dingxingdi Skill: continual-experience-driven-plan-refinement
1. Capability Definition & Real Case
- Professional Definition: The capability to perform test-time evolution and experience reuse by retrieving, integrating, and updating strategic reasoning patterns or distilled 'Thought Templates' from a continuous stream of task trajectories. It involves an abstract pattern-matching loop to resolve vague human intents, adapt to new variations of past tasks, and refine procedural strategies avoiding known historical failures.
- Dimension Hierarchy: Open-World Real-World Planning->Self-Evolving Strategic Planning->continual-experience-driven-plan-refinement
Real Case
[Case 1]
- Initial Environment: A household simulation environment where an agent possesses a long-term memory of past navigation and pick-up tasks. The agent has previously completed a task to 'Put a chilled tomato from the fridge into the microwave' which involved locating the fridge, retrieving the item, and navigating to the kitchen appliance.
- Real Question: Put a green cup with a fork inside it on the kitchen counter.
- Real Trajectory: 1. Think: Apply the 'Locate-Retrieve-Move-Place' strategy from the tomato task. 2. Refine: Retrieve the fridge-to-microwave sequence but swap 'microwave' for 'counter' and 'tomato' for 'cup'. 3. Act: Find the green cup on the table; find the fork; place fork in cup; move to the kitchen; place on counter.
- Real Answer: Task successfully completed by reusing the navigation-placement decomposition strategy.
- Why this demonstrates the capability: This case demonstrates experience reuse where the agent does not merely solve a task in isolation but retrieves a procedural 'how-to' strategy from a previous, semantically distinct task (tomato) to solve a new one (cup/fork). The agent reuses the macro-level planning structure, significantly reducing the search cost for sub-goals.
[Case 2]
- Initial Environment: A symbolic reasoning environment for mathematical problem solving. The agent has a memory bank containing a failed trajectory for solving '2x^2 - 5x + 1 = 0' where it initially attempted a simple factorization that failed before correctly applying the quadratic formula.
- Real Question: Solve the equation 5x^2 - 1x + 7 = 0.
- Real Trajectory: 1. Think: Identify this as a quadratic equation task. 2. Refine: Retrieve the previous record; note the 'Factorization Failed' reflection tag to avoid that path. 3. Act: Directly apply the quadratic formula using the coefficients [5, -1, 7].
- Real Answer: x = (1 ± i√139) / 10
- Why this demonstrates the capability: This illustrates the 'Think-Act-Refine' loop's ability to learn from negative feedback. By recognizing the task structure and the specific failure boundary from its history, the agent avoids a 'stuck loop' and moves directly to the optimal strategy, demonstrating procedural refinement.
[Case 3]
- Initial Environment: A multi-agent recommendation environment where the Manager Agent has access to a 'Thought Pattern' repository (Experience Memory). The user provides a query with a 'Usage Scenario' but an ambiguous product scope.
- Real Question: Can you suggest some blouses for a gathering with friends? I'm not sure about the specific wearing scene.
- Real Trajectory: 1. [Pattern Matching]: Strategic retrieval identifies the 'Ambiguous Demand' thought pattern. 2. [Phased Planning]: Decomposes the goal into Phase 1 (Knowledge acquisition for gathering styles) and Phase 2 (Targeted recommendations). 3. [Phase 1 Execution]: Identifies 'Casual' and 'Semi-formal' as common gathering subsets. 4. [Phase 2 Execution]: Retrieves items for 'Casual Blouses' and 'Semi-formal Blouses' separately.
- Real Answer: Recommended List 1 (Casual Blouses for relaxed hangouts); Recommended List 2 (Semi-formal Blouses for dinner gatherings).
- Why this demonstrates the capability: The agent leverages experience-driven schemas to transform ambiguous or multifaceted human intents into structured plans. Instead of a 'flat' planning failure on a vague prompt, the agent uses distilled memory to execute a hierarchical intent-to-attribute conversion.
Pipeline Execution Instructions
To synthesize data for this capability, you must strictly follow a 3-phase pipeline. Do not hallucinate steps. Read the corresponding reference file for each phase sequentially:
Phase 1: Environment Exploration Read the exploration guidelines to discover raw knowledge seeds:
references/EXPLORATION.mdPhase 2: Trajectory Selection Once Phase 1 is complete, read the selection criteria to evaluate the trajectory:
references/SELECTION.mdPhase 3: Data Synthesis Once a trajectory passes Phase 2, read the synthesis instructions to generate the final data:
references/SYNTHESIS.md