Dear @John6666 I am incredibly grateful to you for taking the time to write out such a detailed, rigorous breakdown. This is exactly the feedback I was hoping for when releasing Agent-ProSAT and the ProSAT dataset.
I genuinely appreciate you diving into the architecture. You’ve essentially provided a blueprint for the next steps and answered questions I was actively wondering how to approach. THANK YOU!
I want to confirm I understand correctly: your core thesis is to decouple the canonical audit record (what is computed) from the training view (how it is explained) as the agent works on prosat.py
, right? Given that Agent-ProSAT represents an iterative autoresearch loop where the LLM writes and refines the solver code based on harness feedback, I realize I was making the agent’s job unnecessarily hard. By being obsessed with rigorous determinism, I was forcing it to simultaneously figure out the logic and bake in the natural language formatting.
Here my summary and plan of action through the lens of your comments:
Updating Agent Directives: I will instruct the agent to generate code that strictly outputs a structured ExecutionWitness
(JSON events). A separate, non-agentic renderer will handle the Views (A, B, C, D). Upgrading the Assessment Loop: I’ll implement your 3-way verification cascade (Accept / Reject / Defer) in the evaluation harness to save token overhead and avoid forcing binary decisions on ambiguous cases.
Four-Arm Schema Ablation: Once the agent generates the JSON-emitting solvers, I’ll run the empirical ablation on the four views and track the generator-to-evaluation lineage in a manifest.
Two quick questions for you on the verification cascade:
When you hit a “Defer” case (e.g., multiple rules fit the demonstrations), do you route that to a stronger LLM dynamically during the run, or queue them up for an offline/human review batch?
Do you think a harness like my earlier work (autoresearch-MIL: GitHub - habanwer/autoresearch-MIL: AI agents running research on single-GPU nanochat training automatically · GitHub ) would improve the aforementioned roadmap?
Thanks again for the fantastic insights and pointing me toward the Code Execution as Grounded Supervision and Think Like You Execute precedents. I’m going to start building out the JSON audit schema and the 3-way verification cascade around the next week.
I’ll update the thread once the first ablation results are in!
Best, Shehab