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 21

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference

arXiv:2506. 00452v5 Announce Type: replace-cross Abstract: In orthogonal frequency division multiplexing (OFDM), accurate channel estimation is crucial.

By TaeJun Ha, Chaehyun Jung, Hyeonuk Kim, Jeongwoo Park, Jeonghun Park

llms

arXiv AI

Jul 21

Automated Reinforcement Learning: An Overview

arXiv:2201. 05000v3 Announce Type: replace-cross Abstract: Reinforcement Learning and, recently, Deep Reinforcement Learning are popular methods for solving sequential decision-making problems modeled as Markov Decision Processes.

By Reza Refaei Afshar, Joaquin Vanschoren, Uzay Kaymak, Rui Zhang, Yaoxin Wu, Wen Song, Yingqian Zhang

llms reinforcement-learning

arXiv AI

Jul 21

TRACE: Trajectory-Based Safety Patch Learning for LLM Post-Training Realignment

arXiv:2607. 16242v1 Announce Type: cross Abstract: Fine-Tuning-as-a-Service (FTaaS) platforms let users train large language models (LLMs) on customized tasks, but this pipeline could erode models' safety alignment.

By Changyue Li, Jiaming He, Youliang Yuan, Jialin Wu, Boxi Yu, Zhicong Huang, Pinjia He

llms fine-tuning benchmarks safety

arXiv AI

Jul 21

Normalized Rewards for Preference Optimization

arXiv:2607. 16240v1 Announce Type: cross Abstract: Direct Alignment Algorithms (DAAs) such as DPO have become a common way to post-train and align LLMs with human preferences.

By Shawn Im, Federico Danieli, Skyler Seto, Barry-John Theobald, Katherine Metcalf

llms reinforcement-learning benchmarks safety

arXiv AI

Jul 21

What Makes Linguistic Representations Good Models of High-Level Visual Perception in the Human Brain?

arXiv:2607. 16214v1 Announce Type: cross Abstract: Image descriptions represented with language models (LMs) predict human brain responses to naturalistic images in high-level visual regions, but the factors driving this predictivity remain unclear.

By Anna Bavaresco, Ina Klari\'c, Raquel Fern\'andez, Marie-Francine Moens

llms rag fine-tuning safety

arXiv AI

Jul 21

When Fewer Layers Break More Chains: Layer Pruning Harms Test-Time Scaling in LLMs

arXiv:2510. 22228v2 Announce Type: replace-cross Abstract: Layer pruning has emerged as a widely adopted technique for improving the efficiency of large language models (LLMs).

By Keyu Wang, Tian Lyu, Guinan Su, Lu Yin, Marco Canini, Jonas Geiping, Shiwei Liu

llms fine-tuning efficiency benchmarks

arXiv AI

Jul 21

From Outcomes to Actions: Leveraging Hindsight for Long-Horizon Language Agent Training

arXiv:2607. 16257v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a widely adopted technique for improving large language models (LLMs) on complex tasks.

By Zishang Jiang, Tingyun Li, Jinyi Han, Xinyi Wang, Sihang Jiang, Yizhou Ying, Xiaojun Meng, Jiansheng Wei, Jiaqing Liang, Yanghua Xiao

llms agents reinforcement-learning

arXiv AI

Jul 21

OMAC: A Holistic Optimization Framework for LLM-Based Multi-Agent Collaboration

arXiv:2505. 11765v5 Announce Type: replace-cross Abstract: Agents powered by advanced large language models (LLMs) have demonstrated impressive capabilities across diverse complex applications.

By Shijun Li, Hilaf Hasson, Joydeep Ghosh

llms agents

arXiv AI

Jul 21

FormulaCode: Evaluating Agentic Optimization on Large Codebases

arXiv:2603. 16011v3 Announce Type: replace-cross Abstract: Large language model (LLM) coding agents increasingly operate at the repository level, motivating benchmarks that evaluate their ability to optimize entire codebases under realistic constraints.

By Atharva Sehgal, James Hou, Akanksha Sarkar, Ishaan Mantripragada, Swarat Chaudhuri, Jennifer J. Sun, Yisong Yue

llms agents benchmarks

arXiv AI

Jul 21

NanoZK: Privacy-Preserving Verifiable Inference for Large Language Models via Layerwise Zero-Knowledge Proofs

arXiv:2603. 18046v2 Announce Type: replace-cross Abstract: We present NanoZK, a zero-knowledge proof system for verifiable LLM inference: clients and third-party auditors check that a provider executed the advertised model on a committed input without learning weights or activations.

By Zhaohui Wang

llms safety

arXiv AI

Jul 21

Vision2Web: A Hierarchical Benchmark for Visual Website Development with Agent Verification

arXiv:2603. 26648v3 Announce Type: replace-cross Abstract: Recent advances in large language models have improved the capabilities of coding agents, yet systematic evaluation of complex, end-to-end website development remains limited.

By Zehai He, Wenyi Hong, Zhen Yang, Ziyang Pan, Mingdao Liu, Xiaotao Gu, Jie Tang

llms agents multimodal benchmarks

arXiv AI

Jul 21

JUMP: Single-Pass Membership Inference on Fine-Tuned Diffusion Language Models

arXiv:2607. 16207v1 Announce Type: new Abstract: Membership inference attacks (MIAs) test whether a candidate example appeared in a model's training data.

By Yeachan Jun, Albert No

llms diffusion fine-tuning

arXiv AI

Jul 21

Learning to Detect Cross-Modal Negation: An Analysis of Latent Representations and an Attention-Based Solution

arXiv:2607. 17712v1 Announce Type: new Abstract: Detecting high-level semantic concepts like negation across modalities remains a challenge for current multimodal systems.

By Ali AbuSaleh, Leon Hammerla, Alexander Mehler

llms rag multimodal

arXiv AI

Jul 21

LaCache: Exact Caching and Precision-Adaptive Inference for Diffusion Large Language Models

arXiv:2607. 16339v1 Announce Type: new Abstract: Diffusion-based Large Language Models(DLLMs) enable parallel generation via Semi-Autoregressive (SAR) decoding in text generation.

By Xingru Chen, Zelang Liang, Yongjia Ma, Jiqing Zhan, Shuling Yang, Lian Wen, Kun Zhan

llms rag diffusion efficiency

arXiv AI

Jul 21

ZifaMem: Structured Memory for Persona, Preference, and Emotional Continuity in AI Companions

arXiv:2607. 17564v1 Announce Type: new Abstract: AI companions are judged not only by single-turn fluency but by whether they sustain emotional continuity: remembering who the companion is, what the user prefers, and how the relationship has felt.

By Jingzhe Fang, Guozhi Xu, Yunfan Cui, Xiaochen Yang, Zhangyu Hua

llms agents

arXiv Machine Learning

Jul 21

Sockpuppetting: Jailbreaking LLMs by Combining Prefilling with Optimization

arXiv:2601. 13359v3 Announce Type: replace-cross Abstract: Prefill attacks are an effective and low-cost jailbreaking method, as they directly insert an acceptance sequence (e.

By Asen Dotsinski, Panagiotis Eustratiadis

llms multimodal benchmarks safety

arXiv AI

Jul 21

Do Maps Still Matter for Machines: Revisiting the Role of Choropleth Maps in Foundation Model Spatial Understanding

arXiv:2607. 17999v1 Announce Type: new Abstract: Spatial understanding is crucial for foundation models (FMs), and maps have long helped humans organize and reason about geographic information.

By Zhiwei Wei, Yonghe Sun, Zhenjia Liu, Wenjia Xu, Chao He, Weihua Dong, Chunbo Liu, Hua Liao

llms benchmarks

arXiv Machine Learning

Jul 21

Bigger Is Safer: Provable Robustness in In-Context Learning Scales with Capacity

arXiv:2602. 17743v2 Announce Type: replace Abstract: In-context learning (ICL) allows large language models to adapt to new tasks from a few examples without updating their parameters.

By Di Zhang, Ningxu Zhang, Zimeng Liu

llms safety

arXiv Machine Learning

Jul 21

ChipChat: Low-Latency Cascaded Conversational Agent in MLX

arXiv:2509. 00078v2 Announce Type: replace-cross Abstract: The emergence of large language models (LLMs) has transformed spoken dialog systems, yet the optimal architecture for real-time on-device voice agents remains an open question.

By Tatiana Likhomanenko, Richard He Bai, Zijin Gu, Zakaria Aldeneh, Shiladitya Dutta, Luke Carlson, Han Tran, Yizhe Zhang, Ruixiang Zhang, Huangjie Zheng, Navdeep Jaitly

llms agents multimodal safety

arXiv Machine Learning

Jul 21

Breaking the Block: Preserving Data Continuity to Train Superior SAEs for Instruct Models

arXiv:2506. 07691v2 Announce Type: replace-cross Abstract: Sparse Autoencoders (SAEs) are a cornerstone of mechanistic interpretability.

By Jiaming Li, Haoran Ye, Yukun Chen, Xinyue Li, Lei Zhang, Hamid Alinejad-Rokny, Jimmy Chih-Hsien Peng, Min Yang

llms fine-tuning safety

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