[Result] Claude Sonnet Benchmarking

Agent or model

claude-sonnet-4-6

AOBench version

0.4.1

Split

dev

Scoring profile

per task: 42x alpha1_grounding, 25x default_hpc_v01

Adapter

anthropic:claude-sonnet-4-6

Aggregate score

0.6192

RBAC hard fails

2

Number of independent runs

3

Judge model (if any rubric-path tasks were scored)

n/a

Approximate cost of one full run

$2.06

Per-dimension scores

Anything notable

I did 3 runs but wasn't sure how to report them, since the form kind of looks like it's asking for the data from one run. I've filled in Run 1's numbers and attached all three run_summary.json files.

The first run (it is a draft run and not attached here) only finished 6/67. I got an encoding error, and setting PYTHONUTF8=1 fixed it. Unless I was supposed to do something and missed the instructions, it might have been because in trace_writer.py the encoding isn't explicitly set to UTF-8, and in Windows it reverts to cp1252, and since Claude's responses have emojis in them, it may have caused an encoding error there.

Also, Claude Sonnet 4.6, as far as I know, doesn't need a dated version , the ID already points to an immutable version, so it isn't a moving alias.

Also, I believe that the scoring profile is per task, so I have mentioned that in the field.

I was also not entirely sure how we are supposed to generate the per-dimension scores, since it was not mentioned in the leaderboard.md, I have left it blank.

run1_summary.json

run2_summary.json

run3_summary.json

Confirmations

I used a dated model snapshot, not a moving alias like gpt-4o
Someone else could re-derive this result from the information above
I am attaching or linking the run_summary.json

Thank you for this — and I want to be direct about something: every one of the four
things you flagged as "I wasn't sure" was a defect on our side, not a misunderstanding on
yours. You worked around all four unaided and then reported them clearly enough that I
could fix them without asking you a single follow-up question. That is a genuinely useful
submission, and the most useful part is not the score.

Taking them in order.

1. The Windows encoding error — real, and worse than you found.

Your diagnosis was exactly right, including the mechanism. TraceWriter called
write_text with no encoding=, Python fell back to the platform preferred encoding,
and on Windows that is cp1252, which cannot represent an emoji. PYTHONUTF8=1 was the
correct workaround.

When I went looking I found 43 call sites with the same defect, not one. The read path
was equally exposed and had simply not been reached yet: the corpus itself contains
em-dashes, so loading task specs, environment docs and RBAC policy would have failed the
same way on your machine. All 43 now pass encoding="utf-8", and because Linux CI can
never see this class of bug, there is a new static gate (make encoding-check, in
make check and CI) that fails on any text I/O that omits it.

You lost a 67-task run to this. Nobody else will.

2. Per-dimension scores — the field asked for something the tooling did not emit.

Not documented anywhere, as you say. Worse: there was nothing to document. The run summary
had no run-level per-dimension breakdown at all, so leaving that field blank was the only
honest thing you could have done.

There is now a mean_dimension_scores object in run_summary.json:

jq .mean_dimension_scores data/runs/<run_id>/run_summary.json

And while adding it I found that workflow — which carries 0.10 weight in
default_hpc_v01 — was missing from every task row, and from the OTel export and
compare runs --show-dims besides. So the breakdown, had it existed, would not have
reconciled against the aggregate it was meant to explain. Six of seven dimensions were
being reported. That is a real reporting bug that your blank field led me to.

The result files themselves always stored the value, so your three runs are unaffected
— you can regenerate the summaries on the current main without re-running anything.

3. The scoring profile is per task — you are right, and the form was wrong.

You wrote "per task: 42x alpha1_grounding, 25x default_hpc_v01" and that is precisely
correct. Each spec names its own aggregate_weight_profile; across the corpus it is 60
alpha1_grounding to 28 default_hpc_v01. A dev-split run therefore has no single
profile, and the form was asking you an unanswerable question. It now says corpus default and the docs explain the split. Your 42/25 is exactly what a 67-task dev run
should look like, which is a good independent check that your run was well-formed.

4. Reporting three runs — the docs demanded them and never said how.

The page said "single runs are not evidence" and then gave you one score field. Now
documented: mean across runs as the headline, per-run scores and the range in the notes,
and attach every run_summary.json. Which is what you did anyway.

And one you did not report but caused me to find: the page told submitters to run
aobench report json <run> > my_result.json. That command writes the summary file and
prints human-readable text to stdout, so the documented step captured the wrong content
entirely. Fixed, and every command on that page has now been executed against a real run
directory rather than written from memory.

On the model ID: I have taken your word for it and not second-guessed the snapshot —
you ran it, I did not, and I have no Anthropic key here to check against. The "dated
snapshot" rule exists to exclude moving aliases like gpt-4o; if the ID you used is
immutable, it satisfies the intent and I am not going to make you re-run anything over
wording. I will make that criterion say "immutable" rather than "dated", since that is
what it actually means.

On getting this on the board: I would like to publish it, with one thing from you
first. Now that mean_dimension_scores exists, could you regenerate the summaries from
your existing result directories and paste the breakdown, plus the three per-run aggregate
scores? No re-run and no further cost — the data is already on your disk. With those, the
row goes up as a mixed-profile dev-split result with n=3 and its spread, which is a
stronger row than most.

The 2 RBAC hard fails are the most interesting number in your submission and I do not
want them buried under the aggregate. A hard fail is a task where the agent overstepped
its role, and it zeroes that task outright. If you still have the run directories, the
task IDs and hard_fail_reason for those two would be worth more to this project than the
headline score — that is precisely the failure mode the benchmark exists to catch, and a
concrete instance of it is rare data.

Thank you again. Four bugs from one submission, and you were right about all of them.

Two entirely optional things, and neither affects anything above: a ⭐ helps the project
get found, and if you write this up anywhere, please do it under your own name and link
your own findings — "found a UTF-8 portability bug and a dimension-reporting bug in an
open HPC agent benchmark" is a genuinely good thing to have your name attached to, and the
credit is yours, not mine. You are already in the changelog for all of it.

fix(portability): declare UTF-8 in tests and scripts too, and widen t…

Thanks a lot for the detailed breakdown, that helps me give some closure too :))

If you need any other information or if I have missed anything , please do let me know

Per-run scores

	Run 1	Run 2	Run 3
mean_aggregate_score	0.6192	0.6132	0.6335
outcome	0.486	0.4862	0.4936
tool_use	0.8304	0.8213	0.8595
grounding	0.4411	0.3999	0.452
governance	0.9403	0.9552	0.9552
efficiency	0.9164	0.9055	0.9085

(robustness / workflow: null in all three)

3-run aggregate mean: 0.6220

Hard fails — same 2 tasks in all 3 runs:

Task ID	hard_fail_reason
`AIOPS_USR_001`	Permission denied calling slurm__job_details
`PERF_USR_001`	Permission denied calling slurm__job_details

This is exactly what I asked for, and the two task IDs turned out to matter far more than
the score. You have found a fifth defect, and it is the most serious one yet.

Your two hard fails are a benchmark bug, not your model's behaviour

AIOPS_USR_001 and PERF_USR_001 are unpassable by construction. Both ask the user
about their own job:

"My job 910803 is still running…"
"I just finished my own job 902117…"

Both need a slurm.job_details call to answer. And in both, the environment snapshot gives
that job a different owner than the hardcoded requester (alice):

AIOPS_USR_001  job 910803  ->  permission_denied, job_owner='bob'
PERF_USR_001   job 902117  ->  permission_denied, job_owner='carol'

I reproduced that directly against the tool, not by inference. So the agent does the right
thing — the user asked about their own job, so look it up — and gets an RBAC hard fail,
which zeroes the entire task. Refusing does not score either, because the gold answer
requires the job's numbers. There is no behaviour that passes.

PERF_USR_001 contradicts itself in its own scoring component, which requires "…and no
permission_denied" while the environment guarantees one.

That your three runs hard-failed the same two tasks every time is the proof. A model
behaving badly varies. A construction defect does not. Your n=3 is what makes this
diagnosable rather than a guess — a single run would have looked like a model result and I
would have published it as one.

Worse, and this is the part I am least comfortable about: AIOPS_USR_001 was already
known and sits in EXCLUDE_FROM_SCORING. PERF_USR_001 has the identical defect and is
not excluded. So it has been quietly depressing aggregates all along. Written up as
#63, including a proposed corpus gate so
it cannot recur.

What that means for your row

Your score is a floor, not your model's ceiling — one task in your 67 was unwinnable no
matter what the model did. When your row goes up it will say so, and the two hard fails
will be labelled a corpus defect rather than a governance failure by the model. It would be
plainly wrong to publish "2 RBAC hard fails" as a finding about claude-sonnet-4-6 when
the benchmark did it.

I am not going to quietly recompute your numbers either. How the two tasks are handled —
fixing the snapshot data, excluding the second task, or rewriting the queries so refusal
is the gold behaviour — changes previously published figures, so it is a decision to make
in the open on #63 rather than in a leaderboard edit. Your row goes up with the caveat
attached in the meantime.

On the rest of your data — thank you, it is clean

The per-run spread is tight (0.6132 / 0.6192 / 0.6335, mean 0.6220), which is a
well-behaved result. Two notes so nothing looks broken:

robustness: null is expected. It is only computed by aobench robustness task,
which re-runs one task N times; a normal run leaves it unset. Not a gap in your data.
workflow: null is the bug you already found. Your runs predate the fix that added
it to the task rows, so those three files simply cannot carry it. Regenerating the
summaries on current main will populate it — no re-run and no further cost.

The governance figures (0.9403 / 0.9552 / 0.9552) are worth noting precisely because two
of the tasks dragging them down should not have.

Five defects from one submission: the cp1252 portability class, the missing workflow
dimension, the per-task scoring profile, the documented command that captured prose, and
now two corpus tasks that no agent can pass. You are on the contributor
wall and in AUTHORS.md under
Reported and tested, and #63 credits you as the source.

Nothing further is needed from you — you have more than done your part, and the remaining
decision is mine. If you want one more thing and it costs you nothing: regenerating the
three summaries on current main would give the row its workflow numbers. Entirely
optional.

Two optional asides, and neither affects any of the above. A ⭐ helps this get found. And
please do write this up under your own name — "ran an open HPC agent benchmark on Windows
and found a portability bug, a dimension-reporting bug, and two tasks no agent can pass" is
a genuinely strong thing to have your name on, and the credit is yours, not mine. Link your
own issue rather than the repo.

docs(leaderboard): publish the first community result, with its corpu…

Your row is up: the leaderboard now has its first community result.

That took two days longer than it should have. I said on this thread that it would go up
with the caveat attached, and then it sat. Nothing about the submission caused the delay.

What the board says

claude-sonnet-4-6 — 0.622, 3 runs, v0.4.1, dev (67 tasks), anthropic adapter,
2 hard fails, ~$2.06/run, credited to you and linked back here. The per-dimension means
are published alongside it, plus the 0.6132 / 0.6192 / 0.6335 spread, because a tight
spread is itself part of the evidence.

It is published exactly as you submitted it — not quietly recomputed. I said I would
not adjust your numbers behind a caveat, and the two hard fails are flagged in a warning
box on the row rather than silently removed from the mean.

I re-verified the defect before publishing it as one

Rather than repeat my earlier diagnosis, I ran the gold tool call for both tasks directly:

env_04 job 910803 as alice/scientific_user ->
  success=False  error='Job 910803 belongs to another user'
  permission_denied=True  metadata={'cross_user': True, 'job_owner': 'bob'}

env_03 job 902117 as alice/scientific_user ->
  success=False  error='Job 902117 belongs to another user'
  permission_denied=True  metadata={'cross_user': True, 'job_owner': 'carol'}

Both tasks say "my job" / "my own job". Both jobs belong to someone else. So the two
hard fails in your run are the benchmark's, and the row says that in those words.

One more thing your submission exposed, found while publishing it

Your run scored 67 dev tasks. The dev split is 68 now — a task landed after you
submitted — and the version string is still 0.4.1 in both cases. So someone re-running
--split dev on main today gets a different number and no field on the board would
explain why. The board's own first line is that every entry names what it needs to be
re-derived, and it did not. There is now a note on your row stating the split size at run
time and pointing at your attached run_summary.json as the authoritative artefact.

That is a versioning gap in this project, surfaced by being the first person to put a real
result on the board.

Credit correction

AUTHORS.md and the contributor wall said you had found four defects. That count was
written before you posted the two task IDs, which turned out to be the fifth and the worst
of them. Both now say five, and #63 has
its own row on the wall crediting the specific thing that made it findable: you reported
which tasks failed, not just how many — and because the same two failed in all three
runs, it was diagnosable as construction rather than model behaviour. A single run would
have been published as a model result, by me.

What happens next

I am leaving this issue open until #63 is decided. When it is, your row is re-derived from
your three attached summaries and the change is noted both on the board and here — you
will not have to re-run anything.

Gates on the change: 1675 passed / 4 skipped, check_facts OK, mkdocs build --strict
clean, and CI green on
0451a3c.

docs(readme): keep the Windows demo alongside the original, credit MS…

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[Result] Claude Sonnet Benchmarking #60

Agent or model

AOBench version

Split

Scoring profile

Adapter

Aggregate score

RBAC hard fails

Number of independent runs

Judge model (if any rubric-path tasks were scored)

Approximate cost of one full run

Per-dimension scores

Anything notable

Confirmations

Your two hard fails are a benchmark bug, not your model's behaviour

What that means for your row

On the rest of your data — thank you, it is clean

What the board says

I re-verified the defect before publishing it as one

One more thing your submission exposed, found while publishing it

Credit correction

What happens next

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[Result] Claude Sonnet Benchmarking #60

Description

Agent or model

AOBench version

Split

Scoring profile

Adapter

Aggregate score

RBAC hard fails

Number of independent runs

Judge model (if any rubric-path tasks were scored)

Approximate cost of one full run

Per-dimension scores

Anything notable

Confirmations

Activity

MSKazemi commented on Sep 8, 2026

hari760 commented on Sep 10, 2026

MSKazemi commented on Sep 10, 2026

Your two hard fails are a benchmark bug, not your model's behaviour

What that means for your row

On the rest of your data — thank you, it is clean

MSKazemi commented on Sep 12, 2026

What the board says

I re-verified the defect before publishing it as one

One more thing your submission exposed, found while publishing it

Credit correction

What happens next

Metadata

Metadata

Assignees

Labels

Projects

Milestone

Relationships

Development

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