Search: “llm”

VEX-Bench: Benchmarking Verification Complexity of LLM-Generated Misinformation

arXiv:2609.35028v1 Announce Type: new Abstract: Large language models (LLMs) have made misinformation inexpensive to produce but not to verify, creating a growing asymmetry in the information ecosyste

29 Sept 2026

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Applications

AgentRecommender: LLM Agents Enable Customizable Recommender Systems on the User Side

DGX agent

arXiv:2609.31166v1 Announce Type: cross Abstract: Recommender systems have traditionally been developed for platforms. However, this has given rise to many phenomena that may be advantageous for platf

applicationsarxiv-cs-ai

28 Sept 2026

HybridInfer: Thermal-Aware Reinforcement-Learning Tier Routing for On-Device, Edge, and Cloud LLM Inference

arXiv:2609.30270v1 Announce Type: new Abstract: On-device inference with small language models keeps user data local, works offline, and incurs no per-query cost, so the on-device tier is preferred wh

28 Sept 2026

The Price of Thought: Does Test-Time Reasoning Pay in LLM Trading?

arXiv:2609.30705v1 Announce Type: new Abstract: While inference-time reasoning in large language models (LLMs) promises better decision making, its higher computational cost may not yield better econo

28 Sept 2026

Towards Understanding LLM-Based Log Anomaly Detection: An Empirical Study of Performance, Efficiency, and Robustness

arXiv:2609.31371v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated promising performance in log anomaly detection, yet how their adaptation strategies, architectures, and d

28 Sept 2026

ADM-Planner: LLM-Guided Long-Horizon Planning for Mobile Manipulators with Attention-Enhanced Dynamic Memory

model-releasesarxiv-cs-ro

arXiv:2609.29212v1 Announce Type: new Abstract: Large language models can decompose mobile-manipulation goals into long action sequences, but the resulting plans remain reliable only while their world

BLADE: Distilled LLM Regularization for Calibrated Knowledge Graph Completion

arXiv:2609.29487v1 Announce Type: new Abstract: Knowledge graph completion models optimize ranking, although many downstream applications require calibrated probabilities. We present BLADE, a variatio

researcharxiv-cs-lg

Cultural Divergence Preservation: Diagnosing Flattening and Caricature in LLM-Simulated Survey Populations

arXiv:2609.29928v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as synthetic survey respondents to estimate population response distributions. In cross-cultural su

researcharxiv-cs-ai

LLM Agents Can Easily Tamper With Their Own Traces

arXiv:2609.30266v1 Announce Type: cross Abstract: Asynchronous monitoring, incident investigations, and compliance audits primarily rely on agent traces to reconstruct what happened. These analyses as

SPARQL-LLM: Real-Time SPARQL Query Generation from Natural Language Questions

arXiv:2512.14277v2 Announce Type: replace-cross Abstract: The advent of large language models is contributing to the emergence of novel approaches that promise to better tackle the challenge of genera

Delegated Misalignment: How Multi-Agent Structures Amplify LLM Safety Risks

arXiv:2609.27900v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in multi-agent systems where a principal agent decomposes tasks and delegates them to subordinate

24 Sept 2026

Improving LLM-based Autonomous Web Agents with Filtering

arXiv:2609.27770v1 Announce Type: new Abstract: Autonomous web agents, powered by Large Language Models (LLMs), have garnered significant attention for automating various web-based tasks with multi-st

24 Sept 2026

Reporting Under Pressure: Separating Factual and Tonal Sycophancy in LLM Statistical Analysis

arXiv:2609.27756v1 Announce Type: new Abstract: Large language models are increasingly asked to analyze data and report what the results mean, a task distinct from the belief- or preference-alignment

safetyarxiv-cs-ai

24 Sept 2026

Calibration as a First-Class Criterion in LLM Evaluation

arXiv:2609.26489v1 Announce Type: new Abstract: Calibration of language models -- the alignment between expressed or implicit confidence and empirical correctness -- is a well-studied subfield within

safetyarxiv-cs-cl

Applications

Explainable Recommendations at Scale: LLM Rationales for YouTube Music Artist Discovery

arXiv:2609.23877v1 Announce Type: new Abstract: Modern music streaming platforms face a persistent tradeoff: exploiting familiar content versus driving the exploration of novel items. While users freq

applicationsarxiv-cs-ai

Local Ai

OptiSkill: A Hierarchical and Evolving SkillBank for LLM-Based Optimization Modeling

arXiv:2609.22987v1 Announce Type: new Abstract: Automated operations research (OR) modeling requires LLMs to translate natural-language decision problems into correct mathematical programs. Existing m

local-aiarxiv-cs-ai

SafetyFlow: An Agent-Flow System for Automated LLM Safety Benchmarking

arXiv:2508.15526v2 Announce Type: replace Abstract: The rapid proliferation of large language models (LLMs) has intensified the requirement for reliable safety evaluation to uncover model vulnerabilit

Do LLMs Choose Like Humans? Using Cognitive Theory to Evaluate LLM Decision-Making

arXiv:2609.22225v1 Announce Type: new Abstract: Large language models (LLMs) exhibit a range of human-like decision-making behaviors, but whether these reflect similar underlying mechanisms or surface

LLM-based Conversational AI Knowledge Assistant for MyBuddy Humanoid Robot

arXiv:2609.24742v1 Announce Type: new Abstract: Humanoid robots are increasingly being popular and developed for human-centered applications, yet their ability to provide intelligent conversations and

researcharxiv-cs-ro

Measured Joules, Learned Routes: Learning to Route for Energy-Efficient LLM Serving

arXiv:2609.23085v1 Announce Type: cross Abstract: Large language models (LLMs) and agentic AI systems are creating rapidly growing inference energy demands as model sizes grow and reasoning trajectori

ParA-LLM: A Unified Approach to Paralinguistic and Acoustic Speech Understanding

arXiv:2609.22771v1 Announce Type: cross Abstract: Recent advances in Audio LLMs have achieved human-level speech recognition, yet existing systems struggle to capture paralinguistic aspects such as sp

Applications

The Corroboration Illusion: When More News Makes LLM Forecasts Less True

arXiv:2609.22246v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to forecast real-world events by retrieving and reasoning over news. We show that this dependence on

applicationsarxiv-cs-cl

Auditing a KB Elicitation of Frontier LLM Knowledge: A Multi-dimensional Analysis of GPTKB v1.5

arXiv:2510.07024v3 Announce Type: replace-cross Abstract: LLMs are remarkable artifacts that have revolutionized a range of knowledge-intensive tasks. A significant contributor is their factual knowle

ExpBoN: Exponential-Noise Best-of-n for Efficient Test-Time LLM Alignment

arXiv:2609.21899v1 Announce Type: new Abstract: Best-of-n (BoN) sampling is a simple yet effective inference-time alignment method, but hard maximization provides only coarse control over the trade-of

safetyarxiv-cs-lg

Has anyone tried ingesting 1k-100k scientific papers into LLM wiki?

model-releasesr-localllama

I want to try using AI to find possibly overlooked interventions for irritable bowel syndrome (IBS) and create a cocktail of interventions that AI thinks would give the highest expected benefit, attac

Jev introduces a new shape of LLM - System One, aka Decision Models

model-releasessimon-willison

Last week TypeSafe AI unveiled Jev, their first example of a new category of model that they are calling 'System One models' (I'm with Maggie Appleton, I think 'decision models' is a better name for t

Local Ai

Newer updates from the last post: Use Local LLM within VScode agent chat

github repo: https://github.com/athulg93/vscode-localllm Alright, So i have made some major updates to this extension Update the git flow: Found out that there were some issues and the / part was a me

local-air-ollama

Local Ai

SpecQuant: Speculative Decoding with Multi-Parent Quantization for Adaptive LLM Inference

arXiv:2609.21704v1 Announce Type: new Abstract: Running large language models (LLMs) locally continues to be limited by restrictions of compute and memory on consumer hardware. The popular acceleratio

local-aiarxiv-cs-lg

To the dozens of 3x 3090 Local LLM people - I found our current best fit

model-releasesr-localllama

I have been anti-low quant. I can feel the difference, I swear. I am also anti-quantized KV cache. I have been burned there - I think the tradeoffs compound, especially at longer contexts. Due to this

20 Sept 2026

ALIBI: Adversarial Legitimacy Injection in Binary Input against LLM Malware Analyzers

arXiv:2609.19722v1 Announce Type: cross Abstract: Large language models are being integrated into malware triage workflows as reasoning components that summarize static evidence and produce analyst-fa

BuildBench: Benchmarking LLM Agents on Compiling Real-World Open-Source Software

arXiv:2509.25248v2 Announce Type: replace-cross Abstract: Automatically compiling open-source software (OSS) projects is a vital, labor-intensive, and complex task, which makes it a good challenge for

Tutorials

Designing Against Deskilling: Metacognitive Feedback Reduces Cognitive Offloading to LLM Assistants

arXiv:2609.20143v1 Announce Type: cross Abstract: Cognitive offloading to AI can reduce opportunities to practice skills, creating risks of deskilling. However, it remains unclear how to prevent deski

tutorialsarxiv-cs-ai

SAFARI: An Industrial Benchmark for LLM-Assisted Hazard Analysis and Risk Assessment

arXiv:2609.20584v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly considered for safety-critical engineering, yet their reliability in regulated functional-safety workflows

Applications

Semantic Layer Induction from Raw Telemetry via Hierarchical LLM and RAG Abstraction

arXiv:2609.19615v1 Announce Type: cross Abstract: Modern applications generate massive volumes of raw telemetry data, but translating those noisy, heterogeneous event streams into actionable business

applicationsarxiv-cs-ai

STR-Agent: An LLM-Driven Agent for QoS-Aware Routing in LEO Satellite Networks

arXiv:2609.20347v1 Announce Type: cross Abstract: LEO satellite networks feature dynamic topologies, time-varying links, and diverse service requirements, which make conventional routing schemes diffi

safetyarxiv-cs-ai

The Role of Fine-grained Harm Signals in LLM Safety

arXiv:2609.19366v1 Announce Type: new Abstract: Prior work has shown that internal harmfulness representations in large language models vary across risk categories, while sharing a common general harm

safetyarxiv-cs-cl

What studies isolate back-and-forth LLM interaction from one-way sharing and self-refinement [D]

model-releasesr-machinelearning

I'm trying to find out whether back-and-forth between two different LLMs improves task success beyond strong alternatives under a controlled resource budget — and I'd rather hear that it's already set

A Four-Stage Decomposition of Word-Problem Solving and Mechanistic Fragility in LLM Math Reasoning

arXiv:2609.17804v1 Announce Type: new Abstract: Large language models solve grade-school math word problems with high accuracy, yet a single irrelevant clause inserted into the problem can collapse it

researcharxiv-cs-ai

A Zeroth-Order Paradigm for LLM Preference Alignment

arXiv:2609.19144v1 Announce Type: cross Abstract: Direct preference alignment methods are widely used to align large language models (LLMs) with human preferences because of their computational and me

DyMT-ESB: Dynamic Multi-Turn Evaluation of Social Bias in User-LLM Interactions

arXiv:2609.18649v1 Announce Type: new Abstract: Warning: This paper contains examples of stereotypes and social bias. LLMs are increasingly used in interactive settings by the general public, making t

safetyarxiv-cs-cl

Encoder Awakening via Adapters: Effective Domain-Adaptive Fine-tuning of Speech-LLMs

arXiv:2609.17981v1 Announce Type: cross Abstract: Speech Large Language Models (Speech-LLMs), typically built from a pre-trained speech encoder, a modality projector, and an LLM fine-tuned with Low-Ra

researcharxiv-cs-cl

Future of general LLM work (interp/inference/alignment) vs agentic/physical AI (VLA, multimodal) for career [D]

I'm at a crossroads with two grad school options that would take me in somewhat different research directions, and I wanted some general advice on these fields, their growth, and industry alignment. I

safetyr-machinelearning

Agents

Hypothesis-Driven Autonomous Materials Synthesis with Multimodal LLM Agents

arXiv:2609.18598v1 Announce Type: cross Abstract: Self-driving laboratories can explore synthesis conditions autonomously, but their decision-making layer is typically a black-box optimizer, and the o

agentsarxiv-cs-ai

Tutorials

LIGE-GR: A Smooth Leap from Ranking to Generative Recommendation in the LLM Era

arXiv:2609.18148v1 Announce Type: new Abstract: The remarkable success of large language models (LLMs) has provided important inspiration for the next generation of recommender systems. Structurally,

tutorialsarxiv-cs-lg

Monitoring and Discovering Reward Hacking with Internal Representations during LLM Evaluations

arXiv:2609.19101v1 Announce Type: new Abstract: As models scale, reward hacking becomes more frequent, more sophisticated, and more consequential. Does it leave a telltale signature in model represent

Beyond the Name: Demographic Leakage in De-Identified Resumes and Evaluation Artifacts in LLM Bias Audits

arXiv:2609.16501v1 Announce Type: cross Abstract: De-identified resume screening assumes that redacting explicit fields prevents ethnocultural inference; however, recent audits attribute residual leak

safetyarxiv-cs-ai

16 Sept 2026

CoAdapt: An LLM-based Framework for Adaptive Collaborative Perception in IIoT Robotic Swarms

arXiv:2609.16852v1 Announce Type: new Abstract: Industrial IoT environments increasingly deploy autonomous mobile robots for tasks such as material handling, product assembly, or infrastructure inspec

16 Sept 2026

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