Stage II Diagnostic Harness Repair Objective
The Stage II training objective optimizes JIT-Agent to imitate successful teacher repair transitions by conditioning on the full prior diagnostic history:
where contains validated repair trajectories R_{K^star} = { (tilde{h}^{(j)}, g^{(j)}, Delta^{star(j+1)}) }_{j=0}^{K^star - 1}, is the task generation context, and is the target patch proposed by the teacher. Because the horizon is bounded by , this objective trains the generator to execute targeted, high-leverage code revisions that restore protocol validity without restarting harness synthesis from scratch.
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Prep Sessions
Dynamic Agent Scaffolding: Synthesis, Diagnostic Repair, and Evolutionary Optimization @ University of Michigan - Ann Arbor
Ch.2 Multi-Stage Harness Optimization - Dynamic Agent Scaffolding: Synthesis, Diagnostic Repair, and Evolutionary Optimization @ University of Michigan - Ann Arbor
Stage II: Diagnostic Feedback and Bounded Harness Repair - Dynamic Agent Scaffolding: Synthesis, Diagnostic Repair, and Evolutionary Optimization @ University of Michigan - Ann Arbor
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Stage II Bounded Repair Dataset Curation
Stage II Diagnostic Harness Repair Objective
Harness Protocol Spaces and Syntactic Subsets
Bounded Diagnostic Code Repair in JIT-Agent
In Stage II data preparation, what specific diagnostic information is compiled alongside failed Stage I harness generations into the failure corpus?
In the Stage II harness repair framework, proposed sequential revisions from the teacher model are deterministically applied to the failed harness using an Apply function.
What is the primary motivation for enforcing bounded repair trajectory limits and discarding candidates that require wholesale redesign during dataset curation?
Explain the criteria and iterative validation process used to retain repair trajectories in the Stage II dataset, referencing the formalization of K*.
Match each mathematical notation from Stage II bounded repair curation to its corresponding description.
Order the steps involved in curating harness repair trajectories for the Stage II dataset.
Determine whether this trajectory is retained in the Stage II dataset and justify your determination based on Stage II curation criteria.
Stage II Diagnostic Harness Repair Objective
Learn After
In the Stage II loss formulation, what information does JIT-Agent condition on when predicting the next patch at step k+1?
In Stage II diagnostic harness repair, the maximum repair trajectory horizon is unbounded to permit indefinite iterative trial-and-error attempts.
In the Stage II loss objective, what does Delta*(k+1) represent, and which agent or module proposes it?
Explain the motivation for imposing a bounded horizon of K* <= 2 on harness repair trajectories in Stage II rather than restarting synthesis from scratch.
Match each mathematical component from the Stage II training objective to its corresponding definition.
The Stage II training objective optimizes JIT-Agent to imitate successful teacher repair transitions by conditioning on the full prior ___ history.
Order the sequence of elements that comprise a repair trajectory transition step k in the Stage II formulation, from initial inputs to final patch application.