大模型推理能力

Enhancing data efficiency in reinforcement learning: a novel imagination mechanism based on mesh information propagation

Zihang Wang, Maowei Jiang

专题命中 逻辑推理 ：reasoning(abstract)；logical reasoning(abstract)；分类 cs.AI、cs.LG

Comments 10 pages. arXiv admin note: text overlap with arXiv:2007.05929 by other authors

Why Do We Need Neuro-symbolic AI to Model Pragmatic Analogies?

Thilini Wijesiriwardene, Amit Sheth, Valerie L. Shalin, Amitava Das

专题命中 逻辑推理 ：reasoning(abstract)；logical reasoning(abstract)；分类 cs.CL、cs.AI

Comments 12 pages 3 figures

Automated scholarly paper review: Concepts, technologies, and challenges

Jialiang Lin, Jiaxin Song, Zhangping Zhou, Yidong Chen, Xiaodong Shi

专题命中 逻辑推理 ：reasoning(abstract)；logical reasoning(abstract)；分类 cs.CL、cs.AI

Comments Please cite the version of Information Fusion

Journal ref Information Fusion, Vol. 98, 101830 (2023)

Emergence of Symbols in Neural Networks for Semantic Understanding and Communication

Yang Chen, Liangxuan Guo, Shan Yu

专题命中 逻辑推理 ：reasoning(abstract)；planning(abstract)；分类 cs.CL、cs.AI

From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought

Lionel Wong, Gabriel Grand, Alexander K. Lew, Noah D. Goodman, Vikash K. Mansinghka, Jacob Andreas, Joshua B. Tenenbaum

专题命中 逻辑推理 ：reasoning(abstract)；planning(abstract)；分类 cs.CL、cs.AI

Language to Rewards for Robotic Skill Synthesis

Wenhao Yu, Nimrod Gileadi, Chuyuan Fu, Sean Kirmani, Kuang-Huei Lee, Montse Gonzalez Arenas, Hao-Tien Lewis Chiang, Tom Erez, Leonard Hasenclever, Jan Humplik, Brian Ichter, Ted Xiao, Peng Xu, Andy Zeng, Tingnan Zhang, Nicolas Heess, Dorsa Sadigh, Jie Tan, Yuval Tassa, Fei Xia

专题命中 逻辑推理 ：reasoning(abstract)；logical reasoning(abstract)；分类 cs.AI、cs.LG

Comments https://language-to-reward.github.io/

Large Language Models as Tax Attorneys: A Case Study in Legal Capabilities Emergence

John J. Nay, David Karamardian, Sarah B. Lawsky, Wenting Tao, Meghana Bhat, Raghav Jain, Aaron Travis Lee, Jonathan H. Choi, Jungo Kasai

专题命中 逻辑推理 ：reasoning(abstract)；logical reasoning(abstract)；分类 cs.CL、cs.AI

Fluid Transformers and Creative Analogies: Exploring Large Language Models' Capacity for Augmenting Cross-Domain Analogical Creativity

Zijian Ding, Arvind Srinivasan, Stephen MacNeil, Joel Chan

专题命中 逻辑推理 ：reasoning(abstract)；logical reasoning(abstract)；分类 cs.CL、cs.AI

Concept-aware Training Improves In-context Learning Ability of Language Models

Michal Štefánik, Marek Kadlčík

专题命中 逻辑推理 ：reasoning(abstract)；logical reasoning(abstract)；分类 cs.CL、cs.AI

Comments Work in progress

Grounding and Distinguishing Conceptual Vocabulary Through Similarity Learning in Embodied Simulations

Sadaf Ghaffari, Nikhil Krishnaswamy

专题命中 逻辑推理 ：reasoning(abstract)；logical reasoning(abstract)；分类 cs.CL、cs.LG

Comments Accepted at IWCS Conference

Knowledge Authoring for Rules and Actions

Yuheng Wang, Paul Fodor, Michael Kifer

专题命中 逻辑推理 ：reasoning(abstract)；logical reasoning(abstract)；分类 cs.CL、cs.AI

Scallop: A Language for Neurosymbolic Programming

Ziyang Li, Jiani Huang, Mayur Naik

专题命中 逻辑推理 ：reasoning(abstract)；logical reasoning(abstract)；分类 cs.AI、cs.LG

Attribution-Scores and Causal Counterfactuals as Explanations in Artificial Intelligence

Leopoldo Bertossi

专题命中 逻辑推理 ：reasoning(abstract)；logical reasoning(abstract)；分类 cs.AI、cs.LG

Comments Submitted as chapter contribution. In this version some additional comments were added, and some wrong equation references corrected

Scientific and Creative Analogies in Pretrained Language Models

Tamara Czinczoll, Helen Yannakoudakis, Pushkar Mishra, Ekaterina Shutova

专题命中 逻辑推理 ：reasoning(abstract)；logical reasoning(abstract)；分类 cs.CL、cs.LG

Comments To be published in Findings of EMNLP 2022

DANLI: Deliberative Agent for Following Natural Language Instructions

Yichi Zhang, Jianing Yang, Jiayi Pan, Shane Storks, Nikhil Devraj, Ziqiao Ma, Keunwoo Peter Yu, Yuwei Bao, Joyce Chai

专题命中 逻辑推理 ：reasoning(abstract)；planning(abstract)；分类 cs.CL、cs.AI

Comments Accepted in EMNLP 2022

Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples

Linlin Liu, Xin Li, Ruidan He, Lidong Bing, Shafiq Joty, Luo Si

专题命中 逻辑推理 ：reasoning(abstract)；logical reasoning(abstract)；分类 cs.CL、cs.AI

Comments Accepted by EMNLP 2022

A Direct Approximation of AIXI Using Logical State Abstractions

Samuel Yang-Zhao, Tianyu Wang, Kee Siong Ng

专题命中 逻辑推理 ：reasoning(abstract)；planning(abstract)；分类 cs.AI、cs.LG

Abstraction for Deep Reinforcement Learning

Murray Shanahan, Melanie Mitchell

专题命中 逻辑推理 ：reasoning(abstract)；logical reasoning(abstract)；分类 cs.AI、cs.LG

Comments To appear in Proceedings IJCAI 2022

ExAIS: Executable AI Semantics

Richard Schumi, Jun Sun

专题命中 逻辑推理 ：reasoning(abstract)；logical reasoning(abstract)；分类 cs.AI、cs.LG

Persistent Anti-Muslim Bias in Large Language Models

Abubakar Abid, Maheen Farooqi, James Zou

专题命中 逻辑推理 ：reasoning(abstract)；logical reasoning(abstract)；分类 cs.CL、cs.LG

Neurosymbolic AI: The 3rd Wave

Artur d'Avila Garcez, Luis C. Lamb

专题命中 逻辑推理 ：reasoning(abstract)；logical reasoning(abstract)；分类 cs.AI、cs.LG

Comments 37 pages

Towards Analogy-Based Explanations in Machine Learning

Eyke Hüllermeier

专题命中 逻辑推理 ：reasoning(abstract)；logical reasoning(abstract)；分类 cs.AI、cs.LG

Comments 11 pages, 4 figures

History-Aware Question Answering in a Blocks World Dialogue System

Benjamin Kane, Georgiy Platonov, Lenhart K. Schubert

专题命中 逻辑推理 ：reasoning(abstract)；planning(abstract)；分类 cs.CL、cs.AI

Comments 16 pages, 4 figures

A Hybrid Knowledge-Grounded Framework for Safety and Traceability in Prescription Verification

一种混合知识引导的框架用于处方验证中的安全性和可追溯性

Yichi Zhu, Kan Ling, Xu Liu, Hengrun Zhang, Huiqun Yu, Guisheng Fan

机构 * School of Information Science and Engineering, East China University of Science and Technology（信息科学与工程学院，东华大学） 纠错

专题命中 逻辑推理 ：reasoning(abstract,comments)；logical reasoning(abstract)；分类 cs.AI

AI总结 本文提出PharmGraph-Auditor框架，通过混合制药知识库和知识引导的验证链，提升处方验证的安全性和可追溯性。

Comments 11 pages, 7 figures.Framework for safe prescription auditing and hybrid knowledge-grounded reasoning

Can LLMs Solve ASP Problems? Insights from a Benchmarking Study (Extended Version)

Lin Ren, Guohui Xiao, Guilin Qi, Yishuai Geng, Haohan Xue

机构 * School of Computer Science and Engineering, Southeast University, Nanjing, China（计算机科学与工程学院，东南大学，南京，中国） ； Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China（新一代人工智能技术及其跨学科应用关键实验室（东南大学），教育部，中国） 纠错

专题命中 逻辑推理 ：reasoning(abstract,comments)；logical reasoning(abstract)；分类 cs.AI

Comments Accepted for publication at the 22nd International Conference on Principles of Knowledge Representation and Reasoning (KR 2025). The code is available at https://github.com/HomuraT/ASPBench

Goal-Oriented Logic-based Semantic Communication for Neuro-Symbolic Reasoning with Applications onto Autonomous Driving

面向目标的基于逻辑的语义通信及用于自动驾驶的神经符号推理

Ahmet Faruk Saz, Duo Xu, Faramarz Fekri

专题命中 逻辑推理 ：reasoning(title)

AI总结 本文针对自动驾驶场景，提出基于一阶逻辑的目标导向语义通信方法，通过选择关键证据传输实现安全决策，在相同通信预算下优于均匀证据选择。

Chart Specification: Structural Representations for Incentivizing VLM Reasoning in Chart-to-Code Generation

图表规范：用于促进VLM在图表到代码生成中推理的结构表示

Minggui He, Mingchen Dai, Jian Zhang, Yilun Liu, Shimin Tao, Pufan Zeng, Osamu Yoshie, Yuya Ieiri

机构 * Waseda University（早稻田大学） ； University of Science and Technology of China（中国科学技术大学） ； NanKai University（南开大学） 纠错

专题命中 逻辑推理 ：reasoning(title)

AI总结 本文提出图表规范，通过结构化中间表示提升VLM在图表到代码生成中的结构保真度，实验显示其在数据效率和性能上优于现有方法。

Comments Accepted by Neurocomputing

EvoPlan: Evolutionary Neuro-Symbolic Robot Planning with Spatio-Temporal Guarantees

EvoPlan：具有时空保证的进化神经符号机器人规划

Bhavya Sai Nukapotula, Samin Moosavi, Haoze Wang, Luke Duncan, Diya Shakkottai, Varun Murali, Srinivas Shakkottai

机构 * Texas A&M University（德克萨斯农工大学） 纠错

专题命中 逻辑推理 ：planning(title)

AI总结 研究针对基于LLM的机器人规划器不足及经典PDDL规划器问题，提出含离线STL约束挖掘、进化PDDL规划器和受约束执行循环的神经符号框架，能保证计划属性，在基准测试中表现良好且可在无云环境下部署。

Neuro-Symbolic Reasoning for Vulnerability Detection

用于漏洞检测的神经符号推理

Yanjie Zhao, Hongjie Chen, Li Lu, Zhou Yang, Xiao Cheng, Haoyu Wang

专题命中 逻辑推理 ：reasoning(title)

AI总结 研究基于大语言模型的漏洞检测不可靠性，提出神经符号框架LeanGuard，通过将代码解释与安全义务判定分离，神经侧过滤事实，符号侧形式验证，证据感知裁决器权衡结果，在五个CWE类上实例化框架。

On the Timed Temporal Logic Planning of Coupled Multi-Agent Systems

关于耦合多智能体系统的时序时序逻辑规划

Alexandros Nikou, Dimitris Boskos, Jana Tumova, Dimos V. Dimarogonas

专题命中 逻辑推理 ：planning(title)

AI总结 本文提出一种自动控制器合成方法，用于满足耦合约束的多智能体系统。通过设计分布式抽象和形式验证技术，计算满足高阶任务的个体运行。