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neural-symbolic

8 articles · Page 1 of 1
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Aug 6, 2026 · Artificial Intelligence

Training‑Free Beats 14B Model: Sonar‑TS Fills Scale Gap in Time‑Series QA

The paper introduces Sonar‑TS, a training‑free neural‑symbolic system that tackles the newly defined NLQ4TSDB problem—natural‑language queries over database‑scale time‑series—by converting shape intents into searchable symbols and verifying candidates with executable code, achieving up to 3.8× higher scores than the strongest Text‑to‑SQL baseline while highlighting remaining challenges in shape understanding.

LLMSQLSonar-TS
0 likes · 10 min read
Training‑Free Beats 14B Model: Sonar‑TS Fills Scale Gap in Time‑Series QA
Data Party THU
Data Party THU
Jun 22, 2026 · Artificial Intelligence

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
0 likes · 7 min read
From Reasoning to Physical Execution: Peking University Papers Push LLMs Toward Fully Automated Labs
DataFunSummit
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.

NLQ4TSDBSonar-TSText-to-SQL
0 likes · 9 min read
Sonar-TS: A New Text-to-SQL Paradigm for Time‑Series Databases
AI Algorithm Path
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 AgentsLangGraphPython
0 likes · 17 min read
Exploring Different AI Agent Architectures: From Reactive to Cognitive
DataFunTalk
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.

AIKnowledge GraphRNNLogic
0 likes · 10 min read
RNNLogic: Learning Logic Rules for Knowledge Graph Reasoning
DataFunTalk
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.

AIKnowledge GraphMachine learning
0 likes · 20 min read
Neural–Symbolic Learning and Multimodal Knowledge Discovery: Recent Advances, Methods, and Challenges
Qunar Tech Salon
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 researchNLPinterview
0 likes · 16 min read
Interview with Dr. Lv Zhengdong on Neural‑Symbolic Systems and the Future of Natural Language Understanding