Intentional Interaction Tasks

• Intentional Interaction Tasks are defined as activities where agents intentionally infer and act based on explicit goals, integrating context, past experience, and dynamic feedback.
• They employ architectures such as vision-language model integration, memory graphs, and adaptive pipelines to achieve few-shot adaptation and context-sensitive decision making.
• Practical applications in robotics, gesture synthesis, and human-AI partnerships demonstrate robust performance improvements and enhanced user control through transparent intent expression.

Intentional interaction tasks are defined as activities in which an agent—human or machine—infers, communicates, or utilizes deliberate goals, strategies, or preferences to shape the selection, sequencing, and adaptation of actions within a dynamic environment. Intentionality is operationalized through explicit or implicit reasoning about context, affordances, past experiences, and future objectives, enabling flexible, context-appropriate behavior even in the absence of direct instruction. Recent research spans robotics, social media interfaces, multimodal communication, code generation, cognitive modeling, visualization authoring, and human-computer interaction, converging on a central aim: closing the gap between raw input/output behaviors and intentional, task-aligned decision making.

1. Architectural Foundations of Intentional Interaction

Core architectures for intentional interaction incorporate perception, semantic reasoning, adaptive memory, and planning:

• Vision-LLM Integration: Frameworks like INTENTION (Wang et al., 6 Aug 2025) employ a pre-trained large VLM (e.g., GPT-4V) for deep scene understanding. The Intuitive Perceptor converts visual and linguistic input into graph-structured representations, encoding objects, spatial relations, and inferred affordances.
• Memory Graphs: Episodic structures (MemoGraph) encode prior interactions as graphs $g_n$ with nodes $\mathcal{N}$, relations $\mathcal{L}$, instruction $\mathcal{I}$, action $a_n$, and outcome $C_n$, enabling fast generalization and few-shot adaptation.
• Algorithmic Pipeline: Learning phases continuously log (state, action, outcome) tuples, while inference phases retrieve and match current scene graphs to stored memories, applying similarity metrics across instruction, node, and link embeddings:

$$\mathcal{S}_W = \alpha \cdot \mathcal{S}_N + \beta \cdot \mathcal{S}_L + \gamma \cdot \mathcal{S}_I\,,$$

Systems utilizing such frameworks execute agents capable of "interactive intuition," inferring appropriate actions in novel contexts by leveraging both present perception and accumulated experience.

2. Intent Expression, Communication, and Adaptation

Intentionality in user-facing systems demands expressive, transparent interfaces and adaptation mechanisms:

• Natural Language Intent Articulation: Intent-based user interfaces (IUIs) (Ding, 28 Apr 2024), Bonsai feedbuilding (Malki et al., 13 Sep 2025), and IntentFlow (Kim et al., 29 Jul 2025) parse and scaffold intent expressions through structured prompts, editable intent panels, tagging, and feedback, facilitating clear mapping from goals to system configuration and output.
• Editable and Transparent Control: Systems expose intermediate steps—feed sources, ranking weights, reasoning paths—enabling users to iteratively refine, delete, or reuse intent specifications, with dynamic visual linking to output segments for traceability.
• Human-in-the-loop Decoding: HiLDe (González et al., 28 May 2025) foregrounds local model decision points during code generation, allowing inspection, alternative selection, and correction, with explanations and downstream propagation to preserve context alignment.

These mechanisms support intentional adjustment of system behavior, fostering agency and fine-grained control even in complex tasks or evolving intent scenarios.

3. Memory, Generalization, and Continual Learning

Intentional interaction relies on mechanisms enabling memory-driven adaptation and skill transfer:

• MemoGraph and Episodic Memory: In INTENTION (Wang et al., 6 Aug 2025), every interaction tuple enriches a topological memory graph, supporting retrieval by graph matching, reusing past policies proportionally to similarity scores, and facilitating continual adaptation without retraining for each new task.
• Multi-task Skill Acquisition: The Intentional Unintentional Agent (Cabi et al., 2017) simultaneously learns policies for both on-policy ("intentional") and off-policy ("unintentional") tasks through unified actor-critic architectures and joint replay buffers, resulting in improved sample efficiency and transfer performance:

$$\nabla_\theta J(\theta) \approx \mathbb{E}_{\rho^\beta} \bigg[\sum_i \nabla_\theta \mu^i_\theta(s)\, \nabla_a Q^i_\mu(s, a)\bigg|_{a = \mu^i_\theta(s)}\bigg]\,,$$

• Graph Matching and Similarity Metrics: Episodic memory enables context-sensitive retrieval and action selection by computing embeddings for instruction, node, and link spaces, with weighted combinations dictating task-relevant action inference.

These architectures allow few-shot adaptation to unfamiliar contexts and explicit leveraging of prior experience, a hallmark of human-like intentionality.

4. Cognitive, Emotional, and Social Dimensions

Intentionality encompasses not only physical action, but psychological and social constructs:

• Cognitive Chains: Frameworks such as CogIntAc (Peng et al., 2022) situate intention as the driver for action, with emotion as a mediating or resulting factor. Tasks include action abduction, emotion prediction conditioned on action satisfaction, and action generation based on intention-emotion fusion.
• Mutual Intention-Emotion Modeling: RAIN (Peng et al., 2022) integrates historical intention information via LSTM and fusion mechanisms, improving emotion prediction and intention recognition through joint learning and explicit context modeling.
• Compliance and Agency: Cognitive and motor compliance are treated as adjustable parameters in predictive coding models (Chame et al., 2019), enabling flexible negotiation between self-driven intention and external adaptation—a trade-off empirically demonstrated in collaborative human-robot tasks.

In embodied and social interaction, intentionality supports adaptive negotiation, communication, and satisficing between agents, whether via explicit instruction, implicit force signals, or emergent roles.

5. Practical Applications and Evaluation

Intentional interaction frameworks have demonstrated robust performance and adaptability across domains:

• Robotics: INTENTION (Wang et al., 6 Aug 2025) achieved 92%–96% success in instructed manipulation, and 72% in “interactive intuition” scenarios lacking task instructions, outperforming planning and LLM-driven baselines.
• Gesture Synthesis: Intentional-Gesture (Liu et al., 21 May 2025) integrates communicative intention annotations and semantic supervision, achieving SOTA results in FGD and MOS across speakers, surpassing rhythm-only and context-only systems.
• XR Accessibility: Gaze+Blink (Rolff et al., 20 Jan 2025) enables hands-free interaction with competitive speed and, when employing blink classification, substantially reduced error rates.
• Collaborative HRI: Communication is essential for escaping local minima in cooperative control; explicit signaling and perceptual control theory (Moore, 2023) formalize conditions under which intention sharing fails and communication must drive intention adaptation.
• Human-AI Partnership: Deep Cognition (Ye et al., 21 Jul 2025) demonstrates that cognitive oversight and real-time intervention (as opposed to “input-wait-output”) yield significant gains: up to +44.6% in fine-grained interaction and +25.0% transparency, with strong improvements in research report quality.
• Data Visualization Authoring: Intent is unified and operationalized as the type of change induced on visualization targets, with frameworks linking intents to user interaction techniques and underlying components (Song et al., 2 Sep 2024).

6. Conceptual Foundations and Directions

Intentional interaction research builds on foundational concepts such as shared plans in ITL (Ramaraj et al., 2021), resistance as a marker of agent intentionality in HCI (Müller, 2016), and formalization of intent in hierarchical interfaces (Ding, 28 Apr 2024, Song et al., 2 Sep 2024). Intent is defined as an overarching goal at a causal chain’s higher level, not mere prescriptive action. Contemporary work advocates frictionful, resistive, or negotiated interfaces to foster intention recognition.

The field is evolving toward systems that:

• Scaffold intent articulation and iterative refinement across expertise levels and task complexities.
• Leverage adaptive memory, episodic data, and LLM-based reasoning for context-sensitive action selection.
• Support bidirectional, transparent, and interruptible interaction paradigms, especially in research, coding, and creative tasks.
• Facilitate robust transfer learning, cross-domain generalization, and composed intent-expression in multi-agent and multi-modal environments.

Open challenges include intent grounding in ambiguous contexts, balancing cognitive and physical compliance in adaptation, reconciling explicit and implicit signals, and engineering systems for both efficiency and agency in real-world deployment.

Table: Representative Frameworks for Intentional Interaction Tasks

Framework/Domain	Core Mechanism	Impact/Result
INTENTION (robotics) (Wang et al., 6 Aug 2025)	VLM-based scene graph, MemoGraph, Perceptor	72%–96% task success, scalable adaption
Bonsai (social media) (Malki et al., 13 Sep 2025)	Planning/sourcing/curation/ranking pipeline	Intent-aligned feeds, increased agency
HiLDe (code gen) (González et al., 28 May 2025)	Human-in-the-loop decision point editing	31% fewer vulnerabilities, increased control
Intentional-Gesture (Liu et al., 21 May 2025)	Intention-annotated datasets, semantic tokenizer	SOTA FGD/MOS, expressive avator synthesis
IU Agent (RL, control) (Cabi et al., 2017)	Multi-task actor-critic, reward vectors	Faster learning, task generalization
Deep Cognition (Ye et al., 21 Jul 2025)	Transparent, interruptible research system	+44.6% fine-grained interaction
CogIntAc (Peng et al., 2022)	Intention-affect-action chain, multi-task fusion	Enhanced emotion and intent prediction

This overview delineates the technical and conceptual landscape of intentional interaction tasks, emphasizing adaptive, memory-driven decision architectures and user-facing interface mechanisms that support explicit, tractable, and contextually robust intent realization.