Papers › Cognitive Graph for Multi-Hop Reading Comprehension at Scale
Cognitive Graph for Multi-Hop Reading Comprehension at Scale
Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, Jie Tang
We propose a new CogQA framework for multi-hop question answering in web-scale documents. Inspired by the dual process theory in cognitive science, the framework gradually builds a \textit{cognitive graph} in an iterative process by coordinating an implicit extraction module (System 1) and an explicit reasoning module (System 2). While giving accurate answers, our framework further provides explainable reasoning paths. Specifically, our implementation based on BERT and graph neural network efficiently handles millions of documents for multi-hop reasoning questions in the HotpotQA fullwiki dataset, achieving a winning joint F₁ score of 34.9 on the leaderboard, compared to 23.6 of the best competitor.
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Code
Syntology Ran 5 of 9 code samples harvested from 2 repositories linked to this paper; 4 have no recorded run. Of those that ran: 1 ran · honoured contract; 2 ran · violated contract; 2 ran · our draft was wrong.
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Code Syntology ran Syntology
9 samples harvested; 5 ran; 1 honoured the contract we drafted; 4 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Harvested from 2 repositories linked to this paper, official or community; each sample names its own and says which. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Question Answering | HotpotQA | Cognitive Graph QA | ANS-EM | 0.371 | #50 of 72 | Archive leaderboard | report |
| Question Answering | HotpotQA | Cognitive Graph QA | ANS-F1 | 0.489 | #50 of 72 | Archive leaderboard | report |
| Question Answering | HotpotQA | Cognitive Graph QA | JOINT-EM | 0.124 | #50 of 72 | Archive leaderboard | report |
| Question Answering | HotpotQA | Cognitive Graph QA | JOINT-F1 | 0.349 | #50 of 72 | Archive leaderboard | report |
| Question Answering | HotpotQA | Cognitive Graph QA | SUP-EM | 0.228 | #50 of 72 | Archive leaderboard | report |
| Question Answering | HotpotQA | Cognitive Graph QA | SUP-F1 | 0.577 | #50 of 72 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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