Meet, Compare, or Abstain: LatWeave for Deterministic Multi-Hop Question Answering on Knowledge Lattices

arXiv:2609.27225 · cs.CL, cs.AI, cs.IR · Submitted 2026-09-23 · Read on arXiv

cs.CL, cs.AI, cs.IR

Submitted: 2026-09-23

Updated: 2026-09-23

Terminology

• RAG — 32× in this paper · explained in 3 episodes
• Abstention — 21× in this paper · explained in 1 episode
• Benchmark — 11× in this paper · explained in 2 episodes
• Hallucination — 11× in this paper · explained in 3 episodes
• Knowledge Graph — 11× in this paper · explained in 7 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Accuracy — 8× in this paper · explained in 1 episode
• Grounding — 7× in this paper · explained in 3 episodes
• Core — 5× in this paper · explained in 2 episodes
• Calibration — 4× in this paper · explained in 7 episodes
• Knowledge graphs — 4× in this paper · explained in 2 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Twelfth International Conference on Learning Representations — 3× in this paper
• Embeddings — 2× in this paper · explained in 2 episodes
• Formal Concept Analysis — 2× in this paper
• Hypergraph — 2× in this paper · explained in 2 episodes
• Provenance — 2× in this paper · explained in 4 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Abstention mechanism — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Continual Learning — 1× in this paper · explained in 2 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Empirical Methods in Natural Language Processing — 1× in this paper
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Graph Construction — 1× in this paper · explained in 3 episodes
• Meeting of the Association for Computational Linguistics — 1× in this paper
• Multi-hop Reasoning — 1× in this paper · explained in 3 episodes
• Retrieval-Augmented Generation (RAG) — 1× in this paper · explained in 16 episodes
• Web Search and Data Mining — 1× in this paper

Sources

• Conformal Risk Control
• AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora
• From Local to Global: A Graph RAG Approach to Query-Focused Summarization
• S-Path-RAG: Semantic-Aware Shortest-Path Retrieval Augmented Generation for Multi-Hop Knowledge Graph Question Answering
• Selective Classification for Deep Neural Networks
• LightRAG: Simple and Fast Retrieval-Augmented Generation
• From RAG to Memory: Non-Parametric Continual Learning for Large Language Models
• Reasoning by Exploration: A Unified Approach to Retrieval and Generation over Graphs
• Open-RAG: Enhanced Retrieval-Augmented Reasoning with Open-Source Large Language Models
• Cooperative Retrieval-Augmented Generation for Question Answering: Mutual Information Exchange and Ranking by Contrasting Layers
• HyperRAG: Reasoning N-ary Facts over Hypergraphs for Retrieval Augmented Generation
• When to Trust: A Causality-Aware Calibration Framework for Accurate Knowledge Graph Retrieval-Augmented Generation
• Efficient Multi-Hop Question Answering over Knowledge Graphs via LLM Planning and Embedding-Guided Search
• Reliable Visual Question Answering: Abstain Rather Than Answer Incorrectly
• CompactRAG: Reducing LLM Calls and Token Overhead in Multi-Hop Question Answering

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