From Reasoning to Physical Execution: Peking University Papers Push LLMs Toward Fully Automated Labs

The article analyzes how two Peking University papers presented at ICML 2026 and ACL 2026 introduce BioProBench and BioProAgent to benchmark and enable large language models to safely perform complex wet‑lab experiments, achieving high physical compliance and integrating into a multi‑agent AI4S LAB platform.

AI for ScienceBioProAgentBioProBench

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From Reasoning to Physical Execution: Peking University Papers Push LLMs Toward Fully Automated Labs

DataFunSummit

Jun 10, 2026 · Databases

Sonar-TS: A New Text-to-SQL Paradigm for Time‑Series Databases

The paper defines the NLQ4TSDB problem of letting non‑expert users query massive time‑series data with natural language, builds the large‑scale NLQTSBench benchmark, proposes the neural‑symbolic Sonar‑TS framework that searches then verifies, and shows it outperforms existing baselines while highlighting remaining challenges.

NLQ4TSDBNeural-symbolicSonar-TS

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Sonar-TS: A New Text-to-SQL Paradigm for Time‑Series Databases

AI Algorithm Path

Apr 26, 2025 · Artificial Intelligence

Exploring Different AI Agent Architectures: From Reactive to Cognitive

This tutorial explains AI agent architectures, compares reactive, deliberative, hybrid, neural‑symbolic and cognitive designs, shows their trade‑offs, provides Python code examples for each, and links these patterns to LangGraph design templates for building scalable intelligent systems.

AI agentsLangGraphNeural-symbolic

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Exploring Different AI Agent Architectures: From Reactive to Cognitive

DataFunTalk

May 2, 2022 · Artificial Intelligence

RNNLogic: Learning Logic Rules for Knowledge Graph Reasoning

This article reviews recent advances in knowledge graph reasoning, introduces the RNNLogic framework that jointly learns a rule‑generating LSTM and a stochastic logic programming predictor, and demonstrates its competitive performance and interpretability on benchmark datasets while outlining future neural‑symbolic directions.

AINeural-symbolicRNNLogic

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RNNLogic: Learning Logic Rules for Knowledge Graph Reasoning

DataFunTalk

Dec 26, 2021 · Artificial Intelligence

Neural–Symbolic Learning and Multimodal Knowledge Discovery: Recent Advances, Methods, and Challenges

This talk reviews recent progress in neural‑symbolic learning and multimodal knowledge discovery, highlighting examples such as GPT‑3 reasoning failures, the need for symbolic knowledge, historical developments, various integration methods, challenges in multimodal knowledge graphs, and future research directions.

AINeural-symbolicknowledge graph

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Neural–Symbolic Learning and Multimodal Knowledge Discovery: Recent Advances, Methods, and Challenges

DataFunTalk

Sep 16, 2018 · Artificial Intelligence

Interview with Dr. Lv Zhengdong, Founder & CTO of DeepCuriosity on AI and Natural Language Processing

In an interview at the 2018 AI Pioneer Conference, Dr. Lv Zhengdong discusses his background, the development of neural‑symbolic AI at DeepCuriosity, its applications in law, public security and finance, challenges in NLP, and his perspective on the future trajectory of artificial intelligence.

AIDeep LearningInterview

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Interview with Dr. Lv Zhengdong, Founder & CTO of DeepCuriosity on AI and Natural Language Processing

Qunar Tech Salon

Jul 3, 2017 · Artificial Intelligence

Interview with Dr. Lv Zhengdong on Neural‑Symbolic Systems and the Future of Natural Language Understanding

Dr. Lv Zhengdong discusses the challenges of true language understanding, the integration of symbolic reasoning with neural networks, recent advances in neural‑symbolic models, and the practical prospects of NLP in domains such as law and finance, emphasizing the need for hybrid approaches.

AI ResearchInterviewNLP

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Interview with Dr. Lv Zhengdong on Neural‑Symbolic Systems and the Future of Natural Language Understanding