DBCooker automates synthesis of database native functions via LLM-guided characterization, coding plans, hybrid filling, and progressive validation, delivering 34.55% higher accuracy than baselines on SQLite, PostgreSQL, and DuckDB while generating functions absent from SQLite 3.50.
Title resolution pending
5 Pith papers cite this work. Polarity classification is still indexing.
citation-role summary
citation-polarity summary
years
2026 5roles
baseline 1polarities
baseline 1representative citing papers
Formalizes design space for human-LLM collaborative planning along mode, scope, and level axes; evaluates AMBIPOM prototype via user study and benchmark revealing hybrid workflows and trade-offs.
ECHO reframes multimedia event extraction as multi-agent iterative refinement over an explicit Multimedia Event Hypergraph with a decoupled Link-then-Bind strategy, delivering 7.3 and 15.5 F1 gains on event mention and argument role.
MAGIQ introduces a post-quantum governance system for multi-agent AI that supports policy budgets, session enforcement, message attribution, and UC-based security proofs while comparing overhead to SAGA.
A multi-agent framework decomposes multimodal empathetic response generation into structured reasoning steps and uses global reflection to reduce emotional biases, outperforming prior methods on IEMOCAP and MELD benchmarks.
citing papers explorer
-
Automating Database-Native Function Code Synthesis with LLMs
DBCooker automates synthesis of database native functions via LLM-guided characterization, coding plans, hybrid filling, and progressive validation, delivering 34.55% higher accuracy than baselines on SQLite, PostgreSQL, and DuckDB while generating functions absent from SQLite 3.50.
-
How to Steer Your Multi-Agent System: Human-LLM Collaborative Planning
Formalizes design space for human-LLM collaborative planning along mode, scope, and level axes; evaluates AMBIPOM prototype via user study and benchmark revealing hybrid workflows and trade-offs.
-
ECHO: Event-Centric Hypergraph Operations via Multi-Agent Collaboration for Multimedia Event Extraction
ECHO reframes multimedia event extraction as multi-agent iterative refinement over an explicit Multimedia Event Hypergraph with a decoupled Link-then-Bind strategy, delivering 7.3 and 15.5 F1 gains on event mention and argument role.
-
MAGIQ: A Post-Quantum Multi-Agentic AI Governance System with Provable Security
MAGIQ introduces a post-quantum governance system for multi-agent AI that supports policy budgets, session enforcement, message attribution, and UC-based security proofs while comparing overhead to SAGA.
-
A Multi-Agent Framework with Structured Reasoning and Reflective Refinement for Multimodal Empathetic Response Generation
A multi-agent framework decomposes multimodal empathetic response generation into structured reasoning steps and uses global reflection to reduce emotional biases, outperforming prior methods on IEMOCAP and MELD benchmarks.