Natural language processing

Classical and neural NLP: translation, question answering, tokenization and the evaluation of language understanding.

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arXiv Machine Learning

Jun 10

Encoding the Euler Characteristic Transform

arXiv:2606. 10824v1 Announce Type: new Abstract: The Euler Characteristic Curve (ECC) records the Euler characteristic of a linearly embedded cell complex as a function of filtration height in a given direction, and the Euler Characteristic Transform (ECT) is the injective shape descriptor obtained by collecting ECCs over many directions.

By Nello Blaser, Odin Hoff Gardaa, Lars M. Salbu, Elena Xinyi Wang, Bastian Rieck

llms nlp benchmarks safety

arXiv Machine Learning

Jun 10

The Order Matters: Sequential Fine-Tuning of LLaMA for Coherent Automated Essay Scoring

arXiv:2606. 10327v1 Announce Type: cross Abstract: Automated Essay Scoring (AES) systems must judge interdependent discourse elements (e.

By Ali Keramati, Mark Warschauer

llms nlp fine-tuning efficiency

arXiv Machine Learning

Jun 10

Pushing the limits of one-dimensional NMR spectroscopy for automated structure elucidation using artificial intelligence

arXiv:2512. 18531v2 Announce Type: replace-cross Abstract: One-dimensional NMR spectroscopy is one of the most widely used techniques for the characterization of organic compounds and natural products.

By Frank Hu, Jonathan M. Tubb, Dimitris Argyropoulos, Sergey Golotvin, Mikhail Elyashberg, Grant M. Rotskoff, Matthew W. Kanan, Thomas E. Markland

llms nlp fine-tuning

arXiv AI

Jun 10

Deployment-Time Memorization in Foundation-Model Agents

arXiv:2606. 10062v1 Announce Type: new Abstract: Foundation-model agents are increasingly long-lived systems that remember users across interactions, making memorization an explicit deployment-time function rather than solely a property of model weights.

By Lei (Rachel), Chen, Guilin Zhang, Kai Zhao, Dalmo Cirne, Andy Olsen, Xu Chu, Zeke Miller, Alet Blanken, Amine Anoun, Jerry Ting

llms agents nlp safety

arXiv AI

Jun 10

Less Context, Better Agents: Efficient Context Engineering for Long-Horizon Tool-Using LLM Agents

arXiv:2606. 10209v1 Announce Type: new Abstract: Large language models deployed as autonomous agents for enterprise workflows face a key challenge: verbose tool responses from enterprise systems can cause context overflow, stale-state errors, and high inference cost.

By Abhilasha Lodha, Mahsa Pahlavikhah Varnosfaderani, Abir Chakraborty, Abhinav Mithal

llms agents nlp efficiency benchmarks

arXiv AI

Jun 10

Detecting Speculative Language in Biomedical Texts using Recurrent Neural Tensor Networks

arXiv:2606. 10471v1 Announce Type: cross Abstract: In this investigation, we delve into the automated detection of speculative language within biomedical articles by utilizing distributed sentence representations and advanced deep learning techniques.

By Dhruv Dixit

nlp

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 AI

Jun 10

UniDexTok: A Unified Dexterous Hand Tokenizer from Real Data

arXiv:2606. 10683v1 Announce Type: cross Abstract: Dexterous hands are essential for fine-grained manipulation, but their hardware designs vary substantially across embodiments.

By Dong Fang, Youjun Wu, Yuanxin Zhong, Rui Zhang, Yunlong Wang, Xiaosong Jia, Yu-Gang Jiang

nlp robotics

arXiv AI

Jun 10

LongMoE: Longitudinal Multimodal Learning via Trajectory-Aware Mixture-of-Experts

arXiv:2606. 09907v1 Announce Type: cross Abstract: Multimodal clinical learning is increasingly important for integrating diverse patient data, including imaging, text, and personalised health records.

By Maxx Richard Rahman, Prakhar Kumar, Wolfgang Maass

nlp multimodal

arXiv AI

Jun 10

A Unified Multi-Modal Framework for Intelligent Financial Systems: Integrating Reinforcement Learning, High-Frequency Trading, and Game-Theoretic Approaches with Cross-Modal Sentiment Analysis

arXiv:2606. 10412v1 Announce Type: new Abstract: The rapid evolution of financial technology demands sophisticated artificial intelligence systems capable of handling diverse challenges across multiple domains simultaneously.

By Fanrong Liu, Zhang Yuwei, Mingni Luo

llms rag nlp reinforcement-learning multimodal 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

Spatial-Omni: Spatial Audio Understanding Integration in Multimodal LLMs via FOA Encoding

arXiv:2606. 10738v1 Announce Type: cross Abstract: Recent multimodal large language models mainly process audio as monaural signals, thereby discarding the spatial cues contained in spatial audio for sound localization, spatial relation reasoning, and spatial scene understanding.

By Zhiyuan Zhu, Yixuan Chen, Yiwen Shao, Wenxiang Guo, Changhao Pan, Yu Zhang, Yuxiang Wang, Wei Liu, Houhua Zhang, Chengkuan Zeng, Wenbo Cheng, Yunxi Liu, Rui Yang, Steve Yves, Liefeng Bo, Zhou Zhao

llms nlp multimodal

arXiv Machine Learning

Jun 10

AI Application Gives Users Real-Time Feedback on the Level of Peace in the Social Media Videos They Watch

arXiv:2601. 05232v3 Announce Type: replace-cross Abstract: Most people now get their news from videos on social media, such as YouTube and Facebook, rather than through curated journalism.

By P. Gilda (Columbia University), P. Dungarwal (Columbia University), A. Thongkham (Columbia University), E. T. Ajayi (St John's University), S. Choudhary (Columbia University), T. M. Terol (Columbia University), C. Lam (Columbia University), J. P. Araujo (Columbia University), M. McFadyen-Mungalln (Columbia University), L. S. Liebovitch (Columbia University), P. T. Coleman (Columbia University), H. West (Columbia University), K. Sieck (Toyota Research Institute), S. Carter (Toyota Research Institute)

llms nlp

arXiv AI

Jun 10

Divide and Cooperate: Role-Decomposed Multi-Agent LLM Training with Cross-Agent Learning Signals

arXiv:2606. 10684v1 Announce Type: cross Abstract: Modern language agents which perform multi-step reasoning have shown strong performance in knowledge-intensive question answering.

By Jaewan Park, Solbee Cho, Jay-Yoon Lee

llms agents nlp fine-tuning benchmarks

arXiv AI

Jun 10

The Confident Liar: Diagnosing Multi-Agent Debate with Log-Probabilities and LLM-as-Judge

arXiv:2606. 10296v1 Announce Type: cross Abstract: Multi-agent debate systems are typically evaluated only on whether the final answer is correct, overlooking the quality of the intermediate reasoning that debate is designed to produce.

By Ali Keramati, Justin Cheok, Jacob Horne, Mark Warschauer

llms agents nlp

arXiv AI

Jun 10

Learning Evidence Highlighting for Frozen LLMs

arXiv:2604. 22565v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) can reason well, yet often miss decisive evidence when it is buried in long, noisy contexts.

By Shaoang Li, Yanhang Shi, Yufei Li, Mingfu Liang, Xiaohan Wei, Yunchen Pu, Fei Tian, Chonglin Sun, Frank Shyu, Luke Simon, Sandeep Pandey, Xi Liu, Jian Li

llms nlp reinforcement-learning

arXiv AI

Jun 10

TruthRL: Incentivizing Truthful LLMs via Reinforcement Learning

arXiv:2509. 25760v2 Announce Type: replace-cross Abstract: While large language models (LLMs) have demonstrated strong performance on factoid question answering, they are still prone to hallucination and untruthful responses, particularly when tasks demand information outside their parametric knowledge.

By Zhepei Wei, Xiao Yang, Kai Sun, Jiaqi Wang, Rulin Shao, Jingxiang Chen, Mohammad Kachuee, Teja Gollapudi, Yiwei Liao, Nicolas Scheffer, Rakesh Wanga, Anuj Kumar, Yu Meng, Wen-tau Yih, Xin Luna Dong

llms nlp reinforcement-learning benchmarks safety

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

llms nlp

arXiv Machine Learning

Jun 10

Entropy, Disagreement, and the Limits of Foundation Models in Genomics

arXiv:2604. 04287v2 Announce Type: replace Abstract: Foundation models in genomics have shown mixed success compared to their counterparts in natural language processing.

By Maxime Rochkoulets, Lovro Vr\v{c}ek, Mile \v{S}iki\'c

rag nlp

arXiv AI

Jun 10

HIPIF: Hierarchical Planning and Information Folding for Long-Horizon LLM Agent Learning

arXiv:2606. 10507v1 Announce Type: new Abstract: While Large Language Models (LLMs) have demonstrated strong capabilities as autonomous agents across a wide range of tasks, their performance often degrades in multi-turn long-horizon agentic tasks.

By Juncheng Diao, Zhicong Lu, Peiguang Li, Yongwei Zhou, Changyuan Tian, Qingbin Li, Rongxiang Weng, Jingang Wang, Xunliang Cai

llms agents nlp reinforcement-learning benchmarks

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