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 15

Where's the Plan? Locating Latent Planning in Language Models with Lightweight Mechanistic Interventions

arXiv:2605. 07984v2 Announce Type: replace-cross Abstract: We study planning site formation in language models -- where internal representations of structurally-constrained future tokens form during the forward pass, and whether they causally drive generation.

By Nicole Ma, Nick Rui

llms

arXiv AI

Jun 15

Succeeding at Scale: Enterprise Retrieval Benchmark Construction and Index-Preserving Query Adaptation for Multi-Tenant Search

arXiv:2601. 04646v4 Announce Type: replace-cross Abstract: Large-scale multi-tenant retrieval systems generate extensive query logs but lack curated relevance labels for effective domain adaptation, resulting in substantial underutilized "dark data.

By Prateek Jain, Shabari S Nair, Ritesh Goru, Prakhar Agarwal, Ajay Yadav, Yoga Sri Varshan Varadharajan, Constantine Caramanis

llms fine-tuning benchmarks

arXiv Machine Learning

Jun 15

Zero-shot generalization of transformer neural operators to larger domains

arXiv:2606. 14597v1 Announce Type: new Abstract: Transformer-based neural operators have shown remarkable performance for approximating solution operators of partial differential equations on complex geometries.

By Armand de Villeroch\'e, Sibo Cheng, Vincent Le Guen, Marc Bocquet, Rem-Sophia Mouradi, Patrick Armand, Alban Farchi, Patrick Massin

llms rag benchmarks safety

arXiv Machine Learning

Jun 15

Code Correctness Signals in LLM Hidden States: Pre-Generation Probing and Repair Geometry

arXiv:2606. 14530v1 Announce Type: new Abstract: Large language models encode rich information in their hidden states.

By Carlo Di Cicco

llms

arXiv Machine Learning

Jun 15

Graph-based Target Back-Propagation for Context Adaptation in Multi-LLM Agentic Systems

arXiv:2606. 14155v1 Announce Type: new Abstract: Context adaptation automates prompt engineering in LLM-based systems by iteratively revising tunable prompts from task feedback, without modifying model weights.

By Tan Zhu, Tong Yao, Kananart Kuwaranancharoen, Amit Singh, Yushang Lai, Deepa Mohan, Shankara Bhargava

llms agents benchmarks

arXiv AI

Jun 15

Poker Arena: Multi-Axis Profiling of Strategic Reasoning and Memory in LLMs

arXiv:2606. 13815v1 Announce Type: new Abstract: Strategic reasoning under uncertainty underpins consequential decisions in negotiation, finance, and policy, but prevailing game-play benchmarks collapse heterogeneous reasoning dimensions into a single scalar, leaving the capability structure of frontier LLMs unexamined.

By Pratham Singla, Shivank Garg, Vihan Singh

llms benchmarks

arXiv AI

Jun 15

Sentinel: Decoding Context Utilization via Attention Probing for Efficient LLM Context Compression

arXiv:2505. 23277v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) often suffers from long and noisy retrieved contexts.

By Yong Zhang, Heng Li, Yanwen Huang, Ning Cheng, Yang Guo, Yun Zhu, Yanmeng Wang, Shaojun Wang, Jing Xiao

llms rag

arXiv Machine Learning

Jun 15

Persona-Pruner: Sculpting Lightweight Models for Role-Playing

arXiv:2606. 14695v1 Announce Type: new Abstract: Language Models (LMs) have shown remarkable potential as role-playing chatbots, delivering consistent, stylized interactions when given a specification of a character or user persona.

By Jinsu Kim, Jihoon Tack, Noah Lee, Jongheon Jeong

llms efficiency benchmarks

arXiv AI

Jun 15

AgentCyberRange: Benchmarking Frontier AI Systems in Realistic Cyber Ranges

arXiv:2606. 14295v1 Announce Type: cross Abstract: Frontier AI systems are increasingly capable of cybersecurity tasks, including codebase inspection, vulnerability detection, and exploitation.

By Fengyu Liu, Jiarun Dai, Yihe Fan, Wuyuao Mai, Ziao Li, Bofei Chen, Jie Zhang, Zheng Lou, Bocheng Xiang, Qiyi Zhang, Xudong Pan, Geng Hong, Yuan Zhang, Min Yang

llms agents benchmarks

arXiv AI

Jun 15

STaR-DRO: Stateful Tsallis Reweighting for Group-Robust Structured Prediction

arXiv:2604. 09737v2 Announce Type: replace-cross Abstract: Structured prediction with large language models requires outputs that are label-accurate, ontology-constrained, structurally valid, and evidence-grounded under label imbalance and heterogeneous group difficulty.

By Samah Fodeh, Ganesh Puthiaraju, Elyas Irankhah, Afshan Khan, Sreeraj Ramachandran, Linhai Ma, Srivani Talakokkul, Sarah Schellhorn

llms fine-tuning safety

arXiv Machine Learning

Jun 15

Detecting Lookahead Bias in LLM Forecasts

arXiv:2512. 23847v2 Announce Type: replace-cross Abstract: We develop a statistical procedure to detect lookahead bias in economic forecasts generated by large language models (LLMs).

By Zhenyu Gao, Wenxi Jiang, Yutong Yan

llms safety

arXiv AI

Jun 15

A Two-Stage Statistical Framework for Evaluating Associative Interference in Large Language Models

arXiv:2606. 14117v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly evaluated for bias using adaptations of human psychological paradigms, yet methodological limitations-particularly the conflation of refusal behavior with task performance-have hindered clear interpretation.

By Achraf Cohen, Andrew Kincaid

llms safety

arXiv Machine Learning

Jun 15

NeST: Neuron Selective Tuning for LLM Safety

arXiv:2602. 16835v2 Announce Type: replace-cross Abstract: Safety alignment is essential for the responsible deployment of Large Language Models (LLMs).

By Sasha Behrouzi, Lichao Wu, Mohamadreza Rostami, Ahmad-Reza Sadeghi

llms fine-tuning multimodal safety

arXiv AI

Jun 15

3D-RFT: Reinforcement Fine-Tuning for Video-based 3D Scene Understanding

arXiv:2603. 04976v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards ( RLVR ) has emerged as a transformative paradigm for enhancing the reasoning capabilities of Large Language Models ( LLMs), yet its potential in 3D scene understanding remains under-explored.

By Xiongkun Linghu, Jiangyong Huang, Baoxiong Jia, Siyuan Huang

llms reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Jun 15

SANA: What Matters for QA Agents over Massive Data Lakes?

arXiv:2606. 13904v1 Announce Type: cross Abstract: Exploratory question answering (EQA) over data lakes requires an LLM agent to discover relevant sources, analyze retrieved data, and adapt its actions based on intermediate results.

By Austin Senna Wijaya, Jiaxiang Liu, Haonan Wang, Eugene Wu

llms agents nlp benchmarks

arXiv AI

Jun 15

Discovery under Hypothesis Redundancy: A Geometric Theory of Discovery Bottlenecks

arXiv:2606. 14386v1 Announce Type: cross Abstract: Scientific discovery saturates when new hypotheses cease to provide independent information, even if the nominal hypothesis space remains large.

By Li Xia, Baoxun Wang

llms benchmarks safety

arXiv AI

Jun 15

VikingMem: A Memory Base Management System for Stateful LLM-based Applications

arXiv:2605. 29640v3 Announce Type: replace Abstract: Large Language Models have revolutionized interactive applications; however, their finite context windows pose a critical data management challenge for maintaining stateful, long-term interactions.

By Jiajie Fu, Junwen Chen, Mengzhao Wang, Aoxiang He, Maojia Sheng, Xiangyu Ke, Yifan Zhu, Yunjun Gao

llms agents benchmarks

arXiv AI

Jun 15

SuperThoughts: Reasoning Tokens in Superposition

arXiv:2606. 13862v1 Announce Type: cross Abstract: Long Chain-of-Thought (CoT) reasoning improves LLM problem-solving but is computationally expensive due to sequential token generation.

By Zheyang Xiong, Shivam Garg, Max Yu, Vaishnavi Shrivastava, Haoyu Zhao, Anastasios Kyrillidis, Dimitris Papailiopoulos

llms efficiency

arXiv Machine Learning

Jun 15

From Uncertain Judgments to Calibrated Rankings: Conformal Elo Estimation for LLM Evaluation

arXiv:2606. 13221v2 Announce Type: replace Abstract: Evaluating new large language models typically requires costly human annotation campaigns at scale.

By Bora Kargi, David Salinas

llms safety

arXiv Machine Learning

Jun 15

IntSeqBERT: Learning Arithmetic Structure in OEIS via Modulo-Spectrum Embeddings

arXiv:2603. 05556v2 Announce Type: replace Abstract: Integer sequences in the OEIS span values from single-digit constants to astronomical factorials and exponentials, making prediction challenging for standard tokenised models that cannot handle out-of-vocabulary values or exploit periodic arithmetic structure.

By Kazuhisa Nakasho

llms rag benchmarks

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