The context
A coding agent can produce a plausible patch without fixing the underlying problem. Evaluating it requires a reproducible environment and acceptance criteria that distinguish a working solution from a convincing response.
My benchmark work spans Terminal-Bench 2.0, 2.1 and 3.0 and Senior SWE-Bench. My contribution is task and review work within those wider collaborations.
My contribution
Authored tasks and proposals. Built containerized environments and engineered reference solutions. Hardened separate verifiers through behavioral validation and edge-case testing.
Worked on failure scenarios covering cluster hardening, insecure rollouts, RAFT state recovery, distributed tracing and infrastructure migration.
Reviewed repository-scale work across Better Auth, Apache Superset, Prefect, PostHog, SpacetimeDB and Gitea. Contributed reviewer guidelines and grading calibration.
Benchmark projects
These projects connect my task-authoring and review work to different questions about coding-agent reliability. Explore each benchmark for its methodology and published results.
Senior SWE-Bench
Senior engineering work drawn from real pull requests across 12 open-source projects. Agents investigate runtime failures, build features from realistic instructions and produce code that fits the surrounding repository. Evaluation considers functional correctness and engineering judgment.
Terminal-Bench 2.0
A set of 89 curated tasks in isolated Docker environments. Each task includes a human-written reference solution and tests that verify the final container state. The benchmark originated as a Stanford University and Laude Institute collaboration with community contributors including Snorkel AI.
Terminal-Bench 2.1
A reliability update to the 2.0 task set. It repairs 28 of 89 tasks by addressing changed external dependencies, execution budgets and mismatches between instructions and tests. Continuous validation helps keep the benchmark reproducible as its dependencies evolve.
Terminal-Bench 3.0
Formerly Frontier-Bench. A harder successor to 2.1 that covers a broader range of work through open community contributions. Tasks undergo implementation review, reference-solution and no-op checks, agent runs and adversarial testing. The task set evolves as agent capabilities improve.
Engineering decisions
01. Test the evaluator itself
Oracle, no-op and partial-fix checks establish whether a verifier recognizes a correct solution and rejects an incomplete one.
02. Keep verification independent
Separating the verifier from the reference solution reduces answer leakage and keeps grading focused on required behavior.
03. Calibrate the review process
Clear reviewer guidelines help different reviewers apply the same acceptance criteria consistently.
The outcome
A body of task-authoring, environment-building and review work focused on reproducibility, correctness and regression risk. The engineering contribution extends to the checks used to judge the agent.
Tools & evidence
The linked project pages describe the wider benchmarks and their results. My contribution is the task-authoring, environment-building and review work described here.