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 14

FineInstructions: Scaling Synthetic Instructions to Pre-Training Scale

arXiv:2601. 22146v2 Announce Type: replace-cross Abstract: Due to limited supervised training data, large language models (LLMs) are typically pre-trained via a self-supervised "predict the next word" objective on a vast amount of unstructured text data.

By Ajay Patel, Colin Raffel, Chris Callison-Burch

llms benchmarks

arXiv Machine Learning

Jul 14

SCOPE-RL: Optimizing Reasoning Paths Before and After Success

arXiv:2607. 11506v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) optimizes LLMs using sparse verifiable final-answer rewards.

By Xiaojian Liu, Han Xu, Jianqiang Xia, Zhixuan Li, Ke Xu, Yiwei Dai, Xinran Chen, Changwo Wu, Yuchen Li

llms reinforcement-learning

arXiv AI

Jul 14

Lesioned Multimodal Language Models Reproduce Aphasic Picture-Naming Patterns

arXiv:2607. 11621v1 Announce Type: new Abstract: Aphasia following stroke commonly produces systematic naming errors with characteristic profiles, but whether general-purpose language models not designed for clinical simulation can reproduce these patterns remains untested.

By Yong Yang, Xiang Guan, Sophie Arheix-Parras, Saeed Ahmadi, Roger Newman-Norlund, Leonardo Bonilha, Christopher Rorden, Julius Fridriksson, Rutvik H. Desai, Srihari Nelakuditi

llms multimodal

arXiv Machine Learning

Jul 14

Adaptive Routing for Efficient Diffusion Transformer-Based PNI Prediction

arXiv:2607. 11533v1 Announce Type: cross Abstract: Perineural invasion (PNI) is a critical prognostic factor in cholangiocarcinoma.

By Youngung Han, Dohyun Kweon, Kyeonghun Kim, Hyunsu Go, Jina Jeong, Suah Park, Induk Um, Junga Kim, Anna Jung, Yului Jeong, Sungha Park, Jinyong Jun, Pa Hong, Woo Kyoung Jeong, Won Jae Lee, Ken Ying-Kai Liao, Hyuk-Jae Lee, Nam-Joon Kim

llms diffusion

arXiv AI

Jul 14

TENET: One Step Toward Test-Driven Development for Repository-Level Code Generation

arXiv:2509. 24148v3 Announce Type: replace-cross Abstract: Test-Driven Development (TDD) is a widely adopted practice that requires developers to create and execute tests alongside implementation.

By Yiran Hu, Nan Jiang, Shanchao Liang, Yi Wu, Lin Tan

llms agents

arXiv AI

Jul 14

GrandCode: Achieving Grandmaster Level in Competitive Programming via Agentic Reinforcement Learning

arXiv:2604. 02721v2 Announce Type: replace Abstract: Competitive programming remains one of the last few human strongholds in coding against AI.

By DeepReinforce Team, Xiaoya Li, Guoyin Wang, Songqiao Su, Chris Shum, Jiwei Li

llms agents nlp reinforcement-learning

arXiv Machine Learning

Jul 14

Diagnosing and Mitigating Thinking Collapse in On-Policy Self-Distillation

arXiv:2607. 10805v1 Announce Type: cross Abstract: On-Policy Self-Distillation (OPSD) has emerged as a crucial paradigm for enhancing and aligning Large Language Models (LLMs).

By Keqin Peng, Chen Li, Yuanxin Ouyang, Yancheng Yuan, Liang Ding

llms efficiency benchmarks

arXiv AI

Jul 14

Playful AI in Professional Email: A Field Experiment on Tone and Recipient Engagement

arXiv:2607. 11749v1 Announce Type: new Abstract: Large language models (LLMs) are rapidly reshaping workplace communication, yet whether AI-assisted writing changes how recipients actually behave, and through what channel, remains unknown.

By Ziv Ben-Zion, Teddy Lazebnik

llms

arXiv Machine Learning

Jul 14

MLPs are Hebbians: Constructing Efficient Fact-Storing MLPs for Transformers

arXiv:2607. 10034v1 Announce Type: new Abstract: Large language models (LLMs) store factual knowledge in their parameters.

By Roberto Garcia, Jerry Liu, Ronny Junkins, Sabri Eyuboglu, Atri Rudra, Christopher R\'e

llms rag safety

arXiv Machine Learning

Jul 14

Serving the Long Tail: Training-Free LLM Candidate Generation for Vacation Rental Marketplaces

arXiv:2607. 09877v1 Announce Type: new Abstract: Vacation rental marketplaces face a structural imbalance on the supply side: a small fraction of properties receive most user interactions, while the long tail of new, niche, and seasonal listings generates too little behavioral signal for collaborative filtering to serve effectively.

By Syed Mohammed Arshad Zaidi, Eric Rincon, Shayan Hassantabar

llms

arXiv Machine Learning

Jul 14

The Silent Freeze: Predicting When Low-Precision Training Stops Learning

arXiv:2607. 09800v1 Announce Type: new Abstract: Training in reduced floating-point precision can silently halt learning: when a gradient-descent weight update falls below half the unit in the last place (ULP) of the weight, it rounds away and that coordinate freezes while its gradient is still nonzero.

By Zekai Shang

llms

arXiv Machine Learning

Jul 14

Gauge dependence and structured-output corruption in sign-branched repetition penalties: measurements across models, inference stacks, and alternative repetition controls

arXiv:2607. 09791v1 Announce Type: new Abstract: The multiplicative repetition penalty shipped across the LLM inference ecosystem (HuggingFace, vLLM, llama.

By Peter Hollows

llms reinforcement-learning benchmarks

arXiv AI

Jul 14

StanceMoE: Mixture-of-Experts Architecture for Stance Detection

arXiv:2604. 00878v2 Announce Type: replace-cross Abstract: Actor-level stance detection aims to determine an author expressed position toward specific geopolitical actors mentioned or implicated in a text.

By Abdullah Al Shafi, Md. Milon Islam, Sk. Imran Hossain, K. M. Azharul Hasan

llms fine-tuning

arXiv AI

Jul 14

Turbo Connection: Reasoning as Information Flow from Higher to Lower Layers

arXiv:2602. 17993v2 Announce Type: replace-cross Abstract: Complex problems, whether in math, logic, or planning, are solved by humans through a sequence of steps where the result of one step informs the next.

By Mohan Tang, Sidi Lu

llms fine-tuning benchmarks

arXiv AI

Jul 14

Agentic Routing: The Harness-Native Data Flywheel

arXiv:2607. 11399v1 Announce Type: cross Abstract: Large language model agents are increasingly executed not by a single model call, but by an execution harness that manages observation, context, control, action, state, and verification.

By Xinchen Liu, Hang Zhou, Yingjie Zong, Yuchuan Tian, Liuyang Song, Shuo Zhang, Yulong Li, Wei He, Mengyu Zheng, Runke Liu, Siyang Cheng, Xiang Kuang, Hailin Hu, Kai Han, Yunhe Wang

llms agents benchmarks

arXiv AI

Jul 14

Automated Textbook Auditing with Multi-Agent LLM Systems

arXiv:2607. 11276v1 Announce Type: cross Abstract: Ensuring the quality of educational materials requires more than standard proofreading: textbooks must be audited for factual accuracy, domain-specific technical correctness, and linguistic quality simultaneously -- a task that general-purpose grammar checkers cannot address.

By Ciprian Cristescu, Adrian-Marius Dumitran, Angela-Liliana Dumitran, Gabriel Stefan

llms agents safety

arXiv AI

Jul 14

DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs

arXiv:2607. 11228v1 Announce Type: cross Abstract: While Large Vision-Language Models (LVLMs) demonstrate remarkable capabilities, they remain highly susceptible to embedded social biases.

By Anqi Li, Jie Zhang, Zhongqi Wang, Songkai Xue, Jiahao Wang, Shiguang Shan, Xilin Chen

llms agents reinforcement-learning multimodal benchmarks safety

arXiv AI

Jul 14

VIA: Visual Interface Agent for Robot Control

arXiv:2607. 11119v1 Announce Type: cross Abstract: Robot manipulation is a complex task that requires visual understanding, physical reasoning, planning, and closed-loop control.

By Hengyuan Hu, Priya Sundaresan, Jensen Gao, Dorsa Sadigh

llms agents robotics fine-tuning multimodal

arXiv AI

Jul 14

Do Video-LLMs Actually Watch? Diagnosing Character-Tracking Failures in Long-Form Video

arXiv:2607. 11078v1 Announce Type: cross Abstract: Can a Video Large Language Model (Video-LLM) follow one person through a long video, keeping track of who they are well enough to report, in order, how their outfit changes across a full TV episode?

By Mohammad Al-Ratrout, Shayla Sharmin, Aditya Raikwar, Roghayeh Leila Barmaki

llms benchmarks

arXiv AI

Jul 14

MemDecay: Region-Aware KV Cache Eviction for Efficient LLM Agent Inference

arXiv:2607. 10582v1 Announce Type: cross Abstract: Large language model (LLM) agents accumulate heterogeneous context, including system instructions, plans, user turns, retrieved documents, tool outputs, and intermediate reasoning, whose key-value (KV) cache can become a major memory bottleneck.

By Venkatesha Matam, Keon Kim

llms agents efficiency

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