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 Machine Learning

Jun 19

Granularity-Regulated Adaptive Computational Efficiency for Optimal Verification in Test-Time Scaling

arXiv:2606. 19354v1 Announce Type: cross Abstract: Test-time scaling (TTS) has emerged as a powerful paradigm for improving the reasoning performance of large language models (LLMs) by investing additional compute at inference time.

By Ardit Krasniqi, Luan Vejsiu, Elira Dervishi

llms benchmarks

arXiv Machine Learning

Jun 19

Where Does Social Reasoning Come From? Capability Provenance in Language Models

arXiv:2606. 19625v1 Announce Type: cross Abstract: We use training-data attribution as an interpretable tool for capability discovery, mapping which regions of the pretraining corpus support social-reasoning versus STEM-reasoning in OLMo3-7B.

By Glenn Matlin, Chandreyi Chakraborty, Saehee Eom, Mika Okamoto, Rayan Castilla, Louis Jaburi, Alvin Deng, Taywon Min, Lucia Quirke, Stella Biderman, Mark Riedl

llms benchmarks

arXiv AI

Jun 19

Wisdom of Committee: Diverse Distillation from Large Foundation Models and Domain Experts

arXiv:2402. 14035v4 Announce Type: replace-cross Abstract: Knowledge distillation from foundation models to compact domain models is challenging due to substantial gaps in capacity, architecture, and modality.

By Zichang Liu, Qingyun Liu, Yuening Li, Liang Liu, Anshumali Shrivastava, Shuchao Bi, Lichan Hong, Ed H. Chi, Zhe Zhao

llms efficiency

arXiv AI

Jun 19

Rethinking Shrinkage Bias in LLM FP4 Pretraining: Geometric Origin, Systemic Impact, and UFP4 Recipe

arXiv:2606. 20381v1 Announce Type: new Abstract: FP4 training promises substantial reductions in memory and computation cost for LLM pretraining, yet current FP4 hardware paths and recipes, including NVIDIA Blackwell/Rubin-class systems and AMD MI350-series GPUs, remain centered on E2M1 data elements.

By Qian Zhao, Kunlong Chen, Changxin Tian, Zhonghui Jiang, Haitao Zhang, Chaofan Yu, Peijie Jiang, Mingliang Gong, Jia Liu, Ziqi Liu, Zhiqiang Zhang, Jun Zhou

llms efficiency benchmarks safety

arXiv Machine Learning

Jun 19

Quantifying Aleatoric Uncertainty of In-Context Learning for Robust Measure of LLM Prediction Confidence

arXiv:2606. 19353v1 Announce Type: cross Abstract: In-Context Learning (ICL) allows LLMs to adapt to new tasks from a few demonstrations, but its reliability remains a concern: predictions are highly sensitive to both prompt design and the model's ability to understand the context, obscuring whether failures arise from data properties or model limitations.

By Jinseok Chung, Minkyoung Song, Hyunji Jung, Namhoon Lee

llms benchmarks safety

arXiv AI

Jun 19

NRITYAM: Language Models Meet Art and Heritage of Dance

arXiv:2606. 19727v1 Announce Type: cross Abstract: Language models have become essential tools in shaping modern workflows.

By Punit Kumar Singh, Niladri Ghosh, Advait Joshi{\i}nst, Shailee Choudhary, Michael F\"arber, Haiqin Yang

llms multimodal benchmarks

arXiv AI

Jun 19

VERITAS: Verifier-Guided Proof Search for Zero-Shot Formal Theorem Proving

arXiv:2606. 19399v1 Announce Type: cross Abstract: LLM-based formal provers often collapse rich verifier signals (syntax errors, type mismatches, partial goal progress) into a binary pass/fail bit.

By Manish Acharya, Zhenyu Liao, Yueke Zhang, Kevin Leach, Yu Huang, Yifan Zhang

llms benchmarks

arXiv AI

Jun 19

CombEval: A Framework for Evaluating Combinatorial Counting in Large Language Models

arXiv:2606. 19788v1 Announce Type: new Abstract: We present CombEval, a dynamic benchmark for evaluating combinatorial counting in large language models.

By Yuxu Zhou, Ond\v{r}ej Ku\v{z}elka, Yuyi Wang, Yuanhong Wang, Yi Chang

llms benchmarks

arXiv AI

Jun 19

GLARE: A Natural Language Interface for Querying Global Explanations

arXiv:2606. 19735v1 Announce Type: new Abstract: While global explanations are crucial for understanding vision models across datasets, classes, and decision contexts, their complex and monolithic nature often hinders practical exploration.

By Bhavan Vasu, Rajesh Mangannavar

llms data-engineering

arXiv AI

Jun 19

DeXposure-Claw: An Agentic System for DeFi Risk Supervision

arXiv:2606. 19501v1 Announce Type: new Abstract: Decentralized finance exposes supervisors to fast-moving, networked credit risks.

By Aijie Shu, Bowei Chen, Wenbin Wu, Cathy Yi-Hsuan Chen, Fengxiang He

llms agents

arXiv Machine Learning

Jun 19

Probe-and-Refine Tuning of Repository Guidance for Coding Agents

arXiv:2606. 20512v1 Announce Type: cross Abstract: LLM-based coding agents need higher-level operational knowledge about a repository (which files house which subsystems, how to run the test suite, which workflows have historically led to wrong fixes) that does not exist in the code itself.

By Asa Shepard, Jeannie Albrecht

llms rag agents

arXiv AI

Jun 19

Manifold Bandits: Bayesian Curriculum Learning over the Latent Geometry of Large Language Models

arXiv:2606. 19750v1 Announce Type: cross Abstract: Reinforcement learning (RL) is a central approach for improving reasoning capabilities in large language models (LLMs), where training efficiency depends critically on how problems are sampled during optimization.

By Darrien McKenzie, Nicklas Hansen, Xiaolong Wang

llms reinforcement-learning

arXiv AI

Jun 19

Large Language Models Do Not Always Need Readable Language

arXiv:2606. 19857v1 Announce Type: cross Abstract: Large language models (LLMs) are commonly prompted and interfaced with human-readable natural language, even when the intended reader is another model.

By Jiayi Zhu, Haoxuan Peng, Junxi Wang, Liang Ke, Chen Zhang, Linfeng Zhang

llms agents

arXiv AI

Jun 19

DeFrame: Debiasing Large Language Models Against Framing Effects

arXiv:2602. 04306v2 Announce Type: replace-cross Abstract: As large language models (LLMs) are increasingly deployed in real-world applications, ensuring their fair responses across demographics has become crucial.

By Kahee Lim, Soyeon Kim, Steven Euijong Whang

llms benchmarks safety

arXiv AI

Jun 19

Evaluating and Enhancing Negation Comprehension in Remote Sensing MLLMs

arXiv:2606. 20177v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable success in various Remote Sensing (RS) tasks.

By Haochen Han, Jue Wang, Alex Jinpeng Wang, Fangming Liu

llms multimodal benchmarks safety

arXiv AI

Jun 19

Mitigating Legibility Tax with Decoupled Prover-Verifier Games

arXiv:2602. 23248v2 Announce Type: replace Abstract: As large language models become increasingly capable, it is critical that their outputs can be easily checked by less capable systems.

By Yegon Kim, Juho Lee

llms

arXiv AI

Jun 19

Vero: An Open RL Recipe for General Visual Reasoning

arXiv:2604. 04917v3 Announce Type: replace-cross Abstract: What does it take to build a visual reasoner that works across charts, science, spatial understanding, and open-ended tasks?

By Gabriel Sarch, Linrong Cai, Qunzhong Wang, Haoyang Wu, Danqi Chen, Zhuang Liu

llms reinforcement-learning efficiency multimodal benchmarks

arXiv AI

Jun 19

Beyond Uniform Forgetting: A Study of Sequential Direct Preference Optimization Across Preference Settings

arXiv:2606. 19744v1 Announce Type: cross Abstract: Aligning language models with human preferences often requires optimising multiple behavioural objectives.

By Pranav Bhandari, Nicolas Fay, Amitava Datta, Usman Naseem, Mehwish Nasim

llms reinforcement-learning fine-tuning safety

arXiv AI

Jun 19

MedRLM: Recursive Multimodal Health Intelligence for Long-Context Clinical Reasoning, Sensor-Guided Screening, Evidence-Grounded Decision Support, and Community-to-Tertiary Referral Optimization

arXiv:2606. 20164v1 Announce Type: cross Abstract: Real-world clinical decision support requires reasoning over heterogeneous and longitudinal patient information rather than answering isolated medical questions.

By Aueaphum Aueawatthanaphisut

llms rag agents nlp multimodal

arXiv AI

Jun 19

Interpretable and Verifiable Hardware Generation with LLM-Driven Stepwise Refinement

arXiv:2606. 19387v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved remarkable success in software development.

By You Li, Samuel Mandell, David Z. Pan

llms agents safety

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