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 13

Fictional Worldbuilding: Multi-Agent LLM Collaboration with Hierarchical Context Compression and Iterative Review

arXiv:2607. 09403v1 Announce Type: new Abstract: Worldbuilding, the construction of coherent fictional worlds, is a foundational task in game design and literary creation.

By Jingbo Chen, He Wang, Wei Yuan, Yuqiao Lai, Zhenyan Lu

llms agents

arXiv AI

Jul 13

Failure as a Process: An Anatomy of CLI Coding Agent Trajectories

arXiv:2607. 09510v1 Announce Type: cross Abstract: Large language model (LLM) coding agents are increasingly deployed to autonomously perform software engineering tasks in terminal-based environments, making their reliability a growing concern.

By Xiangxin Zhao, Han Li, Shuaiting Li, Tianyi Zhao, Earl T. Barr, Federica Sarro, He Ye

llms agents

arXiv AI

Jul 13

All Explanations are Wrong, But Many Are Useful: Exploring the Rashomon Explanation Set with Large Language Models

arXiv:2607. 09502v1 Announce Type: cross Abstract: Explaining machine-learning models is increasingly important for decision-making and consumer trust, yet it is widely believed to come at a cost: existing Explainable AI (XAI) methods suffer from a persistent accuracy-explainability trade-off.

By Pan Li

llms agents benchmarks

arXiv AI

Jul 13

On Locality and Length Generalization in Visual Reasoning

arXiv:2607. 09061v1 Announce Type: cross Abstract: A striking feature of the human visual system is that it ingests visual information through a series of local foveated glimpses, rather than a single global computation.

By Pulkit Madan, Sanjay Haresh, Reza Ebrahimi, Sunny Panchal, Apratim Bhattacharyya, Roland Memisevic

llms computer-vision

arXiv AI

Jul 13

The Patchwork Problem in LLM-Generated Code

arXiv:2607. 08981v1 Announce Type: cross Abstract: LLM-generated code often compiles, passes tests, and appears correct, yet breaks once deployed.

By Viraaji Mothukuri, Reza M. Parizi

llms

arXiv Machine Learning

Jul 13

Mixture of Probes: Learning from Privileged Modalities in Multimodal LLMs Through Probing

arXiv:2607. 08839v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) are typically designed under the assumption that all modalities available during training will also be accessible at inference.

By Dominick Reilly, Qiyu Wu, Hiromi Wakaki, Srijan Das, Yuki Mistufuji

llms multimodal safety

arXiv AI

Jul 13

A Sovereign, Open-Source Foundation Model for German and English

arXiv:2607. 09424v1 Announce Type: cross Abstract: We present Soofi S 30B-A3B, a sovereign, open-source Mixture-of-Experts (MoE) hybrid Mamba Transformer foundation model for German and English.

By The Soofi-Team, :, Benedikt Droste, David Fitzek, Ruben H\"arle, Lukas Helff, Maximilian Idahl, Alex Jude, Abbas Goher Khan, Maurice Kraus, Timm Ruland, Richard Rutmann, Sebastian Sztwiertnia, Markus Frey, Daniil Gurgurov, Jan Pfister, Tom R\"ohr, Sebastian von Rohrscheidt, J\"org Bienert, Nicolas Flores-Herr, Simon Gottschalk, Andreas Hotho, Kristian Kersting, Joachim K\"ohler, Alexander L\"oser, Wolfgang Nejdl, Simon Ostermann, Jan Plogsties, Patrick Putzky, Mehdi Ali, Michael Fromm, Max L\"ubbering

llms efficiency benchmarks

arXiv AI

Jul 13

HiPO: Hierarchical Preference Optimization for Adaptive Reasoning in LLMs

arXiv:2604. 20140v2 Announce Type: replace Abstract: Direct Preference Optimization (DPO) is an effective framework for aligning large language models with human preferences, but it struggles with complex reasoning tasks.

By Darsh Kachroo, Arjun Prasaath Anbazhagan, Adriana Caraeni, Brennan Lagasse, Kevin Zhu

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 13

What VGGT Knows About Overlap: Probing Geometric Foundation Models for Co-Visibility

arXiv:2607. 09503v1 Announce Type: cross Abstract: A fundamental challenge in 3D reconstruction and robotic localization is co-visibility: determining which image pairs share overlapping visible surfaces, particularly in scenarios with minimal overlap.

By Filippo Ziliotto, Luciano Serafini, Lamberto Ballan, Tommaso Campari

llms robotics benchmarks

arXiv AI

Jul 13

SLIDERS: Systematic Reviews via Automated Evidence Synthesis and Reconciliation

arXiv:2604. 22294v2 Announce Type: replace-cross Abstract: Systematic reviews -- which requires comprehensive evidence collection and synthesis from large document corpora in response to targeted research questions -- are foundational in finance, social sciences, and other technical fields.

By Harshit Joshi, Priyank Shethia, Jadelynn Dao, Monica S. Lam

llms rag agents benchmarks

arXiv AI

Jul 13

Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting

arXiv:2511. 00651v2 Announce Type: replace Abstract: Telecom networks are rapidly growing in scale and complexity, making effective management, operation, and optimization increasingly challenging.

By Chenhua Shi, Bhavika Jalli, Gregor Macdonald, John Zou, Wanlu Lei, Mridul Jain, Joji Philip

llms agents fine-tuning

arXiv AI

Jul 13

Sticky Routing: Training MoE Models for Memory-Efficient Inference

arXiv:2607. 08780v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models activate only a sparse subset of experts per token, yet consecutive tokens frequently activate different experts -- causing constant weight swapping between slow storage and fast memory on edge devices.

By Ali Kayyam

llms fine-tuning

arXiv AI

Jul 13

Conceptual Networks for Cross-Linguistic Idiomatic Expressions:A Feature-Based Graph Approach

arXiv:2607. 09576v1 Announce Type: cross Abstract: We present an interpretable network-based framework for representing idiomatic and figurative meaning across eight typologically diverse languages, totaling 160 conventional expressions, the large majority of which are idiomatic.

By Kiran Pala, Punam Silu, Lixun Yu

llms rag benchmarks

arXiv AI

Jul 13

A Unified Approach to Interpreting Knowledge Distillation for Large Language Models via Interactions

arXiv:2607. 08776v1 Announce Type: cross Abstract: Despite the success of knowledge distillation (KD) in Large Language Models (LLMs), the underlying mechanism behind its efficacy remains unclear.

By Qingzhuo Wang, Ruiyang Qin, Zhenxin Qin, Wen Shen, Zhihua Wei

llms efficiency benchmarks

arXiv Machine Learning

Jul 13

Forking-Sequences: Statistically and Computationally Efficient Multi-Horizon Forecasting with Reduced Volatility

arXiv:2510. 04487v5 Announce Type: replace Abstract: While accuracy is a critical requirement for time series forecasting, an equally important desideratum is reasonable forecast volatility across forecast creation dates (FCDs).

By Willa Potosnak, Malcolm Wolff, Mengfei Cao, Ruijun Ma, Tatiana Konstantinova, Dmitry Efimov, Michael W. Mahoney, Boris Oreshkin, Kin G. Olivares

llms benchmarks

arXiv AI

Jul 13

Evolution of Accuracy and Visual-Cognitive Errors in a Decade of Vision-Language AI Models

arXiv:2607. 09654v1 Announce Type: cross Abstract: Vision language models (VLMs) have made remarkable progress in visual reasoning during the last decade.

By Shravan Murlidaran, Miguel P. Eckstein

llms computer-vision multimodal benchmarks safety

arXiv AI

Jul 13

Attention to Detail: Evaluating Energy, Performance, and Accuracy Trade-offs Across vLLM Configurations

arXiv:2607. 09172v1 Announce Type: cross Abstract: Large Language Models are reshaping how software is developed and maintained.

By Nada Zine, Tristan Coignion, Vincenzo Stoico, Cl\'ement Quinton, Romain Rouvoy, Patricia Lago

llms

arXiv AI

Jul 13

Parameter-Efficient Vision-Language Adaptation with Continuous Metadata Conditioning for Animal Re-Identification

arXiv:2607. 09443v1 Announce Type: cross Abstract: Long-term animal re-identification (ReID) must remain robust to gradual morphological evolution and seasonal appearance shifts.

By Anil Osman Tur, Tonje Knutsen Sordalen, Kim Tallaksen Halvorsen, Cigdem Beyan

llms rag multimodal benchmarks safety

arXiv Machine Learning

Jul 13

Neural Collapse Is Forbidden: Information Floors in Language Models

arXiv:2607. 09487v1 Announce Type: new Abstract: Within-class variance in language-model representations is commonly read as incomplete neural collapse.

By Bruno Abrahao

llms

arXiv AI

Jul 13

Communication-Efficient Digital-Twin Coordination for Heterogeneous LLM Embodied Agents over Computing Power Networks

arXiv:2607. 09330v1 Announce Type: new Abstract: Embodied agent teams powered by heterogeneous large language models (LLMs) are being widely deployed in physical artificial intelligence such as smart factories, warehouses, and service robotics.

By Nuocheng Yang, Sihua Wang, Zihan Chen, Tony Q. S. Quek, Changchuan Yin

llms agents reinforcement-learning robotics

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