Figure 2: The overall architecture of the intentional agent.For engineering implementation purposes, the intentional agent maintains an Inner Thought Flow (ITF) TtT\_{t} as part of its structured internal state. We use ITF as an engineering representation of the intermediate state used for subsequent generation; we do not treat it as evidence that the agent has human consciousness or cognition. ITF is mainly prompt-driven at inference, and the base LLM is not fine-tuned to learn it. During silence, updates are initiated by a timed heartbeat rather than new external events. The thought generator updates from retained information without a new external event; the prompt keeps expectation separate from observation, prohibits unsupported external events, and requires prediction mismatches to be addressed in the subsequent thought. At each update, the retained state is combined with current perceptual and prediction-error information; the thought generator produces the next thought, after which the action generator produces a possible action state. This mechanism is somewhat similar to the framework by Liu et al. [Liu et al. (2025)](https://arxiv.org/html/#bib.bib7). However, their approach still relies on the passive triggering of inner thoughts by external events as a starting point to address the optimisation of dialogue flows. In contrast, the intentional agent focuses on how the agent initiates intentional actions towards the external world. Under our internal dynamics framework, this implies how the agent acts upon the environment even when it is situated alone. Here, TtT\_{t}, emotion vector EtE\_{t}, action vector AtA\_{t}, and activated memory content MtM\_{t} together constitute the retained internal state St←{Tt,Et,At,Mt}S\_{t}\\leftarrow\\{T\_{t},E\_{t},A\_{t},M\_{t}\\}. External perception NtN\_{t} is supplied separately to the current update and can be empty. Conventional temporal models usually encode interaction history in a learnt hidden vector whose individual dimensions have no predefined meanings. In our framework, information used for subsequent updates is organised into explicit, named components rather than compressed into a single opaque hidden representation. These components can therefore be inspected and changed separately. At each update, the base LLM used as the thought generator receives the current state to generate the next thought, after which the action generator produces the next action state. The updated thought and action then enter the affective and facial generation process. Because TtT\_{t} largely contains working memory, the memory module primarily stores long-term memory. During the generation of Tt+1T\_{t+1}, TtT\_{t} and the possible NtN\_{t} are inputted into the memory module to calculate cosine similarity for extracting the most similar memory elements. Subsequently, a LLM integrates the text of the memory element itself, its corresponding temporal attributes, and potential executed actions into a descriptive text segment MtM\_{t}. After this, TtT\_{t} is stored by the memory module. 出處: Beyond End-to-End Black Box Mapping: An Intentional Agent Framework for Cognitive-driven Facial Reaction Generation（arXiv）Figure 6: Qualitative frame-by-frame comparison. In this exchange, the speaker presents a difficult-task dilemma, asking what happens when more time is spent on the harder task, while the listener begins formulating a response. Ground Truth shows natural temporal variation, whereas Event-Triggered and Heuristic-Only are more repetitive or neutral. Full Architecture generates a continuous, context-sensitive trajectory that evolves towards a mildly tense or concerned expression as the dilemma unfolds, without reproducing the ground-truth sequence frame by frame. Expression amplitudes are scaled for visual clarity.### F.7 Comparison with Existing Automatic Metrics 出處: Beyond End-to-End Black Box Mapping: An Intentional Agent Framework for Cognitive-driven Facial Reaction Generation（arXiv）
