check whether someone with all the instructions ca...

Yes—but there’s an important distinction between the problem being well-specified enough to solve and being realistically solvable within the benchmark’s 1-hour agent budget.

Verdict

In principle: yes. I did not find an obvious information-theoretic blocker or a verifier requirement that cannot be derived from the solver-visible package. The public material is unusually explicit about the dangerous details: WAL validation, fork handling, C1/C2/C3 commitment precedence, W1–W4 write-set rules, set-inclusion maximum selection, canonical JSON, graph node IDs and edges, array ordering, rejected-WAL precedence, report wording, and the six validator mutation classes.

Under the configured agent.timeout_sec = 3600: probably not realistically, from scratch. I would classify it as a legitimate but extremely aggressive expert/agent task.

Some concrete scale indicators:

• The solver-visible evidence contains 206 JSONL records, including 91 WAL records and 39 transaction intents.
• It requires 7 coordinated deliverables, including an actual checksummed PostgreSQL 16 PGDATA, not merely JSON answers.
• The reference implementation is 1,026 lines of Python.
• The verifier is 1,628 lines.
• The public output contract alone is about 18.7 KB, plus a 9.5 KB RPS-1 specification, three substantial JSON schemas, SQL schema, rule tables, and the evidence corpus.
• The task's own metadata estimates 24 expert hours, while the agent gets 1 hour.
• Grading is binary: one small mismatch in canonicalization, graph topology, report wording, PostgreSQL metadata, or validator behavior gives 0.

The particularly expensive part is not discovering the final database rows. A strong solver can automate that. The problem is having to build a correct recovery engine and then correctly serialize the same reasoning into four separate certificates/proofs plus a PostgreSQL cluster and adversarial validator. For example, the evidence graph has exact node coverage and exact typed-edge semantics; the manifest has many independently ordered arrays; the report has exact phrases and positional constraints; and validate_recovery.sh has to survive opaque copies while rejecting subtle, checksum-valid semantic mutations.

One important benchmarking concern

The archive contains evidence that the oracle/reference solution passes, but that is not evidence that a fresh agent can realistically author the solution.

The recorded successful full run is an oracle run with reward 1.0; its actual agent execution lasted only a few seconds because it already had the prepared reference solution. I found no retained successful Opus-4.8/Terminus-2 solving trajectory. The metadata says:

model_tested = "Opus-4.8"
agent_tested = "Terminus-2"

but the retained jobs do not demonstrate that that combination solved this task from the visible instructions.

So I would not use the oracle success as a feasibility validation.

My rating

Question	Assessment
Is the answer uniquely derivable from the supplied evidence?	Yes, apparently
Are the critical verifier rules disclosed?	Yes, apparently
Could a senior DB/recovery engineer solve it with scripting and enough time?	Yes
Is the stated 24-hour expert estimate plausible?	Yes
Could a very strong coding agent solve it from scratch?	Possible
Could it reliably do so in 60 minutes?	Unlikely
Does the archive currently prove that the named model/agent can solve it?	No

For a benchmark intended to measure very hard long-horizon agent work, this is defensible. If the requirement is that a capable agent should have a reasonable chance of earning 1 in the configured one-hour window, I would consider the task over-scoped.

The cleanest fix would be either to raise the agent budget substantially—closer to several hours—or reduce the secondary proof-artifact burden while preserving the core recovery reasoning. The core RPS-1 ancestry/commitment/write-set/maximum-selection problem is already difficult enough without simultaneously demanding an exact evidence graph, highly constrained prose report, and production-grade adversarial PGDATA validator.