Latest NLP Research Papers

The newest NLP papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks NLP so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• A Unifying Lens on Supervised Fine-Tuning Through Target Distribution Design
  Tong Xie, Yuanhao Ban, Yunqi Hong, Sohyun An et al. · arXiv · Jun 9, 2026

  Supervised fine-tuning (SFT) typically maximizes the likelihood of every token in a demonstrated trajectory. However, an observed token can be non-unique, noisy, or misaligned with the model prior. Strictly fitting toward this one-hot targe…

• Data Journalist Agent: Transforming Data into Verifiable Multimodal Stories
  Kevin Qinghong Lin, Batu EI, Yuhong Shi, Pan Lu et al. · arXiv · Jun 9, 2026

  Data tells stories that shape society; the data journalist's job is to turn raw information into stories non-experts can trust. A high-quality news feature takes a newsroom team weeks: hunting for context, running statistics, choosing an an…

• Multi-Faceted Interactivity Alignment in Full-Duplex Speech Models
  Atsumoto Ohashi, Neil Zeghidour, Alexandre Défossez, Eugene Kharitonov · arXiv · Jun 9, 2026

  Full-duplex spoken dialogue models can listen and speak simultaneously, making them a promising architecture for natural conversation. However, current models are trained solely with supervised learning through token-level likelihood maximi…

• Provenance-Grounded Gating and Adaptive Recovery in Synthetic Post-Training Data Curation
  Soham Bhattacharjee, Karun Sharma, Vinay Kumar Sankarapu, Pratinav Seth · arXiv · Jun 9, 2026

  Synthetic post-training pipelines commonly filter generated samples with reward models or holistic LLM judges, yet two practices remain rarely examined together: whether the filtering signal is grounded in the source evidence that induced e…

• TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning
  Heming Zou, Qi Wang, Yun Qu, Yuhang Jiang et al. · arXiv · Jun 9, 2026

  Reinforcement learning with verifiable rewards (RLVR) is a promising approach for enhancing reasoning and agentic behavior in large language models. However, rollout-intensive policy optimization is often limited by insufficient reward cont…

• PhantomBench: Benchmarking the Non-existential Threat of Language Models
  Haeji Jung, Hila Gonen · arXiv · Jun 9, 2026

  Hallucinations, where language models (LMs) generate factually ungrounded responses, pose serious risks, as users tend to blindly rely on them. This is particularly concerning in high-stakes domains, where consequences of such model behavio…

• The Shibboleth Effect: Auditing the Cross-Lingual Distributional Skew of Large Language Models
  Hakan Mehmetcik · arXiv · Jun 9, 2026

  This study investigates cross-lingual distributional skew (the Shibboleth Effect) in frontier large language models (LLMs) subjected to sustained adversarial conditions. We develop a multi-agent geopolitical wargame, the Cerulean Sea Crisis…

• VISTA: A Versatile Interactive User Simulation Toolkit for Agent Evaluation
  Yunan Lu, Ryan Shea, Yusen Zhang, Zhou Yu · arXiv · Jun 9, 2026

  Evaluation remains a critical bottleneck for interactive agent development. Existing evaluation methods often rely on static benchmarks, which fail to capture the dynamic, multi-step nature of agentic behavior and struggle to expose meaning…

• A History-Aware Visually Grounded Critic for Computer Use Agents
  Jaewoo Lee, Zaid Khan, Archiki Prasad, Justin Chih-Yao Chen et al. · arXiv · Jun 9, 2026

  Various test-time interventions for Computer Use Agents (CUAs), including critic models, have been developed to improve performance through pre-execution action evaluation in complex Graphical User Interface (GUI) environments. However, exi…

• Modeling Complex Behaviors: Multi-Personality Composition and Dynamic Switching in Vision-Language Models
  Peiqi Jia, Haonan Jia, Ziqi Miao, Linkang Du et al. · arXiv · Jun 9, 2026

  With the widespread deployment of Multimodal Large Language Models (MLLMs) in social interaction, understanding and controlling their behavior under complex personality conditions is essential. This paper introduces explicit personality con…

• T1-Bench: Benchmarking Multi-Scenario Agents in Real-World Domains
  Genta Indra Winata, Amartya Chakraborty, Yuzhen Lin, Swasthi P Rao et al. · arXiv · Jun 9, 2026

  Recent advances in reasoning and tool-calling capabilities of large language models (LLMs) have enabled increasingly capable agentic systems. However, existing benchmarks remain limited in task complexity, realism, and domain diversity, and…

• Attention Amnesia in Hybrid LLMs: When CoT Fine-Tuning Breaks Long-Range Recall, and How to Fix It
  Xinyu Zhou, Boyu Zhu, Yi Xu, Zhiwei Li et al. · arXiv · Jun 9, 2026

  Chain-of-thought (CoT) supervised fine-tuning (SFT) is widely adopted to improve reasoning ability, yet we find that it systematically degrades long-context recall in hybrid linear-attention models. Across architectures including HypeNet an…

• Does Reasoning Preserve Alignment? On the Trustworthiness of Large Reasoning Models
  Prajakta Kini, Avinash Reddy, Souradip Chakraborty, Satya Sai Srinath Namburi GNVV et al. · arXiv · Jun 9, 2026

  Instruction-tuned LLMs are increasingly converted into reasoning models through post-training to improve multi-step task performance. This conversion is usually optimized for reasoning accuracy, without explicitly preserving the alignment b…

• AuRA: Internalizing Audio Understanding into LLMs as LoRA
  Bo Cheng, Lei Shi, Zhanyu Ma, Yuan Wu et al. · arXiv · Jun 9, 2026

  Recent efforts to extend large language models (LLMs) to speech inputs typically rely on cascaded ASR-LLM pipelines, end-to-end speech-language models, or bridge/distillation-based adaptation. While these routes respectively reuse strong pr…

• Generative Archetype-Grounded Item Representations for Sequential Recommendation
  Yifan Li, Jiahong Liu, Xinni Zhang, Hao Chen et al. · arXiv · Jun 9, 2026

  Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior. However, the limited quality of item representations remains a critical bottleneck. While pre-trained large language models…

• Measuring Human Value Expression in Social Media Texts: Calibrated LLM Annotation and Encoder Transfer
  Maria Milkova, Maksim Rudnev · arXiv · Jun 9, 2026

  Measuring subjective constructs in naturally occurring social media text requires annotation procedures that are theoretically grounded, empirically validated, and transferable to an encoder model for scalable prediction. Using non-English …

• Who Brought Easter Eggs to Eid? Auditing Cultural Translation of Math Word Problems Across Diverse Languages and Regions
  Parisa Suchdev, Juniper Lovato · arXiv · Jun 9, 2026

  Large language models are increasingly used to adapt math word problems for personalized learning at scale, but it remains an open question whether those adaptations are consistent across models, preserve cultural diversity at scale, and re…

• Mind the Gap: Can Frontier LLMs Pass a Standardized Office Proficiency Exam?
  Tengchao Lv, Dongdong Zhang, Jiayu Ding, Yilin Jia et al. · arXiv · Jun 9, 2026

  The deployment of Large Language Model (LLM) agents for computer automation is accelerating, yet their ability to navigate complex, professional-grade productivity software is largely untested. We argue that Office automation is an ideal en…

• It Takes One to Bias Them All: Breaking Bad with One-Shot GRPO
  Naihao Deng, Yilun Zhu, Naichen Shi, Clayton Scott et al. · arXiv · Jun 9, 2026

  Warning: This paper contains several toxic and offensive statements. Modern large language models (LLMs) are typically aligned through large-scale post-training to ensure fair and reliable behavior. In this work, we investigate how easily s…

• Trace Only What You Need: Structure-Aware On-Demand Hypergraph Memory for Long-Document Question Answering
  Xiangjun Zai, Xingyu Tan, Chen Chen, Xiaoyang Wang et al. · arXiv · Jun 9, 2026

  Long-document question answering (QA) requires large language models (LLMs) to reason over evidence scattered across lengthy documents, where answers often depend on event order, section-level context, and cross-part evidence connections. A…

• Pushing the Limits of LLM Tool Calling via Experiential Knowledge Integration and Activation
  Yupu Hao, Zhuoran Jin, Huanxuan Liao, Kang Liu et al. · arXiv · Jun 9, 2026

  Large language models (LLMs) rely on tool use to act as autonomous agents, yet often fail in multi-step execution due to insufficient tool-related knowledge and ineffective knowledge activation. Therefore, we present a systematic study on h…

• Causally Evaluating the Learnability of Formal Language Tasks
  Vésteinn Snæbjarnarson, Anej Svete, Josef Valvoda, Reda Boumasmoud et al. · arXiv · Jun 8, 2026

  Language models, as multi-task learners, acquire a wide range of abilities during training. A fundamental question is how much task-specific data is needed to learn a given task. Answering this for natural language is difficult: tasks are h…

• SIGA: Self-Evolving Coding-Agent Adapters for Scientific Simulation
  Matthew Ho, Brian Liu, Jixuan Chen, Audrey Wang et al. · arXiv · Jun 8, 2026

  Advanced scientific simulators expose specialized input languages that turn simulation goals into executable configurations, but learning them can cost domain scientists hours to days. We study simulator setup as a problem of agent-tool int…

• Data Synthesis and Parameter-Efficient Fine-Tuning for Low-Resource NMT: A Case Study on Q'eqchi' Mayan
  Alexander Chulzhanov, Soeren Eberhardt, Arjun Mukherjee · arXiv · Jun 8, 2026

  Neural machine translation for digitally low-resource Indigenous languages is often hindered by extreme data scarcity, prompting reliance on extractive web-scraping. To ensure data sovereignty, this study introduces a data synthesis methodo…

• iOSWorld: A Benchmark for Personally Intelligent Phone Agents
  Lawrence Keunho Jang, Mareks Woodside, Geronimo Carom, Andrew Keunwoo Jang et al. · arXiv · Jun 8, 2026

  A useful phone agent needs to be personally intelligent. It should reason over a user's identity, history, and preferences as they exist on the device, not just follow isolated instructions in an impersonal sandbox. Existing mobile agent be…

• Collaborative Human-Agent Protocol (CHAP)
  Arsalan Shahid, Gordon Suttie, Philip Black · arXiv · Jun 8, 2026

  Foundation models are moving from response generation into operational roles. They plan across steps, call tools, request human input, coordinate with other agents, and increasingly carry responsibility for work that affects customers, clai…

• Multi-Turn Evaluation of Deep Research Agents Under Process-Level Feedback
  Rishabh Sabharwal, Hongru Wang, Amos Storkey, Jeff Z. Pan · arXiv · Jun 8, 2026

  Existing benchmarks for deep research agents (DRAs) assess only single-shot outputs, ignoring a key question: can DRAs improve their reports when guided by feedback? To investigate this, we conduct a multi-turn evaluation of DRAs under two …

• The Neutral Mask: How RLHF Provides Shallow Alignment while Leaving Partisan Structure Intact in a Large Language Model
  Wendy K. Tam · arXiv · Jun 8, 2026

  The ambition behind alignment training is to make large language models safe and useful. The primary mechanism, reinforcement learning from human feedback (RLHF), shapes the behavior of deployed language models by aligning them with ``human…

• IS-CoT: Breaking the Long-form Generation Collapse via Interleaved Structural Thinking
  Zechen Sun, Yuyang Sun, Zecheng Tang, Juntao Li et al. · arXiv · Jun 8, 2026

  Generating coherent and controllable long-form content remains a persistent challenge for Large Language Models (LLMs). While reasoning-enhanced models have demonstrated success in logic-intensive domains, our evaluation reveals that they s…

• BrainSurgery: Reproducible and Reliable Declarative Weight Manipulations for Model Editing and Upcycling
  Gianluca Barmina, Annemette Broch Pirchert, Andrea Blasi Núñez, Lukas Galke Poech et al. · arXiv · Jun 8, 2026

  As deep learning models scale, managing, inspecting, and modifying large checkpoints has become increasingly challenging. Researchers often need to alter model weights for layer restructuring, precision casting, low-rank factorization, and …