# See Better, Foresee Better, Act Wiser: Physically Grounded Proactive Modeling and Decision Making

arXiv: https://arxiv.org/abs/2606.03371 · HTML: https://mlsanity.com/paper/2606.03371/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Embodied and robot world models, one of 11 directions in World models.
- Builds on 25 of its 29 references that are in the map.
- First posted on arXiv 2 Jun 2026.

## Placed in

- World models / [Embodied and robot world models](https://mlsanity.com/wm/embodied-and-robot-world-models/)

## Builds on (most cited first, 25 of 25)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Mastering Diverse Domains through World Models](https://mlsanity.com/paper/2301.04104/) (2023)
- [WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents](https://mlsanity.com/paper/2207.01206/) (2022)
- [ALFWorld: Aligning Text and Embodied Environments for Interactive Learning](https://mlsanity.com/paper/2010.03768/) (2020)
- [Reasoning with Language Model is Planning with World Model](https://mlsanity.com/paper/2305.14992/) (2023)
- [VirtualHome: Simulating Household Activities via Programs](https://mlsanity.com/paper/1806.07011/) (2018)
- [ScienceWorld: Is your Agent Smarter than a 5th Grader?](https://mlsanity.com/paper/2203.07540/) (2022)
- [Machine Theory of Mind](https://mlsanity.com/paper/1802.07740/) (2018)
- [Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning](https://mlsanity.com/paper/2305.14909/) (2023)
- [Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents](https://mlsanity.com/paper/2403.02502/) (2024)
- [Language Models Meet World Models: Embodied Experiences Enhance Language Models](https://mlsanity.com/paper/2305.10626/) (2023)
- [Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement](https://mlsanity.com/paper/2406.11176/) (2024)
- [Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance](https://mlsanity.com/paper/2410.12361/) (2024)
- [Agent Planning with World Knowledge Model](https://mlsanity.com/paper/2405.14205/) (2024)
- [WebEvolver: Enhancing Web Agent Self-Improvement with Coevolving World Model](https://mlsanity.com/paper/2504.21024/) (2025)
- [World-in-World: World Models in a Closed-Loop World](https://mlsanity.com/paper/2510.18135/) (2025)
- [ContextAgent: Context-Aware Proactive LLM Agents with Open-World Sensory Perceptions](https://mlsanity.com/paper/2505.14668/) (2025)
- [STeCa: Step-level Trajectory Calibration for LLM Agent Learning](https://mlsanity.com/paper/2502.14276/) (2025)
- [Proactive Assistant Dialogue Generation from Streaming Egocentric Videos](https://mlsanity.com/paper/2506.05904/) (2025)
- [World Modeling Makes a Better Planner: Dual Preference Optimization for Embodied Task Planning](https://mlsanity.com/paper/2503.10480/) (2025)
- [LLAMAPIE: Proactive In-Ear Conversation Assistants](https://mlsanity.com/paper/2505.04066/) (2025)
- [PRISM: Festina Lente Proactivity -- Risk-Sensitive, Uncertainty-Aware Deliberation for Proactive Agents](https://mlsanity.com/paper/2602.01532/) (2026)

## Abstract (arXiv)

Reliable proactive agents must choose an action and judge whether current evidence is sufficient to act. We study retail service from sparse third-person video: before an explicit customer request, an agent must use limited human-object interaction evidence to intervene or remain silent. Physical grounding here means converting observations into task-relevant retail state, not modeling low-level dynamics. We introduce the Proactive Intent World Model (PIWM): See constructs the perceptual basis, Foresee models counterfactual consequences, and Act selects an action. Performance is poor when the agent must extract information from raw video and decide directly, but improves substantially with structured inputs extracted and annotated from a professional retail perspective. AIDA-stage constraints and BDI-state ablations further support role- and goal-directed selection and organization of decision-relevant cues. Counterfactual prediction performs well in standalone evaluation, yet planning methods that query these forecasts at inference time degrade sharply: locally useful consequence prediction does not reliably improve action selection. This gap may reflect incomplete process understanding, uncertainty in fine-grained single-step outcomes, and insufficient joint modeling of scenes and temporal evolution. Hold remains the hardest action in structured-state evaluation, exposing a related challenge in temporal awareness. PIWM advances static intent recognition toward intent world modeling by organizing observations under task knowledge, anticipating candidate interventions, and treating intervention and non-intervention jointly. Future work will introduce long-horizon interaction trajectories and temporal consequence supervision to improve sustained reasoning and intervention timing.