T3Mermaid to ASCII art (mermaid-ascii)T3Kimi K3, and what we can still learn from the pelican benchmarkT3Firefox in WebAssemblyT3Spot birds not golfT3[AINews] Kimi K3 2.8T-A50B: the largest open model ever released; Opus 4.8-class at Sonnet 5 pricingT1From physical surfaces to human-centric heat stress: LST and UTCI heat mapping reveals nonlinear effects of urban morphologyT1DualHNIE: Dual-Channel Hypergraph Learning for Node Importance Estimation in Heterogeneous Knowledge GraphsT1GenTL: A General Transfer Learning Model for Building Thermal DynamicsT1A short review on the maximum clique problem algorithms with classical, AI, and quantum methodsT1Man, Machine, and Masterpiece: Artistic Ownership in the AI EraT1HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective OptimizationT1How Well Does AI-Generated Feedback Work? Intrinsic and Extrinsic Evaluation across more than 20,000 EFL Essay DraftsT3Mermaid to ASCII art (mermaid-ascii)T3Kimi K3, and what we can still learn from the pelican benchmarkT3Firefox in WebAssemblyT3Spot birds not golfT3[AINews] Kimi K3 2.8T-A50B: the largest open model ever released; Opus 4.8-class at Sonnet 5 pricingT1From physical surfaces to human-centric heat stress: LST and UTCI heat mapping reveals nonlinear effects of urban morphologyT1DualHNIE: Dual-Channel Hypergraph Learning for Node Importance Estimation in Heterogeneous Knowledge GraphsT1GenTL: A General Transfer Learning Model for Building Thermal DynamicsT1A short review on the maximum clique problem algorithms with classical, AI, and quantum methodsT1Man, Machine, and Masterpiece: Artistic Ownership in the AI EraT1HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective OptimizationT1How Well Does AI-Generated Feedback Work? Intrinsic and Extrinsic Evaluation across more than 20,000 EFL Essay Drafts

Intelligent Multi-Agent System for Research Automation

Primary research

#353

T1digested

Canonical URL: https://www.semanticscholar.org/paper/6080633c613ba9deada38a4b9e2687d036d07cff
Topic: Multi-Agent Systems
First seen: 2026-07-16 23:33:01
Last seen: 2026-07-16 23:33:01

Source raw items (1)

Semantic Scholar2026-07-16 23:31:51
Intelligent Multi-Agent System for Research Automation
This project proposes a domain-aware multi-agentresearch system that intelligently routes user queries to specialized AI agents across multiple domains such as biomedical, legal, market research, academic research, and technical programming. Traditional large language models (LLMs) often generate hallucinated or unverifiable responses due to lack of domain-specific grounding and validation [3]. To address this limitation, the system introduces an intelligent Query Router that classifies queries using hybrid techniques such as keyword analysis and embedding similarity, assigning confidence scores and selecting the most relevant domain agents [1]. Each domain agent interacts with domain-specific data sources and generates structured, citationbacked responses. A validation layer further verifies outputs to ensure reliability and reduce hallucinations.