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

From Preimage Search To Source-Grounded Feature Inversion

arXiv:2607. 12526v1 Announce Type: new Abstract: Interpreting a neural network requires understanding what its internal features extract from a particular input.

By Kaixiang Shu

llms

arXiv AI

Jul 15

An Omnilingual-ASR-Based Speech-LLM System for the 2nd MLC-SLM Challenge

arXiv:2607. 12468v1 Announce Type: cross Abstract: We describe our submission to Task 1 of the 2nd MLCSLM Challenge: a cascaded diarization-then-recognition system that combines DiariZen-Large-s80 (WavLM-Large) segmentation, CAM++ embedding-based two-speaker clustering, and a LoRA-adapted omniASR LLM 7B v2 recognizer, with no oracle segmentation or speaker labels at test time.

By Shuming Fang, Shuifei Zeng

llms rag computer-vision fine-tuning multimodal

arXiv AI

Jul 15

Do LLMs Know What They Know? Measuring Metacognitive Efficiency with Signal Detection Theory

arXiv:2603. 25112v2 Announce Type: replace-cross Abstract: Standard evaluation of LLM confidence relies on calibration metrics (ECE, Brier score) that conflate how much a model knows (Type-1 accuracy) with how well its confidence signal tracks that knowledge (Type-2 metacognitive sensitivity).

By Jon-Paul Cacioli

llms

arXiv AI

Jul 15

Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution

arXiv:2607. 13034v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflows, yet they rarely ask how much effort a task actually requires.

By Junjie Yin, Xinyu Feng

llms agents benchmarks

arXiv AI

Jul 15

From Controlled to the Wild: Evaluation of Pentesting Agents for the Real-World

arXiv:2605. 10834v2 Announce Type: replace Abstract: AI pentesting agents are increasingly credible as offensive security systems, but current benchmarks still provide limited guidance on which will perform best in real-world targets.

By Pedro Conde, Henrique Branquinho, Valerio Mazzone, Bruno Mendes, Andr\'e Baptista, Nuno Moniz

llms agents benchmarks

arXiv Machine Learning

Jul 15

Verifier-Based Reinforcement Fine-Tuning of Reasoning Models for Thermal Energy Storage Control

arXiv:2607. 12856v1 Announce Type: new Abstract: Buildings are expected to shift cooling loads in response to grid conditions.

By Takumi Shioda, Kohei Terashima, Tatsuo Nagai

llms reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jul 15

Auditing Data Leakage in Whole-Slide Image Multimodal Benchmarks

arXiv:2607. 12278v1 Announce Type: cross Abstract: Recent vision-language models (VLMs) for computational pathology report striking zero-shot performance on whole-slide image (WSI) visual question answering (VQA) benchmarks.

By Wenhao Zhang, Zhongliang Zhou, John Kang, Sheng Li

llms nlp multimodal benchmarks

arXiv Machine Learning

Jul 15

SlimPer: Make Personalization Model Slim and Smart

arXiv:2607. 12281v1 Announce Type: cross Abstract: Transformer-style architectures are increasingly adopted for industrial recommendation systems, yet they inherit a design premise misaligned with the task: generative models rely on per-token autoregressive prediction, which justifies maintaining large intermediate tensors that scale with sequence length.

By Siqi Wang, Xianjie Chen, Shaofeng Deng, Albert Chen, Romil Shah, Jiawei Huang, Zhaoqin Wang, Zhang Zhang, Yiqun Liu, Meilei Jiang, Anish Dubey, Moyan Mei, Tongxin Wang, Nathan Berrebbi, Misael Manjarres, Armand Sauzay, Shardul Kothapalli, Aryaman Vinchhi, Kevin Johnstone, Juheon Lee, Gufan Yin, Ziheng Huang, Justin Lin, Mert Terzihan, Yilin Qi, Cynthia Yang, Colin Peppler, Qi Ding, Ruohan Sun, Ge Song, Litao Deng, Parichay Kapoor, Matt Ma, Huihui Cheng, Jiyuan Zhang, Yanli Zhao, Yiping Han, Fangqiu Han, Ning Yao, Arun Singh, Jordan Edwards, Zhengyu Su, Abhishek Kumar, Guangdeng Liao, Ankit Asthana

llms rag multimodal safety

arXiv AI

Jul 15

CrochetBench: Can Vision-Language Models Move from Describing to Doing in Crochet Domain?

arXiv:2511. 09483v3 Announce Type: replace Abstract: While multimodal large language models can describe visual content, their ability to generate executable procedures remains underexplored.

By Peiyu Li, Xiaobao Huang, Ting Hua, Nitesh V. Chawla

llms multimodal benchmarks

arXiv AI

Jul 15

Optimization Is Not All You Need

arXiv:2607. 11977v1 Announce Type: new Abstract: In 2019, OpenAI released two million GPT-2 outputs-ungrammatical, half broken-to aid the detection of machine-generated text.

By Minh Hua, Rita Raley

llms benchmarks safety

arXiv AI

Jul 15

RFM-Editing 2: Text-Guided Audio Editing with Rectified Flow Matching and Coarse-to-Fine Diffusion Transformers

arXiv:2606. 20101v3 Announce Type: replace-cross Abstract: Audio editing aims to modify specific content in an existing audio clip according to a text instruction or description while preserving the remaining acoustic content.

By Liting Gao, Yonggang Zhu, Yaru Chen, Dongyu Wang, Shubin Zhang, Zhenbo Li, Jean-Yves Guillemaut, Wenwu Wang

llms diffusion multimodal safety

arXiv AI

Jul 15

G-SHARE: A Guideline-Based Structured Reasoning Framework for Human-Factor Event Diagnosis

arXiv:2607. 11892v1 Announce Type: cross Abstract: Human-factor event diagnosis is essential for learning from operational events in nuclear power plants, yet its quality depends strongly on expert interpretation of narrative reports and guideline-based reasoning.

By Xingyu Xiao, Mao Du, Jiejuan Tong, Jingang Liang, Haitao Wang

llms benchmarks safety

arXiv AI

Jul 15

Comparing Semantic Navigation in Humans and Large Language Models using Natural Language Processing

arXiv:2607. 12195v1 Announce Type: cross Abstract: Semantic memory retrieval can be conceptualized as navigation through conceptual space.

By Gabriel Paris-Colombo, Rodrigo M. Cabral-Carvalho, Felipe D. Toro-Hern\'andez

llms rag nlp

arXiv AI

Jul 15

PM-Bench: Evaluating Prospective Memory in LLM Agents

arXiv:2607. 12385v1 Announce Type: new Abstract: A significant challenge in agentic AI is prospective memory: the ability to execute an intention at a specific future cue or state while other activities are ongoing.

By Genglin Liu, Saadia Gabriel

llms agents benchmarks

arXiv AI

Jul 15

MaxSAT-Based Feedback for Guiding Vision-Language Models in Sudoku

arXiv:2607. 12711v1 Announce Type: new Abstract: Vision--Language Models (VLMs) have recently demonstrated promising performance on structured visual reasoning tasks, including grid-based puzzles.

By Pedro Orvalho, Guillem Aleny\`a, Felip Many\`a

llms multimodal

arXiv AI

Jul 15

IQA-T1: Tool-based Visual Evidence Reasoning for Image Quality Assessment

arXiv:2607. 12375v1 Announce Type: cross Abstract: Image Quality Assessment (IQA) in open-world environments remains challenging due to limited generalization and interpretability.

By Jinjian Wu, Jiaqi Tang, Wei Wei, Yingying Yan, Jianmin Chen, Botong Geng, Lei Zhang, Qifeng Chen

llms multimodal benchmarks safety

arXiv AI

Jul 15

Evaluating Health Misinformation in Low-Resource Languages: Integrating Small Language Models with a Culturally-Sensitive Responsible NLP Framework (Bangla as a Case Study)

arXiv:2607. 12336v1 Announce Type: cross Abstract: Artificial Intelligence (AI) technologies, while serving as a foundational enabler for modern social media and digital health services, exert a bivalent effect by simultaneously acting as a combatant against and a spread vector for misinformation.

By Farnaz Farid, Raihan Alam, Al Al-Areqi, Farhad Ahamed, Muhammad Hassan Khan, Sadia Hossain, Irena Veljanova, Anika Tabassum Binte Hossain

llms nlp fine-tuning

arXiv AI

Jul 15

Egocentric Bias in Vision-Language Models

arXiv:2602. 15892v2 Announce Type: replace-cross Abstract: Visual perspective taking--inferring how the world appears from another's viewpoint--is foundational to social cognition.

By Maijunxian Wang, Yijiang Li, Bingyang Wang, Tianwei Zhao, Ran Ji, Qingying Gao, Emmy Liu, Hokin Deng, Dezhi Luo

llms agents multimodal benchmarks safety

arXiv AI

Jul 15

Accelerating Masked Diffusion Large Language Models: A Survey of Efficient Inference Techniques

arXiv:2607. 12829v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) offer a theoretical advantage in parallel generation over standard autoregressive models.

By Daehoon Gwak, Minhyung Lee, Junwoo Park, Jaegul Choo

llms diffusion efficiency benchmarks

arXiv Machine Learning

Jul 15

ReDiTT: Retrieval Augmented Conditional Diffusion Transformers for Asynchronous Time Series

arXiv:2607. 12391v1 Announce Type: new Abstract: We present a diffusion based model for asynchronous time series prediction, where the goal is to predict the next inter event time and event type.

By Saiyue Lyu, Zhitian Zhang, Ruizhi Deng, Thibaut Durand

llms rag diffusion

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