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

Jun 10

Closing the Modality Gap in Zero-Shot HAR: Contrastive Training and Separability-Optimized Prototypes on IMU Data

arXiv:2606. 10789v1 Announce Type: new Abstract: Zero-shot learning (ZSL) for inertial measurement unit (IMU)-based human activity recognition (HAR) faces a central challenge: bridging the gap between sensor embeddings and semantic class representations.

By Anik Ghosh

llms rag benchmarks safety

arXiv Machine Learning

Jun 10

Dexterous Point Policy: Learning Point-based Dexterous Hand Policies from Human Demonstrations

arXiv:2606. 10614v1 Announce Type: cross Abstract: Robotic foundation models pre-trained on human demonstration videos have shown promise, but a significant embodiment gap remains when the resulting policies are deployed on real robots.

By Beomjun Kim, Seong Hyeon Park, Seunghoon Sim, Seungjun Moon, Sanghyeok Lee, Jinwoo Shin

llms agents robotics fine-tuning benchmarks

arXiv Machine Learning

Jun 10

One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data

arXiv:2606. 10678v1 Announce Type: new Abstract: Transformer-based models have emerged as leading paradigms in time-series forecasting in recent years, employing self-attention mechanisms to capture long-range dependencies.

By Amrijit Biswas, Mustafa Kamal, Robin Krambroeckers, M. M. Lutfe Elahi, Sifat Momen, Nabeel Mohammed, Shafin Rahman

llms benchmarks safety

arXiv AI

Jun 10

PromptEmbedder: Efficient and Transferable Text Embedding via Dual-LLM Soft Prompting

arXiv:2605. 28066v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable efficacy in text embedding, yet current adaptation methods like LoRA face significant bottlenecks in computational efficiency and cross-architecture transferability.

By Yu-Che Tsai, Kuan-Yu Chen, Yuan-Hao Chen, Yu-Han Chang, Ching-Yu Tsai, Yu-Hsiang Chuang, Shou-De Lin

llms rag fine-tuning benchmarks safety

arXiv AI

Jun 10

Advancing the State-of-the-Art in Empirical Privacy Auditing

arXiv:2606. 10481v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning of large language models (LLMs) can exhibit problematic memorization of individual training examples.

By Nicole Mitchell, Galen Andrew, Arun Ganesh, Brendan McMahan, Peter Kairouz

llms fine-tuning benchmarks safety

arXiv Machine Learning

Jun 10

N-GRPO: Embedding-Level Neighbor Mixing for Enhanced Policy Optimization

arXiv:2606. 10768v1 Announce Type: new Abstract: The success of Large Language Models in mathematical reasoning relies heavily on the generation of diverse and valid solution paths during the rollout phase.

By Xukun Zhu, Hang Yu, Peng Di, Linchao Zhu

llms rag benchmarks

arXiv AI

Jun 10

Baseline-Free Policy Optimization for Neural Combinatorial Optimization

arXiv:2606. 10321v1 Announce Type: cross Abstract: Neural combinatorial optimization (NCO) trains autoregressive policies to solve routing problems.

By Carlos S. Sep\'ulveda, Gonzalo A. Ruz

llms reinforcement-learning benchmarks safety

arXiv AI

Jun 10

EstRTL: Functional Estimation Guided RTL Code Generation

arXiv:2606. 09867v1 Announce Type: cross Abstract: Optimizing register transfer level (RTL) code is of vital importance in hardware design.

By Qi Xiong, Renzhi Chen, Bowei Wang, Yuqing Xiong, Libo Huang, Lei Wang

llms agents fine-tuning

arXiv AI

Jun 10

Co-GLANCE: Uncertainty-Aware Active Perception for Heterogeneous Robot Teaming

arXiv:2606. 09919v1 Announce Type: cross Abstract: Perceptual uncertainty is a central challenge for heterogeneous robot teams operating in unstructured outdoor environments, where no single viewpoint affords reliable scene understanding.

By Michal P. Podolinsky, Neel P. Bhatt, Pranay Samineni, Rohan Siva, Christian Ellis, Ufuk Topcu

llms computer-vision robotics efficiency multimodal

arXiv AI

Jun 10

ActiveMem: Distributed Active Memory for Long-Horizon LLM Reasoning

arXiv:2606. 10532v1 Announce Type: new Abstract: Memory is essential for enabling large language model (LLM) agents to handle long-horizon reasoning tasks.

By Yunhan Jiang, Wenbin Duan, Shasha Guo, Liang Pang, Xiaoqian Sun, Huawei Shen

llms agents efficiency benchmarks

arXiv AI

Jun 10

One Token per Multimodal Evidence: Latent Memory for Resource-Constrained QA

arXiv:2606. 10572v1 Announce Type: new Abstract: External memory effectively grounds large language models (LLMs) and vision-language models (VLMs)-based question answering (QA) in relevant multimodal evidence.

By Zhi Zheng, Ziqiao Meng, Hao Luan, Wei Liu, Wee Sun Lee

llms rag nlp efficiency multimodal benchmarks

arXiv Machine Learning

Jun 10

Operator Fusion for LLM Inference on the Tensix Architecture

arXiv:2606. 09879v1 Announce Type: new Abstract: This study addresses on-device inference bottlenecks of Transformer models on Tenstorrent's Tensix architecture and proposes an operator fusion strategy that enhances data locality.

By Qingbo Wu, Ke Li, Wenzhu Wang, Jie Yu, Ruian Zhang, Lili Liu

llms

arXiv Machine Learning

Jun 10

Mechanistic Analysis of Alignment Algorithms in Language Models

arXiv:2606. 09850v1 Announce Type: new Abstract: Post-training alignment algorithms are predominantly evaluated as black boxes, obscuring how they reshape language models' internal computations.

By Aarush Sinha, Ishan Garg, Veeraraju Elluru, Arth Singh, Kushal Garg

llms diffusion reinforcement-learning safety

arXiv AI

Jun 10

One Lens, Many Worlds : A Capability-Typed Interface for World-Model Interpretability

arXiv:2606. 09936v1 Announce Type: cross Abstract: World models are now built on substantially different computational substrates.

By Bhavith Chandra Challagundla, Sanskar Pandey, Param Thakkar, Rishikesh Mallagundla, Yugandhar Reddy Gogireddy, Wenhao Lu, Hindol Roy Choudhury, Shravani Challagundla, Mohamed Deraz Nasr, Spursh Deshpande

llms rag diffusion safety

arXiv AI

Jun 10

Structure-Preserving Learning Improves Geometry Generalization in Neural PDEs

arXiv:2602. 02788v2 Announce Type: replace-cross Abstract: We aim to develop physics foundation models for science and engineering that provide real-time solutions to Partial Differential Equations (PDEs) which preserve structure and accuracy under adaptation to unseen geometries.

By Benjamin D. Shaffer, Shawn Koohy, Brooks Kinch, M. Ani Hsieh, Nathaniel Trask

llms benchmarks safety

arXiv AI

Jun 10

Density Ridge Selective Prediction for LLM and VLM Hallucination Detection under Calibration Label Scarcity

arXiv:2606. 10198v1 Announce Type: cross Abstract: Hallucination detection in large language and vision-language models is increasingly framed as selective prediction, where a detector assigns a confidence score and abstains when confidence is low.

By Nina I. Shamsi

llms multimodal benchmarks safety

arXiv AI

Jun 10

LLM-Aided Joint Secrecy Precoding and Trajectory for RSMA-Based Heterogeneous UAV Networks

arXiv:2507. 17188v2 Announce Type: replace-cross Abstract: This paper investigates secure communications in rate-splitting multiple access (RSMA) enabled heterogeneous UAV networks, where multiple UAVs collaboratively serve ground terminals in the presence of eavesdroppers.

By Lijie Zheng, Ji He, Shih Yu Chang, Yulong Shen

llms agents reinforcement-learning

arXiv AI

Jun 10

Interpreting and Steering a Text-to-Speech Language Model with Sparse Autoencoders

arXiv:2606. 10029v1 Announce Type: cross Abstract: Language models increasingly serve as the backbone of text-to-speech (TTS) systems, yet we understand little about the representations they build when text and generated speech tokens share a single residual stream.

By Nikita Koriagin, Georgii Aparin, Nikita Balagansky, Daniil Gavrilov

llms diffusion multimodal safety

arXiv AI

Jun 10

Decentralized Multi-Agent Systems with Shared Context

arXiv:2606. 10662v1 Announce Type: cross Abstract: Multi-agent systems (MAS) can scale large language model reasoning at test time by decomposing complex problems into parallel subtasks.

By Yuzhen Mao, Azalia Mirhoseini

llms agents

arXiv Machine Learning

Jun 10

Pre-AF 13: An Interpretable Atrial Fibrillation Risk Score Mined from Discharge Reports

arXiv:2606. 10725v1 Announce Type: new Abstract: Background.

By Olga Shakhmatova, Dmitrii Kriukov, Daniil Larionov, Nikita Khromov, Iaroslav Bespalov, Alexander Zolotarev, Kirill Grishchenkov, Ekaterina Ivanova, Miron Kuznetsov, Ilya Sochenkov, Elizaveta Panchenko, Artem Shelmanov, Dmitry V. Dylov

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