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 Machine Learning

Jul 15

Hierarchical Synthetic Tabular Data Generation: A Hybrid Top-Down and Bottom-Up Framework

arXiv:2605. 28198v2 Announce Type: replace Abstract: Existing approaches for synthetic tabular data generation are based on either purely generative models or LLMs, both of which struggle with data heterogeneity, logical consistency, rare-event coverage, and robustness in low-data regimes.

By Junfeng Nie, Alvin Jin, Xiaohui Chen

llms multimodal benchmarks safety

arXiv AI

Jul 15

GRID: Grammar-Railed Decoding for Enterprise SQL Generation

arXiv:2607. 11951v1 Announce Type: new Abstract: Large language models can write SQL, but enterprise deployment demands more than plausible text: outputs must be syntactically valid, must respect per-role and per-schema policy, must carry provable (not best-effort) guarantees, must not slow down as generations grow, and must leave a compliance-grade record of every decision.

By Mohsen Arjmandi

llms benchmarks data-engineering

arXiv Machine Learning

Jul 15

An Agentic AI Scientific Community for Automated Neural Operator Discovery

arXiv:2607. 12122v1 Announce Type: new Abstract: We present an agentic approach to autonomous neural operator discovery based on an AI scientific community, which consists of a swarm of virtual laboratories that interact under a citation-based economy of influence.

By Luis Loo, Ulisses Braga-Neto

llms agents benchmarks

arXiv Machine Learning

Jul 15

Toward Localizing and Repairing Bias in Transformer Attention Heads

arXiv:2607. 12863v1 Announce Type: cross Abstract: Transformer language models are increasingly used as software components, yet biased outputs remain difficult to localize and repair inside the model.

By Sigma Jahan

llms safety

arXiv Machine Learning

Jul 15

RAFP: Identifying LLM Lineages via Rare-Region Fingerprints

arXiv:2505. 12682v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly released under restricted licenses, creating a growing need for robust model ownership verification.

By Yun-Yun Tsai, Jia Hao Liang, Chuan Guo, Junfeng Yang, Laurens van der Maaten

llms fine-tuning efficiency safety

arXiv AI

Jul 15

Do You Remember? Toward Memory-Centric Multimodal AI

arXiv:2607. 11919v1 Announce Type: cross Abstract: Human memory is reconstructive, not a faithful recording.

By Xuguang Yu, Weigang Zheng, Minyue Yu

llms diffusion fine-tuning efficiency multimodal safety

arXiv AI

Jul 15

Adaptive Testing for LLM Evaluation: A Psychometric Alternative to Static Benchmarks

arXiv:2511. 04689v3 Announce Type: replace-cross Abstract: Evaluating large language models (LLMs) typically requires thousands of benchmark items, making the process expensive, slow, and increasingly impractical at scale.

By Peiyu Li, Xiuxiu Tang, Si Chen, Ying Cheng, Ronald Metoyer, Ting Hua, Nitesh V. Chawla

llms benchmarks

arXiv AI

Jul 15

Xray-Visual Models: Scaling Vision models on Industry Scale Data

arXiv:2602. 16918v2 Announce Type: replace-cross Abstract: We present Xray-Visual, a unified vision model architecture for large-scale image and video understanding trained on industry-scale social media data.

By Shlok Mishra, Tsung-Yu Lin, Linda Wang, Hongli Xu, Yimin Liu, Michael Hsu, Chaitanya Ahuja, Hao Yuan, Jianpeng Cheng, Hong-You Chen, Haoyuan Xu, Chao Li, Sreya Dutta Roy, Abhijeet Awasthi, Jihye Moon, Don Husa, Michael Ge, Sumedha Singla, Arkabandhu Chowdhury, Phong Dingh, Satya Narayan Shukla, Yonghuan Yang, David Jacobs, Qi Guo, Jun Xiao, Xiangjun Fan, Aashu Singh

llms computer-vision multimodal benchmarks safety

arXiv AI

Jul 15

Navigating the Mirage: A Dual-Path Agentic Framework for Robust Misleading Chart Question Answering

arXiv:2603. 28583v2 Announce Type: replace-cross Abstract: Despite the success of Vision-Language Models (VLMs), misleading charts remain a significant challenge due to their deceptive visual structures and distorted data representations.

By Yanjie Zhang, Yafei Li, Rui Sheng, Zixin Chen, Yanna Lin, Huamin Qu, Lei Chen, Yushi Sun

llms agents computer-vision nlp fine-tuning efficiency multimodal benchmarks safety

arXiv AI

Jul 15

When Close Enough Is Not Enough: Autoregressive Drift in Quantum Circuit Synthesis

arXiv:2607. 12780v1 Announce Type: cross Abstract: Quantum circuit optimization for fault-tolerant computing requires exact functional equivalence while minimizing expensive non-Clifford resources such as T gates.

By Mehdi Saeedi, Eddie Richter, Paul Hartke

llms nlp fine-tuning

arXiv AI

Jul 15

SkillSelect-Serve: QoS-Aware Budgeted Skill Service Recommendation for LLM Agents

arXiv:2607. 00011v2 Announce Type: replace-cross Abstract: Reusable agent skills are emerging as a service-oriented capability layer for Large Language Model (LLM) agents.

By Jingyuan Zheng, Dongjing Wang, Xin Zhang, Hao Chen, Youhuizi Li, Xudong Shen, Haiping Zhang, Butian Huang, Dongjin Yu, Guandong Xu

llms agents

arXiv AI

Jul 15

Ontology-Amplified Distillation and Contextuality Auditing for Sovereign Enterprise Language Models: A Combined Proof-of-Mechanism and Negative-Results Method Study

arXiv:2607. 11948v1 Announce Type: new Abstract: Regulated financial institutions operating under data-residency rules need tenant-owned language models that can run inside the institution's perimeter.

By Thanh Luong Tuan

llms agents reinforcement-learning fine-tuning efficiency safety

arXiv Machine Learning

Jul 15

Hierarchical Latent Structures in Data Generation Process Unify Mechanistic Phenomena across Scale

arXiv:2603. 06592v2 Announce Type: replace-cross Abstract: Contemporary studies in mechanistic interpretability have uncovered many puzzling phenomena in the neural information processing of Transformer-based language models, such as induction heads, function vectors, and the Hydra effect.

By Jonas Rohweder, Subhabrata Dutta, Iryna Gurevych

llms safety

arXiv Machine Learning

Jul 15

Learning Mechanistic Reasoning for Chemical Reactions with Large Language Models

arXiv:2607. 12771v1 Announce Type: new Abstract: Reaction mechanisms consist of the step-by-step sequences of elementary reactions that explain chemical transformations.

By Xingyu Dang, Haocheng Tang, Junmei Wang, Yanjun Li

llms fine-tuning benchmarks safety

arXiv AI

Jul 15

Self-Regulated Reading with AI Support: An Eight-Week Study with Students

arXiv:2602. 09907v2 Announce Type: replace-cross Abstract: College students increasingly use AI chatbots to support academic reading, yet we lack granular understanding of how these interactions shape their reading experience and cognitive engagement.

By Yue Fu, Joel Wester, Niels Van Berkel, Alexis Hiniker

llms

arXiv AI

Jul 15

Declarative by Design, Assistable Only by Convention: Benchmarking Multi-Agent Frameworks for AI-Assistability

arXiv:2602. 11198v2 Announce Type: replace-cross Abstract: Multi-agent frameworks (MAFs) promise to simplify LLM-driven software development, yet no principled metric captures how well AI coding assistants can generate correct, framework-specific code.

By Shafiuddin Rehan Ahmed, Sourabh Deshpande

llms agents benchmarks safety

arXiv AI

Jul 15

Efficiently Learning Branching Networks for Multitask Algorithmic Reasoning

arXiv:2512. 01113v2 Announce Type: replace-cross Abstract: Algorithmic reasoning -- the ability to perform step-by-step logical inference -- is a synthetic benchmark for evaluating multi-step reasoning abilities, designed for graph neural networks and also for transformer models.

By Dongyue Li, Zhenshuo Zhang, Minxuan Duan, Edgar Dobriban, Hongyang R. Zhang

llms benchmarks

arXiv AI

Jul 15

Critic Experience Bank: Self-Evolving Step-Level Confidence Estimation for LLM Agents

arXiv:2607. 12397v1 Announce Type: new Abstract: LLM agents act in external environments where each action changes the state that later decisions condition on, and where a single wrong step can waste interaction budget or trigger irreversible side effects long before the final failure is observed.

By Yaopei Zeng, Congchao Wang, JianHang Chen, Nan Wang, Yurui Chang, Lu Lin

llms agents benchmarks

arXiv AI

Jul 15

Cost-Governed RAG: Unified Per-Tenant Cost Attribution Across Retrieval and Generation in Multi-Tenant LLM Systems

arXiv:2607. 12188v1 Announce Type: new Abstract: Enterprise Retrieval-Augmented Generation (RAG) deployments face a critical governance gap: while LLM generation cost is metered per token, the retrieval layer - vector memory, similarity compute, and embedding API calls - remains an unattributed shared cost, enabling invisible cross-subsidization among tenants.

By Navnit Shukla

llms rag efficiency

arXiv AI

Jul 15

A Threshold Exceedance Framework for CBRN Uplift Evaluation in Frontier Language Models

arXiv:2607. 12200v1 Announce Type: new Abstract: As frontier language models advance, policymakers and model developers need methods for assessing whether model access materially increases a non-expert actor's ability to plan high-consequence Chemical, Biological, Radiological, or Nuclear (CBRN) misuse relative to public tools alone.

By Rahul Gupta, Abhinav Mohanty, Payal Motwani, Venkatesh Saligrama, Satyapriya Krishna, Connor Harris, Gary Anthony Ackerman, Brandon Behlendorf, Tom Hobson, Theodore Wilson, Spyros Matsoukas

llms

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