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

Jul 22

CLT-Forge: A Scalable Library for Cross-Layer Transcoders and Attribution Graphs

arXiv:2603. 21014v2 Announce Type: replace Abstract: Mechanistic interpretability seeks to understand how Large Language Models (LLMs) represent and process information.

By Florent Draye, Vedant Palit, Abir Harrasse, Tung-Yu Wu, Jiarui Liu, Punya Syon Pandey, Roderick Wu, Chih-Hao Hsu, Terry Jingchen Zhang, Zhijing Jin, Bernhard Sch\"olkopf

llms safety

arXiv AI

Jul 22

Enhancing Transformer-based Routing by Encoding Distance via Relative Positional Encoding

arXiv:2607. 18909v1 Announce Type: new Abstract: This paper explores Relative Positional Encoding (RPE) as an additive bias in Transformer architectures to solve the Team Orienteering Problem.

By Leyre Enc\'io, Daniel Fuertes, Carlos R. del-Blanco, Fernando Jaureguizar

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arXiv AI

Jul 22

AutoJourn: Multi-Perspective Summarisation, Bias Detection and Bias Neutralisation for LLM-Generated News in Automated Journalism

arXiv:2607. 18983v1 Announce Type: cross Abstract: We present AutoJourn, a demonstration system for multi-perspective news generation and bias-aware evaluation using large language models (LLMs).

By Himel Ghosh, Ahmed Mosharafa, Georg Groh

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arXiv AI

Jul 22

Saving the legacy of Hero Ibash: Evaluating Four Language Models for Aminoacian

arXiv:2402. 18121v2 Announce Type: replace-cross Abstract: This study assesses four cutting-edge language models in the underexplored Aminoacian language.

By Yunze Xiao, Yiyang Pan

llms nlp benchmarks

arXiv AI

Jul 22

MUX: Continuous Reasoning via Multiplexed Tokens

arXiv:2607. 18264v1 Announce Type: new Abstract: Language models solve complex problems by articulating intermediate reasoning steps in natural language.

By Ayhan Suleymanzade, Halil Alperen Gozeten, Michael Bronstein, \.Ismail \.Ilkan Ceylan, Jinwoo Kim

llms diffusion efficiency benchmarks

arXiv AI

Jul 22

Relay-Bench: Evaluating LLMs on Multi-Domain Reasoning Chains

arXiv:2607. 18438v1 Announce Type: cross Abstract: Introducing Relay-Bench, an unsaturated, holistic, text-only benchmark that measures LLMs' ability to complete an assortment of tasks from distinct domains in a single prompt.

By Liam Swayne

llms multimodal benchmarks

arXiv AI

Jul 22

Beyond Single-Dimensional Compression: The Compound Sparsity Frontier of Large Language Models

arXiv:2607. 18280v1 Announce Type: cross Abstract: Large language models (LLMs) are often compressed through static parameter pruning or dynamic token-level computation, yet aggressive sparsification can trigger rapid performance degradation beyond an essential sparsity boundary.

By Chao Han, Haozhe Hu, Xiaoyu Shen

llms nlp efficiency benchmarks

arXiv AI

Jul 22

An Analysis of Residual-Stream Geometry Across Transformer Depth

arXiv:2607. 18348v1 Announce Type: cross Abstract: We propose a transition-centred geometric analysis of transformer residual streams.

By Sunit Bhattacharya, Ravi Shankar Kolli

llms

arXiv AI

Jul 22

AI Tour Meeting: Group Travel Planning by LLM Agents

arXiv:2607. 18806v1 Announce Type: new Abstract: This paper proposes AI Tour Meeting, a group travel planning framework powered by multiple Large Language Model (LLM)-based agents.

By Daisuke Kikuta

llms agents

arXiv AI

Jul 22

PRISP: Privacy-Safe Few-Shot Personalization via Lightweight Adaptation

arXiv:2601. 06471v2 Announce Type: replace-cross Abstract: Large language model (LLM) personalization aims to adapt general-purpose models to individual users.

By Junho Park, Dohoon Kim, Taesup Moon

llms fine-tuning benchmarks safety

arXiv Machine Learning

Jul 22

RAPT: Model-Predictive Out-of-Distribution Detection and Failure Diagnosis for Sim-to-Real Humanoid Deployment

arXiv:2602. 01515v2 Announce Type: replace-cross Abstract: Deploying learned control policies is risky because policies that appear robust in simulation can confidently enter out-of-distribution (OOD) states after Sim-to-Real transfer, causing silent failures and potential hardware damage.

By Humphrey Munn, Brendan Tidd, Peter Bohm, Marcus Gallagher, David Howard

llms robotics

arXiv AI

Jul 22

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image

arXiv:2607. 19261v1 Announce Type: cross Abstract: Whole-slide image (WSI) diagnosis requires identifying diagnostically relevant regions, examining them across magnifications, and integrating multi-scale evidence.

By Dankai Liao, Tianyi Zhang, Yufeng Wu, Xinyue Zhang, Qiaochu Xue, Zeyu Liu, Dachun Zhao, Linghan Cai, Yueming Jin

llms agents diffusion multimodal benchmarks

arXiv AI

Jul 22

Towards an Automated Test of LLM Security Knowledge

arXiv:2607. 18496v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for a range of software, hardware and human-centered security tasks.

By Shufan Chai, Liangliang Sun, Jessica Staddon

llms benchmarks

arXiv AI

Jul 22

OntoBook: Ontology-Grounded Synthetic Textbooks for Medical Encoder Pretraining

arXiv:2607. 18927v1 Announce Type: new Abstract: We present OntoBook, a method that converts medical ontology structure into pretraining signal for encoder language models.

By Rian Touchent (ALMAnaCH), \'Eric de la Clergerie (ALMAnaCH)

llms benchmarks safety

arXiv AI

Jul 22

Estimating Rare Events in Language Models with Proper Evaluation

arXiv:2607. 18454v1 Announce Type: cross Abstract: Quantifying the risk of rare failures in language models, such as those triggered by adversarial distribution shifts or very large-scale deployments, requires estimating probabilities far too small for random sampling.

By Nikita Y. Parulekar, Anqi Liu

llms safety

arXiv AI

Jul 22

Beyond Score Prediction: LLM-Based Essay Scoring and Feedback Generation via Reinforcement Learning with Rubric Rewards

arXiv:2607. 19219v1 Announce Type: cross Abstract: Large language models (LLMs) have been widely applied to automated essay scoring (AES) and automated feedback generation (AFG).

By Xuefeng Jin, Jiashuo Zhang, Teng Cao, Bin Yang

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 22

Large Language Models Explore by Latent Distilling

arXiv:2604. 24927v2 Announce Type: replace-cross Abstract: Generating diverse responses is crucial for test-time scaling of large language models (LLMs), yet standard stochastic sampling mostly yields surface-level lexical variation, limiting semantic exploration.

By Yuanhao Zeng, Ao Lu, Lufei Li, Zheng Zhang, Yexin Li, Kan Ren

llms benchmarks

arXiv AI

Jul 22

Assessment in Team Problem-Solving Exercises in Computing Education

arXiv:2607. 19209v1 Announce Type: cross Abstract: This full paper in the research-to-practice track presents methods for assessing student teams in tabletop exercises (TTXs).

By Valdemar \v{S}v\'abensk\'y, Jan Vykopal, Sukrit Leelaluk, Pavel \v{C}eleda, Fumiya Okubo, Atsushi Shimada

llms

arXiv Machine Learning

Jul 22

On the Limits of Support-Preserving Alignment and Bounded Filtering

arXiv:2607. 18295v1 Announce Type: new Abstract: We study whether alignment schemes that reshape a base model's output distribution, combined with bounded safety filters, can drive the probability of harmful behavior to zero in modern large language models.

By Aryan Dutt, Rui Mao, Anupam Chattopadhyay

llms benchmarks safety

arXiv AI

Jul 22

CodeRescue: Budget-Calibrated Recovery Routing for Coding Agents

arXiv:2607. 19338v1 Announce Type: new Abstract: Coding agents increasingly operate in executable environments where a failed attempt produces actionable feedback rather than merely an incorrect answer.

By Qijia He, Jiayi Cheng, Chenqian Le, Rui Wang, Xunmei Liu, Yixian Chen, Jie Mei, Zhihao Wang, Xupeng Chen, Yuhuan Chen, Tao Wang

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