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 10

MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Clinical Tabular Prediction

arXiv:2603. 02221v2 Announce Type: replace-cross Abstract: In clinical tabular prediction, classical machine learning models with feature engineering often outperform neural methods.

By Zizheng Zhang, Yiming Li, Justin Xu, Jinyu Wang, Rui Wang, Lei Song, Jiang Bian, David W Eyre, Jingjing Fu

llms benchmarks safety

arXiv AI

Jun 10

Effective Reinforcement Learning for Agentic Search by Recycling Zero-Variance Queries During Training

arXiv:2606. 10709v1 Announce Type: cross Abstract: The use of GRPO-style algorithms has become the standard strategy for training LLM search agents under outcome-only rewards.

By Jo\~ao Coelho, Jo\~ao Magalh\~aes, Bruno Martins, Chenyan Xiong

llms agents reinforcement-learning benchmarks

arXiv AI

Jun 10

Whisfusion: Parallel ASR Decoding with Masked Diffusion

arXiv:2508. 07048v2 Announce Type: replace-cross Abstract: Autoregressive (AR) encoder-decoder models dominate high-quality multilingual ASR, but their left-to-right decoders make inference latency scale with transcript length.

By Taeyoun Kwon, Junhyuk Ahn, Taegeun Yun, Heeju Jwa, Yoonchae Choi, Siwon Park, Jongchan Kim, Hyungon Ryu, Hyuk-Jae Lee, Nam-Joon Kim

llms rag diffusion efficiency multimodal benchmarks

arXiv AI

Jun 10

BadRobot: Jailbreaking Embodied LLM Agents in the Physical World

arXiv:2407. 20242v5 Announce Type: replace-cross Abstract: Embodied AI represents systems where AI is integrated into physical entities.

By Hangtao Zhang, Chenyu Zhu, Xianlong Wang, Ziqi Zhou, Changgan Yin, Minghui Li, Lulu Xue, Yichen Wang, Shengshan Hu, Aishan Liu, Peijin Guo, Leo Yu Zhang

llms agents robotics benchmarks safety

arXiv AI

Jun 10

Flaws in the LLM Automation Narrative

arXiv:2606. 11166v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly described as performing at the level of human experts on knowledge economy tasks.

By George Perrett, Javae Elliott, Jennifer Hill, Marc Scott

llms benchmarks

arXiv AI

Jun 10

CITRAS: Covariate-Informed Transformer for Time Series Forecasting

arXiv:2503. 24007v4 Announce Type: replace-cross Abstract: In time series forecasting, covariates represent external factors that influence target variables.

By Yosuke Yamaguchi, Issei Suemitsu, Wenpeng Wei

llms

arXiv AI

Jun 10

Bittensor Agent Arenas as a Trajectory Primitive: Distilling a Shopping Agent from ShoppingBench Subnet Traces

arXiv:2606. 10064v1 Announce Type: cross Abstract: Small-model agentic post-training is bottlenecked less by the algorithm than by the trajectory substrate it consumes.

By Shardul Bansal, Seth Schilbe, Jarrod Barnes

llms agents reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Jun 10

TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning

arXiv:2606. 11119v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is a promising approach for enhancing reasoning and agentic behavior in large language models.

By Heming Zou, Qi Wang, Yun Qu, Yuhang Jiang, Lizhou Cai, Yixiu Mao, Ru Peng, Xin Xu, Weijie Liu, Kai Yang, Saiyong Yang, Xiangyang Ji

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Jun 10

Spatiotemporal Graph Transformer for 3D Neighborhood Interaction and Quality Prediction in Metal Additive Manufacturing

arXiv:2606. 10227v1 Announce Type: new Abstract: Metal additive manufacturing enables the fabrication of complex parts, but achieving consistent build quality remains challenging due to interactions induced by repeated layer-wise melting, solidification, and reheating across the 3D build.

By Joyce Karen Pelaez, Siqi Zhang, Hoo Sang Ko

llms multimodal

arXiv Machine Learning

Jun 10

Hasse Diagrams for Attention: A Partial Order Framework for Designing Transformer Masks

arXiv:2606. 09951v1 Announce Type: new Abstract: During the training of large Transformer models, attention masks regulate the scope and direction of information flow across a sequence.

By Chentao Li, Han Guo

llms

arXiv AI

Jun 10

Earth-OneVision: Extending Remote Sensing Multimodal Large Language Models to More Sensor Modalities and Tasks

arXiv:2606. 10819v1 Announce Type: cross Abstract: RS-MLLMs enable natural-language understanding and spatial reasoning over earth observation imagery.

By Miaoxin Cai, Guanqun Wang, Wei Zhang, Guangyao Zhou, Yin Zhuang, Tong Zhang, Hao Wang, He Chen, Jun Li

llms multimodal benchmarks safety

arXiv AI

Jun 10

Beyond APIs: Probing the Limits of MLLMs in Physical Tool Use

arXiv:2606. 10803v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) excel at utilizing digital APIs and increasingly serve as the "brain" of embodied AI, instructing robots to interact with the physical world.

By Zhixin Ma, Yutong Zhou, Yongqi Li, Chong-Wah Ngo, Wenjie Li

llms agents robotics multimodal benchmarks

arXiv AI

Jun 10

Catching One in Five: LLM-as-Judge Blind Spots in Production Multi-Turn Transaction Agents

arXiv:2606. 10315v1 Announce Type: cross Abstract: LLM-as-judge is the default instrument for evaluating conversational agents, yet its reliability is almost always reported as agreement with human ratings, not recall of real defects.

By Sawyer Zhang, Alexander Wang, Sophie Lei

llms agents safety

arXiv AI

Jun 10

ReasonAlloc: Hierarchical Decoding-Time KV Cache Budget Allocation for Reasoning Models

arXiv:2606. 11164v1 Announce Type: new Abstract: Long chain-of-thought (CoT) trajectories in large language model (LLM) reasoning cause severe inference bottlenecks due to rapid key-value (KV) cache growth.

By Wenhao Liu, Hao Shi, Yunhe Li, Weizhi Fei, Xiangyuan Wang, Mengzhe Ruan, Hanxu Hou, Peisong Wang, Linqi Song, Shuang Qiu

llms efficiency benchmarks

arXiv AI

Jun 10

Atomic Intent Reasoning: Bringing LLM Semantics to Industrial Cross-Domain Recommendations

arXiv:2606. 10357v1 Announce Type: cross Abstract: Cross-domain recommendation is a core problem in content-to-e-commerce platforms.

By Zhuohang Jiang, Yuxin Chen, Shijie Wang, Haohao Qu, Zhou Jindong, Wenqi Fan, Li Qing, Dongxu Liang, Jun Wang

llms efficiency benchmarks

arXiv AI

Jun 10

LakeQA: An Exploratory QA Benchmark over a Million-Scale Data Lake

arXiv:2606. 10460v1 Announce Type: cross Abstract: Recent large language models (LLMs) have shown rapid progress in reading-based question answering (QA), where evidence is explicitly provided or can be trivially retrieved.

By Haonan Wang, Jiaxiang Liu, Yurong Liu, Austin Senna Wijaya, Tianle Zhou, Eden Wu, Yijia Chen, Wanting You, Reya Vir, Daniela Pinto, Grace Fan, Yusen Zhang, Juliana Freire, Eugene Wu

llms agents nlp benchmarks

arXiv AI

Jun 10

When Attribution Patching Lies: Diagnosis and a Second-Order Correction

arXiv:2606. 09899v1 Announce Type: cross Abstract: A central goal of mechanistic interpretability is to identify which internal components causally drive a language model's behavior.

By Luyang Zhang, Jialu Wang

llms benchmarks safety

arXiv AI

Jun 10

Blurry Window Attention

arXiv:2606. 09862v1 Announce Type: cross Abstract: The Softmax Attention operation in Transformer language models has a quadratic complexity in the sequence length and a growing state size in the form of KV cache, which becomes a bottleneck in long context scenarios.

By Axel Laborieux, Christos Sourmpis, Juan Gabriel Kostelec, Qinghai Guo

llms efficiency

arXiv AI

Jun 10

Alignment Collapse Under KV Cache Quantization: Diagnosis and Mitigation

arXiv:2606. 09864v1 Announce Type: cross Abstract: Key-value (KV) cache quantization is widely used to reduce Large Language Model (LLM) inference memory, yet existing evaluations solely focus on measuring perplexity and accuracy without assessing the safety impact.

By Bruce Changlong Xu, Adarsh Kumarappan, Mu Zhou

llms efficiency benchmarks safety

arXiv AI

Jun 10

Conditional Vendi Score: Prompt-Aware Diversity Evaluation for Generative AI Models and LLMs

arXiv:2411. 02817v2 Announce Type: replace-cross Abstract: Generative models guided by text prompts are widely evaluated for fidelity and prompt alignment, yet their ability to produce outputs remains underexplored.

By Mohammad Jalali, Azim Ospanov, Amin Gohari, Farzan Farnia

llms diffusion safety

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