Large language models

Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.

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arXiv AI

Jun 30

Evidence-Driven LLM Agent for C-to-Synthesizable-C Conversion and Verification

arXiv:2606. 28409v1 Announce Type: cross Abstract: Software-compilable C programs routinely fail to complete the four-stage pipeline of a high-level synthesis (HLS) toolchain -- compilation, C simulation (CSim), synthesis, and C/RTL co-simulation (CoSim) -- because HLS accepts only a synthesizable subset of C (HLS-C).

By Zhe Zhao, Hongbing Lang, Zhihan Xiao, Luke Ztz Hu, John Imoleayo Adebisi, Songping Mai

llms rag agents benchmarks

arXiv Machine Learning

Jun 30

An Integrated Two-Stage Deep-Learning Tool for Rapid Post-Hurricane Damage Identification and Repair Scheduling

arXiv:2606. 29117v1 Announce Type: cross Abstract: Post-hurricane damage assessment and repair scheduling can require computationally intensive simulation and optimization.

By Hooman Torkaman, Ellis Oti Boateng, Jignesh Solanki, Anurag Srivastava

llms benchmarks

arXiv AI

Jun 30

When More Sampling Hurts: The Modal Ceiling and Correlation Ceiling of Test-Time Scaling

arXiv:2606. 28661v1 Announce Type: cross Abstract: People overthink; language models over-sample, and the extra effort can talk both into a worse answer.

By Yong Yi Bay, Kathleen A. Yearick

llms benchmarks

arXiv AI

Jun 30

Correct codes for the wrong reasons? validating LLMs as measurement instruments for theoretical constructs

arXiv:2606. 28574v1 Announce Type: cross Abstract: When a large language model (LLM) codes a construct in text as a human annotator would, that agreement makes the LLM a reliable coder.

By Manuel Pita

llms

arXiv AI

Jun 30

Latent Bridges for Multi-Table Question Answering

arXiv:2606. 28916v1 Announce Type: cross Abstract: We introduce GRAB, a constructor-encoder-bridge pipeline for table question answering.

By Simone Varriale, Tamara Cucumides, Floris Geerts, Paolo Papotti

llms nlp

arXiv AI

Jun 30

Fine-Tuning General-Purpose Large Language Models for Agricultural Applications:A Reproducible Framework and Evaluation Protocol Based on Qwen3-8B

arXiv:2606. 28992v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) have demonstrated strong abilities in opendomain question answering, information extraction, and text generation.

By Zhaoyang Li, Ruijie Zhang, Jiaqi Liu, Zhaoji Sun

llms nlp fine-tuning safety

arXiv Machine Learning

Jun 30

Adaptive Block Diffusion: Resolving Training-Inference Mismatch in Diffusion Language Models

arXiv:2606. 29275v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) are typically trained under fixed context structures, restricting denoising to predetermined token subsets.

By Gagan Jain

llms diffusion

arXiv AI

Jun 30

A Hybrid Framework for Song Lyric Annotation Based on Human-LLM Alignment

arXiv:2606. 29273v1 Announce Type: cross Abstract: Emotion recognition of song lyrics is a challenging task since lyrics may not necessarily align with the overall emotion of a song.

By Rashini Liyanarachchi, Frank Tran, Md Mahmudul Hasan, Aditya Joshi, Erik Meijering

llms safety

arXiv AI

Jun 30

Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction

arXiv:2606. 29347v1 Announce Type: cross Abstract: Adaptive Financial Transformer (AFT) is proposed for stock return prediction under non-stationary financial markets.

By Dishan Sarkar

llms benchmarks safety

arXiv AI

Jun 30

Mechanistically Eliciting Latent Behaviors in Language Models

arXiv:2606. 29604v1 Announce Type: cross Abstract: We aim to discover diverse, generalizable perturbations of LLM internals that can surface hidden behavioral modes.

By Andrew Mack, Nina Panickssery, Alexander Matt Turner

llms safety

arXiv Machine Learning

Jun 30

Two kinds of robustness are not the same: disentangling fault tolerance and low-SNR robustness in multi-domain event detection on real data

arXiv:2606. 29339v1 Announce Type: cross Abstract: Reliable event detection underpins induced-seismicity monitoring for Carbon dioxide Capture and Storage (CCS) and geothermal operations, distributed acoustic sensing (DAS), and industrial condition monitoring.

By Isao Kurosawa

llms benchmarks

arXiv AI

Jun 30

Towards Continual Motion-Language Agents: LoRA Variants for Incremental Motion Understanding and Generation

arXiv:2606. 30266v1 Announce Type: cross Abstract: Motion-language agents must possess the bidirectional capability to both understand human movement (motion-to-text, M2T) and generate it from natural language (text-to-motion, T2M).

By Bertram Taetz, Hugo Albuquerque Cosme da Silva, Gabriele Bleser-Taetz

llms agents fine-tuning benchmarks

arXiv AI

Jun 30

On the Faithfulness of Post-Hoc Concept Bottleneck Models

arXiv:2606. 30498v1 Announce Type: cross Abstract: Human decision-making interprets the world through high-level concepts, such as recognizing a bird by its belly color.

By Laines Schmalwasser, Jan Blunk, Niklas Penzel, Julia Niebling, Joachim Denzler

llms multimodal benchmarks

arXiv AI

Jun 30

Geo-Expert: Towards Expert-Level Geological Reasoning via Parameter-Efficient Fine-Tuning

arXiv:2605. 24844v2 Announce Type: replace Abstract: While general-purpose Large Language Models (LLMs) applied to Geology often hallucinate when reasoning about subsurface structures and deep-time evolution, current AI in Earth sciences predominantly targets surface remote sensing and GIS.

By Chenyou Guo, Zongqi Liu, Yizhou Zhang, Zhaorui Jiang, Ze Liu

llms fine-tuning benchmarks

arXiv AI

Jun 30

Knowing Bias, Doing Better: Mitigating Social Bias in LLMs via Know-Bias Neuron Enhancement

arXiv:2601. 21864v2 Announce Type: replace Abstract: Large language models (LLMs) exhibit social biases that reinforce harmful stereotypes, limiting their safe deployment.

By Jinhao Pan, Chahat Raj, Anjishnu Mukherjee, Sina Mansouri, Bowen Wei, Shloka Yada, Ziwei Zhu

llms benchmarks safety

arXiv AI

Jun 30

CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use Agents

arXiv:2511. 02734v3 Announce Type: replace Abstract: Current evaluations of Large Language Model (LLM) agents primarily emphasize task completion, often overlooking resource efficiency and adaptability.

By Jiayu Liu, Cheng Qian, Zhaochen Su, Qing Zong, Shijue Huang, Bingxiang He, Yi R. Fung

llms agents benchmarks

arXiv AI

Jun 30

When Web Agents Finish but Still Fail: Reproducible Triggers and Trace Diagnostics for Parallel Web Exploration

arXiv:2606. 20724v2 Announce Type: replace Abstract: Long-horizon web agents often fail in ways hidden by final-answer evaluation: they may visit useful pages, produce a well-formed answer, and terminate confidently while still missing fields, over-including unsupported items, or relying on stale evidence.

By Aagam Sogani, Botao Rui, Swetha Vaidyanathan, Rishi Agarwal, Minghao Yan, Shivaram Venkataraman

llms agents benchmarks

arXiv AI

Jun 30

Modeling Earth-Scale Human-Like Societies with One Billion Agents

arXiv:2506. 12078v2 Announce Type: replace-cross Abstract: Understanding the dynamic evolution of complex social phenomena requires both high-fidelity modeling of human behavior and large-scale simulations.

By Haoxiang Guan, Jiyan He, Liyang Fan, Zhenzhen Ren, Shaobin He, Xin Yu, Yuan Chen, Xueyin Xu, Shuxin Zheng, Yan Gao, Enhong Chen, Tie-Yan Liu, Zhen Liu

llms agents diffusion

arXiv AI

Jun 30

Are LLMs Reliable Rankers? Rank Manipulation via Two-Stage Token Optimization

arXiv:2510. 06732v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used as rerankers in information retrieval, yet their ranking behavior can be steered by small, natural-sounding prompts.

By Tiancheng Xing, Jerry Li, Yixuan Du, Xiyang Hu

llms rag robotics safety

arXiv AI

Jun 30

Semantic Prompting: Agentic Incremental Narrative Refinement through Spatial Semantic Interaction

arXiv:2604. 19971v2 Announce Type: replace-cross Abstract: Interactive spatial layouts empower users to synthesize information and organize findings for sensemaking.

By Xuxin Tang, Ibrahim Tahmid, Eric Krokos, Kirsten Whitley, Xuan Wang, Chris North

llms agents safety

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