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 16

Toward Vibe Medicine: A Self-Evolving Multi-Agent Framework for Clinical Decision Support

arXiv:2606. 15504v1 Announce Type: new Abstract: In recent years, the advances of large language models and autonomous agents have revolutionized the healthcare field, facilitating diagnosis and improving treatment results.

By Qianxue Zhang, Yiming Ren, Shihuan Qin, Xiao Zhang, Liao Zhang, Jinyang Huang, Zhengliang Liu, Chenbin Liu, Hongying Feng, Jingyuan Chen, Yuzhen Ding, Weihang You, Hanqi Jiang, Yi Pan, Yifan Zhou, Junhao Chen, Lifeng Chen, Wei Liu, Tianming Liu, Zengren Zhao, Lian Zhang

llms agents multimodal safety

arXiv AI

Jun 16

Frame-Conditioned Moral Computation in LLaMA 3.1-8B-Instruct: A Mechanistic Interpretability Audit of Ethical Reasoning

arXiv:2606. 15507v1 Announce Type: new Abstract: Behavioral audits of Large Language Models on moral prompts measure what the model says, not the internal computation producing it.

By Ali Dasdan, Manan Shah, W. Russell Neuman, Chad Coleman, Kund Meghani, Safinah Ali

llms reinforcement-learning safety

arXiv AI

Jun 16

Skill-to-LoRA: From Using Skills to Learning Behaviors for Token-Efficient LLM Agents

arXiv:2606. 16769v1 Announce Type: new Abstract: Agent skills are commonly distributed as SKILL.

By Tianyi Zhang, Zhonghao Qi

llms agents fine-tuning safety

arXiv AI

Jun 16

StarOR: Synergizing Tree Search and Test-Time Reinforcement Learning for Optimization Modeling

arXiv:2606. 15197v1 Announce Type: cross Abstract: Optimization modeling is inherently hierarchical, requiring a precise sequence of symbolic commitments.

By Jiajun Li, Yu Ding, Shisi Guan, Ran Hou, Wanyuan Wang

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 16

Vocabulary Dropout for Curriculum Diversity in LLM Co-Evolution

arXiv:2604. 03472v3 Announce Type: replace-cross Abstract: Co-evolutionary self-play, where one language model generates problems and another solves them, promises autonomous curriculum learning without human supervision.

By Jacob Dineen, Aswin RRV, Zhikun Xu, Ben Zhou

llms agents reinforcement-learning benchmarks

arXiv AI

Jun 16

VibeThinker-3B: Exploring the Frontier of Verifiable Reasoning in Small Language Models

arXiv:2606. 16140v1 Announce Type: new Abstract: This technical report introduces VibeThinker-3B, a compact dense model with 3B parameters developed to investigate how far verifiable reasoning can be pushed within a strictly small-model regime.

By Sen Xu, Shixi Liu, Wei Wang, Jixin Min, Yingwei Dai, Zhibin Yin, Yirong Chen, Xin Zhou, Junlin Zhang

llms reinforcement-learning fine-tuning efficiency

arXiv AI

Jun 16

Graphical-Probabilistic Modeling of Generative Flows in LLM-Native Software Systems

arXiv:2606. 15943v1 Announce Type: cross Abstract: Engineering LLM-native software remains a challenging and immature field.

By V\'ictor A. Braberman, Flavia Bonomo-Braberman

llms

arXiv AI

Jun 16

Towards Advanced Mathematical Reasoning for LLMs via First-Order Logic Theorem Proving

arXiv:2506. 17104v2 Announce Type: replace Abstract: Large language models (LLMs) have shown promising first-order logic (FOL) reasoning capabilities with applications in various areas.

By Chuxue Cao, Mengze Li, Juntao Dai, Jinluan Yang, Zijian Zhao, Shengyu Zhang, Weijie Shi, Chengzhong Liu, Sirui Han, Yike Guo

llms benchmarks

arXiv AI

Jun 16

Trust Between AI Agents: Measuring Formation, Breakage, and Recovery, with Implications for Governing Multi-Agent Systems

arXiv:2606. 14923v1 Announce Type: new Abstract: As language-model agents increasingly work in teams, each agent must decide how much to trust its teammates.

By Yujiao Chen

llms agents safety

arXiv AI

Jun 16

PrologMCP: A Standardized Prolog Tool Interface for LLM Agents

arXiv:2606. 14935v1 Announce Type: new Abstract: Frontier reasoning-tuned language models still fail on deductive tasks at depth, and the cost of improved performance through extended internal reasoning scales poorly.

By Agnieszka Mensfelt, Adarsh Prabhakaran, Adrian Haret, Vince Trencsenyi, Kostas Stathis

llms agents

arXiv AI

Jun 16

PO-PDDL: Learning Symbolic POMDPs from Visual Demonstrations for Robot Planning Under Uncertainty

arXiv:2606. 15654v1 Announce Type: cross Abstract: Real-world robot task planning must operate under both stochastic action execution and partial observability, yet constructing Partially Observable Markov Decision Process (POMDP) models for real robotics domains remains difficult and labor-intensive.

By Wenjing Tang, Xuanjin Jin, Yuan Liu, Renming Huang, Cewu Lu, Panpan Cai

llms reinforcement-learning robotics

arXiv Machine Learning

Jun 16

Dynamic Link Prediction with Temporally Enhanced Signed Graph Neural Networks

arXiv:2605. 26290v2 Announce Type: replace Abstract: Temporal signed networks (TSNs) model the time evolution of cooperative and adversarial relationships that arise in applications such as social media analysis, trust and reputation systems, and financial transaction networks.

By Derek Regier, Andrew Polyak, Aresh Dadlani, Khosro Salmani

llms rag safety

arXiv Machine Learning

Jun 16

Understanding Cross-Modal Contributions in Continual Vision-Language Models: A Theoretical Perspective

arXiv:2606. 14883v1 Announce Type: cross Abstract: Continual vision-language models are commonly addressed through sequential fine-tuning; however, although this paradigm enables adaptation to new environments (tasks), it inherently emphasizes the contribution of previously learned environments (tasks) at the expense of the stability required to preserve previously acquired knowledge.

By Salimeh Sekeh, Mary Wisell

llms fine-tuning multimodal

arXiv Machine Learning

Jun 16

Multi-User Dueling Bandits: A Fair Approach using Nash Social Welfare

arXiv:2605. 01961v2 Announce Type: replace Abstract: Learning from human preference data is becoming a useful tool, from fine-tuning large language models to training reinforcement learning agents.

By Maheed H. Ahmed, Mahsa Ghasemi

llms agents reinforcement-learning fine-tuning safety

arXiv AI

Jun 16

VinQA: Visual Elements Interleaved Long-form Answer Generation for Real-World Multimodal Document QA

arXiv:2606. 16092v1 Announce Type: cross Abstract: Real-world documents combine text with tables, charts, photographs, and diagrams arranged in diverse layouts, yet existing research on multimodal large language models (MLLMs) for document QA predominantly produces text-only responses, underutilizing these visual elements.

By Young Rok Jang, Hyesoo Kong, Kyunghwan An, Jae Sub Huh, Gyeonghun Kim, Stanley Jungkyu Choi

llms fine-tuning multimodal

arXiv AI

Jun 16

Bridging the Gap: Enabling Natural Language Queries for NoSQL Databases through Text-to-NoSQL Translation

arXiv:2502. 11201v3 Announce Type: replace-cross Abstract: NoSQL databases are core data infrastructure, yet natural-language access to them remains underdeveloped: correct query generation must recover how a non-relational data model represents entities, nested paths, arrays, missing fields, and dynamic keys.

By Jinwei Lu, Jiawei Lu, Chen Zhang, Zhiqian Qin, Haodi Zhang, Yuanfeng Song, Raymond Chi-Wing Wong

llms benchmarks data-engineering

arXiv AI

Jun 16

G-Loss: Graph-Guided Fine-Tuning of Language Models

arXiv:2604. 25853v3 Announce Type: replace-cross Abstract: Traditional loss functions, including cross-entropy, contrastive, triplet, and su pervised contrastive losses, used for fine-tuning pre-trained language models such as BERT, operate only within local neighborhoods and fail to account for the global semantic structure.

By Aditya Sharma, Vinti Agarwal, Rajesh Kumar

llms rag nlp fine-tuning benchmarks

arXiv AI

Jun 16

Understanding, Detecting, and Repairing Real-World In-Context-Learning-Based Text-to-SQL Errors

arXiv:2501. 09310v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have been adopted for text-to-SQL tasks, utilizing their in-context learning (ICL) capability to translate natural language questions into SQL queries.

By Jiawei Shen, Chengcheng Wan, Ruoyi Qiao, Jiazhen Zou, Hang Xu, Yuchen Shao, Yueling Zhang, Weikai Miao, Geguang Pu

llms benchmarks data-engineering

arXiv AI

Jun 16

Vernier: Probing Representational Misalignment Behind Lexical Gaps in Causal Reasoning

arXiv:2606. 15733v1 Announce Type: cross Abstract: Instruction-tuned language models can answer the same causal-reasoning question differently after its English variable names are replaced by type-preserving placeholders, although the structural causal model and the gold answer are unchanged.

By Zhenyu Yu

llms

arXiv Machine Learning

Jun 16

A Decision-Theoretic View of Test-Time Training: When, How Far, and Which Directions to Adapt

arXiv:2606. 15569v1 Announce Type: new Abstract: Test-time training (TTT) adapts a pretrained model to each prompt via parameter updates, improving accuracy under pretraining-to-test distribution shifts.

By Tomoya Wakayama

llms benchmarks

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