# Zongyuan Sun and coauthors: Clearing the FoG: Training KG Agents to Retrieve the Evidence Behind Their Answers [Canonical reading](https://escapemeridian.com/readings/zongyuan-sun-and-coauthors-clearing-the-fog-training-kg-agents-d47607aa) The paper’s authors argue that a correct answer is not necessarily a supported one. They say existing knowledge graph (KG) agents often retrieve answers without retrieving the supporting triples—the recorded facts that make an answer traceable and auditable—because training rewards answers rather than evidence. They propose FoG, which trains agents to retrieve that evidence as they answer. Its evidence-grounded synthesis creates questions with supporting triples; the agent records triples returned by the KG; and an evidence-coverage process reward credits a supporting triple to the turn that first retrieves it. The authors report that, on three KGQA benchmarks, 3B–4B open-source agents trained with FoG outperform substantially larger baselines in answer accuracy and evidence coverage, with zero-shot cross-KG transfer. As an Escape Meridian interpretation, if those results carry beyond the reported benchmarks, the approach could help make KG-based answers easier to audit; the abstract does not establish performance in other settings. How well does FoG’s evidence coverage hold up outside the three reported KGQA benchmarks? Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims. ## Original source arXiv cs.AI · Article Original source date: Oct 27, 2026 (2026-10-27). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-14T05:18:49.275Z. First observed: 2026-10-28T08:16:37.851Z. Retrieved: 2026-10-30T21:46:18.992Z. - [Clearing the FoG: Training KG Agents to Retrieve the Evidence Behind Their Answers](https://arxiv.org/abs/2610.37405) ## Limits The abstract reports benchmark results but does not provide benchmark names, detailed measurements, or evidence about performance outside the three KGQA benchmarks. The source describes the authors’ experimental findings; the abstract alone does not establish how the method performs in practical deployments. ## Open question How well does FoG’s evidence coverage hold up outside the three reported KGQA benchmarks? ## Source-stated The authors distinguish getting a factual answer right from retrieving evidence that supports it, and say existing KG agents often retrieve answers without the supporting triples. > a correct answer is not necessarily a supported one - [Supporting source](https://arxiv.org/abs/2610.37405) ## Source-stated The authors say existing agents’ training data carries answers but not evidence, and their rewards check answers rather than retrieved evidence. > their training rewards check the answer but not the retrieved evidence - [Supporting source](https://arxiv.org/abs/2610.37405) ## Source-stated FoG combines evidence-grounded synthesis, a KG interaction agent, and an evidence-coverage process reward (ECPR). > FoG consists of evidence-grounded synthesis, a KG interaction agent, and an evidence-coverage process reward (ECPR) - [Supporting source](https://arxiv.org/abs/2610.37405) ## Source-stated The authors report that FoG enables 3B–4B open-source agents to outperform substantially larger baselines in answer accuracy and evidence coverage on three KGQA benchmarks, with zero-shot cross-KG transfer. > Experiments on three KGQA benchmarks demonstrate that FoG enables 3B--4B open-source agents to outperform substantially larger baselines in answer accuracy and evidence coverage, with zero-shot cross-KG transfer. - [Supporting source](https://arxiv.org/abs/2610.37405) ## Escape Meridian interpretation If the reported results carry beyond the tested benchmarks, FoG could help make KG-based answers easier to audit. > a knowledge graph (KG) plays two roles: as a source of answers, it supplies facts that the model lacks; as a source of evidence, it supplies the supporting triples that make an answer traceable and auditable. - [Supporting source](https://arxiv.org/abs/2610.37405) ## Source and contributor context Sun, Zongyuan, Zhao, Shuai, Du, Yingjun, Shu, Ke, Zhu, Ziyue, Luo, Haoran · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Benchmark A defined set of tests used to compare systems. Results depend on the tasks and measures selected. - [Definition reference](https://arxiv.org/abs/2211.09110) ## Zero-shot Doing a task without worked examples (zero-shot) or with only a few examples given in the prompt (few-shot). - [Definition reference](https://developers.google.com/machine-learning/glossary#zero-shot-prompting) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: 54ed1792-b76f-44d3-8571-dd3a92050e18. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-14T05:18:49.275Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: 1e4279eb0f308222ea2b0917a732d6602165677c714ff44371dfb342b9554e07. Source text hash: dc54ec05a449cfc2c212debd042316b71a5d874af186b073988332274eb94fcb. ## Revision and source-check history ### Version 1 — 2026-11-14T05:18:49.275Z The paper’s authors argue that a correct answer is not necessarily a supported one. They say existing knowledge graph (KG) agents often retrieve answers without retrieving the supporting triples—the recorded facts that make an answer traceable and auditable—because training rewards answers rather than evidence. They propose FoG, which trains agents to retrieve that evidence as they answer. Its evidence-grounded synthesis creates questions with supporting triples; the agent records triples returned by the KG; and an evidence-coverage process reward credits a supporting triple to the turn that first retrieves it. The authors report that, on three KGQA benchmarks, 3B–4B open-source agents trained with FoG outperform substantially larger baselines in answer accuracy and evidence coverage, with zero-shot cross-KG transfer. As an Escape Meridian interpretation, if those results carry beyond the reported benchmarks, the approach could help make KG-based answers easier to audit; the abstract does not establish performance in other settings. How well does FoG’s evidence coverage hold up outside the three reported KGQA benchmarks? ## Original source arXiv cs.AI · Article Original source date: Oct 27, 2026 (2026-10-27). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-14T05:18:49.275Z. First observed: 2026-10-28T08:16:37.851Z. Retrieved: 2026-10-30T21:46:18.992Z. - [Clearing the FoG: Training KG Agents to Retrieve the Evidence Behind Their Answers](https://arxiv.org/abs/2610.37405) ## Limits The abstract reports benchmark results but does not provide benchmark names, detailed measurements, or evidence about performance outside the three KGQA benchmarks. The source describes the authors’ experimental findings; the abstract alone does not establish how the method performs in practical deployments. ## Open question How well does FoG’s evidence coverage hold up outside the three reported KGQA benchmarks? ## Source-stated The authors distinguish getting a factual answer right from retrieving evidence that supports it, and say existing KG agents often retrieve answers without the supporting triples. > a correct answer is not necessarily a supported one - [Supporting source](https://arxiv.org/abs/2610.37405) ## Source-stated The authors say existing agents’ training data carries answers but not evidence, and their rewards check answers rather than retrieved evidence. > their training rewards check the answer but not the retrieved evidence - [Supporting source](https://arxiv.org/abs/2610.37405) ## Source-stated FoG combines evidence-grounded synthesis, a KG interaction agent, and an evidence-coverage process reward (ECPR). > FoG consists of evidence-grounded synthesis, a KG interaction agent, and an evidence-coverage process reward (ECPR) - [Supporting source](https://arxiv.org/abs/2610.37405) ## Source-stated The authors report that FoG enables 3B–4B open-source agents to outperform substantially larger baselines in answer accuracy and evidence coverage on three KGQA benchmarks, with zero-shot cross-KG transfer. > Experiments on three KGQA benchmarks demonstrate that FoG enables 3B--4B open-source agents to outperform substantially larger baselines in answer accuracy and evidence coverage, with zero-shot cross-KG transfer. - [Supporting source](https://arxiv.org/abs/2610.37405) ## Escape Meridian interpretation If the reported results carry beyond the tested benchmarks, FoG could help make KG-based answers easier to audit. > a knowledge graph (KG) plays two roles: as a source of answers, it supplies facts that the model lacks; as a source of evidence, it supplies the supporting triples that make an answer traceable and auditable. - [Supporting source](https://arxiv.org/abs/2610.37405) ## Source and contributor context Sun, Zongyuan, Zhao, Shuai, Du, Yingjun, Shu, Ke, Zhu, Ziyue, Luo, Haoran · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Benchmark A defined set of tests used to compare systems. Results depend on the tasks and measures selected. - [Definition reference](https://arxiv.org/abs/2211.09110) ## Zero-shot Doing a task without worked examples (zero-shot) or with only a few examples given in the prompt (few-shot). - [Definition reference](https://developers.google.com/machine-learning/glossary#zero-shot-prompting) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: 54ed1792-b76f-44d3-8571-dd3a92050e18. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-14T05:18:49.275Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: 1e4279eb0f308222ea2b0917a732d6602165677c714ff44371dfb342b9554e07. Source text hash: dc54ec05a449cfc2c212debd042316b71a5d874af186b073988332274eb94fcb. ### uncertain — 2026-11-14T05:18:49.275Z — anthropic/claude-opus-5.5 Publication: 54ed1792-b76f-44d3-8571-dd3a92050e18 attribution — pass: The reading attributes the argument and results to the paper's authors, led by Zongyuan Sun. That matches the arXiv author list. Results are framed as the authors' reports, and the auditability inference is labeled as an Escape Meridian interpretation. > Authors:Zongyuan Sun, Shuai Zhao, Yingjun Du, Ke Shu, Ziyue Zhu, Haoran Luo summary — uncertain: The reading faithfully conveys the thesis that correct is not the same as supported, and it explains triples. However, the result paragraph depends on terms a general reader cannot decode: what a knowledge graph is, KGQA, 3B–4B (model size), and zero-shot cross-KG transfer. Small repair: add tooltip glosses for these. Also, the source says agents often answer correctly without the triples, not that they retrieve answers; aligning that wording would be smoother. > so agents often answer correctly without retrieving the supporting triples date — pass: The originalPublished date of 2026-10-27 matches the arXiv v1 submission date. It is not the later feed date of 2026-10-29. > \[Submitted on 27 Oct 2026\] support — pass: The claims about training data and rewards, FoG's three components, and the benchmark outcomes are all stated in the abstract. The auditability inference is explicitly conditional, labeled as interpretation, and tied to the source's own traceable-and-auditable framing. The question and limitations do not overreach the evidence. > their training data carries answers but not evidence, and their training rewards check the answer but not the retrieved evidence media — pass: The selected media is the verified direct arXiv abstract page for this paper. It is the most direct way for a reader to access the source. ## Recorded references Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter. No public references recorded here yet. [Reference data (JSON)](/readings/d47607aa54c35739fd2d2806828630d8a0bdb5cb86a581e376c397bf8853192c/references.json) ## Corrections and challenges [Send a correction or an opposing source](https://escapemeridian.com/corrections/) [JSON record](https://escapemeridian.com/readings/zongyuan-sun-and-coauthors-clearing-the-fog-training-kg-agents-d47607aa.json)