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 21

ZifaMem: Structured Memory for Persona, Preference, and Emotional Continuity in AI Companions

arXiv:2607. 17564v1 Announce Type: new Abstract: AI companions are judged not only by single-turn fluency but by whether they sustain emotional continuity: remembering who the companion is, what the user prefers, and how the relationship has felt.

By Jingzhe Fang, Guozhi Xu, Yunfan Cui, Xiaochen Yang, Zhangyu Hua

llms agents

arXiv Machine Learning

Jul 21

ChipChat: Low-Latency Cascaded Conversational Agent in MLX

arXiv:2509. 00078v2 Announce Type: replace-cross Abstract: The emergence of large language models (LLMs) has transformed spoken dialog systems, yet the optimal architecture for real-time on-device voice agents remains an open question.

By Tatiana Likhomanenko, Richard He Bai, Zijin Gu, Zakaria Aldeneh, Shiladitya Dutta, Luke Carlson, Han Tran, Yizhe Zhang, Ruixiang Zhang, Huangjie Zheng, Navdeep Jaitly

llms agents multimodal safety

arXiv Machine Learning

Jul 21

Breaking the Block: Preserving Data Continuity to Train Superior SAEs for Instruct Models

arXiv:2506. 07691v2 Announce Type: replace-cross Abstract: Sparse Autoencoders (SAEs) are a cornerstone of mechanistic interpretability.

By Jiaming Li, Haoran Ye, Yukun Chen, Xinyue Li, Lei Zhang, Hamid Alinejad-Rokny, Jimmy Chih-Hsien Peng, Min Yang

llms fine-tuning safety

arXiv Machine Learning

Jul 21

Auditing Invisible Weight Updates with Reference Traces

arXiv:2607. 09800v2 Announce Type: replace Abstract: Master weights and stochastic rounding bypass invisible stored-weight updates but do not locate lost direct-storage proposals or parameters worth protecting.

By Zekai Shang

llms

arXiv AI

Jul 21

Do Maps Still Matter for Machines: Revisiting the Role of Choropleth Maps in Foundation Model Spatial Understanding

arXiv:2607. 17999v1 Announce Type: new Abstract: Spatial understanding is crucial for foundation models (FMs), and maps have long helped humans organize and reason about geographic information.

By Zhiwei Wei, Yonghe Sun, Zhenjia Liu, Wenjia Xu, Chao He, Weihua Dong, Chunbo Liu, Hua Liao

llms benchmarks

arXiv AI

Jul 21

Natural Language Access to Domain-Specific Metadata: A Reusable Framework for LLM Query Generation

arXiv:2607. 18029v1 Announce Type: cross Abstract: Researchers need to answer ad-hoc questions about the contents of domain-specific archives but often lack the expertise to write structured queries on the metadata.

By Blake G. Fitch, Cato Elia Kurtz

llms agents fine-tuning benchmarks safety data-engineering

arXiv AI

Jul 21

Re-Sonance: A Dysarthric Asynchronous Real-Time Speech Conversion System Based on a Three-Stage Cascaded ASR-LLM-TTS Architecture

arXiv:2607. 17615v1 Announce Type: cross Abstract: Individuals with dysarthria face significant challenges in professional speaking scenarios such as conferences, presentations, and meetings, where real-time communication is crucial.

By Yuxuan Wu, Yifan Xu, Junkun Wang, Jiayong Jiang, Xin Zhao, Zhaojie Luo

llms multimodal

arXiv AI

Jul 21

Token-Level Cross-Modal Transformer with Contrastive Multi-Task Learning for Breast Cancer Subtype Classification and Survival Prediction

arXiv:2607. 16233v1 Announce Type: cross Abstract: Integrating heterogeneous genomic and clinical modalities for joint cancer subtype classification and survival prediction remains a key challenge in precision oncology.

By Suxing Liu Byungwon Min

llms multimodal

arXiv AI

Jul 21

Otap:Structure-Aware Optimal Transport for Evaluating Planning and Execution in Agent Trajectories

arXiv:2607. 17082v1 Announce Type: new Abstract: Large language model agents solve tasks by generating trajectories that interleave planning, tool calls, and intermediate results.

By Babak Barazandeh, Subhabrata Majumdar, George Michailidis

llms agents benchmarks

arXiv AI

Jul 21

Masked Diffusion Language Models are Strong and Steerable Text-Based World Models for Agentic RL

arXiv:2607. 16204v1 Announce Type: new Abstract: Recent growth in reinforcement learning (RL) has surfaced a need for diverse, specialized training environments.

By Darshan Deshpande

llms agents diffusion reinforcement-learning fine-tuning efficiency safety

arXiv Machine Learning

Jul 21

GlyRAG: Context-Aware Retrieval-Augmented Framework for Blood Glucose Forecasting

arXiv:2601. 05353v2 Announce Type: replace Abstract: Accurate blood glucose forecasting using continuous glucose monitoring (CGM) data can support the early prediction of dysglycemic risk.

By Shovito Barua Soumma, Hassan Ghasemzadeh

llms rag agents

arXiv AI

Jul 21

Can AI Agents Really Complete RTL-to-GDS? Lessons from Benchmarking Tool-Interactive EDA Workflows

arXiv:2607. 17528v1 Announce Type: new Abstract: LLM-driven agent systems have emerged as a promising paradigm for electronic design automation (EDA), demonstrating strong potential for automating complex design workflows.

By Jinyuan Deng, Zhengrui Chen, Xufeng Wei, Tianyu Xing, Chenyi Wen, Cheng Zhuo

llms agents benchmarks

arXiv Machine Learning

Jul 21

NIRVANA: Structured Pruning Reimagined for Large Language Model Compression

arXiv:2509. 14230v2 Announce Type: replace Abstract: While structured pruning presents a highly effective pathway for accelerating Large Language Model (LLM) inference, existing methods frequently suffer from significant performance degradation and demand computationally retraining to recover capabilities.

By Mengting Ai, Tianxin Wei, Sirui Chen, Jingrui He

llms fine-tuning efficiency benchmarks

arXiv Machine Learning

Jul 21

Patch Policy: Efficient Embodied Control via Dense Visual Representations

arXiv:2607. 18236v1 Announce Type: cross Abstract: Pretrained dense visual features from Vision Transformers (ViTs) are powerful yet have been underutilized in robot learning.

By Gaoyue Zhou, Zichen Jeff Cui, Ada Langford, Bowen Tan, Yann LeCun, Lerrel Pinto

llms robotics fine-tuning efficiency multimodal benchmarks

arXiv Machine Learning

Jul 21

SciForma: Structure-Faithful Generation of Scientific Diagrams

arXiv:2607. 18091v1 Announce Type: cross Abstract: Structural fidelity is essential to scientific methodology diagrams.

By Yuxuan Luo, Peng Zhang, Xinjie Zhang, Xun Guo, Zhouhui Lian, Yan Lu

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Jul 21

An Adjoint-Sensitivity Framework for Lost-in-the-Middle Phenomena in Causal Residual Transformers

arXiv:2607. 17696v1 Announce Type: cross Abstract: We develop an adjoint-sensitivity framework for positional influence in causal residual Transformers and separate unconditional analytic results from conditional boundary-shape conclusions.

By Cheng Huan, Hongwei Yuan

llms safety

arXiv Machine Learning

Jul 21

AGG: Jacobian-Aggregated Group Gradient for Efficient GRPO Training of Diffusion Models

arXiv:2607. 17572v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) is a powerful reinforcement learning algorithm for aligning generative models with human preferences.

By Ruiyi Ding, Jie Li, He Kang, Ziyan Liu, Chengru Song, Yuan chen

llms diffusion reinforcement-learning benchmarks

arXiv Machine Learning

Jul 21

Node4All: Learning Node Representation Beyond Datasets

arXiv:2607. 17272v1 Announce Type: new Abstract: Node representation learning has advanced rapidly, yet most existing methods rely on per-dataset training and hyperparameter tuning.

By Dooho Lee, Jaemin Yoo

llms benchmarks

arXiv AI

Jul 21

SR-Agent: An Experience-Driven Agentic Framework for Post-Ranking Strategies Refinement in E-Commerce Recommendation

arXiv:2607. 17719v1 Announce Type: new Abstract: User experience is a first-class objective in industrial e-commerce recommender systems (RS).

By Hanchen Yang, Kaiwen Yang, Junpeng Zhuang, Yang He, Keting Cen, Bochao Liu, Zhongbo Sun, An Liu, Zhongteng Han, Chenyi Lei

llms agents

arXiv Machine Learning

Jul 21

Grounded verification of chemical and materials reasoning: detection is the bottleneck

arXiv:2607. 17417v1 Announce Type: new Abstract: Large language models confabulate chemical objects (molecular formulas, space groups, formation energies) in fluent reasoning traces, concentrated on long-tail entities where confidence is least trustworthy.

By Can Polat, Mustafa Kurban, Erchin Serpedin, Hasan Kurban

llms

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