VeriTrace: Evolving Mental Models for Deep Research... | AI Research

What the paper is about

Deep research agents face vast, interdependent, and pervasively uncertain information. Existing systems explore what evolving intermediate representations should look like, but leave their evolution to the LLM's implicit reasoning. Without explicit regulation, the intermediate layer is easily contaminated by mixed-quality information and propagates errors along its dependencies, so model scale often ends up substituting for absent regulation. We argue that an agent's mental model should instead evolve through explicit feedback that continuously aligns task understanding with reality, and identify three regulatory loops: interpretive update, deviation feedback, and schema revision. We realise this in VeriTrace, a cognitive-graph framework that explicitly implements the three loops. Using matched Qwen3.5-27B backbones, VeriTrace improves over the strongest matched baseline by 4.22 pp on DeepResearch Bench (DRB) Insight (1.49 pp Overall) and by 5.9 pp Overall win rate on DeepConsult. With Config-DeepSeek, it achieves the strongest reproducible open-source result on DRB.

What it covers

Deep research agents face vast, interdependent, and pervasively uncertain information. Existing systems explore what evolving intermediate representations should look like, but leave their evolution to the LLM's implicit reasoning. Without explicit regulation, the intermediate layer is easily contaminated by mixed-quality information and propagates errors along its dependencies, so model scale often ends up substituting for absent regulation. We argue that an agent's mental model should instead evolve through explicit feedback that continuously aligns task understanding with reality, and identify three regulatory loops: interpretive update, deviation feedback, and schema revision. We realise this in VeriTrace, a cognitive-graph framework that explicitly implements the three loops. Using matched Qwen3.5-27B backbones, VeriTrace improves over the strongest matched baseline by 4.22 pp on DeepResearch Bench (DRB) Insight (1.49 pp Overall) and by 5.9 pp Overall win rate on DeepConsult. With Config-DeepSeek, it achieves the strongest reproducible open-source result on DRB.
--> Computer Science > Artificial Intelligence arXiv:2605.26081 (cs) [Submitted on 25 May 2026] Title: VeriTrace: Evolving Mental Models for Deep Research Agents Authors: Haolang Zhao , Yunbo Long , Lukas Beckenbauer , Alexandra Brintrup View a PDF of the paper titled VeriTrace: Evolving Mental Models for Deep Research Agents, by Haolang Zhao and Yunbo Long and Lukas Beckenbauer and Alexandra Brintrup View PDF Abstract: Deep research agents face vast, interdependent, and pervasively uncertain information. Existing systems explore what evolving intermediate representations should look like, but leave their evolution to the LLM's implicit reasoning. Without explicit regulation, the intermediate layer is easily contaminated by mixed-quality information and propagates errors along its dependencies, so model scale often ends up substituting for absent regulation. We argue that an agent's mental model should instead evolve through explicit feedback that continuously aligns task understanding with reality, and identify three regulatory loops: interpretive update, deviation feedback, and schema revision. We realise this in VeriTrace, a cognitive-graph framework that explicitly implements the three loops. Using matched Qwen3.5-27B backbones, VeriTrace improves over the strongest matched baseline by 4.22 pp on DeepResearch Bench (DRB) Insight (1.49 pp Overall) and by 5.9 pp Overall win rate on DeepConsult. With Config-DeepSeek, it achieves the strongest reproducible open-source result on DRB. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2605.26081 [cs.AI] (or arXiv:2605.26081v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2605.26081 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Haolang Zhao [ view email ] [v1] Mon, 25 May 2026 17:46:57 UTC (4,434 KB) Full-text links: Access Paper: View a PDF of the paper titled VeriTrace: Evolving Mental Models for Deep Research Agents, by Haolang Zhao and Yunbo Long and Lukas Beckenbauer and Alexandra Brintrup View PDF TeX Source view license Current browse context: cs.AI
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