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 9

TF-Engram: A Train-Free Engram with SSD-Backed Memory for Large Language Models

arXiv:2607. 07388v1 Announce Type: cross Abstract: Large Language Models (LLMs) store factual knowledge and domain-specific patterns implicitly in dense Transformer parameters, making knowledge expansion costly through pretraining, fine-tuning, retrieval augmentation, or longer contexts.

By Yutang Ma, Kecheng Huang, Xikun Jiang, Zili Shao

llms fine-tuning efficiency

arXiv Machine Learning

Jul 9

Behavior Leverage Imbalance in Multi-Teacher On-Policy Distillation

arXiv:2607. 07050v1 Announce Type: cross Abstract: Agentic language models must learn when to call tools, when to consume tool responses, and when to answer directly.

By Jiabin Shen, Guang Chen, Chengjun Mao

llms agents efficiency

arXiv AI

Jul 9

Latent graph encoding of multimodal neuroimaging features with generative AI architectures

arXiv:2607. 07027v1 Announce Type: cross Abstract: While generative models enable encoding of complex neuroimaging data for feature generation and reconstruction, developing optimal architectural frameworks with appropriate encoding and latent space processes is crucial for studying structural and functional properties of the brain.

By Ishaan Batta, Meenu Ajith, Vince Calhoun

llms diffusion multimodal safety

arXiv AI

Jul 9

Diversity Without Fidelity: A Solver-Sampler Mismatch in Multi-Agent LLM Negotiation Simulation

arXiv:2604. 11840v3 Announce Type: replace-cross Abstract: Language models are increasingly used to simulate people: survey respondents, negotiators, stakeholders in policy exercises.

By Sandro Andric

llms agents

arXiv AI

Jul 9

From Content to Audience: A Multimodal Annotation Framework for Broadcast Television Analytics

arXiv:2603. 26772v2 Announce Type: replace-cross Abstract: Automated semantic annotation of broadcast television content presents distinctive challenges, combining structured audiovisual composition, domain-specific editorial patterns, and strict operational constraints.

By Paolo Cupini, Francesco Pierri

llms nlp multimodal benchmarks

arXiv Machine Learning

Jul 9

MedPMC: A Systematic Framework for Scaling High-Fidelity Medical Multimodal Data for Foundation Models

arXiv:2607. 07673v1 Announce Type: cross Abstract: Medicine is inherently multimodal, requiring clinicians to synthesize information across diverse data streams.

By Hyunjae Kim, Dain Kim, Pan Xiao, Serina S. Applebaum, Younjoon Chung, Xuguang Ai, Yu Yin, Roy Jiang, Yuexi Du, Yawen Wei, Yiming Kong, Tuo Guo, Zhiyuan Cao, Mengmeng Du, Yuelei Fu, Yan Hu, Rui Shi, Gui Yang, Kevin W. Jin, Yuntian Liu, Yuxuan Tian, Jonathan Marquez, Zhen Chen, Sheng Zhang, Hoifung Poon, Hua Xu, Jaewoo Kang, Qingyu Chen

llms multimodal benchmarks safety

arXiv Machine Learning

Jul 9

Counterfactual Modeling with Fine-Tuned LLMs for Health Intervention Design and Sensor Data Augmentation

arXiv:2601. 14590v3 Announce Type: replace Abstract: Counterfactual explanations (CFEs) provide human-centric interpretability by identifying the minimal, actionable changes required to alter a machine learning model's prediction.

By Shovito Barua Soumma, Asiful Arefeen, Stephanie M. Carpenter, Melanie Hingle, Hassan Ghasemzadeh

llms fine-tuning multimodal safety

arXiv Machine Learning

Jul 9

Efficient Long-Horizon Learning for Learned Optimization

arXiv:2607. 06772v1 Announce Type: new Abstract: Learned optimization aims to improve upon hand-designed optimizers (e.

By Xiaolong Huang, Benjamin Th\'erien, James Harrison, Eugene Belilovsky

llms computer-vision

arXiv Machine Learning

Jul 9

SNLP: Layer-Parallel Inference via Structured Newton Corrections

arXiv:2605. 17842v3 Announce Type: replace Abstract: Autoregressive language models execute Transformer layers sequentially, creating a latency bottleneck that is not removed by conventional tensor or pipeline parallelism.

By Ligong Han, Kai Xu, Hao Wang, Akash Srivastava

llms fine-tuning

arXiv Machine Learning

Jul 9

LEMUR 2: Unlocking Neural Network Diversity for AI

arXiv:2607. 06839v1 Announce Type: new Abstract: Existing NAS benchmarks (e.

By Tolgay Atinc Uzun, Waleed Khalid, Saif U Din, Sai Revanth Mulukuledu, Akashdeep Singh, Chandini Vysyaraju, Raghuvir Duvvuri, Avi Goyal, Yashkumar Rajeshbhai Lukhi, Muhammad A. Hussain, Krunal Jesani, Usha Shrestha, Yash Mittal, Roman Kochnev, Pritam Kadam, Mohsin Ikram, Harsh R. Moradiya, Alice Arslanian, Dmitry Ignatov, Radu Timofte

llms rag diffusion fine-tuning multimodal benchmarks

arXiv AI

Jul 9

Healthier LLMs: Retrieval-Augmented Generation for Public Health Question Answering

arXiv:2607. 06641v1 Announce Type: cross Abstract: Large language models (LLMs) achieve promising results on medical question answering benchmarks, yet their use in public health is constrained by hallucinations and the rapid evolution of official guidance.

By Felix Feldman, Joshua Harris, Timothy Laurence, Leo Loman, Ollie Higgins, Fan Grayson, Poonam Soma, Bethany Pace-Bonello, Michael Borowitz, Toby Nonnenmacher

llms rag nlp benchmarks safety

arXiv Machine Learning

Jul 9

GIFT: Geometry-Informed Low-precision Gradient Communication for LLM Pretraining

arXiv:2607. 07494v1 Announce Type: cross Abstract: Gradient communication is a primary scaling bottleneck in large language model (LLM) pretraining.

By Jieying Wang, Shuyuan Fan, Mingkai Zheng, Zhao Zhang

llms efficiency

arXiv AI

Jul 9

Entropy Pacing Policy Optimization for Multi-Task Agentic Reinforcement Learning

arXiv:2607. 07178v1 Announce Type: cross Abstract: Recent breakthroughs of Reinforcement Learning (RL) have highlighted its potential for complex agentic Large Language Model (LLM) tasks.

By Zetian Hu, Shunyu Liu, Junjie Zhang, Yongcheng Jing, Ting-En Lin, Yongbin Li, Dacheng Tao

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Jul 9

Comparative Study of Domain-adapted VLMs for General Document Visual Question Answering

arXiv:2607. 07179v1 Announce Type: cross Abstract: Document Visual Question Answering (DocVQA) presents a complex multimodal challenge, requiring models to exploit visual, textual, and layout information from documents.

By Miguel Lopez-Duran, Elena Marrero, Julian Fierrez, Marta Robledo-Moreno, Ruben Vera-Rodriguez, Daniel DeAlcala, Aythami Morales, Ruben Tolosana, Oscar Delgado, Alvaro Ortigosa, Javier Ortega-Garcia

llms nlp fine-tuning multimodal

arXiv AI

Jul 9

AnyPoC: Universal Proof-of-Concept Test Generation for Scalable LLM-Based Bug Detection

arXiv:2604. 11950v2 Announce Type: replace-cross Abstract: While recent LLM-based agents can identify many candidate bugs in source code, their reports remain static hypotheses that require manual validation, limiting the practicality of automated bug detection.

By Zijie Zhao, Chenyuan Yang, Weidong Wang, Yihan Yang, Ziqi Zhang, Lingming Zhang

llms rag agents benchmarks safety

arXiv AI

Jul 9

SpaCellAgent: A Self-Evolving LLM-Based Multi-Agent Framework for Trajectory Analysis

arXiv:2607. 07467v1 Announce Type: new Abstract: Spatial and Single-cell transcriptomics are transformative in deciphering cellular dynamics.

By Songhan Wang, Haoang Chi, He Li, Zhiheng Zhang, Jiayan Yuan, Cheems Wang, Hao Peng, Xinwang Liu, Wenjing Yang

llms agents

arXiv AI

Jul 9

DASH: Dynamic Audio-Driven Semantic Chunking for Efficient Omnimodal Token Compression

arXiv:2603. 15685v2 Announce Type: replace-cross Abstract: Omnimodal large language models (OmniLLMs) jointly process audio and visual streams, but the resulting long multimodal token sequences make inference prohibitively expensive.

By Bingzhou Li, Tao Huang

llms rag computer-vision efficiency multimodal safety

arXiv AI

Jul 9

When Agents Remember Too Much: Memory Poisoning Attacks on Large Language Model Agents

arXiv:2607. 06595v1 Announce Type: cross Abstract: Personal AI agents powered by large language models can reason and act using available tools to access emails, manage calendars, and push code to remote repositories, all with minimal oversight.

By George Torres, Sharad Shrestha, Satyajayant Misra

llms agents benchmarks

arXiv Machine Learning

Jul 9

Multi-Class vs. Multi-Label BERT for CVE-to-CWE Mapping: How Taxonomy Structure Shapes the Errors

arXiv:2607. 07573v1 Announce Type: new Abstract: Assigning Common Weakness Enumeration (CWE) categories to Common Vulnerabilities and Exposures (CVE) records remains an important but largely manual step in vulnerability analysis.

By Ana Schwengber Kelm, Christian Bockermann, J\"org Frochte

llms nlp

arXiv AI

Jul 9

TriRoute: Unified Learned Routing for Joint Adaptive Attention, Experts, and KV-Cache Allocation

arXiv:2607. 06601v1 Announce Type: cross Abstract: Conditional computation can decouple language model quality from per-token inference cost, yet leading techniques act on a single axis in isolation: Mixture-of-Experts (MoE) sparsifies the FFN, Mixture-of-Depths (MoD) skips whole transformer blocks, and KV-cache quantization compresses attention memory.

By Andrii Balashov, Olena Ponomarova

llms efficiency

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