Search: “llm”

APEXA: Execution-Integrity Enforcement for Multi-Agent LLM Automation of Synchrotron Data Reduction

arXiv:2609.24165v1 Announce Type: new Abstract: Synchrotron data reduction, detector calibration followed by azimuthal integration of terabyte-scale diffraction series, is a multi-step, expert-bound b

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Agents

Behavior is Not Enough: A Mechanism-Based Evaluation of Social Norm Emergence in LLM Societies

DGX agent

arXiv:2609.26481v1 Announce Type: cross Abstract: Social norms cannot be identified from behavior alone: the same cooperative equilibrium may reflect shared expectations, strategic incentives, or simp

agentsarxiv-cs-cl

23 Sept 2026

Local Ai

Beyond Scalar Sensitivity: Activation-Aware Mixed-Precision LLM Quantization with Cross-Layer Refinement

arXiv:2609.25916v1 Announce Type: new Abstract: Mixed-precision weight quantization is commonly formulated as a Multiple-Choice Knapsack Problem (MCKP), yet existing solvers rely on scalar sensitivity

local-aiarxiv-cs-lg

CompKV: Compensation-Aware KV Selection for Long-Context LLM Inference

arXiv:2609.26300v1 Announce Type: new Abstract: Despite their strong performance, large language models (LLMs) are bottlenecked by KV cache memory traffic during long-context inference. Sparse attenti

researcharxiv-cs-lg

Do Not Trust the Benchmark: Limitations of General LLM Rankings and a Case for Task-Specific Evaluation

arXiv:2609.23201v1 Announce Type: new Abstract: Benchmark scores increasingly influence the development, marketing, and selection of large language models (LLMs). Yet an overall score is interpretable

gwBenchmarks: Stress-Testing LLM Agents on High-Precision Gravitational Wave Astronomy

arXiv:2605.11269v2 Announce Type: replace-cross Abstract: Modern gravitational wave astronomy relies on modeling tasks that often require months of graduate-level effort, including building fast wavef

Reasoning Topology Matters: A Controlled Study of LLM-Based Cybersecurity Analysis

arXiv:2609.24710v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in cybersecurity, where accurate analysis often requires multi-step and context-dependent reasoning

Trains but Doesn't Learn: A Post-Training Delivery Benchmark for LLM Agents as Forward-Deployed Engineers

arXiv:2609.25237v1 Announce Type: cross Abstract: Post-training is becoming a service (PTaaS): a customer hands an operator data and a goal, and a forward-deployed engineer (FDE) returns a fine-tuned,

Understanding Reliability in LLM-based Human Behavior Simulation

arXiv:2609.25066v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to simulate human survey responses and behavioral reactions, yet unreliable simulations can mislead s

safetyarxiv-cs-cl

Applications

Augmented Hypothesis Testing with Persona-Based LLM Simulations

arXiv:2609.24629v1 Announce Type: new Abstract: A/B testing requires large sample sizes, long timelines, and significant costs. When auxiliary predictions of experimental outcomes are available from m

applicationsarxiv-cs-lg

Beyond Relevance: Structured Semantic Supervision for Product Search with LLM-Augmented Annotations

arXiv:2609.23646v1 Announce Type: cross Abstract: E-commerce search requires distinguishing products that are merely related to a query from those that directly satisfy the user's shopping intent. We

CSC: Calibrated Simplicity for Conflict-Aware Social Bot Detection in the LLM Era

arXiv:2609.23320v1 Announce Type: new Abstract: Social bot detection is essential for protecting online platforms from misinformation amplification, coordinated manipulation, and distorted public disc

researcharxiv-cs-lg

Enforcing Narrative Reliability and Epistemic Pacing in LLM-Driven Detective Games via Structured Knowledge Trees

arXiv:2609.23043v1 Announce Type: cross Abstract: Large Language Models (LLMs) enable open-ended dialogue in interactive games, but their non-deterministic outputs make it difficult to preserve author

agentsarxiv-cs-cl

Fairness Beyond Anonymization? Demographic Leakage in German LLM-Generated Resumes

arXiv:2609.22188v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into AI-assisted hiring pipelines, including automated resume generation and screening. Under t

From Tables to Quantified Statements: Evaluating LLM Inference Generation through Executable Verification

arXiv:2609.23966v1 Announce Type: new Abstract: LLMs can generate fluent descriptions from tables, but their outputs may remain logically unsupported by the structured data. We introduce STAT-TO-TEXT,

From UNDRR Reports to Event Records: Schema-Constrained LLM Extraction of Georeferenced Disasters

arXiv:2609.23853v1 Announce Type: new Abstract: Disaster-risk-reduction archives describe hazard events in prose that databases such as EM-DAT (Delforge et al., 2025) cannot ingest directly. We presen

QLoRA Fine-Tuning of Ministral LLM for Sequence-to-Function Protein Annotation

arXiv:2609.24538v1 Announce Type: new Abstract: Functional annotation of newly sequenced proteins remains a bottleneck in molecular biology: the number of sequences in public repositories grows far fa

ReliCAD: From Uncertain LLM Generation to Reliable Parametric CAD Modeling

arXiv:2609.22325v1 Announce Type: new Abstract: Large language models have shown considerable potential for natural-language-driven parametric CAD modeling. However, a fundamental contradiction exists

agentsarxiv-cs-ro

Testing the Construct Validity of a Functional Valence Axis in LLM Agents

arXiv:2609.22850v1 Announce Type: new Abstract: Contrastive activation directions are often interpreted from what they decode or how strongly they steer behavior. But what evidence is sufficient to id

safetyarxiv-cs-lg

VPRune: Efficient Training-free Pre-LLM Visual Token Pruning

arXiv:2609.24485v1 Announce Type: new Abstract: Visual token pruning is a promising approach to reducing the inference cost of large vision-language models (LVLMs), yet aggressive token reduction ofte

safetyarxiv-cs-cv

An Interpretable Memory Decision Controller for LLM Agents Based on Three-Signal Complementarity: Decoupling Confidence and Consistency

arXiv:2609.22043v1 Announce Type: new Abstract: Memory systems for large language models have focused predominantly on efficient retrieval, whereas the decision of whether retrieved memories should be

Gradient-Stable Attention Heads Signal LLM Correctness

arXiv:2602.13699v2 Announce Type: replace Abstract: Large language models (LLMs) often generate plausible yet incorrect answers, posing risks in safety-critical settings such as medicine. Human evalua

safetyarxiv-cs-lg

GVPO++: Group Variance Policy Optimization for LLM Post-Training and On-Policy Distillation

arXiv:2609.21432v1 Announce Type: new Abstract: Post-training plays a pivotal role in enhancing the reasoning capabilities and task-specific expertise of large language models (LLMs). Despite recent a

safetyarxiv-cs-ai

LEMCA: LLM-Guided Synthesis of Efficient Mode-Switching Control Architectures

arXiv:2609.21319v1 Announce Type: new Abstract: Physical control tasks in the natural world, such as driving or object manipulation, frequently exhibit dramatic variations in sensory and compute compl

researcharxiv-cs-ro

LLM-Generated Feature Pools for Time Series Anomaly Detection

arXiv:2609.21801v1 Announce Type: new Abstract: We study how far a simple statistical pipeline can go on univariate time series anomaly detection under a strict selection protocol. The method extracts

researcharxiv-cs-ai

SignGPT: Toward LLM-Mediated Sign Language Interaction through Gloss-Free Translation and Generation

model-releasesarxiv-cs-cv

arXiv:2609.21709v1 Announce Type: new Abstract: Large language models (LLMs) provide limited support for sign language interaction. Unifying sign language translation (SLT) and generation (SLG) to ena

Souper-Model: How Simple Arithmetic Unlocks State-of-the-Art LLM Performance

arXiv:2511.13254v2 Announce Type: replace Abstract: Large Language Models (LLMs) have displayed remarkable capabilities across diverse domains, but their training remains resource- and time-intensive,

You can use any LLM just like JEV

model-releasesr-localllama

You can simply run any GGUF with llama.cpp with n_predict=1 and n_probs=10, disable reasoning, and prompt it such as 'If the following email is spam, respond with 1, if not spam, respond with 0. Do no

CliniCIRCA: A Modular LLM Framework for Constructing Longitudinal Mental Health Patient Journeys from Raw EHR Narratives

arXiv:2609.19585v1 Announce Type: cross Abstract: In mental health care, reasoning over patient journeys is a key task for clinicians. Yet these journeys, encompassing a longitudinal progression of bi

Detecting Soft Errors in Parallel Software with LLM-tuned Instruction Duplication

arXiv:2609.19531v1 Announce Type: cross Abstract: We propose PaRID (PaRallel Instruction Duplication), a software-directed soft error detection framework that requires only compile-time effort for mul

researcharxiv-cs-ai

Digital Twins for Opinion Dynamics: A Generative LLM Framework for Social Networks

model-releasesarxiv-cs-lg

arXiv:2609.19913v1 Announce Type: new Abstract: The study of opinion dynamics in social networks is one of the key challenges in computational social science with direct relevance to understanding pol

From Intent to Action: Benchmarking LLM Safety in Vehicle Voice Command Authorization

arXiv:2609.19630v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into vehicle voice assistants. But linking natural-language requests to vehicle functions creat

LLM-Guided Transformation of Non-Critical Driving Scenes into Safety-Critical Scenarios Using Augmented Reality

arXiv:2609.20318v1 Announce Type: cross Abstract: Testing Autonomous Driving Systems (ADS) requires realistic safety-critical scenarios, but collecting such data from real-world driving is costly and

safetyarxiv-cs-ai

Local Ai

Local Sparsity Enables Unsupervised LLM Safety Detection

arXiv:2609.20129v1 Announce Type: cross Abstract: Deployment-time safety methods for large language models (LLMs) are predominantly supervised and assume access to unsafe training data. Nevertheless,

local-aiarxiv-cs-ai

Sampling Reveals Style: Unsupervised, Training-Free Discovery of Prompt-Conditional Stylistic Axes in LLM Activations

arXiv:2609.19150v1 Announce Type: new Abstract: Large language models (LLMs) encode rich stylistic structure in their hidden activations, but discovering which stylistic dimensions are salient for a g

What Do We Expect from LLMs? Mapping the Design of LLM Benchmarks

arXiv:2609.19182v1 Announce Type: new Abstract: Benchmarks are central to how progress in large language models (LLMs) is assessed and communicated. Yet model rankings alone reveal little about how ev

agentsarxiv-cs-ai

Collective Loss of Control in LLM Agent Systems: An Epidemic Account of Mutation, Contagion, and Recovery

arXiv:2609.18460v1 Announce Type: new Abstract: How does a multi-agent system evolve from a local deviation into collective loss of control? We propose an epidemic explanation organized around acciden

17 Sept 2026

From Pixels to Pairs: A Comprehensive Benchmark of LLM-Based Key-Value Extraction in Noisy Document Settings

arXiv:2609.17538v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for structured information extraction from documents, yet their behavior under realistic OCR noise re

17 Sept 2026

Reasoning through Evolution: Automatic Meta-path Discovery for LLM-based Fake News Detection

arXiv:2609.18597v1 Announce Type: new Abstract: Propagation structures provide crucial evidence for fake news detection, yet existing approaches primarily rely on supervised GNN-based models, which re

agentsarxiv-cs-ai

17 Sept 2026

agent-memoryapplepersistence

Shared Selective Persistent Memory for Agentic LLM Systems

Human

Apple paper: keep specs, schemas, tool config, and constraints; drop session traces. Selective shared memory beat both no memory and full history.

Smarter by the Moment: Environment-Driven Dynamic Policies for Continual LLM Improvement

arXiv:2609.16800v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable progress across diverse domains, but continual adaptation to evolving tasks and environments remai

safetyarxiv-cs-cl

What Breaks Under Pruning in Smart Homes, and When? Evaluating LLM Degradation Across Architectures and Task Complexity

arXiv:2609.17515v1 Announce Type: new Abstract: Pruning can reduce the deployment cost of large language models (LLMs), but its impact on context-grounded tool calling remains poorly understood. We sy

Local Ai

What's the current best LLM uncensoring method?

With the recent Nvidia Huggingface acquisition and frontier AI labs screaming about safety and putting guardrails everywhere, I think it's important that we have local models that aren't affected by a

local-air-localllama

Can We Triage LLM Translation Errors in Classical Texts Without Human References? Source Novelty, GEMBA Scoring, and Budgeted Review through Pali-to-English Translation

arXiv:2609.14963v1 Announce Type: new Abstract: As large language models become capable translators of classical texts, a key challenge is deciding which outputs need expert review when no human refer

Applications

DeepFeature: LLM-Empowered Context-aware Feature Generation for Wearable Biosignals

arXiv:2512.08379v3 Announce Type: replace Abstract: Biosignals collected from wearable devices are widely utilized in healthcare applications. Machine learning models used in these applications often

applicationsarxiv-cs-ai

Externalizing Requirement-to-Repair Artifacts as Observable Traces for LLM-Based Program Repair

arXiv:2609.14913v1 Announce Type: new Abstract: Repository-level repair requires not only correct patches but also inspectable records that explain how issue requirements are translated into code chan

researcharxiv-cs-ai

K-Bench: A Benchmark for LLM Unlearning in Agentic Deployments

arXiv:2609.12808v2 Announce Type: replace Abstract: Unlearning benchmarks such as TOFU and MUSE certify forgetting by reading the model's final answer, where a model that refuses to answer already cou

LayerRoute: Adaptive Layer-Skipping with LoRA-Preserved Quality for Efficient LLM Inference

arXiv:2609.13682v1 Announce Type: cross Abstract: We introduce LayerRoute, a parameter-efficient method for adaptive transformer layer-skipping that combines per-layer hard-gated routing (trained via