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

Jun 9

Decision-Aware Memory Cards: Counterfactual-Inspired Context Selection and Compression for Tool-Using LLM Agents

arXiv:2606. 08151v1 Announce Type: new Abstract: Tool-using LLM agents often fail not because relevant text is absent, but because decisive evidence is not selected, compressed, or surfaced at action time.

By Xinyu Guan, Qianyang Zhao, Yuming Deng

llms rag agents fine-tuning benchmarks

arXiv AI

Jun 9

How Small Can You Go? LoRA Fine-Tuning 270M-8B Models for Merchant Information Extraction in Financial Transactions

arXiv:2606. 08051v1 Announce Type: new Abstract: Financial transaction processing requires extracting structured merchant information from noisy, abbreviated bank transaction strings at scale.

By Donghao Huang, Tomas Drietomsky, Benjamin Barrett, Zhaoxia Wang

llms fine-tuning efficiency benchmarks

arXiv AI

Jun 9

Trajectory-Refined Distillation

arXiv:2606. 08432v1 Announce Type: new Abstract: On-policy distillation (OPD) has become a central post-training tool for large language models (LLMs), providing dense per-token teacher supervision along the student's own rollouts.

By Li Jiang, Haoran Xu, Yichuan Ding, Amy Zhang

llms efficiency benchmarks

arXiv AI

Jun 9

LATTEArena: An Evaluation Framework for LLM-powered Tabular Feature Engineering (Extended Version)

arXiv:2606. 09004v1 Announce Type: new Abstract: Feature engineering remains essential for tabular data analysis, and Large Language Models (LLMs) have emerged as a promising paradigm for automating this process, giving rise to LLM-powered AuTomated Tabular feature Engineering (LATTE).

By Ankai Hao, Ke Chen, Huan Li, Lidan Shou

llms benchmarks

arXiv AI

Jun 9

Learning to Attack and Defend: Adaptive Red Teaming of Language Models via GRPO

arXiv:2606. 09701v1 Announce Type: cross Abstract: AI red teaming must continually adapt to evolving attackers and defenders.

By Blake Bullwinkel, Eugenia Kim, Amanda Minnich, Mark Russinovich

llms reinforcement-learning benchmarks safety

arXiv AI

Jun 9

EvoMaster: A Foundational Evolving Agent Framework for Agentic Science at Scale

arXiv:2604. 17406v3 Announce Type: replace Abstract: The convergence of large language models and agents is catalyzing a new era of scientific discovery: Agentic Science.

By Xinyu Zhu, Yuzhu Cai, Zexi Liu, Cheng Wang, Fengyang Li, Wenkai Jin, Wanxu Liu, Zehao Bing, Bingyang Zheng, Jingyi Chai, Shuo Tang, Rui Ye, Yuwen Du, Xianghe Pang, Yaxin Du, Tingjia Miao, Yuzhi Zhang, Ruoxue Liao, Zhaohan Ding, Linfeng Zhang, Yanfeng Wang, Weinan E, Siheng Chen

llms agents benchmarks

arXiv Machine Learning

Jun 9

Can AI Weather Models Predict Beyond Two Weeks? A Quantitative Benchmark and Analysis of Long Rollouts

arXiv:2605. 30184v2 Announce Type: replace Abstract: While AI weather models excel at short-to-medium range forecasts (up to 15 days), they frequently suffer from ill-defined "instabilities" when rolled out over longer horizons.

By Fanny Lehmann, Firat Ozdemir, Yun Cheng, Torsten Hoefler, Sebastian Schemm, Benedikt Soja, Siddhartha Mishra

llms computer-vision benchmarks

arXiv AI

Jun 9

From Conflict to Consensus: Boosting Medical Reasoning via Multi-Round Agentic RAG

arXiv:2603. 03292v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) exhibit high reasoning capacity in medical question-answering, but their tendency to produce hallucinations and outdated knowledge poses critical risks in healthcare fields.

By Wenhao Wu, Zhentao Tang, Yafu Li, Shixiong Kai, Mingxuan Yuan, Zhenhong Sun, Chunlin Chen, Zhi Wang

llms rag agents reinforcement-learning benchmarks safety

arXiv Machine Learning

Jun 9

OTora: A Unified Red Teaming Framework for Reasoning-Level Denial-of-Service in LLM Agents

arXiv:2605. 08876v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly deployed as autonomous agents that execute tool-augmented, multi-step tasks, where latency is a critical factor for real-world applications.

By Xinyu Li, Ronghui Mu, Lin Li, Tianjin Huang, Gaojie Jin

llms agents safety

arXiv AI

Jun 9

Can You Trust What You See? Human and AI Detection of Synthetic Legal Evidence

arXiv:2606. 07613v1 Announce Type: cross Abstract: Visual evidence has long been treated as a reliable form of legal proof, but advances in artificial intelligence (AI) are undermining that assumption.

By Jinzhe Tan, Ali Ekber Cinar, Karim Benyekhlef

llms diffusion multimodal

arXiv AI

Jun 9

Page image classifier fine-tuned on century-spanning archives of scanned documents for further content-specific processing

arXiv:2606. 07558v1 Announce Type: cross Abstract: Purpose: Digitization projects in the humanities produce vast, heterogeneous archives of historical documents, making manual sorting impractical at scale.

By Kateryna Lutsai, Pavel Stra\v{n}\'ak, David Nov\'ak, Dana K\v{r}iv\'ankov\'a

llms computer-vision fine-tuning multimodal

arXiv Machine Learning

Jun 9

Community-Specific Slang and Entity Detection via Semantic Shift in Fine-Tuned Language Models

arXiv:2606. 07522v1 Announce Type: cross Abstract: We propose an unsupervised method of resolving slang, unique entities, and folklore from online communities by isolating words in the lexicon that have the highest magnitude of semantic shift.

By Julia Kruk, Sanchita Porwal, Amitrajit Bhattacharjee, Mansi Phute

llms fine-tuning

arXiv AI

Jun 9

Emergence via Phase Transitions: Mechanism Landscapes and Universal Convergence Across Complex Systems

arXiv:2606. 07563v1 Announce Type: cross Abstract: Across machine learning, biology, and physics, independently evolving systems often converge toward strikingly similar high-level structures despite radically different microscopic details.

By Truong Xuan Khanh

llms

arXiv AI

Jun 9

Revisiting Training Scale: An Empirical Study of Token Count, Power Consumption, and Parameter Efficiency

arXiv:2601. 06649v2 Announce Type: replace-cross Abstract: Research in machine learning has questioned whether increases in training token counts reliably produce proportional performance gains in large language models.

By Joe Dwyer

llms

arXiv AI

Jun 9

Sound and Complete Neurosymbolic Reasoning with LLM-Grounded Interpretations

arXiv:2507. 09751v3 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but exhibit problems with logical consistency in their output.

By Bradley P. Allen, Prateek Chhikara, Thomas Macaulay Ferguson, Filip Ilievski, Paul Groth

llms rag benchmarks safety

arXiv AI

Jun 9

Efficient Onboard Vision-Language Inference in UAV-Enabled Low-Altitude Economy Networks via LLM-Enhanced Optimization

arXiv:2510. 10028v2 Announce Type: replace-cross Abstract: The rapid advancement of Low-Altitude Economy Networks (LAENets) has enabled a variety of applications, including aerial surveillance, environmental sensing, and semantic data collection.

By Yang Li, Ruichen Zhang, Yinqiu Liu, Guangyuan Liu, Abbas Jamalipour, Xianbin Wang, Dong In Kim

llms nlp reinforcement-learning multimodal

arXiv AI

Jun 9

SpatialWorld: Benchmarking Interactive Spatial Reasoning of Multimodal Agents in Real-World Tasks

arXiv:2606. 09669v1 Announce Type: new Abstract: Spatial reasoning is a foundational capability for multimodal large language models (MLLMs) to perceive and operate within the physical world.

By Hongcheng Gao, Hailong Qu, Jingyi Tang, Jiahao Wang, Zihao Huang, Hengkang Qiao, Shihong Huang, Junming Yang, Yi Li, Hongyixuan Yuan, Wenjie Li, Bohan Zeng, Wenbo Li, Bo Wang, Jianhui Liu, Olive Huang, Haoyang Huang, Wentao Zhang, Guoqing Huang, Nan Duan, Yinpeng Dong

llms agents multimodal benchmarks

arXiv AI

Jun 9

Symbolic Reasoning Frameworks Modulate LLM Risk Aversion in Multi-Agent Strategic Settings

arXiv:2606. 07552v1 Announce Type: cross Abstract: Large language models exhibit innate behavioral tendencies when deployed as strategic agents -- notably a risk-averse "turtle" bias toward defensive play.

By Augustin Chan

llms agents benchmarks safety

arXiv Machine Learning

Jun 9

Constraint-Aware Optimization for Robust Protein Stability Prediction

arXiv:2606. 08100v1 Announce Type: new Abstract: Multimodal $\Delta\Delta G$ predictors integrating protein language models with inverse-folding representations achieve strong in-distribution accuracy on the Megascale dataset but exhibit limited robustness on out-of-distribution (OOD) proteins, persistent forward-reverse bias on paired-mutation benchmarks, and under-representation of rare stabilizing mutations.

By A Shivram, Aneesh S. Chivukula, Manik Gupta, Sourav Chowdhury

llms multimodal benchmarks safety

arXiv AI

Jun 9

vla.cpp: A Unified Inference Runtime for Vision-Language-Action Models

arXiv:2606. 08094v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) policies are typically shipped as Python/PyTorch stacks that assume a workstation-class GPU, a mismatch for the hardware on which robots actually run.

By Khanh D. Nguyen, Hung T. Ho, Chinh T. Nguyen, Thanh Q. Duong, Linh D. Le, Duy M. H. Nguyen, Vien A. Ngo, An T. Le

llms diffusion robotics multimodal benchmarks

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