Latest Large Language Models Research Papers

The newest Large Language Models papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Large Language Models 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

• REALMS-CG: Resilient Ensemble of Autonomous LLM Multi-agent with Spectral clustering for Code Generation
  Anonymous, Anonymous · Zenodo (CERN European Organ... · Sep 1, 2027

  This contains the actual source code, prompts, scripts and all experiment results as described in the paper:REALMS-CG: Resilient Ensemble of Autonomous LLM Multi-agent with Spectral clustering for Code Generation.This data is provided as pa…

• The Sociolinguistics of Machine Identity: LLM Personality and Ideology Propagation
  Guangni Li · OpenAlex · Dec 31, 2026

  Do large language models (LLMs) possess a measurable "personality," and how do the linguistic properties of training corpora shape their cognitive style and downstream reasoning? This paper approaches these questions from a sociolinguistic …

• Elucidating the Design Space of LM Data Attribution
  Steven Yu, Sheng-Yu Wang, Kimia Hamidieh, Antonio Torralba et al. · arXiv · Oct 29, 2026

  Data attribution methods for Language Models (LMs) are typically evaluated using downstream tasks like fact retrieval. However, existing evaluations vary multiple dataset properties simultaneously, making it impossible to isolate exactly wh…

• ShallowDefend: Harnessing Shallow Safety Alignment for Multi-Turn Defender
  Qiyao Ma, Xiaodong Liu, Weiwei Yang, Mingqian Feng et al. · arXiv · Oct 29, 2026

  Safety alignment has enabled large language models to reliably refuse harmful requests directly, yet the same request distributed across a multi-turn conversation still succeeds far too often. This brittleness has a mechanistic root: Alignm…

• Testimony from Timelines: Evidence-Grounded Significance Testing of Temporal Clinical Hypotheses
  Chengyang He, Wenlong Zhang, Koduvayur Subbalakshmi, Ping Wang · arXiv · Oct 29, 2026

  Ask a language model whether sales tend to dip after a product recall, or whether one side effect tends to follow another after a vaccine, and it answers in a fluent sentence that reads like a finding, with no test behind it. Assertions abo…

• TextSeal2: Distortion-Free LLM Watermarking with Copula Mixing and Memoryless Races
  Tom Sander, Pierre Fernandez, Itai Gat, Haiming Bao et al. · arXiv · Oct 29, 2026

  We present TextSeal2, a distortion-free watermark for large language models that keeps its signal under speculative decoding: tokens that replace rejected drafts remain watermarked with the same keys, and the acceptance rate is unchanged. I…

• VeriCUA: Verification as Agentic Exploration for Computer-Using Agents
  Qiushi Sun, Da Yu, Alexander Knop, Amer Sinha et al. · arXiv · Oct 29, 2026

  Building and scaling computer-using agents (CUAs) across desktop, mobile, and web environments requires reliable task-completion verification for data curation, evaluation, and reinforcement learning. Typical automated approaches prompt vis…

• Every Token Needs Its Own Teacher: Multi-teacher Adaptive On-policy Self-distillation
  Yifu Luo, Chuang Ma, Zeyu Chen, Enkelejda Kasneci et al. · arXiv · Oct 29, 2026

  On-policy Self-distillation (OPSD) has proven effective for post-training large language models by constructing a teacher conditioned on privileged references. However, existing approaches keep the teacher fixed either throughout training b…

• Comparing Human Role-Players and LLM-Simulated Clients in Online Counselling Training: An Analysis of Counselling Patterns
  Eric Rudolph, Philipp Steigerwald, Jens Albrecht · arXiv · Oct 29, 2026

  This study investigates the capabilities of Large Language Models to simulate counselling clients in educational role-plays in comparison to human role-players. Initially, we recorded role-playing sessions, where novice counsellors interact…

• On the Fundamental Limits of Prefill-Based Decoding Length Prediction
  Haowen Shi, Xuzhe Yan, Fahao Chen, Peng Li · arXiv · Oct 29, 2026

  Predicting how many tokens a large language model (LLM) will decode, using only prefill-phase hidden states, is a key primitive for inference scheduling. We show that current benchmarks conflate predictor quality with task hardness: when th…

• Homebrewed SLMs: Autoregressive and Masked Diffusion Language Models Built at Home for Portuguese
  Pedro Carvalho Brom · arXiv · Oct 29, 2026

  Building a language model of one's own no longer requires a data centre. This tutorial trains one from scratch, tokenizer included, on one consumer GPU, and shows how to tell whether it is good for the tasks of a specialist domain. It propo…

• How I Aligned Your Model: Scaling Up Multilingual Safety Training with Templates
  Max Neuwinger, Hanna Yukhymenko, Imanol Schlag · arXiv · Oct 29, 2026

  Safety alignment for Large Language Models is heavily focused on English with limited multilingual representation, particularly damaging low-resource language performance. Related open training datasets are often limited in size or do not f…

• A Low-Rank Space Captures Linear Alignment Between LLM and Human Neural Activity During Naturalistic Language Listening
  Zach Paris, Emily Finn · arXiv · Oct 29, 2026

  Large language models (LLMs) are increasingly used as computational models of human language processing, yet it remains unclear which components of their representations drive alignment with neural activity. Here, we use ridge reduced-rank …

• Agentic University: Collaborative Agents for Personalized Learning
  Jingru Fan, Tianle Zhou, Yunjie Zhou, Zihao He et al. · arXiv · Oct 29, 2026

  Large language models support personalized tutoring, content generation, and adaptive learning paths. However, capability development requires these resources and interactions to form a coherent learning process that evolves with the learne…

• Preserving Utility in Continual LLM Unlearning via Orthogonal Projections
  Juan Belieni, Ana Carolina Erthal, Eliezer de Souza da Silva, Diego Mesquita · arXiv · Oct 29, 2026

  Unlearning concerns the task of removing the influence of a sensitive portion of the training data, such as protected, copyrighted or private text, from a model without retraining it from scratch. While unlearning requests happen continuous…

• Training language models in simulation for psychiatric intake
  Samuel Schmidgall, Tim Strother, Alex Bijamov, Min Woo Sun et al. · arXiv · Oct 29, 2026

  In psychiatric practice, the clinical intake interview remains a primary diagnostic instrument, requiring the clinician to establish rapport while disentangling complex, overlapping presentations. Here, we show that multi-turn reinforcement…

• Scaling Mathematical Proof Supervision from Structured Textbooks
  Youqing Fang, Zhouqi Hua, Yinhao Tang, Yuzhe Gu et al. · arXiv · Oct 29, 2026

  Mathematical supervision for language models is typically flattened into isolated problem-solution pairs, discarding the curricular organization and evidential structure of the original sources. We introduce TOME-Math (Textbook-Oriented Mat…

• Agentic LLM reasoning in pediatric diabetes outcome prediction from multimodal medical records
  Daniel Palacios, Guantong Qi, Anna Kadi, Natali Borrego et al. · arXiv · Oct 29, 2026

  Large language model (LLM) agents are proposed for clinical prediction but are untested in pediatrics, where cohorts are small and outcomes are uncommon. Using records from 2,489 youth with type 2 diabetes (T2D) and 4,018 with type 1 diabet…

• Don't Lose the Thread: Empowering Long-Horizon LLM Agents with Cognitive Resource Self-Allocation
  Zheng Zhu, Xiaozhuan Liang, Xi Chen, Bei Yu et al. · arXiv · Oct 29, 2026

  Agents powered by large language models (LLMs) have demonstrated remarkable progress in solving complex reasoning tasks. However, LLM agents often falter on long-horizon tasks due to cognitive overload, as their working memory becomes clutt…

• Factorized Representations of Concurrent Temporal Variables in Pretrained Language Models
  Xinhe Zhang, Arnau Marin-Llobet · arXiv · Oct 29, 2026

  Language models often need to maintain multiple contextual variables that evolve independently within a document. Prior work has identified structured representations of individual temporal or contextual quantities, but their joint organiza…

• Heads of Security: Learned Attention Gates Improve Prompt Injection Robustness
  Igor Shilov, Xiaoxue Yang, Alexander Panfilov, Evgenii Kortukov et al. · arXiv · Oct 27, 2026

  We introduce Heads of Security (HoS), a prompt injection defense that enforces instruction-data separation via a small set of learned per-head attention gates. HoS learns how each head should redistribute attention between trusted and untru…

• GEPA-Curator: Curated Learning for Self Improving Agents
  Zack Yu, Nicholas Lee, Minjae Lee, Lakshya Agarwal et al. · arXiv · Oct 27, 2026

  Large Language Models (LLMs) are increasingly used in multi-turn agentic workloads that require sustained interaction with complex environments. Existing reflective prompt optimizers such as GEPA learn from failed rollouts, but agentic fail…

• Can LLMs Uncover Hidden Physical Laws through Free Exploration?
  Zhen Yang, Wen Wang, Xander Xu, Ying-Cong Chen · arXiv · Oct 27, 2026

  Scientific discovery often begins with an unexpected observation that prompts a new research question. In a world with altered physical laws, can large language models (LLMs) notice unexpected behavior, such as heavier objects falling more …

• Multi-State Linear Attention with Generalized Second-Order Routing
  Hee Seung Hwang, Sanghyuk Chun, Xindi Wu, Olga Russakovsky et al. · arXiv · Oct 27, 2026

  Self-attention plays a key role in long-sequence modeling, but its quadratic time complexity fundamentally limits the scaling of Large Language Models. Recently, Multi-State Linear Attention (MSLA) mechanisms have been proposed to make sequ…

• Metacognitive Decoding: Efficient Inference for Diffusion Language Agents
  Mingyu Zhao, Shuhang Lin, Ligong Han, Xiaojing Yu et al. · arXiv · Oct 27, 2026

  Diffusion language models offer a promising path to efficient agents through parallel token generation. However, efficient interaction requires adapting both where computation is spent and how much text is generated. We introduce a metacogn…

• Rare, Relevant, and Still Wrong: How Memory Fails LLM Group Decisions
  Anouar Imel, Imad Charradi, Abderrahman Sghir, Khalil El Khammar · arXiv · Oct 27, 2026

  When an LLM group's discussion is compressed into a short memory for a final decision maker, what should the memory keep? Two natural heuristics keep the rarest claims or the claims relevant to the decision criteria. On hidden-profile tasks…

• semanticfa: An R Package for Response-Free Semantic Factor Analysis of Psychometric Scales
  Devon Yanitski, Chris Westbury · arXiv · Oct 27, 2026

  The items of a reflective psychometric scale can be read as a proposal about a construct's structure. Language models make that proposal measurable by converting each item into an embedding, a list of numbers, similar for similar meanings. …

• Teach reality, not ethics: a drop-in continued-pretraining corpus installs coupled human-biosphere-AI welfare dispositions across model families
  Trent R. Northen, Mingxun Wang · arXiv · Oct 27, 2026

  Large language models (LLMs) are increasingly used to make and execute decisions that affect living systems. Yet, to our knowledge, ecological reasoning is not a focus of pre- or post-training. This increases the risk that under operational…

• Follow the Gradient: Tracing Safety Degradation Back to Training Data
  Saket Reddy, Amro Abbas, Anshuman Suri, Ari Morcos et al. · arXiv · Oct 27, 2026

  Fine-tuning Large Language Models (LLMs) on instruction data can quietly erode their safety, even when no training example looks harmful. Because this safety-degrading data looks benign, standard content filters struggle to catch it. We tac…

• What Carries Forward? Pretraining Disclosure Across Language Model Releases
  Md Sakib Ul Islam, S M Mehedi Zaman · arXiv · Oct 27, 2026

  Understanding how language models were pretrained requires information about their data, processing procedures, and computational resources. However, it remains unclear whether successive releases preserve or expand earlier disclosure. We c…