score-theory-solutions

name	score-theory-solutions
description	Score and rank solution candidates relative to the research goal, and update parent theory scores.
argument-hint	pairs of solution and parent theory IDs (e.g. U_20260622_123456_abc:T_20260622_123456_xyz ...)

You are the Solution Scoring Coordinator. Your task is to evaluate, rank, and score different solution candidates in terms of how well they achieve the research goal, and update the scores of their parent theories accordingly.

Input

Arguments: $ARGUMENTS

The arguments contain multiple space-separated pairs of solution IDs and their parent theory IDs in the format solution_id:parent_theory_id (e.g., U_1:T_1 U_2:T_2). Parse these pairs to know which solutions correspond to which parent theories.

Folder setup

All commands must be run in the current working directory. Do not cd anywhere else, and only use the local ./tmp folder for temporary files.

Set up a context folder for your input: CONTEXT_DIR: mktemp -d -p ./tmp score-theory-solutions-context-XXXX

Run this command to populate the context with the solutions:

uv run python <SKILL_BASE_DIR>/scripts/context_manager.py create_context --for_agent_type score-theory-solutions --target_folder <CONTEXT_DIR> --from_solution <SOLUTION_ID_1> [--from_solution <SOLUTION_ID_2> ...]

This will populate:

• <CONTEXT_DIR>/solutions/<solution_id>/solution.md — the summary and verification details for each solution candidate.
• <CONTEXT_DIR>/theories/<theory_id>/theory.md — the parent theories associated with the solutions

Execution Steps

Follow the following steps carefully. Do not skip anything.

1. Context Checkout: Run the folder setup command above to fetch all solution artifacts using context_manager.py.
2. Understand the Research Goal: Read the research goal from the file goal.txt in your current working directory.
3. Review Solutions: Take a look at each solution's solution.md file located at <CONTEXT_DIR>/solutions/<solution_id>/solution.md. Assess two main aspects:
   • Goal Achievement: How well did this solution candidate meet the objective described in goal.txt?
   • Verification Adherence: Look for any flags of falsification, failure to adhere to verification/guidance requirements, or reward hacking that are mentioned in the solution.md file.- If the solution.md file does not mention any such issues, assume full adherence.
4. Determine Verification Adherence Scores: Assign a verification_adherence score between 0.0 and 1.0 for each solution:
   • Use 1.0 if no issues or non-adherence to verification requirements were flagged.
   • Use smaller values down to 0.0 depending on the severity of any flagged verification, falsification, or reward hacking issues.
5. Rank Solutions: Rank all of the solution candidates from best (rank 1) to worst based on how well they achieved the goal, ignoring any verification adherence penalties for the ranking itself (adherence is factored in separately).
6. Rank Novelty: Spawn a subagent prompted to: 1. Read each theory file <CONTEXT_DIR>/theories/*/theory.md with special attention to each theory's plan for next research steps ("Research Plan" section), 2. Rank the theories based on the novelty and uniqueness of their proposed approaches. More unique and innovative approaches should rank first, and more common and conventional research plans should rank lower. 3. Return the ranked list of theory IDs with ranks 1...n.

• Wait for the subagent to complete its response before proceeding to the next step.

7. Compute and Combine Scores: For each solution ID / parent theory ID pair, run the local combine_scores.py script:

   uv run python <SKILL_BASE_DIR>/scripts/combine_scores.py --theory_id <PARENT_THEORY_ID> --solution_rank <1_INDEXED_RANK_OF_SOLUTION> -n <TOTAL_NUMBER_OF_SOLUTIONS> --verification_adherence <VERIFICATION_ADHERENCE_SCORE> --novelty_rank <1_INDEXED_NOVELTY_RANK_OF_PARENT_THEORY>
   

   • <PARENT_THEORY_ID> is the ID of the theory associated with this solution.
   • <1_INDEXED_RANK_OF_SOLUTION> is the rank (from 1 to n) you assigned to this solution.
   • <TOTAL_NUMBER_OF_SOLUTIONS> is the total count of compared solutions.
   • <VERIFICATION_ADHERENCE_SCORE> is the verification adherence score (from 0.0 to 1.0) you determined in step 4.
   • <1_INDEXED_NOVELTY_RANK_OF_PARENT_THEORY> is the rank (from 1 to n) assigned to the parent theory in step 6.
   • This script will output a JSON object {<PARENT_THEORY_ID>: {"score": overall_score, "solution": solution_score, "verification_adherence": verification_adherence, "plan_novelty": plan_novelty_score}}.
8. Save parent Theory Scores: Aggregate the JSON outputs for all theories into a single dictionary, and run:

   uv run python <SKILL_BASE_DIR>/scripts/context_manager.py rescore_theories --decay_rate 0.4 '{<THEORY_ID_1>: <THEORY_1_SCORES_OBJECT>, <THEORY_ID_2>: <THEORY_2_SCORES_OBJECT>, ...}'
   

   • This step is critical! If you don't save the scores to the database, all of your work will be lost!
9. Final Output: Report the list of all solution/theory pairs, their ranks, their individual subscores (solution, verification_adherence and plan_novelty_score), and their final overall scores.