Search: “llm”

Dual-Node NVIDIA DGX Spark over Tailscale: A Remote-Access Testbed for Distributed LLM Training and Cyber-Threat-Intelligence Fine-Tuning

arXiv:2608.07226v1 Announce Type: cross Abstract: Compact AI systems make local language-model experimentation increasingly accessible, yet practical evidence for multi-node training on desktop-class

local-aiarxiv-cs-lg

10 Aug 2026

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Human-Centered Explainable AI for TinyML Edge Devices: A Pareto-Based Selection Framework with LLM-Guided Design

DGX agent

arXiv:2608.07091v1 Announce Type: cross Abstract: Edge Artificial Intelligence (Edge AI) enables the deployment of AI models directly on local edge devices, while such deployments are subject to stric

local-aiarxiv-cs-ai

10 Aug 2026

LSEAD: A Privacy-Preserving LLM-Based Speech Analysis Framework for Early Alzheimer's Disease Screening

arXiv:2608.07378v1 Announce Type: cross Abstract: Early diagnosis of Alzheimer's disease (AD) is critical for enabling timely interventions that may slow disease progression and improve patient outcom

10 Aug 2026

When Coordination Becomes a Threat: Communication Attacks in LLM-Controlled Multi-Robot Systems

arXiv:2608.06830v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used as high-level planners in embodied multi-robot systems, enabling robots to interpret natural language

agentsarxiv-cs-ro

10 Aug 2026

EcoAgent-Bench: Evaluating Economic Decision-Making in Budget-Constrained LLM Agents

arXiv:2608.05519v1 Announce Type: new Abstract: Agent benchmarks usually measure task completion and treat resource use as an auxiliary statistic. In deployment, however, the choice among a local look

EdgeXpert: An Edge Device for Memory-Efficient LLM Inference with Mixture-of-Experts and Speculative Decoding

arXiv:2608.05303v1 Announce Type: cross Abstract: On-device deployment of Large Language Models (LLMs) has become essential for personalized edge applications. A primary bottleneck is external memory

local-aiarxiv-cs-cl

Invariant Representation Learning for Source-Free Time Series Forecasting with LLM-Centric Proxy Denoising

arXiv:2510.05589v3 Announce Type: replace-cross Abstract: Effective time series forecasting enables various real-world applications, benefiting from the proliferation of mobile devices. However, the v

MicroEvo: Knowledge-Guided LLM Sampling for Efficient Microarchitecture Design Space Exploration

arXiv:2608.06183v1 Announce Type: new Abstract: Microarchitecture design space exploration suffers from expansive search spaces and expensive PPA evaluation, leaving only a small simulation budget for

safetyarxiv-cs-ai

PPDL: LLM-Based Flows as Probabilistic Programs

arXiv:2608.05234v1 Announce Type: new Abstract: Building reliable applications that leverage large language models (LLMs) remains a significant challenge. While LLMs offer impressive capabilities acro

agentsarxiv-cs-lg

Hardware

An Explainable LLM Agent Layer for Open-World Anomaly Detection in Oil Wells

arXiv:2608.04041v1 Announce Type: new Abstract: Open-World Learning (OWL) pipelines for oil well anomaly detection have recently been shown to combine autoencoder-based detection, multiclass classific

hardwarearxiv-cs-lg

FinRpt: Dataset, Evaluation System and LLM-based Multi-agent Framework for Equity Research Report Generation

arXiv:2511.07322v3 Announce Type: replace-cross Abstract: While LLMs have shown great success in financial tasks like stock prediction and question answering, their application in fully automating Equ

Hallucinations on the Board: Tool-Augmented Evaluation of LLM Chess Commentary

arXiv:2608.04240v1 Announce Type: cross Abstract: Superhuman game engines in domains like chess have made expert-level evaluations easily accessible, yet they communicate what is true without the natu

Hardware

SparseDitto: Customizing GPU Kernels for Different Sparsity Patterns with LLM-Based Agentic System

arXiv:2608.05033v1 Announce Type: cross Abstract: Sparse matrix kernels are fundamental to scientific computing, graph analytics, and machine learning. Their GPU performance depends strongly on the in

hardwarearxiv-cs-lg

Transfer Learning for Named Entity Recognition of Classical Latin through LLM Prompting

model-releasesarxiv-cs-cl

arXiv:2608.04015v1 Announce Type: new Abstract: With the increase in digitized resources of Classical Latin texts and modern breakthroughs of Large Language Models (LLMs), I contribute to ancient lang

A Graph Signal Processing Perspective on Numerical Sequence Representations in LLM In-Context Learning

arXiv:2608.03015v1 Announce Type: cross Abstract: Pretrained large language models (LLMs) have demonstrated in-context learning (ICL) capabilities for numerical inference over sequences serialized as

local-aiarxiv-cs-ai

AI-Assisted Peer Review Across Research Communities: From Reviewer AI Policies to LLM Review Quality

arXiv:2608.03581v1 Announce Type: cross Abstract: AI-assisted peer review is increasingly discussed and adopted as a tool to support the scientific publishing process, yet there is little systematic u

safetyarxiv-cs-ai

Beyond the Hivemind: Escaping LLM Homogeneity via Meta-Persona Anchoring and Sequential Temperature Scaling

arXiv:2608.02618v1 Announce Type: new Abstract: Recent studies have identified an ``Artificial Hivemind'' effect in Large Language Models (LLMs) causing models to converge on a narrow, homogenized con

EduClaw-Bench: A Long-Horizon Benchmark for Pedagogical LLM Agents with Simulated Learners

arXiv:2608.03206v1 Announce Type: cross Abstract: Large language models (LLMs) power educational applications from tutoring to essay scoring, but each is a point solution to a single task, and only re

How Closely Do LLM Reviews Align with Human Peer Review?

arXiv:2608.03659v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate scientific reviews, yet existing evaluations rarely examine whether different providers

LoCA: Forward-Only LLM Tuning after One-Shot Calibration with Local Credit Assignment

arXiv:2608.03020v1 Announce Type: new Abstract: Parameter-efficient post-training reduces the number of trainable parameters, but still requires repeated end-to-end backpropagation through the frozen

Should We Type or Talk to LLM Agents? A Comprehensive Study of Voice and Keyboard Input Perturbations

arXiv:2608.03970v1 Announce Type: new Abstract: Human input reaches language models by typing or speaking, and each channel leaves a distinct signature: orthographic noise for keyboards; for voice, di

Single Canonical Prompts Underestimate LLM Safety's Surface-Form Sensitivity

arXiv:2608.02665v1 Announce Type: cross Abstract: A benchmark score is a measurement instrument, yet most benchmarks read each item at a single canonical surface form. We ask whether that reading is f

Applications

TQLite: Multi-LLM Jury Guided Distillation for Real-time MQM Translation Quality Evaluation

arXiv:2608.02975v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated impressive performance in MQM-based translation quality (TQ) evaluation, and recent advances in large r

applicationsarxiv-cs-ai

TraceCompiler: Skill-Guided Mining and Compilation of LLM Agent Traces into Mostly Deterministic Workflows

arXiv:2608.02680v1 Announce Type: cross Abstract: Tool-using language-model agents repeatedly rediscover procedures they have already executed, producing traces that mix reusable structure with retrie

Device-First Feedback: Toward Mobile-Native LLM-Driven Neural Architecture Search

arXiv:2608.00078v1 Announce Type: new Abstract: Deploying convolutional neural networks generated by large language models (LLMs) on real mobile hardware requires more than GPU validation accuracy: IN

local-aiarxiv-cs-cv

GraphIR: Architecture-Level Search States for LLM-Guided Neural Architecture Evolution

model-releasesarxiv-cs-lg

arXiv:2608.01633v1 Announce Type: new Abstract: Large language models (LLMs) enable neural architecture search (NAS) directly over executable neural network programs. However, code-level flexibility d

MemoryForge: Synthesize Lifelong Memory for Human-Like LLM Agents

arXiv:2608.00007v1 Announce Type: new Abstract: Equipping Large Language Models (LLMs) with human-like personas is crucial for agentic applications, such as role-play and user simulation. Traditional

agentsarxiv-cs-cl

TS-Reasoner: Aligning Time Series Foundation Models with LLM Reasoning

arXiv:2510.03519v2 Announce Type: replace Abstract: Time series reasoning is crucial to decision-making in diverse domains, including finance, energy, and scientific discovery. While existing time ser

safetyarxiv-cs-cl

Efficient LLM Adversarial Training via Low-Rank Defense and Circuit-Guided Surrogates

arXiv:2607.28959v1 Announce Type: cross Abstract: Adversarial training is one of the most effective defenses against adversarial attacks, yet the computational cost remains prohibitive at modern scale

Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction

arXiv:2601.15037v2 Announce Type: replace-cross Abstract: Open-domain Relational Triplet Extraction (ORTE) aims to mine structured knowledge without predefined relation schemas. Large Language Models

Metaphor-Induced Algorithmic Steering: Cross-Domain Procedural Transfer in LLM Code Generation

arXiv:2607.28683v1 Announce Type: cross Abstract: Large language models benefit from elements in natural language, such as metaphors and analogies in training data and inference input to achieve gener

Tutorials

To Facilitate or not to Facilitate: Human and LLM Facilitator Tendencies in Online Discussions

arXiv:2607.28643v1 Announce Type: cross Abstract: Automating facilitation in online discussions is a long-standing social concern given the increasing time we spend on online spaces and the failure of

tutorialsarxiv-cs-cl

LLM2Vec-Gen: Generative Embeddings from Large Language Models

model-releasesarxiv-cs-cl

arXiv:2603.10913v3 Announce Type: replace Abstract: Fine-tuning LLM-based text embedders via contrastive learning maps inputs and outputs into a new representational space, discarding the LLM's output

31 Jul 2026

Reasoning Consensus: Structural Ensembling of LLM Reasoning via Weighted DAG Aggregation

arXiv:2607.27783v1 Announce Type: new Abstract: Large Language Models (LLMs) explore problems through chain-of-thought, but this exploration is buried in unstructured prose. On high-stakes tasks, user

researcharxiv-cs-cl

31 Jul 2026

SDO: Structure-Aware Data Organization for Efficient LLM Post-Training

arXiv:2607.27273v1 Announce Type: new Abstract: Post-training of large language models is expensive, and existing efficiency improvements mainly focus on selecting informative samples or designing tra

researcharxiv-cs-lg

31 Jul 2026

CinemaTraj: Composing Atomic Camera Trajectories for 3D Scenes with LLM Agents

arXiv:2607.26910v1 Announce Type: new Abstract: Automatically generating cinematically expressive camera trajectories through 3D scenes from natural language descriptions is a challenging task of high

safetyarxiv-cs-cv

Generation or Judgement? A Paradigm Perspective on LLM-Based Emotion-Cause Pair Extraction in Conversation

arXiv:2607.26967v1 Announce Type: new Abstract: Emotion-cause pair extraction in conversation (ECPEC) identifies utterance pairs in which one utterance causes an emotion expressed in another. Recent L

researcharxiv-cs-cl

InferScale: GPU-Native KV Injection for Personalized LLM Serving

model-releasesarxiv-cs-lg

arXiv:2607.27090v1 Announce Type: cross Abstract: Large language models are increasingly deployed with persistent personalized context, such as accumulated memory profiles or long conversation histori

MEDIC: Comprehensive Evaluation of Leading Indicators for LLM Safety and Utility in Clinical Applications

model-releasesarxiv-cs-cl

arXiv:2409.07314v4 Announce Type: replace Abstract: While Large Language Models (LLMs) achieve superhuman performance on standardized medical licensing exams, these static benchmarks have become satur

MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent

arXiv:2507.02259v2 Announce Type: replace Abstract: Despite improvements by length extrapolation, efficient attention and memory modules, handling infinitely long documents with linear complexity with

agentsarxiv-cs-cl

On-Policy Distillation for LLM Safety: A Routing Approach to Template-Robust Realignment

arXiv:2607.27081v1 Announce Type: cross Abstract: Fine-tuning is the dominant paradigm for specializing large language models (LLMs), yet it exposes a critical vulnerability: malicious data providers

safetyarxiv-cs-cl

SCALPEL: Semantic Cross-modal Alignment via LLM-Powered Encoder Learning for Medical Vision-Language Representation

arXiv:2607.26885v1 Announce Type: new Abstract: Vision-language pre-training (VLP) serves as a cornerstone for medical multimodal representation learning. However, existing medical VLP frameworks are

safetyarxiv-cs-cv

ToxScreen: Detecting Whether an LLM Has Been Poisoned

model-releasesarxiv-cs-lg

arXiv:2607.26849v1 Announce Type: cross Abstract: As large language models (LLMs) are deployed in high-stakes domains, adversaries may poison training data to implant backdoors: hidden triggers that c

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation

arXiv:2607.26599v1 Announce Type: new Abstract: Estimating heterogeneous treatment effects is central to targeted interventions, such as personalized promotions and precision medicine. We focus on the

local-aiarxiv-cs-lg

A Human-in-the-Loop Corpus for LLM-Based Simplification of Scientific Summaries

arXiv:2607.25630v1 Announce Type: cross Abstract: Interdisciplinary research is accelerating, yet scientific papers remain difficult to understand outside their home fields. We study large language mo

Does Runtime Topology Context Improve LLM-Generated Kubernetes Security Patches?

arXiv:2607.25995v1 Announce Type: cross Abstract: Kubernetes is central to the cloud-native ecosystem, orchestrating containerised workloads. Recent work suggests that large language models (LLMs) can

safetyarxiv-cs-ai

Language as a Material Interface for Creative LLM Interaction

arXiv:2607.24753v1 Announce Type: cross Abstract: Although directive prompting is the predominant way to interact with Large Language Models (LLMs), many creative practices rely on language that is op

researcharxiv-cs-cl

Nudging Sustainable Choices through LLM-Generated Recommendation Explanations

arXiv:2607.25726v1 Announce Type: new Abstract: Recommender systems mediate everyday consumption, offering a promising channel for encouraging sustainable choices. Prior research shows that explanatio