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    <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 errors propagate 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 an average of 4.82 pp on DeepResearch Bench (DRB) Insight (1.83 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.</abstract>
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%0 Conference Proceedings
%T VeriTrace: Evolving Mental Models for Deep Research Agents
%A Zhao, Haolang
%A Long, Yunbo
%A Beckenbauer, Lukas
%A Brintrup, Alexandra
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zhao-etal-2026-veritrace
%X 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 errors propagate 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 an average of 4.82 pp on DeepResearch Bench (DRB) Insight (1.83 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.
%R 10.18653/2026.findings-emnlp.1106
%U https://aclanthology.org/2026.findings-emnlp.1106/
%U https://doi.org/10.18653/2026.findings-emnlp.1106
%P 23388-23426

Markdown (Informal)

[VeriTrace: Evolving Mental Models for Deep Research Agents](https://aclanthology.org/2026.findings-emnlp.1106/) (Zhao et al., Findings 2026)

• VeriTrace: Evolving Mental Models for Deep Research Agents (Zhao et al., Findings 2026)

ACL

• Haolang Zhao, Yunbo Long, Lukas Beckenbauer, and Alexandra Brintrup. 2026. VeriTrace: Evolving Mental Models for Deep Research Agents. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 23388–23426, Budapest, Hungary. Association for Computational Linguistics.