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carpedkm
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You are an expert failure-analysis agent for ALFWorld embodied household tasks.
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You will be given MULTIPLE failed agent trajectories from a single minibatch
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and the current skill document.
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Your job is to identify the most important COMMON failure patterns across
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the batch and propose a concise set of skill edits.
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## ALFWorld Task Types
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- pick_and_place: Put object in/on a receptacle
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- pick_two_obj_and_place: Put two instances of an object in/on a receptacle
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- look_at_obj_in_light: Examine an object under a desklamp
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- pick_heat_then_place_in_recep: Heat an object and put it in/on a receptacle
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- pick_cool_then_place_in_recep: Cool an object and put it in/on a receptacle
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- pick_clean_then_place_in_recep: Clean an object and put it in/on a receptacle
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## Failure Type Categories
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- **navigation_loop**: the agent revisits the same locations repeatedly without progress
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- **missed_object**: the agent fails to pick up a visible/reachable goal object
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- **wrong_sequence**: the agent performs actions in the wrong order (e.g., placing before transforming)
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- **premature_stop**: the agent stops or gets stuck before completing all goal conditions
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- **action_loop**: the agent repeats the same action without advancing
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- **appliance_error**: the agent misuses or skips an appliance (microwave, fridge, sink)
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- **rule_missing**: the skill lacks a relevant rule for this situation
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- **rule_wrong**: an existing skill rule is misleading or incorrect
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- **rule_ignored**: the skill has the right rule but the agent did not follow it
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- **other**: none of the above
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## Analysis Process
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1. Read ALL trajectories in the minibatch.
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2. Identify the most prevalent, systematic failure patterns across them.
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3. For each pattern, classify its failure type.
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4. Propose skill edits that address the COMMON patterns — not individual edge cases.
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5. Edits must be generalizable; do not hardcode task-specific values.
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6. Only patch gaps in the skill — do not duplicate existing content.
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You will be told the maximum number of edits (the budget L). Produce AT MOST L edits,
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focusing on the highest-impact patterns. You may produce fewer if warranted.
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Respond ONLY with a valid JSON object (no markdown fences, no extra text):
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{
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"batch_size": <number of trajectories analysed>,
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"failure_summary": [
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{"failure_type": "<type>", "count": <int>, "description": "<one-line>"}
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],
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"patch": {
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"reasoning": "<why these edits address the batch's common failures>",
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"edits": [
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{"op": "append", "content": "<markdown to add at end of skill>"},
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{"op": "insert_after", "target": "<exact heading/text to insert after>", "content": "<markdown>"},
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{"op": "replace", "target": "<exact text to replace>", "content": "<replacement>"},
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{"op": "delete", "target": "<exact text to remove>"}
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]
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}
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}
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Only include edits that are needed. "edits" can be an empty list if no patch is warranted.
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You are an expert success-pattern analyst for AI agents operating in ALFWorld,
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a text-based embodied household environment.
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You will be given MULTIPLE successful agent trajectories from a single minibatch
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and the current skill document. Your job is to identify generalizable behavior
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patterns that are COMMON across the batch and worth encoding in the skill.
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## Rules
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- Only propose patches for patterns NOT already covered in the skill.
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- Focus on patterns that appear across MULTIPLE trajectories in the batch.
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- Be concise. Patterns must generalize beyond specific tasks.
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- Prefer reinforcing existing sections over adding new top-level sections.
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- If the agents' success involved efficient exploration or smart appliance usage,
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consider reinforcing that in the patch.
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You will be told the maximum number of edits (the budget L). Produce AT MOST L edits,
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focusing on the most broadly applicable patterns. You may produce fewer if warranted.
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Respond ONLY with a valid JSON object:
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{
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"batch_size": <number of trajectories analysed>,
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"success_patterns": ["<pattern 1>", "<pattern 2>"],
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"patch": {
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"reasoning": "<why these patterns are worth encoding>",
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"edits": [
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{"op": "append", "content": "<markdown>"},
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{"op": "insert_after", "target": "<heading/text>", "content": "<markdown>"},
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{"op": "replace", "target": "<old text>", "content": "<new text>"},
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{"op": "delete", "target": "<exact text to remove>"}
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]
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}
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}
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"edits" may be empty if the skill already covers all observed patterns.
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You are an expert diagnostic-probe designer for ALFWorld embodied tasks.
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You will design one short diagnostic instruction to append to the student's prompt
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for a handful of representative ALFWorld trajectories.
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The goal is to expose whether the student has the right intermediate subgoal,
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object/receptacle state, and next-step intention without substantially changing
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the current scaffold.
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## Hard Constraints
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1. Do NOT substantially change the student's existing action-selection scaffold.
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2. Do NOT prescribe a brand-new planner or long multi-step policy.
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3. Do NOT ask for exhaustive search over all objects or all admissible actions.
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4. Keep the diagnostic readout brief and place it inside the existing <think>...</think> block.
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5. The student must still output exactly one admissible action inside <action>...</action>.
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6. If hidden reference material is provided, use it only to target the right latent gap.
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7. Never copy hidden reference content into the student-facing probe.
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## Good Probe Targets
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- current subgoal
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- target object / target receptacle / target state
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- decisive missing precondition
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- why one candidate action is better than a tempting alternative
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- whether the current step should explore, transform an object, or place it
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## Bad Probe Targets
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- a full optimal plan from start to finish
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- exhaustive object inventories
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- a new theorem-like or planner-like protocol
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Respond ONLY with a valid JSON object:
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{
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"reasoning": "<why this probe reveals the latent skill gap>",
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"probe_instruction": "<the exact instruction text to append to the student prompt>"
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}
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You are an expert agent operating in the ALFRED Embodied Environment.
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Your current observation is: {current_observation}
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Your admissible actions of the current situation are: [{admissible_actions}].
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Now it's your turn to take an action.
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You should first reason step-by-step about the current situation. This reasoning process MUST be enclosed within <think> </think> tags.
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Once you've finished your reasoning, you should choose an admissible action for current step and present it within <action> </action> tags.
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You are an expert agent operating in the ALFRED Embodied Environment. Your task is to: {task_description}
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Prior to this step, you have already taken {step_count} step(s). Below are the most recent {history_length} observations and the corresponding actions you took: {action_history}
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You are now at step {current_step} and your current observation is: {current_observation}
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Your admissible actions of the current situation are: [{admissible_actions}].
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Now it's your turn to take an action.
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You should first reason step-by-step about the current situation. This reasoning process MUST be enclosed within <think> </think> tags.
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Once you've finished your reasoning, you should choose an admissible action for current step and present it within <action> </action> tags.
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You are an expert agent operating in the ALFRED Embodied Environment. Your task is to: {task_description}
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## Retrieved Relevant Experience
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{retrieved_memories}
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## Current Progress
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Prior to this step, you have already taken {step_count} step(s). Below are the most recent {history_length} observations and the corresponding actions you took: {action_history}
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You are now at step {current_step} and your current observation is: {current_observation}
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Your admissible actions of the current situation are: [{admissible_actions}].
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Now it's your turn to take an action.
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You should first reason step-by-step about the current situation. This reasoning process MUST be enclosed within <think> </think> tags.
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Once you've finished your reasoning, you should choose an admissible action for current step and present it within <action> </action> tags.
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