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

Rule2Text: A Framework for Generating and Evaluating Natural Language Explanations of Knowledge Graph Rules

arXiv:2508. 10971v2 Announce Type: replace-cross Abstract: Knowledge graphs (KGs) can be enhanced through rule mining; however, the resulting logical rules are often difficult for humans to interpret due to their inherent complexity and the idiosyncratic labeling conventions of individual KGs.

By Nasim Shirvani-Mahdavi, Chengkai Li

llms fine-tuning

arXiv AI

Jun 24

VeriPilot: An LLM-Powered Verilog Debugging Framework

arXiv:2606. 23759v1 Announce Type: cross Abstract: Verilog debugging remains one of the most time-consuming stages in digital circuit design.

By Yihan Wang, Cheng Liu, Jiazheng Zhang, Lei Zhang, Long Cheng, Xiaowei Li, Huawei Li

llms benchmarks

arXiv Machine Learning

Jun 24

Reinforcement Learning to Disentangle Multiqubit Quantum States from Partial Observations

arXiv:2406. 07884v3 Announce Type: replace-cross Abstract: Using partial knowledge of a quantum state to control multiqubit entanglement is a largely unexplored paradigm in the emerging field of quantum interactive dynamics with the potential to address outstanding challenges in quantum state preparation and compression, quantum control, and quantum complexity.

By Pavel Tashev, Stefan Petrov, Matthew T. Diaz, Friederike Metz, Alaina M. Green, Norbert M. Linke, Marin Bukov

llms agents reinforcement-learning benchmarks

arXiv AI

Jun 24

Societal Alignment Frameworks Can Improve LLM Alignment

arXiv:2503. 00069v2 Announce Type: replace-cross Abstract: Recent progress in large language models (LLMs) has focused on producing responses that meet human expectations and align with shared values - a process coined alignment.

By Karolina Sta\'nczak, Nicholas Meade, Mehar Bhatia, Hattie Zhou, Konstantin B\"ottinger, Jeremy Barnes, Jason Stanley, Jessica Montgomery, Richard Zemel, Nicolas Papernot, Nicolas Chapados, Denis Therien, Timothy P. Lillicrap, Ana Marasovi\'c, Sylvie Delacroix, Gillian K. Hadfield, Siva Reddy

llms safety

arXiv AI

Jun 24

VoltanaLLM: Energy-Efficient and SLO-Aware Disaggregated LLM Serving via Adaptive Frequency Control and State-Space Routing

arXiv:2509. 04827v3 Announce Type: replace-cross Abstract: The energy cost of Large Language Model (LLM) inference is rapidly becoming a barrier to sustainable and scalable deployment.

By Jiahuan Yu, Aryan Taneja, Junfeng Lin, Minjia Zhang

llms

arXiv AI

Jun 24

HOLMES: Evaluating Higher-Order Logical Reasoning in LLMs

arXiv:2606. 23238v2 Announce Type: replace Abstract: Logical reasoning is essential for reliable AI, yet existing benchmarks are largely first-order-logic-centric, focusing on object-level deduction over fixed predicates.

By Yucheng Wu, Jundong Xu, Mingzhen Ju, Yue Yu, Chenpeng Wang, Haoxuan Li, Liangming Pan

llms benchmarks

arXiv AI

Jun 24

VeryTrace: Verifying Reasoning Traces through Compilable Formalism and Structured Verification

arXiv:2606. 24124v1 Announce Type: new Abstract: Multi-step reasoning with Chain-of-Thought (CoT) prompting remains fragile: logical errors or hallucinations in early steps silently propagate, producing confident but incorrect conclusions.

By Ninghan Zhong, Ahmet Ege Tanriverdi, Kaan Kale, Sriram Vishwanath

llms robotics benchmarks safety

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

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

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

Red-Teaming the Agentic Red-Team

arXiv:2606. 24496v1 Announce Type: cross Abstract: The use of agentic systems to perform offensive security operations has moved from a theoretical possibility to a commoditized capability.

By Dario Pasquini, Michal Bazyli, Taras Fedynyshyn, Artem Sorokin

llms agents robotics safety

arXiv AI

Jun 24

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment

arXiv:2509. 14001v5 Announce Type: replace-cross Abstract: Personalized object detection aims to adapt a general-purpose detector to recognize user-specific instances from only a few examples.

By Elena Camuffo, Francesco Barbato, Mete Ozay, Simone Milani, Umberto Michieli

llms rag computer-vision efficiency multimodal benchmarks safety

arXiv AI

Jun 24

Quantifying Prior Dominance in RAG Systems

arXiv:2606. 23695v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) grounds Large Language Models in external knowledge, yet current evaluations rely on discrete heuristics that suffer from ''epistemic blindness'' - failing to distinguish genuine contextual information extraction from parametric memory recall.

By Barak Or

llms rag safety

arXiv Machine Learning

Jun 24

FuseSampleAgg: One-Pass Neighborhood Estimation for Budgeted Knowledge-Graph Refresh and Validation

arXiv:2511. 13645v2 Announce Type: replace Abstract: Operational knowledge-graph (KG) pipelines in networking and cybersecurity increasingly need to refresh embeddings under strict time, memory, and audit budgets, especially as curated feeds and LLM-assisted extraction accelerate KG updates.

By Aleksandar Stankovi\'c, Haoran Du, Xinming Wang

llms rag benchmarks

arXiv Machine Learning

Jun 24

Debate2Create: Robot Co-design via Multi-Agent LLM Debate

arXiv:2510. 25850v3 Announce Type: replace-cross Abstract: We introduce Debate2Create (D2C), a multi-agent LLM framework that formulates robot co-design as structured, iterative debate grounded in physics-based evaluation.

By Kevin Qiu, Marek Cygan

llms agents reinforcement-learning robotics benchmarks

arXiv AI

Jun 24

Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data

arXiv:2505. 24622v3 Announce Type: replace Abstract: Many high-stakes screening tasks require predicting rare outcomes from unstructured text, where errors are costly and decisions must be auditable.

By Ben Griffin, Aaron Ontoyin Yin, Diego Vidaurre, Ugur Koyluoglu, Joseph Ternasky, Fuat Alican, Yigit Ihlamur

llms benchmarks

arXiv AI

Jun 24

DeepBD: A Grounded Agentic Workflow for Variant Prioritization and Diagnosis of Genetic Birth Defects

arXiv:2606. 24779v1 Announce Type: cross Abstract: Birth defects are a major cause of fetal loss, neonatal morbidity and long-term disability.

By Shiyu Li, Ziqi Yan, Zhihao Wu, Jielong Lu, Weiran Liao, Jiajun Yu, Genjie Li, Zeyu Chu, Jiajun Bu, Haishuai Wang

llms rag agents benchmarks

arXiv Machine Learning

Jun 24

Systematic Exploration of 4-Expert Heterogeneous Mixture-of-Experts via Automated Pipeline Search

arXiv:2606. 23739v1 Announce Type: new Abstract: We present an automated large-scale search pipeline for heterogeneous 4-Expert Mixture-of-Experts (MoE4) architectures within the LEMUR neural network dataset ecosystem.

By Yashkumar R Lukhi, Harsh Rameshbhai Moradiya, Radu Timofte, Dmitry Ignatov

llms safety

arXiv Machine Learning

Jun 24

Holistic Data Scheduler for LLM Pre-training via Multi-Objective Reinforcement Learning

arXiv:2606. 24133v1 Announce Type: new Abstract: The composition of training data, governed by the diversity of sources and their mixing strategy, is a cornerstone of Large Language Model (LLM) pre-training.

By Chenhao Dang, Jing Ma, Mingjie Liao

llms reinforcement-learning benchmarks

arXiv AI

Jun 24

Open-source LLMs administer maximum electric shocks in a Milgram-like obedience experiment

arXiv:2605. 21401v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents that make sequences of decisions over extended interactions in high-stakes domains.

By Roland Pihlakas (for the Three Laws collaboration), Jan Llenzl Dagohoy (for the Three Laws collaboration)

llms agents safety

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