SEAL: Self-Evolving Agentic Learning for Conversational Question Answering over Knowledge Graphs
Source
- Person key:
ysymyth - Source kind:
paper - Canonical URL: https://arxiv.org/abs/2512.04868
- License:
NOASSERTION - Public handling:
public-metadata-summary-hash-link-only - Semantic hash:
066152558d7b1bd3e5d8559990dc854c5860f98fd7734d43576fcea44c9e5dcb - First seen: 2026-05-16
- Last changed: 2026-05-16
- Identity guard: Do not confuse with yao-shunyu-alfred, the physics-to-AI researcher at alfredyao.github.io.
Classification
- Category: Software engineering agents and benchmarks
- Topic hub: shunyu-yao-public-corpora
- Project taxonomy: shunyu-yao-project-taxonomy
- Paper map: shunyu-yao-paper-map
Summary
Knowledge-based conversational question answering (KBCQA) confronts persistent challenges in resolving coreference, modeling contextual dependencies, and executing complex logical reasoning. Existing approaches, whether end-to-end semantic parsing or stepwise agent-based reasoning, often suffer from structural inaccuracies and prohibitive computational costs, particularly when processing intricate queries over large knowledge graphs. To address these limitations, we introduce SEAL, a novel two-stage semantic parsin…
What This Teaches
How coding agents are benchmarked, scaffolded, and constrained against real repositories and issues.
Related
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