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

CoEvoP&R: Co-Evolving Placement Objectives with Routing Feedback via Large Language Models

arXiv:2607. 17398v1 Announce Type: cross Abstract: Analytical placers rely on differentiable objective functions to guide placement, typically combining intermediate surrogate metrics such as half-perimeter wirelength (HPWL) and cell-density penalties.

By Ruogu Chen, Weihua Xiao, Ramesh Karri, Jie Han

llms

arXiv AI

Jul 21

Interactive Task Alignment as a POMDP

arXiv:2607. 16412v1 Announce Type: new Abstract: Current benchmarks for language models primarily evaluate execution on fully specified tasks.

By Andy Dai, Zexue He, Zhenyu Zhang, Alex Pentland, Jiaxin Pei

llms reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Jul 21

OrderMoE: An expert similarity driven distributed edge MoE inference

arXiv:2607. 17154v1 Announce Type: cross Abstract: Although mixture-of-experts, MoE, models have been increasingly adopted to scale large language models with moderate computation cost, it remains challenging to deploy MoE inference over resource-constrained and bandwidth-limited edge infrastructures.

By Xin Yuan, Ning Li, Quan Chen, Wenchao Xu, Athanasios V. Vasilakos, Song Guo, Haijun Zhang

llms efficiency

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

How Does Alignment Tuning Shape Representations of Sycophancy and Related Cue-Induced Biases in LLMs?

arXiv:2607. 18114v1 Announce Type: cross Abstract: Modern LLMs are alarmingly susceptible to surprisingly simple immaterial changes of input prompts: a casual hint, an incorrectly labeled few-shot example, or a fake prior assistant turn often flips an originally correct answer.

By Prakhar Gupta, Terry Jingchen Zhang, Florent Draye, Bernhard Sch\"olkopf, Zhijing Jin

llms safety

arXiv AI

Jul 21

Exploratory and Assimilating Reflection: Reflective Recall Cycle for Long-term Memory

arXiv:2607. 17879v1 Announce Type: new Abstract: LLM-based autonomous agents require external memory to overcome their statelessness and limited context window for long-term interaction and dynamic knowledge reasoning.

By Ganesh Senrayan, Moyuru Yamada, Ishan Jindal, Kiran Purohit

llms agents benchmarks

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

SyriSign: A Parallel Corpus for Arabic Text to Syrian Arabic Sign Language Translation

arXiv:2603. 29219v2 Announce Type: replace-cross Abstract: Sign language is the primary approach of communication for the Deaf and Hard-of-Hearing (DHH) community.

By Mohammad Amer Khalil, Raghad Nahas, Ahmad Nassar, Khloud Al Jallad

llms rag benchmarks safety

arXiv AI

Jul 21

DSBench: A Comprehensive Benchmark for Evaluating External and In-Cabin Risks

arXiv:2511. 14592v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) show great promise for autonomous driving, but their suitability for safety-critical scenarios is largely unexplored, raising safety concerns.

By Xianhui Meng, Yuchen Zhang, Zhijian Huang, Zheng Lu, Ziling Ji, Yandan Lin, Yaoyao Yin, Hongyuan Zhang, Wei Zhou, Guangfeng Jiang, Li Zhang, Long Chen, Hangjun Ye, Jun Liu, Xiaoshuai Hao

llms agents robotics fine-tuning multimodal benchmarks safety

arXiv AI

Jul 21

Mathematical Discovery in the Wild: AI-Guided Proofs in Banach Space Theory

arXiv:2607. 17388v1 Announce Type: cross Abstract: We investigate the capacity of current language models to contribute to mathematical research.

By Antonio Acuaviva, Pablo Acuaviva

llms

arXiv AI

Jul 21

WuYu-EnvLE-Bench: A Benchmark for Evaluating Large Language Models in Environmental Law Enforcement

arXiv:2607. 17745v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly considered for environmental enforcement, but their ability to produce traceable enforcement decisions remains unclear.

By Ziliang Yang, Yi Zhang, Kaijun Lin, Jiachao Ke, Haihong Xu, Zongguo Wen

llms benchmarks

arXiv Machine Learning

Jul 21

Program Synthesis for Simulation-Based Inference: Joint Model Selection and Parameter Estimation

arXiv:2607. 17540v1 Announce Type: new Abstract: Neural simulation-based inference enables parameter estimation for complex models, but typically requires the user to specify a simulator encoding a fixed model structure.

By Siddharth Mishra-Sharma

llms benchmarks

arXiv AI

Jul 21

SAGA: Synthetic Agentic Graph Architecture for Temporal Benchmark Generation

arXiv:2607. 17288v1 Announce Type: cross Abstract: High quality temporal graph benchmarks with rich semantics and ground-truth anomaly labels are essential for training graph neural networks, yet remain scarce due to privacy constraints and annotation costs.

By Jiacheng Ding, Xiaofei Zhang

llms rag agents benchmarks safety

arXiv AI

Jul 21

RELIC: Revealed Principles for Learning Interpretable Composable Skills in Multi-Agent Planning

arXiv:2607. 16745v1 Announce Type: new Abstract: Multi-agent planning becomes substantially harder when agents must improve specialized decision-making skills while keeping their internal implementations private.

By Nguyen Viet Tuan Kiet, Bui Dinh Pham, Duong Quoc Chinh, Dao Van Tung, Tran Cong Dao, Huynh Thi Thanh Binh

llms agents safety

arXiv AI

Jul 21

AgentBrew: Lifelong Knowledge Brewing from Strong Teachers to Weak LLM Agents

arXiv:2607. 16851v1 Announce Type: new Abstract: Deploying LLM agents typically requires a compact test-time student, even if a stronger teacher is available during training.

By Yangqin Jiang, Chao Huang

llms agents

arXiv AI

Jul 21

HAS: Highlight-guided Attention Steering for Multimodal LLM Video Summarization

arXiv:2607. 17994v1 Announce Type: cross Abstract: Video understanding has become more and more important with the growth of Artificial Intelligence (AI) for video generation.

By Rui Chu, Yingjie Lao

llms diffusion nlp multimodal benchmarks

arXiv AI

Jul 21

RECON: Benchmarking Agent Memory for Compositional Reasoning over Long Contexts

arXiv:2607. 16716v1 Announce Type: new Abstract: Large language models and LLM-based agents are widely used as personal chat assistants, enterprise copilots, and autonomous workflow agents.

By Mihir Shriniwas Arya

llms agents benchmarks

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

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