Search: “llm”

Policy Split: Incentivizing Dual-Mode Exploration in LLM Reinforcement with Dual-Mode Entropy Regularization

arXiv:2604.11510v1 Announce Type: cross Abstract: To encourage diverse exploration in reinforcement learning (RL) for large language models (LLMs) without compromising accuracy, we propose Policy Spli

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Research

Quotation-Based Data Retention Mechanism for Data Privacy in LLM-Empowered Network Services

DGX agent

arXiv:2503.23001v5 Announce Type: replace Abstract: The deployment of large language models (LLMs) for next-generation network optimization introduces novel data governance challenges. mobile network

researcharxiv-cs-lg

14 Apr 2026

Steered LLM Activations are Non-Surjective

arXiv:2604.09839v1 Announce Type: new Abstract: Activation steering is a popular white-box control technique that modifies model activations to elicit an abstract change in output behavior. It has als

Thinking Fast, Thinking Wrong: Intuitiveness Modulates LLM Counterfactual Reasoning in Policy Evaluation

arXiv:2604.10511v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for causal and counterfactual reasoning, yet their reliability in real-world policy evaluation remain

Applications

(This is a big part of what was called emergence in earlier academic work on unexpected LLM ability gains)

applicationsethan-mollick--x

Ethan Mollick discusses the concept of 'emergence' in large language models (LLMs), referring to the phenomenon where AI systems appear to suddenly develop unexpected capabilities as they scale. The p

VeriTrans: Fine-Tuned LLM-Assisted NL-to-PL Translation via a Deterministic Neuro-Symbolic Pipeline

arXiv:2604.10341v1 Announce Type: new Abstract: extbf{VeriTrans} is a reliability-first ML system that compiles natural-language requirements into solver-ready logic with validator-gated reliability.

ZARA: Training-Free Motion Time-Series Reasoning via Evidence-Grounded LLM Agents

arXiv:2508.04038v2 Announce Type: replace Abstract: Motion sensor time-series are central to Human Activity Recognition (HAR), yet conventional approaches are constrained to fixed activity sets and ty

3D-VCD: Hallucination Mitigation in 3D-LLM Embodied Agents through Visual Contrastive Decoding

arXiv:2604.08645v1 Announce Type: cross Abstract: Large multimodal models are increasingly used as the reasoning core of embodied agents operating in 3D environments, yet they remain prone to hallucin

Automated Instruction Revision (AIR): A Structured Comparison of Task Adaptation Strategies for LLM

arXiv:2604.09418v1 Announce Type: new Abstract: This paper studies Automated Instruction Revision (AIR), a rule-induction-based method for adapting large language models (LLMs) to downstream tasks usi

Agents

Every Response Counts: Quantifying Uncertainty of LLM-based Multi-Agent Systems through Tensor Decomposition

arXiv:2604.08708v1 Announce Type: cross Abstract: While Large Language Model-based Multi-Agent Systems (MAS) consistently outperform single-agent systems on complex tasks, their intricate interactions

agentsarxiv-cs-ai

Looking for people with different hardware to help benchmark local LLM behavioral reliability

A Reddit post in the r/ollama community seeking volunteers with diverse hardware setups to participate in a collaborative effort to benchmark the **behavioral reliability** of locally-run large langua

model-releasesr-ollama

QuanBench+: A Unified Multi-Framework Benchmark for LLM-Based Quantum Code Generation

arXiv:2604.08570v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for code generation, yet quantum code generation is still evaluated mostly within single frameworks

Cross-Tokenizer LLM Distillation through a Byte-Level Interface

arXiv:2604.07466v1 Announce Type: new Abstract: Cross-tokenizer distillation (CTD), the transfer of knowledge from a teacher to a student language model when the two use different tokenizers, remains

researcharxiv-cs-cl

HumanLLM: Benchmarking and Improving LLM Anthropomorphism via Human Cognitive Patterns

arXiv:2601.10198v3 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning and generation, serving as the foundation for advanced persona s

safetyarxiv-cs-cl

Hardware

LAsset: An LLM-assisted Security Asset Identification Framework for System-on-Chip (SoC) Verification

arXiv:2601.02624v2 Announce Type: replace-cross Abstract: The growing complexity of modern system-on-chip (SoC) and IP designs is making security assurance difficult day by day. One of the fundamental

hardwarearxiv-cs-ai

Tutorials

Learning is Forgetting: LLM Training As Lossy Compression

arXiv:2604.07569v1 Announce Type: cross Abstract: Despite the increasing prevalence of large language models (LLMs), we still have a limited understanding of how their representational spaces are stru

tutorialsarxiv-cs-cl

LINE: LLM-based Iterative Neuron Explanations for Vision Models

arXiv:2604.08039v1 Announce Type: new Abstract: Interpreting the concepts encoded by individual neurons in deep neural networks is a crucial step towards understanding their complex decision-making pr

safetyarxiv-cs-cv

LLM debates are more fun when social

I was unable to retrieve the specific Reddit post at the provided URL through my search results. The page may require a direct browser session or Reddit login to access, and my web search did not s...

local-air-ollama

LLM Spirals of Delusion: A Benchmarking Audit Study of AI Chatbot Interfaces

arXiv:2604.06188v1 Announce Type: cross Abstract: People increasingly hold sustained, open-ended conversations with large language models (LLMs). Public reports and early studies suggest that, in such

ShadowNPU: System and Algorithm Co-design for NPU-Centric On-Device LLM Inference

arXiv:2508.16703v4 Announce Type: replace-cross Abstract: On-device running Large Language Models (LLMs) is nowadays a critical enabler towards preserving user privacy. We observe that the attention o

local-aiarxiv-cs-ai

WASD: Locating Critical Neurons as Sufficient Conditions for Explaining and Controlling LLM Behavior

arXiv:2603.18474v2 Announce Type: replace Abstract: Precise behavioral control of large language models (LLMs) is critical for complex applications. However, existing methods often incur high training

What Your Local LLM Actually Sees: Debugging Ollama Traffic in Quarkus with mitmproxy

This tutorial demonstrates how to use mitmproxy to inspect the actual HTTP traffic sent from a Quarkus application to a local Ollama model via its OpenAI-compatible endpoint, revealing the real JSO...

local-air-ollama

Listening to @alexocheema from @exolabs talking about running LLMs locally at @aiDotEngineer @swyx 18 months before we get a SOTA LLM at hom…

Alex Cheema is the co-founder of Exo Labs, a startup founded in March 2024 to 'democratize access to AI' through open source multi-device computing clusters. The core product, *exo*, connects al...

local-aiswyx--x

8 Apr 2026

To Defer or Not to Defer? Online Learning with LLMs and Congested Experts

model-releasesarxiv-cs-lg

arXiv:2610.15128v1 Announce Type: new Abstract: Modern AI systems increasingly combine large language models (LLMs) with human experts, using the LLM to handle routine queries while deferring uncertai

14 Oct 2026

PhaseGate: Phase-Aware CPU Retrieval Scheduling for On-Device LLMs on Unified Memory

arXiv:2610.04537v1 Announce Type: new Abstract: On-device assistants run GPU-based LLM inference alongside CPU retrieval on unified-memory systems. Under a saturated local-retrieval workload, four con

local-aiarxiv-cs-lg

6 Oct 2026

Do LLMs Trust the Accuser or the Accusation? Measuring Belief Shifts in Werewolf

arXiv:2609.12446v1 Announce Type: cross Abstract: Social-deduction games such as Werewolf are increasingly used to evaluate LLM agents, but existing evaluations often rely on final game outcomes. We p

14 Sept 2026

Item-Mean Surrogates: Why Richer Persona Data Fail to Improve LLMs as Human Surrogates

arXiv:2608.29455v1 Announce Type: new Abstract: LLMs are increasingly used as human surrogates, often on the premise that richer persona data could make them substitutes or exploratory tools for speci

1 Sept 2026

SMDD-Bench: Can LLMs Solve Real-World Small Molecule Drug Design Tasks?

arXiv:2605.21740v2 Announce Type: replace Abstract: LLM agents have incredible potential for scientific discovery applications. However, the performance of LLM agents on real-world, small molecule dru

26 May 2026

Tabular PDF Information Extraction with Local LLMs and Layout-Aware Parsing: A Reliability Evaluation

arXiv:2604.00003v2 Announce Type: replace-cross Abstract: Extracting structured information from academic PDF documents is non trivial: a single page typically combines free text metadata with tabular

25 May 2026

Jailbreaks as social engineering: 5 case studies suggest LLMs inherit human psychological vulnerabilities from training data [D]

researchr-machinelearning

This r/MachineLearning discussion post examines LLM jailbreaks through the lens of social engineering, arguing that the psychological vulnerabilities found in LLMs are not random artifacts but structu

15 Apr 2026

Flama: 8 years building a Python framework where ML models and LLMs are first-class, now written up on arXiv [P]

local-air-machinelearning

I started Flama in 2018 to take a trained model to a production API with as little ceremony as possible. With 2.0 LLMs became first-class alongside classic models, and together with Miguel Durán-Olive

13 Oct 2026

DeltaLLM: Delta-Key Sparse Attention for Edge LLMs

arXiv:2507.19608v2 Announce Type: replace Abstract: On-device LLM inference is increasingly important for edge platforms but remains challenging due to high computational and memory demands and tight

local-aiarxiv-cs-ai

12 Oct 2026

When LLMs Sit Above Diagnostic Tools: Unrealized Complementarity in Industrial Fault Diagnosis

arXiv:2610.05031v2 Announce Type: replace Abstract: Large language models are increasingly used as integration layers above specialized tools, but a stronger component does not necessarily produce a s

researcharxiv-cs-cl

12 Oct 2026

JEV vs. LLMs as Rubric Judges: Cheaper, Faster, and Wrong in the Same Places

arXiv:2609.29769v1 Announce Type: new Abstract: We ask whether Jev, a typed classifier that returns probabilities over permitted answers without generating text, can replace an LLM rubric judge. We co

researcharxiv-cs-cl

25 Sept 2026

Industry

Today’s LLMs still write like typewriters: one token at a time. This sequential process creates a hard inference bottleneck. We're introduci…

Today’s LLMs still write like typewriters: one token at a time. This sequential process creates a hard inference bottleneck. We're introducing Uno, a diffusion-augmented LLM that delivers autoregressi

industryemad-mostaque--x

17 Sept 2026

Applications

What if LLMs Ate Their Words: Causal History Effects in Multi-Turn Interaction

arXiv:2609.05882v1 Announce Type: cross Abstract: Multi-turn interaction creates a feedback process in which an LLM's previous responses become context for later behavior. Prior work shows substantial

applicationsarxiv-cs-ai

10 Sept 2026

Beyond Detection: Evaluating Defensive LLMs Against AI-Generated Social Engineering in Live Turn-by-Turn Interaction

arXiv:2608.10239v1 Announce Type: new Abstract: Generative AI makes social-engineering attacks more fluent, adaptive, and scalable, increasing the need for LLM-based de- fenders that can protect users

local-aiarxiv-cs-ai

12 Aug 2026

Tutorials

Challenges and Recommendations for LLMs-as-a-Judge in Multilingual Settings and Low-Resource Languages

arXiv:2607.02235v1 Announce Type: cross Abstract: LLM-as-a-Judge has become the dominant evaluation paradigm for many natural language generation tasks, due to shortcomings of conventional metrics and

tutorialsarxiv-cs-ai

3 Jul 2026

CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery

arXiv:2606.03602v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the fundamental limitations of purely statistical methods, such as statistical dis

3 Jun 2026

When LLMs Benchmark Themselves: Deconstructing Self-Bias in Automated Evaluation

arXiv:2509.26600v2 Announce Type: replace-cross Abstract: As LLMs rapidly saturate existing benchmarks, automated benchmark creation using LLMs (LLM-as-a-benchmark) -- where a model generates test inp

27 May 2026

Graph Representation Learning Augmented Model Manipulation on Federated Fine-Tuning of LLMs

model-releasesarxiv-cs-lg

arXiv:2605.07961v1 Announce Type: new Abstract: Federated fine-tuning (FFT) has emerged as a privacy-preserving paradigm for collaboratively adapting large language models (LLMs). Built upon federated

11 May 2026

CoFi-PGMA: Counterfactual Policy Gradients under Filtered Feedback for Multi-Agent LLMs

arXiv:2604.22785v1 Announce Type: new Abstract: Large language model (LLM) deployments increasingly rely on multi-agent architectures in which multiple models either compete through routing mechanisms

safetyarxiv-cs-lg

28 Apr 2026

LLMs Can Get 'Brain Rot': A Pilot Study on Twitter/X

arXiv:2510.13928v2 Announce Type: replace-cross Abstract: We propose and test the LLM Brain Rot Hypothesis: continual exposure to junk web text induces lasting cognitive decline in large language mode

23 Apr 2026

Personalized Benchmarking: Evaluating LLMs by Individual Preferences

arXiv:2604.18943v1 Announce Type: new Abstract: With the rise in capabilities of large language models (LLMs) and their deployment in real-world tasks, evaluating LLM alignment with human preferences

22 Apr 2026

DORA Explorer: Improving the Exploration Ability of LLMs Without Training

arXiv:2604.17244v1 Announce Type: new Abstract: Despite the rapid progress, LLMs for sequential decision-making (i.e., LLM agents) still struggle to produce diverse outputs. This leads to insufficient

21 Apr 2026

Knowledge without Wisdom: Measuring Misalignment between LLMs and Intended Impact

model-releasesarxiv-cs-lg

arXiv:2603.00883v2 Announce Type: replace Abstract: LLMs increasingly excel on AI benchmarks, but doing so does not guarantee validity for downstream tasks. This study contrasts LLM alignment on bench

21 Apr 2026

Evaluating LLMs as Human Surrogates in Controlled Experiments

arXiv:2604.15329v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to simulate human responses in behavioral research, yet it remains unclear when LLM-generated data

20 Apr 2026

LLMbench: A Comparative Close Reading Workbench for Large Language Models

arXiv:2604.15508v1 Announce Type: cross Abstract: LLMbench is a browser-based workbench for the comparative close reading of large language model (LLM) outputs. Where existing tools for LLM comparison