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

Jul 28

The Lattice Representation Hypothesis of Large Language Models

arXiv:2603. 01227v3 Announce Type: replace Abstract: We propose the Lattice Representation Hypothesis of large language models: a symbolic backbone that grounds conceptual hierarchies and logical operations in embedding geometry.

By Bo Xiong

llms rag

arXiv AI

Jul 28

Moral Hazard in Multi-Agent Language Models

arXiv:2607. 23982v1 Announce Type: cross Abstract: Cooperation can fail when socially valuable effort is costly, weakly observable, and mainly benefits others.

By Dane Malenfant

llms agents fine-tuning safety

arXiv AI

Jul 28

Key-Value Means: Transformers with Expandable Block-Recurrent Compressed Memory

arXiv:2605. 09877v4 Announce Type: replace-cross Abstract: Recall presents a difficult choice: transformers have a linearly growing memory that slows each successive token, while linear RNNs typically have fixed costs but limited recall.

By Daniel Goldstein, Navneel Singhal, Eugene Cheah

llms

arXiv AI

Jul 28

Computational Experiments in Number Theory

arXiv:2504. 19451v4 Announce Type: replace-cross Abstract: This paper presents two concrete applications of Artificial Intelligence to algorithmic and analytic number theory.

By Ali Saraeb

llms benchmarks

arXiv AI

Jul 28

Athena-Brain Technical Report: An Efficient Robot Brain for General Intelligence and Embodied Interaction

arXiv:2607. 18985v2 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in language understanding, reasoning, and world knowledge.

By Jialian Li, Junhong Liu, Yuchen Cao, Weiran Guo, Jiaming Song, Xutao Wang, Yi Zhao, Jiangpin Liu, Jie Chen

llms agents reinforcement-learning robotics fine-tuning benchmarks

arXiv Machine Learning

Jul 28

PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling

arXiv:2607. 23447v1 Announce Type: new Abstract: Predicting cellular responses to unseen chemical perturbations is challenging due to unknown targets and mechanisms, high-dimensional expression responses, and limited experimental coverage of the large small-molecule design space.

By Yuche Gao, Jos\'e Miguel Hern\'andez-Lobato, Siyuan Guo

llms benchmarks

arXiv Machine Learning

Jul 28

FlowCTS: On-policy Continuous Trajectory Supervision of Flow Models

arXiv:2607. 24522v1 Announce Type: new Abstract: While on-policy distillation (OPD) effectively addresses sparse rewards and exposure bias in large language model post-training, its extension to flow models remains underexplored.

By Kaiyang Ye, Yuan Ge, Junxiang Zhang, Bei Li, Ziming Zhu, Haishu Zhao, Xiaoqian Liu, Chenglong Wang, Jingbo Zhu, Zhengtao Yu, Tong Xiao

llms computer-vision reinforcement-learning fine-tuning efficiency safety

arXiv AI

Jul 28

ADAGE: A Language-Agnostic Pipeline for Analogical Reasoning Evaluation

arXiv:2607. 23058v1 Announce Type: cross Abstract: Multilingual reasoning evaluation overwhelmingly relies on translating English benchmarks, a practice that introduces linguistic artifacts and fails to test culturally-grounded reasoning.

By Ahmed Haj Ahmed, Alvin Grissom II

llms benchmarks

arXiv AI

Jul 28

Temporal Context Reinstatement Drives Episodic-Like Order Memory in Long-Context Language Models

arXiv:2607. 22575v1 Announce Type: new Abstract: Human episodic memory supports the retrieval of experiences that unfold over extended timescales, yet the computational mechanisms underlying this ability remain debated due to the limited mechanistic accessibility in long-term memory experiments in humans.

By Mathis Pink, Vy Ai Vo, Qinyuan Wu, Jianing Mu, Javier Turek, Uri Hasson, Kenneth A. Norman, Sebastian Michelmann, Alexander Huth, Mariya Toneva

llms safety

arXiv Machine Learning

Jul 28

Self-Boosting Vision-Language Models with Noisy Student On-Policy Self-Distillation

arXiv:2607. 23125v1 Announce Type: new Abstract: Post-training enables vision-language models (VLMs) to understand human instructions and perform various downstream tasks.

By Shuai Wang, Daoan Zhang, Zhe Tang, Hao Cheng, Jiaheng Wei

llms reinforcement-learning efficiency multimodal benchmarks

arXiv AI

Jul 28

Cortex: Compact Behavior Cloning for Quake with Frozen Visual Features

arXiv:2607. 22739v1 Announce Type: cross Abstract: We study how far a deliberately simple behavioral-cloning policy can progress in a visually rich first-person game before adding reinforcement learning or explicit memory.

By Dzmitry Malyshau

llms reinforcement-learning

arXiv AI

Jul 28

Not All LLM Reasoning is Visible in the Chain-of-Thought

arXiv:2607. 22925v1 Announce Type: cross Abstract: A key question for AI safety is whether a language model expresses all of its reasoning in its output tokens.

By Vatsal Baherwani, Tom Goldstein, Ashwinee Panda

llms reinforcement-learning fine-tuning safety

arXiv AI

Jul 28

UP-Fuse: Uncertainty-guided LiDAR-Camera Fusion for 3D Panoptic Segmentation

arXiv:2602. 19349v2 Announce Type: replace-cross Abstract: LiDAR-camera fusion enhances 3D panoptic segmentation by leveraging camera images to complement sparse LiDAR scans, but it also introduces a critical failure mode.

By Rohit Mohan, Florian Drews, Yakov Miron, Daniele Cattaneo, Abhinav Valada

llms computer-vision robotics multimodal benchmarks safety

arXiv AI

Jul 28

Lexical discovery in unknown environments orchestrated by Large Language Models

arXiv:2607. 22591v1 Announce Type: new Abstract: Populations of autonomous agents deployed in unknown environments (e.

By Rafael Sendra-Arranz, I\~naki Dellibarda Varela, Eduardo Rocon, \'Alvaro Guti\'errez, Manuel Cebrian

llms rag agents

arXiv AI

Jul 28

How Context Attribution Handles What the Model Already Knows

arXiv:2607. 23804v1 Announce Type: cross Abstract: Context attribution methods for large language models (LLMs) identify which input context contributes to the model response.

By Quoc-Huy Trinh, Lin Zhu, Sebastian Szyller

llms benchmarks

arXiv AI

Jul 28

Evaluating the Impact of Reviewer Guideline Design on LLM-Based Automated Peer Review

arXiv:2607. 22553v1 Announce Type: cross Abstract: Peer review is an essential process in scientific research, yet the growing workload has made its automation increasingly necessary.

By Haowen Li, Yoichi Ishibashi, Masafumi Oyamada

llms

arXiv AI

Jul 28

Evolving from Lessons: Skill-Augmented Table Graph Reasoning for Operation-wise Table Question Answering

arXiv:2607. 22633v1 Announce Type: new Abstract: Table Question Answering (TableQA) aims to reason over tables to answer user queries.

By Guixin Su, Qiankun Pi, Mayi Xu, Wenli Li, Ming Zhong, Yuanyuan Zhu, Jiawei Jiang, Tieyun Qian

llms nlp efficiency

arXiv AI

Jul 28

VlogReward: Learning Multi-Dimensional Evaluation for Vlog Editing

arXiv:2607. 22632v1 Announce Type: new Abstract: The rapid rise of vlogs as a personalized storytelling medium has created a demand for automated systems to evaluate and refine vlog editing plans.

By Yexiang Liu, Wen Zhong, Sijie Zhu, Xin Gu, Fan Chen, Junxian Duan, Jie Cao, Longyin Wen, Zhenfang Chen

llms reinforcement-learning multimodal benchmarks

arXiv AI

Jul 28

CRAFT: Learn the Schema, Execute the Plan

arXiv:2607. 22642v1 Announce Type: new Abstract: Enterprise coding agents translate natural-language analytical requests into executable code over proprietary APIs, schemas, and metric definitions.

By Aakash Kolekar, Sahika Genc, Shahriar Shariat, Bunyamin Sisman, Tibor Mezi, Barbara Poblete, Shree Vandana Kachroo, Calvin Chi, Parth Parmar, Ari Singer, Prayaas Jain, Cindy Barker, Benoit Dumoulin

llms agents reinforcement-learning fine-tuning

arXiv AI

Jul 28

TRACE: Business Rule-Grounded Reasoning Curriculum for Knowledge-Preserving Parametric Tool Retrieval in Enterprise LLMs

arXiv:2607. 22639v1 Announce Type: new Abstract: Parametric retrieval enables LLMs to retrieve tools implicitly by assigning each API a unique virtual token and training the model to generate it via constrained beam search.

By Sai Shruthi Sistla, Ashutosh Hathidara, Christopher Toukmaji, Mayank Shrivastava, Karthikeyan Asokkumar

llms rag fine-tuning

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