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Graph Topology Meets Energy-Driven Pathfinding: Preserving Contextual Consistency in Retrieval-Augmented Generation

Paper recorded by Signals 4 on 2026-10-21 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-21 on arXiv · recorded by Signals 4 on 2026-10-22

Category: cs.CL · 自然语言处理 · first seen 2026-10-22

Abstract

Retrieval-augmented generation (RAG) frequently integrates knowledge graphs (KGs) for complex multi-hop question answering. However, their performance is increasingly bottlenecked by the knowledge structuring and reasoning processes. (i) Severe semantic loss: Distilling continuous texts into discrete symbols strips away contextual nuances and narrative coherence. (ii) Pronounced modality mismatch:

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#187 most recent of 660 cs.CL papers we have recorded · ↑ newer: ToM2Vec: Continuous Theory-of-Mind Tokens for Multi-Agent Coordination · ↓ older: Your Benchmark Is an Empirical Measure Over Difficulty
Cite this page: Graph Topology Meets Energy-Driven Pathfinding: Preserving Contextual Consistency in Retrieval-Augmented Generation: the #187 most recent of 660 cs.CL papers we have recorded (as of 2026-10-21). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/graph-topology-meets-energy-driven-pathfinding-preserving-contextual-consistency.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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