Papers with graph traversal
Hindsight: Structured Agent Memory that Retains, Recalls, and Reflects (2026.acl-demo)
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Christopher Latimer, Nicolò Boschi, Andrew Neeser, Chris Bartholomew, Gaurav Srivastava, Xuan Wang, Naren Ramakrishnan
| Challenge: | Hindsight organizes long-term memory into four logical networks and exposes three core operations. |
| Approach: | Hindsight organizes long-term memory into four logical networks and exposes three core operations. |
| Outcome: | Hindsight is a working memory system for AI agents that separates facts from beliefs . the system outperforms existing models on LongMemEval and LoCoMo with 83.6% accuracy . |
Graph Meets LLM: A Novel Approach to Collaborative Filtering for Robust Conversational Understanding (2023.emnlp-industry)
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| Challenge: | Defective queries impact the robustness of conversational AI systems such as Alexa, Siri or Google Assistant. |
| Approach: | They propose a Personalized Query Rewriting system that takes into account individual preferences or unique error patterns identified from a user's historical interactions with the conversational AI. |
| Outcome: | The proposed approach has been proven on a large-scale real-world dataset and online A/B experiments. |
TOBUGraph: Knowledge Graph-Based Retrieval for Enhanced LLM Performance Beyond RAG (2025.emnlp-industry)
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Savini Kashmira, Jayanaka L. Dantanarayana, Joshua Brodsky, Ashish Mahendra, Yiping Kang, Krisztian Flautner, Lingjia Tang, Jason Mars
| Challenge: | Retrieval-Augmented Generation (RAG) relies on query-chunk text-to-text similarity in the embedding space for retrieval, can fail to capture deeper semantic relationships across chunks, is highly sensitive to chunking strategies, and is prone to hallucinations. |
| Approach: | They propose a graph-based retrieval framework that first constructs the knowledge graph from unstructured data dynamically and automatically. |
| Outcome: | The proposed framework outperforms multiple RAG implementations in both precision and recall, significantly enhancing user experience through improved retrieval accuracy. |
Conversational Graph Grounded Policy Learning for Open-Domain Conversation Generation (2020.acl-main)
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| Challenge: | Existing word-level policy models that learn dialog policy and language generation from dialog corpora often lead to degeneration issues where the utterances become ungrammatical or repetitive. |
| Approach: | They propose to represent prior dialog transitions as a graph and learn a CG grounded dialog policy that can foster a more coherent and controllable dialog. |
| Outcome: | The proposed framework is able to learn dialog policy in open-domain multi-turn conversation. |
LEDGER: Scaling Agentic Document Editing with Dependency-aware Graph Retrieval (2026.findings-acl)
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| Challenge: | Document editing requires full-context awareness of dependencies, but processing entire documents for each edit incurs prohibitive token costs and latency. |
| Approach: | a framework that constructs lightweight dependency graphs captures semantic relationships and structural hierarchies across document elements is proposed for agentic document editing . a scaLing agentic agentic framework is based on a dependency graph framework that captures dependencies and refactors function dependencies. |
| Outcome: | a new framework achieves 76 consistency versus 56 baseline while reducing token usage by 85 . the framework is based on a framework that captures semantic relationships and structural hierarchies across document elements . it can be used to improve document consistency, but it also reduces token costs and latency . |
MoLoRAG: Bootstrapping Document Understanding via Multi-modal Logic-aware Retrieval (2025.emnlp-main)
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| Challenge: | Document Understanding is a foundational AI capability with broad applications . Large Vision-Language Models (LLMs) can't handle multi-page document comprehension . a logic-aware retrieval framework for multi-modal, multi- page document understanding is proposed . |
| Approach: | They propose a logic-aware retrieval framework for multi-modal, multi-page document understanding . MoLoRAG uses semantic and logical relevance to deliver more accurate retrieval . |
| Outcome: | The proposed framework improves on four DocQA datasets and demonstrates 9.68% accuracy improvement over existing methods. |
CoRAG: Enhancing Hybrid Retrieval-Augmented Generation through a Cooperative Retriever Architecture (2025.findings-emnlp)
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| Challenge: | Existing methods only retrieve related documents from local neighbors or subgraphs in the knowledge base, which often miss relevant information located further away from a global view. |
| Approach: | Hybrid-RAG combines textual documents and graph-structured relational information for RAG . existing methods only retrieve related documents from local neighbors or subgraphs in the knowledge base . |
| Outcome: | Hybrid-RAG combines textual documents and graph-structured relational information . existing methods only retrieve related documents from local neighbors or subgraphs in the knowledge base . |
Interactive Semantic Parsing with Reinforcement Learning for Knowledge Graph Reasoning (2026.findings-acl)
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| Challenge: | Existing approaches to improve LLM reliability rely on factual hallucinations . Existing methods rely only on graph traversal, resulting in imprecise retrieval and heavy post-processing burdens. |
| Approach: | They propose a framework that integrates knowledge Graphs as structured, high-fidelity buffers to enhance LLM reliability. |
| Outcome: | The proposed framework allows logical constraints to be dynamically interleaved with graph search while optimizing via reinforcement learning with only final answer feedback eliminates the need for gold program annotations. |
BYOKG-RAG: Multi-Strategy Graph Retrieval for Knowledge Graph Question Answering (2025.emnlp-main)
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Costas Mavromatis, Soji Adeshina, Vassilis N. Ioannidis, Zhen Han, Qi Zhu, Ian Robinson, Bryan Thompson, Huzefa Rangwala, George Karypis
| Challenge: | Existing approaches to knowledge graph question answering (KGQA) rely on Large Language Model (LLM) agents for graph traversal and retrieval. |
| Approach: | They propose a framework that synergizes Large Language Models with specialized graph retrieval tools to enhance KGQA. |
| Outcome: | The proposed framework outperforms the second-best graph retrieval method by 4.5% points while showing better generalization to custom KGs. |