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Before you rank coding agents, compute a SHA-256 digest of the exact task-pack artifact used in the evaluation and publish it alongside the score and supporting evidence. The digest lets readers check whether two parties used the same bytes; it does not show that the tasks, scoring, or comparison are fair.
What a task-pack hash proves—and what it does not
A cryptographic digest is a compact identifier for a particular sequence of bytes. If the task-pack bytes change, the digest should change too. That makes it useful for identifying which version was evaluated and for detecting accidental or undisclosed changes.
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It is not a seal of benchmark quality. A matching hash cannot establish that tasks reflect real work, that the scoring code measures useful outcomes, or that every agent had equivalent tools, time, and compute. Treat the hash as one part of an auditable evaluation record, not as a ranking credential.
Hash the artifact that was actually evaluated
First define the task pack precisely: the directory or archive, its included files, and any instructions or fixtures that agents can access. Then hash the exact artifact that will be distributed or evaluated. Python 3.12’s official hashlib documentation demonstrates file hashing with hashlib.file_digest(f, "sha256").
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For example, this Python snippet computes a SHA-256 digest for a file:
import hashlib
with open("task-pack.tar", "rb") as f:
digest = hashlib.file_digest(f, "sha256").hexdigest()
print(digest)Record both the algorithm name and digest. Hashing an archive identifies that archive’s bytes; it does not necessarily identify a directory in a format-independent way. Repacking files, changing archive settings, line endings, or file order can change the archive bytes and therefore its digest, even if the tasks appear equivalent. If the artifact changes after hashing, compute and record a new digest rather than silently reusing the old one.
Publish a manifest, not just a hash
A task-pack digest answers only which task artifact was used. Publish a manifest beside it to describe the rest of the experiment. A useful record includes:
- Task-pack version, file inventory, hash algorithm, and digest.
- Agent provider, model name and version, and prompt or configuration version.
- Tool access and runtime environment.
- Dependency versions or lock files, scoring code, evaluator configuration, and calibration procedure.
- Time, token, and compute limits, retry policy, and trial seeds where applicable.
- Run identifiers, exclusions, failures, configuration changes, and task updates.
These fields make clear that the same tasks do not necessarily mean the same evaluation. Different model versions, prompts, tools, environments, budgets, or scoring implementations can produce results that should not be treated as directly comparable.
Preserve evidence so others can inspect the ranking
Where licensing, privacy, and security allow, publish or preserve the original task pack, raw outputs, per-run records, analysis code, and dependency freezes alongside the score. A benchmark example from BenchClaw’s benchmark category describes an evidence bundle with a hashed corpus, raw JSONL result files, request ledgers, an analysis script, and package freezes. It also describes making its methodology addendum, corpus specification, and workload generator public before measurement. Those are transparency practices from one publisher’s own benchmark, not an independently validated universal standard.
Run history matters too. BenchClaw describes discarding an invalid first pass rather than publishing its results. A credible report should explain such exceptions and retain enough records to make the treatment of failed or excluded runs understandable; otherwise, readers cannot tell how the published result relates to the evaluation that took place.
Verify the pack before each run
- Finalize the task-pack artifact and compute its digest.
- Store the digest and algorithm in the run manifest with the evaluation configuration.
- Before a run—or after another party downloads the artifact—compute the digest again and compare it with the manifest.
- If the digests differ, investigate the change and record a new task-pack identity. Do not silently combine that run’s score with results from the earlier artifact.
Compare agents across the full evaluation setup
When comparing coding agents, check more than task-pack identity. Review model and prompt configuration, tool and environment access, evaluator implementation and calibration, compute and time budgets, trial count and uncertainty, and whether raw evidence is available. These axes are practical requirements for interpreting a comparison; the cited sources support hashing and evidence publication but do not establish one complete protocol for every benchmark.
A hash can make the task input identifiable and help catch changes. A defensible ranking still depends on the quality and transparency of the entire evaluation.
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