{"slug": "grace-graph-grounded-reflective-agent-copilot-engine-for-expert-in-the-loop", "title": "GRACE: Graph-Grounded Reflective Agent Copilot Engine for Expert-in-the-Loop Knowledge Expansion", "summary": "Researchers introduced GRACE (Graph-grounded Reflective Agent Copilot Engine), a framework that deconstructs LLM responses into atomic claims and grounds them against trusted knowledge priors in a weighted bipartite graph, classifying claims as Grounded, Refuted, or Boundary to identify hallucinations and novel claims. The framework uses a Return on Attention (RoA) objective to defer uncertain claims to expert review, and verified claims expand the knowledge base iteratively. Evaluations across multiple language models and datasets show GRACE outperforms RAG baselines, mitigating hallucination at the system level.", "body_md": "arXiv:2609.04442v1 Announce Type: new \nAbstract: Large language models deployed in high-stakes settings frequently generate plausible but ungrounded claims. Standard retrieval-augmented generation (RAG) pipelines offer limited remedy, since they retrieve isolated passages without tracking cross-document evidence relationships or quantifying uncertainty. We introduce GRACE (Graph-grounded Reflective Agent Copilot Engine), a framework that deconstructs LLM responses into atomic claims and grounds them against trusted knowledge priors within a weighted bipartite graph. Edge weights encode the closeness of each claim to the priors, enabling weighted centrality analysis that classifies claims as Grounded, Refuted, or Boundary. Such classification identifies not just hallucinations but also novel or contested claims at the frontier of the model's knowledge. To efficiently allocate human or agent resources, we formulate a Return on Attention (RoA) objective that defers a claim to expert review only when its priority-weighted uncertainty exceeds the cost of verification. Claims verified by experts are promoted to new evidence anchors, closing a validator-LLM evolutionary loop that expands the knowledge base across iterations. We evaluate GRACE across multiple language models and on datasets spanning both general and domain-specific knowledge. Our results show that our knowledge base serves as a reliable foundation for retrieval that outperforms RAG baselines, and that the RoA framework efficiently selects valuable boundary knowledge for expert verification. These findings demonstrate that graph-structured representations combined with expert-in-the-loop verification can mitigate hallucination at the system level rather than at the generation level. Code available at https://github.com/johnsk95/grace_code", "url": "https://wpnews.pro/news/grace-graph-grounded-reflective-agent-copilot-engine-for-expert-in-the-loop", "canonical_source": "https://arxiv.org/abs/2609.04442", "published_at": "2026-09-07 04:00:00+00:00", "updated_at": "2026-09-07 04:25:36.368455+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-research", "ai-safety"], "entities": ["GRACE", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/grace-graph-grounded-reflective-agent-copilot-engine-for-expert-in-the-loop", "markdown": "https://wpnews.pro/news/grace-graph-grounded-reflective-agent-copilot-engine-for-expert-in-the-loop.md", "text": "https://wpnews.pro/news/grace-graph-grounded-reflective-agent-copilot-engine-for-expert-in-the-loop.txt", "jsonld": "https://wpnews.pro/news/grace-graph-grounded-reflective-agent-copilot-engine-for-expert-in-the-loop.jsonld"}}