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 7

CompactionRL: Reinforcement Learning with Context Compaction for Long-Horizon Agents

arXiv:2607. 05378v1 Announce Type: new Abstract: Long-horizon agentic LLMs are increasingly limited by finite context windows, as extended interaction trajectories can exceed the maximum context length before a task is completed.

By Yujiang Li, Zhenyu Hou, Yi Jing, Jie Tang, Yuxiao Dong

llms agents reinforcement-learning

arXiv AI

Jul 7

LLM-as-a-Verifier: A General-Purpose Verification Framework

arXiv:2607. 05391v1 Announce Type: new Abstract: Scaling pre-training, post-training, and test-time compute have become the central paradigms for improving the capabilities of LLMs.

By Jacky Kwok, Shulu Li, Pranav Atreya, Yuejiang Liu, Yixing Jiang, Chelsea Finn, Marco Pavone, Ion Stoica, Azalia Mirhoseini

llms agents reinforcement-learning robotics benchmarks

arXiv Machine Learning

Jul 7

Uni-OPD: Unifying On-Policy Distillation with a Dual-Perspective Recipe

arXiv:2605. 03677v2 Announce Type: replace Abstract: On-policy distillation (OPD) has recently emerged as an effective post-training paradigm for consolidating the capabilities of specialized expert models into a single student model.

By Wenjin Hou, Shangpin Peng, Weinong Wang, Zheng Ruan, Yue Zhang, Zhenglin Zhou, Mingqi Gao, Yifei Chen, Kaiqi Wang, Hongming Yang, Chengquan Zhang, Zhuotao Tian, Han Hu, Yi Yang, Fei Wu, Hehe Fan

llms efficiency multimodal benchmarks

arXiv AI

Jul 7

MemTrace: Tracing and Attributing Errors in Large Language Model Memory Systems

arXiv:2605. 28732v2 Announce Type: replace-cross Abstract: Memory is essential for enabling large language models to support long-horizon reasoning, yet existing memory systems remain unreliable and difficult to debug.

By Xinle Deng, Ruobin Zhong, Hujin Peng, Xiaoben Lu, Yanzhe Wu, Guang Li, Buqiang Xu, Yunzhi Yao, Jizhan Fang, Haoliang Cao, Junjie Guo, Yuan Yuan, Ziqing Ma, Yuanqiang Yu, Rui Hu, Baohua Dong, Hangcheng Zhu, Ningyu Zhang

llms rag benchmarks

arXiv AI

Jul 7

ASSEMCAD: Production-Ready CAD Assembly Generation from Natural Language

arXiv:2607. 05123v1 Announce Type: new Abstract: Recent advances in large language models and programmatic CAD have significantly improved Text-to-CAD generation for individual parts.

By Yurui Dong, Shu Zou, Siqi Li, Nianchen Deng, Hongbin Zhou, Xuemeng Yang, Pinlong Cai, Licheng Wen, Xinyu Cai, Botian Shi

llms

arXiv AI

Jul 7

Pretraining Curricula Enable Selective Fine-tuning

arXiv:2607. 04846v1 Announce Type: cross Abstract: Transformers follow implicit curricula whereby some tasks are learned before others.

By Sebastian A. Bruijns, Jirko Rubruck, Mia H. Whitefield, Kai J. Sandbrink, Fazl Barez, Christopher Summerfield

llms fine-tuning safety

arXiv AI

Jul 7

SPORK: Self-Speculative Forking to Accelerate Agentic LLM Inference

arXiv:2607. 03333v1 Announce Type: cross Abstract: LLM agents are becoming a common interface for research, coding, and question answering, yet their Thought-Action-Observation loop is often serial: the model reasons, emits a tool call, then idles the GPU until the result returns.

By Huajun Bai, Weiwei Lv, Huichuan Zheng, Youyou Lu, Jiwu Shu

llms agents nlp benchmarks

arXiv AI

Jul 7

EvoAgentBench: Benchmarking Agent Self-Evolution via Ability Transfer

arXiv:2607. 05202v1 Announce Type: new Abstract: Agent self-evolution in long-horizon LLM systems is largely procedural: useful experience is not merely stored information, but reusable procedures for searching, debugging, and verification.

By Xingze Gao, Chuanrui Hu, Hongda Chen, Pengfei Yao, Zhao Wang, Yi Bai, Zhengwei Wu, Yunyun Han, Xiaofeng Cong, Jie Gui, Yafeng Deng, Teng Li

llms agents benchmarks

arXiv AI

Jul 7

MentalThink: Shaping Thoughts in Mental SVG World

arXiv:2607. 03530v1 Announce Type: new Abstract: We introduce MentalThink, a visual-symbolic reasoning paradigm that equips Multimodal LLMs (MLLMs) with an executable mechanism for "mental" visualization.

By Kangheng Lin, Jisheng Yin, Dingming Li, En Yu, Yana Wei, Han Zhou, Liang Zhao, Hongyu Zhou, Hongbo Peng, Jianjian Sun, Zheng Ge, Xiangyu Zhang, Daxin Jiang, Jingyu Wang

llms reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

Jul 7

Punching Above Their Weight: Classification-Head Fine-Tuning of Tiny Language Models (TLMs) for Verifiable Multiple-Choice Tasks

arXiv:2607. 03801v1 Announce Type: cross Abstract: We define Tiny Language Models (TLMs) as models below roughly 3B parameters that fit on mainstream consumer devices.

By Bhavesh Sood, Jaromir Savelka

llms fine-tuning benchmarks

arXiv AI

Jul 7

Harness-Aware Self-Evolving: Co-Evolving Model Weights, Harness, and Task Solutions

arXiv:2607. 03935v1 Announce Type: new Abstract: Self-evolving frameworks usually optimize task solutions while treating the surrounding harness as fixed.

By Haochen Luo, Yi Huang, Sichun Luo, Fengyuan Liu, Lei Li, Zefa Hu, Junlan Feng, Qi Liu

llms agents benchmarks

arXiv AI

Jul 7

When Simpler Is Better: Evaluating Translation Pipelines for Medieval Latin Manuscripts

arXiv:2607. 03836v1 Announce Type: cross Abstract: Despite remarkable progress in machine translation, Vision Language Models (VLMs) struggle on historical manuscripts, a domain that stresses core Natural Language Processing (NLP) capabilities: low-resource transliteration, archaic vocabulary, and noisy input signals.

By Nguyen Kim Hai Bui, Md. Easin Arafat, Tam\'as G\'abor Orosz, Mufti Mahmud

llms rag computer-vision nlp multimodal benchmarks

arXiv AI

Jul 7

The Remarkable Effectiveness of Providing AI Agents with Natural Language Tools: A Replication Study Validating NLT Performance Across 14 Models

arXiv:2607. 03953v1 Announce Type: cross Abstract: This study independently replicates and extends the Natural Language Tools (NLT) framework of Johnson et al.

By Alexander Somma, Isabelle Plante, Fred Premji

llms agents

arXiv Machine Learning

Jul 7

When Do Foundation Models Pay Off? A Break-Even Analysis of Pretrained Time Series Forecasters

arXiv:2607. 04919v1 Announce Type: new Abstract: Deploying a time series foundation model requires GPU infrastructure, engineering overhead, and carries no guarantee of improvement over XGBoost.

By Nicholas Tan Jerome, Frank Simon

llms fine-tuning benchmarks

arXiv AI

Jul 7

Responsibility Distribution Estimation in Ego-View Accident Videos with Multimodal Large Language Models

arXiv:2607. 03591v1 Announce Type: cross Abstract: Recent studies on multimodal traffic accident understanding have mainly relied on infrastructure-camera footage, satellite imagery, or structured crash records.

By Ryosei Tamura, Andrew Shin

llms agents computer-vision fine-tuning multimodal benchmarks

arXiv AI

Jul 7

Aligning Language Models with Selective Prediction

arXiv:2607. 03528v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as critical decision-making components in high-stakes real-world AI systems, rendering LLM reliability a foremost practical concern.

By Gaoxiang Luo, Yifan Wu, Sinian Zhang, Aryan Deshwal, Ju Sun

llms reinforcement-learning safety

arXiv AI

Jul 7

Harmonic-Aware Transformer for Real-Time Catheter Localization in Interventional Procedures of Magnetic Particle Imaging

arXiv:2607. 02919v1 Announce Type: cross Abstract: Magnetic particle imaging (MPI) enables real-time, radiation-free tracking of magnetic nanoparticle-coated instruments, making it highly suitable for interventional procedures.

By Abuobaida M. Khair, Wenjing Jiang, Xiaoli Yang, Moritz Wildgruber, Xiaopeng Ma

llms efficiency

arXiv AI

Jul 7

No Time Like the Present: Agentic Test-Time Training for LLM Agents

arXiv:2607. 03441v1 Announce Type: cross Abstract: LLM agents often degrade over long episodes: as trajectories grow, they revisit explored states, repeat failed actions, and lose strategies that previously worked.

By Yanbo Wang, Jinhua Hao, Yuze Shi, Kun Yuan, Ming Sun

llms agents fine-tuning

arXiv Machine Learning

Jul 7

Pathways of Visual Information Flow in Vision-Language Models

arXiv:2607. 03358v1 Announce Type: cross Abstract: We study how visual information is routed in vision-language models (VLMs).

By Israfel Salazar, Stella Frank, Dan Oneata, Desmond Elliott, Constanza Fierro

llms multimodal benchmarks

arXiv Machine Learning

Jul 7

Score Distributions, Not Cells: Evaluating Single-Cell Perturbations Under Class Overlap

arXiv:2607. 04595v1 Announce Type: new Abstract: Most classification problems assume the classes are roughly separable, so that an individual sample can usually be assigned to one class.

By Youssef Marrakchi, Davide D'Ascenzo, Sebastiano Cultrera di Montesano

llms benchmarks

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