presentation Sensitivity in LLM Screening

arXiv:2609.16517v1 Announce Type: cross Abstract: Resume screeners must infer job-relevant competence from resumes whose presentation can vary substantially in wording, structure, stylistic polish, an

16 Sept 2026

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Decoy Direction Optimization: A Post-Hoc Defense Against LLM Abliteration

DGX agent

arXiv:2609.16204v1 Announce Type: cross Abstract: Safety guardrails in open-weight language models can be readily bypassed using Refusal Feature Ablation (RFA), a technique that identifies and project

model-releasesarxiv-cs-cl

16 Sept 2026

JustFit: 200K-Token LLM Serving on a 24 GiB Laptop with Just-in-Time State Management

arXiv:2609.17475v1 Announce Type: new Abstract: Capable open-weight models make local coding and reasoning attractive, but their context and execution state strain laptop memory. We present JustFit, a

local-aiarxiv-cs-ai

16 Sept 2026

Bridging Network Psychometrics and Artificial Intelligence: An Ising-Potts Model with LLM-Derived Weights

arXiv:2609.08797v2 Announce Type: replace-cross Abstract: The Potts model extends the Ising model to multinomial data. We introduce a Rater Ising-Potts model that uses agreement indicators between pai

local-aiarxiv-cs-cl

Bridging Thought and Action: Taming Long-Horizon Instability in Open-Source LLM Agents with a MetaTool-Enhanced ROS Framework

arXiv:2609.13335v1 Announce Type: cross Abstract: Large Language Models (LLMs) have enabled more natural human-robot interaction, but open-source models often exhibit unstable long-horizon reasoning a

FLoKD: Adaptive Knowledge Distillation for Federated Low-Rank LLM over Wireless Networks

arXiv:2609.13580v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong capabilities across a wide range of natural language processing tasks. However, conventional fine-

Generate to Explore, Select to Exploit: Aligning LLM-based Headline Generation with Personalized Recommendation

arXiv:2609.15094v1 Announce Type: cross Abstract: In industrial recommendation feeds, presenting a static headline for an item often fails to satisfy the diverse, multimodal interests of the user popu

safetyarxiv-cs-ai

MCPAgentBench: A Real-world Task Benchmark for Evaluating LLM Agent MCP Tool Use

arXiv:2512.24565v4 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly serving as autonomous agents, and their utilization of external tools via the Model Context Protocol (

Per-Matrix Optimality Is Not Enough: Three-Level Optimization for Low-Rank LLM Compression

arXiv:2609.15838v1 Announce Type: cross Abstract: Per-matrix singular value decomposition (SVD) truncation is Eckart-Young optimal in the whitened Frobenius norm, but errors from independently compres

PhysMent: An Interactive Approach For LLM Reasoning In Physics Problems

arXiv:2609.13152v1 Announce Type: new Abstract: Large language models (LLMs) perform strongly on static science benchmarks, yet their ability to reason about the physical world through active experime

Route, Don't Fix: Regime-Dependent Decoding Correction and a Trajectory-Gated Router for Reliable Clinical LLM Answer Selection

arXiv:2609.14825v1 Announce Type: cross Abstract: Large language models (LLMs) are often deemed unsafe for clinical question answering because of their tendency to hallucinate. Retrieval augmentation,

SAILOR: Solver-Assisted Interactive LLM-based Optimization Recovery

arXiv:2609.13945v1 Announce Type: new Abstract: Natural-language descriptions of optimization problems may be incomplete or vague about numerical information that a solver requires, including costs, c

Same Patient, Different Order: Action-Level Reliability of Clinical LLM Agents Under Repeated Runs

arXiv:2609.13582v1 Announce Type: cross Abstract: A clinical agent benchmark can report the same verdict on identical inputs while the agent files a materially different order on each run. Such agents

Self-Indexing Attention for Compression-Compatible Sparse Long-Context LLM Inference

arXiv:2609.13205v1 Announce Type: cross Abstract: Sparse long-context inference requires efficient token retrieval in both prefill and decode. Existing methods often use different retrieval strategies

researcharxiv-cs-cl

The Language-Energy Divide: Measuring Energy Costs of Multilingual LLM Inference

arXiv:2606.21869v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed in multilingual settings, yet the energy costs of serving these models across different

When Can You Trust Your Synthetic Users? Diagnostics and Corrections for LLM Consumer Panels

arXiv:2609.13148v1 Announce Type: cross Abstract: Large language models are increasingly deployed as synthetic consumer panels, promising 97% cost reductions over traditional surveys. Yet aggregate va

Diverse Minds, Divided Networks? Personality Composition, Polarization, and Collective Intelligence in LLM-Based Social Simulations

arXiv:2609.12444v1 Announce Type: cross Abstract: Simulated societies of large language model agents are used to study online polarization, and separately to study collective intelligence, but the two

agentsarxiv-cs-cl

Explainable Prediction from Mobile Sensing Data through LLM-guided Concept Integration

arXiv:2609.11995v1 Announce Type: new Abstract: Mobile sensing enables longitudinal monitoring of behavioral and physiological patterns in everyday settings. However, accurate prediction remains chall

researcharxiv-cs-lg

Granularity-Adaptive Credit Assignment for Long-Horizon LLM Agent Reinforcement Learning

arXiv:2609.12424v1 Announce Type: new Abstract: Reinforcement learning is now the standard way to train large language model agents on long-horizon tasks, where dozens of interdependent actions preced

safetyarxiv-cs-lg

Judging by the Cover: Cleaning LLM Truthfulness Benchmarks to Avoid Surface-Level Feature Leakage

arXiv:2609.13003v1 Announce Type: new Abstract: Binary-choice truth benchmarks ask models to choose between a correct and an incorrect answer, but if the two answers differ systematically in surface-l

researcharxiv-cs-cl

LifeMem: Enabling Lifelong Experience Reuse for LLM Agents

arXiv:2609.12655v1 Announce Type: new Abstract: Large language model agents are expected to continuously adapt to new tasks and environments over their lifetime by reusing past experience. However, ex

agentsarxiv-cs-cl

The Battery Price of edge AI: A study of the Environmental Impact of LLM Inference on Mobile Devices

arXiv:2609.11940v1 Announce Type: cross Abstract: The rapid diffusion of generative artificial intelligence raises privacy, latency, and performance concerns that motivate a shift toward 'local-first'

local-aiarxiv-cs-lg

BenchShield: Formal Model-Backed Instrumentation for Reward Integrity in LLM-Agent Evaluation Infrastructure

arXiv:2609.11028v1 Announce Type: cross Abstract: LM-agent benchmarks increasingly function as interactive evaluation infrastructure. Agents observe state, call tools, modify workspaces, submit artifa

Beyond Static Guarantees: Measuring the Static-Pass Dynamic-Fail Gap in Security-Sensitive and LLM-Generated Python Code

arXiv:2609.10762v1 Announce Type: cross Abstract: Advances in large language models (LLMs) fuel the quest for scalable methods to assess the security of generated and security-sensitive software. Stat

Natural Language Access to Domain-Specific Metadata: A Reusable Framework for LLM Query Generation

arXiv:2607.18029v2 Announce Type: replace-cross Abstract: Researchers need to answer ad-hoc questions about the contents of domain-specific archives but often lack the expertise to write structured qu

Rescaling Confidence: What Scale Design Reveals About LLM Metacognition

arXiv:2603.09309v3 Announce Type: replace Abstract: Verbalized confidence, in which LLMs report a numerical certainty score, is widely used to estimate uncertainty in black-box settings, yet the confi

RAG-Safety-Bench: Reliable Evaluation of Retrieval-Augmented LLM Safety

arXiv:2609.11758v1 Announce Type: new Abstract: Allowing large language models (LLMs) to retrieve information from a set of trusted documents can increase reliability and reduce hallucination. However

11 Sept 2026

The PIMMUR Principles: Ensuring Validity in Collective Behavior of LLM Societies

arXiv:2509.18052v4 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used to simulate human collective behavior, yet claims that such simulations are human-like remain lar

agentsarxiv-cs-cl

11 Sept 2026

Privacy-Preserving Split Learning for Federated LLM Fine-Tuning

arXiv:2609.09794v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) on domain-specific data is essential for downstream adaptation. In many deployments, a participant cannot hold

local-aiarxiv-cs-lg

Reducing Hallucinations in LLM-based Scientific Literature Analysis Using Peer Context Outlier Detection

arXiv:2604.01461v2 Announce Type: replace Abstract: Reducing hallucinations in Large Language Models (LLMs) is essential for accurate data extraction from large text corpora. Current methods, like pro

SWORD: Wikidata-based Distortions Reveal Hidden Cross-Lingual Inconsistencies in LLM Factual Error Rejection

arXiv:2609.09349v1 Announce Type: new Abstract: Modern LLMs demonstrate impressive multilingual performance, yet standard benchmarks primarily reward selecting correct answers rather than evaluating g

The Audit Decides the Verdict: Instrument Effects Rival Demographic Bias in LLM Decision Audits

arXiv:2609.09048v1 Announce Type: cross Abstract: Whether a language model looks demographically biased can depend on how the audit asks its question. A charitable-aid benchmark reports that the same

We're Cooked! - Probing LLM Political Alignment Via Conflict-Framed Recipe Translation

arXiv:2609.07568v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed for translation tasks, yet their implicit political positioning in such contexts remains unders

When Does Defendant Statement Matter? A Study of Bias and Persuasion in LLM-Simulated Jurors

arXiv:2609.09887v1 Announce Type: new Abstract: LLMs have been used to simulate human decision-making in professional settings, yet their behaviors in common-law jury trials remain unexplored. We stud

Why Do LLM Agents Fail in Exploring New Environments? A World-Modeling Perspective

arXiv:2510.15047v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) as agents often fail to improve in new environments. We identify and characterize a failure mode we call explorat

safetyarxiv-cs-cl

Diagnosing LLM Reranker Behavior Under Fixed Evidence Pools

arXiv:2602.18613v2 Announce Type: replace Abstract: Standard reranking evaluations study how a reranker orders candidates returned by an upstream retriever. This setup couples ranking behavior with re

safetyarxiv-cs-lg

Don't let FOMO win if you're interested in local llm from a hobby/learning aspect

model-releasesr-localllama

Just a reminder for those out there itching to get into local llms - don't let FOMO or 'gear acquisition syndrom' take over. No matter the hobby, it's so easy to get stuck in a trap where we buy more

How Long Until Your Robot Ignores You? A Safety Benchmark for LLM Orchestrators in Human-Humanoid Collaboration

model-releasesarxiv-cs-ro

arXiv:2609.07288v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly employed to orchestrate robot behavior through natural-language interfaces, yet no benchmark exists to eva

LatentMD: Benchmarking Markdown Boundary Failures in LLM-Generated Text

arXiv:2609.06993v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly generate Markdown that is consumed by renderers, agents, code extractors, and structured downstream pipeline

ReST-RL: Reinforcing LLM Reasoning through Unified Self-Training and Value-Guided Search

arXiv:2508.19576v3 Announce Type: replace-cross Abstract: With respect to improving the reasoning accuracy of LLMs, the representative reinforcement learning (RL) method - Group Relative Policy Optimi

safetyarxiv-cs-lg

Robustness of LLM-Generated SystemVerilog Assertions to Semantics-Preserving RTL Transformations

arXiv:2609.05658v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly being explored for automating SystemVerilog Assertion (SVA) generation, yet most evaluations report correc

The Oversight Gap: What LLM Safety Monitors Miss, and Why It Is Not Capability

arXiv:2609.07162v1 Announce Type: new Abstract: Several properties safety monitors are asked to certify, among them cross-tenant noninterference, sandbagging and evaluation awareness, are 2-safety hyp

VEX-Bench: Benchmarking LLM Agents for Assessing Exploitability of Software Supply Chain Vulnerabilities

arXiv:2609.08040v1 Announce Type: cross Abstract: The software supply chain has become an increasingly exposed attack surface because of its reliance on intricate yet fragile dependencies. Existing de

Aplaud: Adaptive Personalized Low-Rank Decomposition for User-Specific LLM

arXiv:2609.04738v1 Announce Type: new Abstract: In this paper, we study the problem of personalized survey response prediction using fine-tuned large language models (LLMs). This task poses unique cha

HarvestBench: Measuring Whether LLM Agents Will Pay to Avoid Killing Animals

arXiv:2609.04444v1 Announce Type: new Abstract: Benchmarks for the side effects an agent causes on the way to a goal already exist, but HarvestBench is the first to put a price on avoiding the side ef

Moral Competence Before Moral Content: Why LLM Agents Lack the Prerequisites for Coherent Alignment

arXiv:2609.05036v1 Announce Type: new Abstract: AI alignment requires AI systems to adhere to human norms, values, or intentions. Under value pluralism there is no correct target, but a shared prerequ

safetyarxiv-cs-ai

REFINE: LLM Refinement over Budgeted Text-Attributed Graphs for Personalized Medical Concept Representation

arXiv:2609.04415v1 Announce Type: cross Abstract: Learning rich medical concept representations is essential for EHR prediction. Text-attributed knowledge graphs (TKGs) provide a natural foundation by

safetyarxiv-cs-ai

Clean Engineering, Unstable Measurement: A Preregistered Reliability Failure of Black-Box LLM Observers on Shared Endpoints

arXiv:2609.04198v1 Announce Type: new Abstract: Language-model judges now gate training data, score generations, and drive leaderboards. The judge is then a measurement instrument, resting on one rare