arXiv (All)
open access
Outcome-Anchored Credit Reallocation for Agentic Knowledge Graph Question Answering
Agentic knowledge graph question answering (KGQA) requires large language models (LLMs) to solve complex questions through multi-turn interactions with a knowledge graph (KG). Outcome-supervised reinforcement learning derives verifiable outcome rewards from final answers but uniformly assigns each r
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- WEQA: Wearable hEalth Question Answering with Query-Adaptive Agentic Reasoning#282828
- Repair Before Reinforce: Context-Augmented Knowledge Graph Reasoning for Multi-Hop Question Answering#1000193
- Towards Researcher Agents for Knowledge-Graph Question Answering#441905
- Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning#1037721
- CARA: Causally Guided Retrieval Agent for Question Answering#1357672
Record · ID 1400213
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive