Search: “llm”

arXiv:2609.04013v1 Announce Type: new Abstract: Early screening of chronic kidney disease (CKD) is critical for timely intervention, yet most machine learning (ML) and deep learning (DL) approaches re

applicationsarxiv-cs-ai

4 Sept 2026

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Research

From Tokens to Semantics: Leveraging Complementary Signals for Hallucination Detection in Black-Box LLMs

DGX agent

arXiv:2609.02679v1 Announce Type: cross Abstract: When LLMs support public-facing or high-stakes workflows, missed fabrications can harm users and institutions, while false alarms consume limited huma

researcharxiv-cs-ai

3 Sept 2026

FUSE: An Evaluating Framework for Dangerous Capabilities of LLMs

arXiv:2609.02168v1 Announce Type: new Abstract: Fragmented safety evaluation undermines the governance of dangerous AI capabilities. We present a modular framework that evaluates each model through th

hLLM: Single Pass Decoding for Generative Reranking

arXiv:2609.01807v1 Announce Type: cross Abstract: Large language models (LLMs) achieve state-of-the-art generative ranking quality, but the ranking they produce must be decoded, and autoregressive dec

ICE: Intervention-Consistent Explanation Evaluation with Statistical Grounding for LLMs

arXiv:2603.18579v2 Announce Type: replace-cross Abstract: Evaluating whether explanations faithfully reflect a model's reasoning remains an open problem. Existing benchmarks use single interventions w

WMLLM: Self-Evolving Optimization Agents via Predict-Then-Act World Modeling

arXiv:2609.01608v1 Announce Type: cross Abstract: Black-box optimization problems remain challenging because of large, weakly structured, and high-dimensional search spaces. Existing methods often suf

Aligned but Flattened: Analyzing the Trade-off between Cultural Alignment and Diversity in LLMs

arXiv:2609.00565v1 Announce Type: cross Abstract: Cultural fine-tuning has become the de facto paradigm for building culture-aware large language models (LLMs), yet existing optimization exclusively f

Automated Event Log Generation from Unstructured Text Using Finetuned LLMs

arXiv:2609.01320v1 Announce Type: new Abstract: Process mining (PM) provides a powerful framework for discovering and optimizing operational processes from event data. However, the efficacy of PM tech

Can LLMs Discover Scientific Laws in Real and Parallel Worlds?

arXiv:2609.01552v1 Announce Type: new Abstract: Scientific equation discovery has long been central to scientific progress, proceeding through iterative cycles of hypothesis generation, observational

CopyShield: A Cross-Level Benchmark of Copyright Defenses in LLMs

model-releasesarxiv-cs-lg

arXiv:2609.01161v1 Announce Type: new Abstract: Large language models can reproduce memorized text verbatim, yet copyright defenses are usually evaluated under incompatible protocols. We introduce Cop

Agents

Detecting Hidden Behaviors in LLMs via Activation-matched Finetuning

arXiv:2609.00351v1 Announce Type: cross Abstract: Large language models can hide hidden behaviors that activate only under narrow conditions, such as backdoor triggers, sleeper-agent deployment cues,

agentsarxiv-cs-ai

HarnessDev: Can LLMs Create and Evolve Their Own Agent Harness?

arXiv:2609.01437v1 Announce Type: cross Abstract: As agents move from research prototypes to deployed tools, their capability increasingly depends on model-external execution infrastructure, commonly

In-Context Neurofeedback: Can LLMs Control Their Internal Representations through Privileged Access?

arXiv:2609.00904v1 Announce Type: new Abstract: Whether large language models (LLMs) can control their own internal representations matters for both machine metacognition and AI safety. A recent study

safetyarxiv-cs-ai

Tutorials

The Structure of Quantization Damage in LLMs: Why the Next Bit Should Be Spent Globally

arXiv:2609.01587v1 Announce Type: cross Abstract: Post-training quantization (PTQ) is widely used to reduce the cost of serving large language models (LLMs), but its accuracy cost is uneven and is oft

tutorialsarxiv-cs-cl

Audit Me If You Can: Query-Efficient Active Fairness Auditing of Black-Box LLMs

arXiv:2601.03087v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) exhibit systematic biases across demographic groups. Auditing is proposed as an accountability tool for black-box

Controlling Refusal Behavior of LLMs via Stiefel-Constrained Rotation Steering

arXiv:2608.30986v1 Announce Type: cross Abstract: Activation steering has emerged as a lightweight approach for controlling model refusal at inference time. A growing line of research explores trainab

DERELAB: Probing Defeasible Reasoning and Confirmation Bias in LLMs with a Generative Benchmark

arXiv:2608.30413v1 Announce Type: new Abstract: Defeasible reasoning is a type of reasoning where inferences are drawn from plausible current evidence, but can be retracted upon the introduction of ne

DVBench: Benchmarking MLLMs for Understanding Dynamic Charts and Narratives in Data Videos

arXiv:2608.29711v1 Announce Type: new Abstract: While MLLMs have made significant strides in chart comprehension and video understanding, current evaluations largely isolate these capabilities, leavin

Graph Evidence Is Not Enough: Diagnosing Native Decoder Use in Graph-Augmented LLMs

arXiv:2608.30437v1 Announce Type: new Abstract: Graph-augmented large language models often assume that graph evidence produced by external computation and placed in the input can be used by the nativ

Applications

LLMODE: Aligning ODEs with LLMs via Gated Token Injection for Irregular Spatio-Temporal Forecasting

arXiv:2608.29640v1 Announce Type: cross Abstract: Large language models (LLMs) have shown promise for spatio-temporal forecasting, but existing approaches often rely on regularly sampled token sequenc

applicationsarxiv-cs-ai

More Capable, Less Faithful: A Multilingual Analysis of Mathematical (Un)Solvability Detection in LLMs

arXiv:2608.30463v1 Announce Type: new Abstract: Solvability detection is one of the most challenging aspects of mathematical reasoning for Large Language Models (LLMs). While prior work has studied th

Recognition-Refusal Misalignment in LLMs: Why Models Answer Structurally Unanswerable Questions

arXiv:2608.29109v1 Announce Type: new Abstract: Large language models often answer structurally unanswerable questions, such as computing cot(-540{eg}) or evaluating (1).startswith('1'), instead of ab

Towards AI-Assisted Research Writing: Benchmarking LLMs for AI/ML Introduction Generation

arXiv:2508.14273v3 Announce Type: replace Abstract: As researchers increasingly adopt LLMs as writing assistants, generating high-quality research paper introductions remains both challenging and esse

Deriving Scaling Laws for OpenEuroLLM Models: Learning Rate, Batch Size and Loss

arXiv:2608.28308v1 Announce Type: new Abstract: We study the scaling behavior of learning rate and batch size in pretraining dense large language models on English-prevalent corpora. Beyond scaling ex

researcharxiv-cs-lg

31 Aug 2026

Representation of syntax in LLMs through the lens of linear distance and similarity-aware entropy

arXiv:2608.27813v1 Announce Type: new Abstract: Structural probes were introduced by Hewitt and Manning to reconstruct syntactic trees from a neural language model's latent representations. They are e

researcharxiv-cs-cl

31 Aug 2026

Applications

Exploring the Role of LLMs in HPC Programming: A Survey

arXiv:2608.26110v1 Announce Type: cross Abstract: Large Language Models (LLMs) are emerging as promising assistants in High-Performance Computing (HPC), where programming remains complex and expertise

applicationsarxiv-cs-ai

Hallucinations in LLMs: A Lifecycle-Based Survey of Causes, Detection, Mitigation, and Prevention

arXiv:2608.26168v1 Announce Type: cross Abstract: The lifecycle of hallucination in LLMs is a concept that enables building solid frameworks on the control and reliability of LLMs in high-stakes envir

Planting a Latent Variable in Natural-Looking Text: a More Realistic Test of Belief States in LLMs and Their Link to Concept Geometry

arXiv:2608.26887v1 Announce Type: new Abstract: LLMs are thought to track 'belief states,' i.e., running probability distributions over the latent variables that govern language (Shai et al., 2024; Sa

researcharxiv-cs-cl

Translation Asymmetry in LLMs as a Data Augmentation Factor: A Case Study for 6 Romansh Language Varieties

arXiv:2603.25489v2 Announce Type: replace Abstract: Recent strategies for low-resource machine translation rely on LLMs to generate synthetic data based on text in higher-resource languages. We revisi

VFA: Empowering Multilingual MLLMs via Vision-Free Adaptation

arXiv:2608.26155v1 Announce Type: cross Abstract: Multimodal large language models have advanced rapidly, yet most remain English-centric, as scaling multilingual multimodal instruction tuning is limi

safetyarxiv-cs-ai

Local Ai

LLMTrace: A Corpus for Classification and Fine-Grained Localization of AI-Written Text

arXiv:2509.21269v2 Announce Type: replace Abstract: The widespread use of human-like text from Large Language Models (LLMs) necessitates the development of robust detection systems. However, progress

local-aiarxiv-cs-cl

27 Aug 2026

When Personality Meets Quantization: A Layer-wise MBTI Analysis of Quantized LLMs

arXiv:2608.25977v1 Announce Type: new Abstract: Personality is increasingly important in large language models (LLMs), as it shapes users' trust, engagement, and emotional experiences. While the Myers

researcharxiv-cs-cl

27 Aug 2026

NeuronTune: Fine-Grained Neuron Modulation for Balanced Safety-Utility Alignment in LLMs

arXiv:2508.09473v2 Announce Type: replace-cross Abstract: Ensuring robust safety alignment while preserving utility is critical for the reliable deployment of Large Language Models (LLMs). However, cu

safetyarxiv-cs-ai

26 Aug 2026

Round-trip Reinforcement Learning: Self-Consistent Training for Better Chemical LLMs

arXiv:2510.01527v2 Announce Type: replace Abstract: Large Language Models (LLMs) are emerging as versatile foundation models for computational chemistry, handling bidirectional tasks like reaction pre

researcharxiv-cs-lg

26 Aug 2026

A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs

arXiv:2506.20073v2 Announce Type: replace-cross Abstract: Spatio-temporal data mining plays a pivotal role in informed decision making across diverse domains. However, existing models are often restri

25 Aug 2026

Redteaming Leading Arabic LLMs with ASAS

arXiv:2608.21985v1 Announce Type: new Abstract: As the adoption of large language models (LLMs) grows in Arabic-speaking regions, ensuring their safety and cultural alignment is increasingly critical.

25 Aug 2026

Sa2VA: Marrying SAM2 with MLLM for Dense Grounded Understanding of Images and Videos

model-releasesarxiv-cs-cv

arXiv:2501.04001v4 Announce Type: replace Abstract: This work presents Sa2VA, the first comprehensive, unified model for dense grounded understanding of both images and videos. Unlike existing multi-m

25 Aug 2026

Fine-tuning LLMs for Tourist Trajectory Prediction using Field Experiment Data

model-releasesarxiv-cs-lg

arXiv:2608.20830v1 Announce Type: cross Abstract: Evaluating mobility interventions at tourist destinations requires predicting visitor behavior under varying conditions. Traditional methods struggle

24 Aug 2026

APPROVE: Visual End-User-in-the-Loop Robot Programming with LLMs

arXiv:2608.19281v1 Announce Type: new Abstract: Programming robots remains challenging for non-experts, as traditional methods require expert knowledge and even block-based interfaces often lack flexi

safetyarxiv-cs-ro

21 Aug 2026

LLMs as Acquisition Policies for Finite-Pool Materials Optimization: A Controlled Study

arXiv:2608.19790v1 Announce Type: new Abstract: Discovering materials with desirable properties often requires searching large candidate spaces while experimental or computational evaluations remain c

21 Aug 2026

Qworld: Question-Specific Evaluation Criteria for LLMs

arXiv:2603.23522v2 Announce Type: replace-cross Abstract: Evaluating large language models (LLMs) on open-ended questions is difficult because response quality depends on the question's context. Binar

21 Aug 2026

Are LLMs Safe Beyond Text: Do Emojis Expose Gaps in Safety Evaluation

arXiv:2608.18164v1 Announce Type: cross Abstract: Safety evaluations of large language models (LLMs) predominantly rely on text-based adversarial prompts, potentially overlooking vulnerabilities arisi

20 Aug 2026

Compress and Forget: bitsandbytes Quantization Amplifies Proactive Interference in LLMs

arXiv:2608.18578v1 Announce Type: new Abstract: Proactive interference (PI) is a documented failure mode in large language models in which retrieval of a repeatedly overwritten value degrades as prior

20 Aug 2026

FedLNS: Leverage LayerNorm Signature Modeling to Mitigate Adversarial Manipulation in Federated LLMs

model-releasesarxiv-cs-lg

arXiv:2608.18736v1 Announce Type: new Abstract: Federated training enables language models to learn from distributed private text, but the server cannot directly verify the local supervision or optimi

20 Aug 2026

Encoded but Not Actionable: Auditing the Decode-Generate-Steer Gap in Frozen LLMs for Geometric Constraints

arXiv:2608.17843v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong performance on structured reasoning tasks, but what they encode and whether it informs model beh

19 Aug 2026

Leveraging Association Context Retrieval in Knowledge Edit- ing to Build White-Box Attacks on LLMs

arXiv:2608.17836v1 Announce Type: new Abstract: As large language models (LLMs) are granted increasing autonomy, it is essential to investigate methods that can induce unsafe behavior. We propose a no

researcharxiv-cs-lg

19 Aug 2026

LLMs for Medical Consultation Are Evaluated Too Late: The Preformulation Gap

arXiv:2608.17330v1 Announce Type: new Abstract: Large language models for medical consultation are often evaluated after a clinical problem has already been made clear, although real consultations may

19 Aug 2026

A Human-Centred Approach to Benchmarking LLMs for Parenting Advice

arXiv:2608.14622v1 Announce Type: new Abstract: People are increasingly using large language models (LLMs) to seek advice, including for parenting. Parenting is a critical and socially sensitive domai