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 24

CineCap: Structured Reasoning with Spatio-Temporal Anchors for Cinematographic Video Captioning

arXiv:2606. 24636v1 Announce Type: new Abstract: Cinematographic captioning aims to describe how a video is filmed using professional film-language concepts such as camera movement, shot size, depth of field, composition, and shooting angle.

By Xinyu Mao, Yuhui Zeng, Xiaokun Liu, Wenyu Qin, Meng Wang, Xin Tao, Pengfei Wan, Xiaohan Xing, Max Meng

llms diffusion reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Jun 24

Does Mixture-of-Experts Actually Help Inference on Consumer and Edge Hardware? An Empirical Study

arXiv:2606. 21428v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) language models are often described as ideal for resource-constrained inference.

By Alfarizy Alfarizy, Hung Truong Thanh Nguyen, Ren\'e Richard, Roozbeh Razavi-Far, Hung Cao

llms efficiency benchmarks

arXiv AI

Jun 24

Evaluating LLM Usage for Efficient and Explainable Numerical and Classified Implicit Sentiment Analysis of Product Desirability

arXiv:2606. 23701v1 Announce Type: cross Abstract: Qualitative product feedback can reveal nuanced user experiences, but its implicit sentiment is difficult to measure.

By Sherri Weitl-Harms, John Hastings

llms nlp safety

arXiv Machine Learning

Jun 24

Hybrid Sequence Modeling and Reinforced Verification for Controllable Target-Conditioned Decision Making

arXiv:2508. 16420v3 Announce Type: replace Abstract: Target-conditioned sequence models provide a simple interface for controllable offline decision making, but the requested target return can be an unreliable control signal, especially when the target return lies in underrepresented regions of the dataset.

By Yue Pei, Hongming Zhang, Chao Gao, Martin M\"uller, Yingying Zhang, Mengxiao Zhu, Hao Sheng, Ziliang Chen, Liang Lin, Haogang Zhu

llms benchmarks safety

arXiv Machine Learning

Jun 24

When Top-1 Fails: Calibrating LoRA Monitors for Masked Diffusion LMs

arXiv:2606. 24119v1 Announce Type: new Abstract: Discrete diffusion language model (DLM) fine-tuning inherits inexpensive diagnostics from denoising-time confidence monitors, but their PEFT-training meaning is untested.

By Lucky Verma, Pratik Yadav

llms diffusion fine-tuning

arXiv AI

Jun 24

video-SALMONN-R$^3$: Learning to ReWatch, ReAsk, and ReAnswer for Efficient Video Understanding

arXiv:2606. 24477v1 Announce Type: cross Abstract: Video large language models (LLMs) are often constrained by computation and memory budgets, leading them to use reduced frame rates and spatial resolutions, which may cause them to miss critical information for question answering (QA).

By Yixuan Li, Guangzhi Sun, Yudong Yang, Wei Li, Zejun MA, Chao Zhang

llms nlp reinforcement-learning fine-tuning

arXiv AI

Jun 24

THEIA: Learning Complete Kleene Three-Valued Logic in a Pure-Neural Modular Architecture

arXiv:2604. 11284v5 Announce Type: replace-cross Abstract: We present THEIA, a 2.

By Augustus Haoyang Li

llms

arXiv AI

Jun 24

Beyond Trajectory Imitation: Strategy-Guided Policy Optimization for LLM Reasoning

arXiv:2606. 24064v1 Announce Type: new Abstract: Distilling reasoning capabilities from strong to weak language models typically involves imitating specific solution trajectories, effectively transferring what to answer rather than how to reason.

By Tianyuan Shi, Canbin Huang, Bei Li, Xin Chen, Xiaojun Quan, Jingang Wang, Qifan Wang

llms agents reinforcement-learning fine-tuning efficiency benchmarks

arXiv AI

Jun 24

NoContactNoWorries: Estimating Contact through Vision and Proprioception for In-Hand Dexterous Manipulation

arXiv:2606. 24450v1 Announce Type: cross Abstract: Perceiving physical contact is fundamental to dexterous manipulation.

By Soham Patil, Avirup Das, Sourabh Bhosale, Spandan Roy

llms agents reinforcement-learning robotics multimodal

arXiv AI

Jun 24

Poster: Exploring the Limits of Audio-Based Detection of Turkish Phone Call Scams

arXiv:2606. 24523v1 Announce Type: cross Abstract: Scam phone calls exploit vulnerable communities worldwide, yet research on detection has focused almost exclusively on English and other high-resource languages.

By Arda Eren, Micheal Cheung, Youqian Zhang, Grace Ngai, Eugene Yujun Fu

llms multimodal safety

arXiv AI

Jun 24

IPO Finance Agent: Evaluation of LLM Financial Analysts beyond Finance Agent v2, with Automated Rubric Generation -- the Case of the SpaceX (SPCX) IPO

arXiv:2606. 23032v2 Announce Type: replace Abstract: Finance Agent v2 (by Vals AI) has emerged as the reference benchmark for evaluating both Anthropic Claude and OpenAI ChatGPT frontier language models on financial tasks.

By Mostapha Benhenda

llms agents benchmarks safety

arXiv AI

Jun 24

LLM-MINE: Large Language Model based Alzheimer's Disease and Related Dementias Phenotypes Mining from Clinical Notes

arXiv:2603. 13673v2 Announce Type: replace Abstract: Accurate extraction of Alzheimer's Disease and Related Dementias (ADRD) phenotypes from electronic health records (EHR) is critical for early-stage detection and disease staging.

By Mingchen Shao, Yuzhang Xie, Carl Yang, Jiaying Lu

llms

arXiv AI

Jun 24

Attention in Motion: Secure Platooning via Transformer-based Misbehavior Detection

arXiv:2512. 15503v3 Announce Type: replace-cross Abstract: Vehicular platooning promises transformative improvements in transportation efficiency and safety through the coordination of multi-vehicle formations enabled by Vehicle-to-Everything (V2X) communication.

By Konstantinos Kalogiannis, Ahmed Mohamed Hussain, Hexu Li, Panos Papadimitratos

llms efficiency benchmarks safety

arXiv AI

Jun 24

From Spatial to Spectral: An Efficient, Frequency-Guided Feature Representation Learner for Small Object Detection

arXiv:2606. 23825v1 Announce Type: cross Abstract: Efficient small object detection is bottlenecked by the inherent feature scarcity of tiny targets, which is further aggravated by operations of spatial-domain detectors that indiscriminately discard critical high-frequency details.

By Yuhan Rui, Shihan Qiao, Yibin Lou, Mingxi Yu, Yutong Wan, Yanqiao Chen, Dongsheng Hou, Zhen Cao, Athena Zhuoming Zhong, Qi Hao

llms computer-vision benchmarks

arXiv AI

Jun 24

Representation Interventions Enable Lifelong Knowledge Memory Control in LLMs

arXiv:2511. 20892v4 Announce Type: replace Abstract: Large language models (LLMs) often produce incorrect or outdated content after being employed.

By Xuyuan Liu, Shengyu Chen, Xinshuai Dong, Yanchi Liu, Xujiang Zhao, Haoyu Wang, Yujun Yan, Haifeng Chen, Zhengzhang Chen

llms benchmarks

arXiv Machine Learning

Jun 24

Polaris: A Godel Agent Framework for Small Language Models through Experience-Abstracted Policy Repair

arXiv:2603. 23129v3 Announce Type: replace Abstract: G\"odel agent realize recursive self-improvement: an agent inspects its own policy and traces and then modifies that policy in a tested loop.

By Aditya Kakade, Vivek Srivastava, Shirish Karande

llms agents benchmarks

arXiv AI

Jun 24

The African Language Tax: Quantifying the Cost, Latency, and Context Penalty of Tokenizing African Languages in Frontier LLMs

arXiv:2606. 24460v1 Announce Type: cross Abstract: Commercial large language models bill, scale latency, and budget context per token.

By Olaoye Anthony Somide

llms nlp benchmarks

arXiv AI

Jun 24

SURGELLM: Rethinking Multi-Task Evaluation through Task-Aware Feature Gating with Class-Balanced Normalization

arXiv:2606. 24259v1 Announce Type: cross Abstract: Fine-tuned encoders deployed across heterogeneous NLP tasks face three compounding problems: mismatched inductive biases, class-imbalance corruption of feature statistics, and no mechanism to condition attention on external lexical knowledge.

By Noor Islam S. Mohammad, Ulug Bayazit

llms nlp fine-tuning efficiency safety

arXiv Machine Learning

Jun 24

Reasoning as Attractor Dynamics: Latent Memory Retrieval via Gibbs-Weighted Energy Minimization

arXiv:2606. 24543v1 Announce Type: new Abstract: Large Language Models (LLMs) are traditionally viewed as autoregressive generators.

By Kanishk Awadhiya

llms safety

arXiv Machine Learning

Jun 24

Bridging Mechanistic Interpretability and Prompt Engineering with Gradient Ascent for Interpretable Persona Control

arXiv:2601. 02896v3 Announce Type: replace Abstract: Controlling emergent behavioral personas (e.

By Harshvardhan Saini, Yiming Tang, Dianbo Liu

llms safety

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