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This research introduces CogWM, an LLM-based cognitive model designed to track users' beliefs, desires, intentions, and emotions during multi-turn interactions beyond simple task completion metrics. By training on 150K samples using Qwen3-14B, the system enables progressive evaluation of conversational agents through explicit cognitive trajectory analysis rather than relying solely on surface-level response fidelity. The findings reveal distinct patterns in how different agent strategies shape user internal states over time, offering a new framework for diagnosing interaction failures and optimizing dialogue systems.
Cognitive World Model for Progressive BDI/E Trajectory Evaluation of Conversational Agents
arXiv:2606.29495v2 Announce Type: replace Abstract: As LLM-based conversational agents advance toward increasingly open-ended and interaction-intensive scenarios, task completion alone provides an incomplete assessment of their effectiveness. The evolution of users' internal states, including beliefs, desires, intentions, and emotions (BDI/E), serves as an intermediate signal connecting agent behaviors with interaction outcomes and reflects how conversational strategies shape users during multi-turn interactions. However, existing evaluation paradigms primarily focus on surface-level responses or final outcomes, providing limited insight into the underlying cognitive processes. This limitation makes it difficult to diagnose why agents succeed or fail and to optimize their interaction strategies. To address this challenge, we propose Cognitive World Model (CogWM), an LLM-based cognitive user model that jointly models users' BDI/E states and corresponding responses, enabling explicit cognitive trajectory tracking. Trained on 150K user-turn samples with Qwen3-14B, CogWM achieves superior performance over existing user simulation baselines in both response fidelity and cognitive state understanding. Interactions with six state-of-the-art LLMs demonstrate that CogWM enables progressive comparison of agents through cognitive trajectories, revealing distinct agent patterns and complementary relationships between cognitive evolution and behavioral outcomes.
Abstract:As LLM-based conversational agents advance toward increasingly open-ended and interaction-intensive scenarios, task completion alone provides an incomplete assessment of their effectiveness. The evolution of users' internal states, including beliefs, desires, intentions, and emotions (BDI/E), serves as an intermediate signal connecting agent behaviors with interaction outcomes and reflects how conversational strategies shape users during multi-turn interactions. However, existing evaluation paradigms primarily focus on surface-level responses or final outcomes, providing limited insight into the underlying cognitive processes. This limitation makes it difficult to diagnose why agents succeed or fail and to optimize their interaction strategies. To address this challenge, we propose Cognitive World Model (CogWM), an LLM-based cognitive user model that jointly models users' BDI/E states and corresponding responses, enabling explicit cognitive trajectory tracking. Trained on 150K user-turn samples with Qwen3-14B, CogWM achieves superior performance over existing user simulation baselines in both response fidelity and cognitive state understanding. Interactions with six state-of-the-art LLMs demonstrate that CogWM enables progressive comparison of agents through cognitive trajectories, revealing distinct agent patterns and complementary relationships between cognitive evolution and behavioral outcomes.
Artificial Intelligence (cs.AI)
I.2.7; I.2.11; H.5.2
arXiv:2606.29495 [cs.AI]
(or arXiv:2606.29495v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.29495 Focus to learn more arXiv-issued DOI via DataCite
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