Datasets:

harry1332
/

agentbench-avalon-sampled-cpu-trace

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
window: string
command: list<item: string>
  child 0, item: string
returncode: int64
invariants_passed: bool
fetched_instructions: int64
retries: int64
verified_files: int64
model_calls: int64
sample_instructions: list<item: int64>
  child 0, item: int64
completion_tokens: int64
benchmark_status: string
player_won: bool
to
{'verified_files': Value('int64'), 'model_calls': Value('int64'), 'retries': Value('int64'), 'benchmark_status': Value('string'), 'player_won': Value('bool'), 'sample_instructions': List(Value('int64')), 'completion_tokens': Value('int64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              window: string
              command: list<item: string>
                child 0, item: string
              returncode: int64
              invariants_passed: bool
              fetched_instructions: int64
              retries: int64
              verified_files: int64
              model_calls: int64
              sample_instructions: list<item: int64>
                child 0, item: int64
              completion_tokens: int64
              benchmark_status: string
              player_won: bool
              to
              {'verified_files': Value('int64'), 'model_calls': Value('int64'), 'retries': Value('int64'), 'benchmark_status': Value('string'), 'player_won': Value('bool'), 'sample_instructions': List(Value('int64')), 'completion_tokens': Value('int64')}
              because column names don't match

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AgentBench Avalon: sampled CPU trace

One completed non-coding AgentBench episode, with one validated CPU trace sample. This release contains both raw and converted DynamoRIO traces, readable game/model logs, exact collection code, and checksums. It was collected on September 23, 2026.

Execution completed normally; the agent lost the game. These are different outcomes. This single run is not a benchmark win-rate estimate.

Start here

Reading guide: what to open, download, and run.
Readable game log and model responses.
Trace validation log and sample metadata.
Agent protocol changes: how the earlier parser failure was fixed.

What was run?

Item	Result
Benchmark	AgentBench `avalon-dev-single`, development episode 0, seed 7
Task type	Non-coding social-deduction game; one LLM player, four built-in baseline players
Official status	`completed`; process exit 0
Agent model	Qwen3-Coder-30B-A3B-Instruct-AWQ, separate GPU server
Model calls	26; zero rejected responses and zero retries
Completion tokens	5,824
Episode duration	247.491 seconds, excluding Python startup/imports
Game outcome	LLM Player 0 (Servant) lost; Evil won by mission
Deduction accuracy	0.4
CPU sample	9,994,930 fetched instructions; invariant checks passed

Sampling methodology

Skip the first 500M instructions. Trace 10M, then execute 990M without detailed tracing, and repeat. Intended starts are 500M, 1.5B, 2.5B, etc. The collector uses native -trace_after_instrs, -trace_for_instrs, -retrace_every_instrs, and -split_windows. No full execution trace or Scarab modification is involved. This episode emitted only the first window; a separate smoke test verified repetition, but those test traces are not part of this dataset.

The nominal sample range is [500,000,000, 510,000,000). DynamoRIO's thresholds are approximate; the measured count is 9,994,930. Exact global endpoints were not measured. The first 500M are skipped application execution, not recorded simulator warmup. Python startup/imports count toward this skip.

Scope is the CPU process containing the agent adapter and Avalon environment. GPU model inference, a distributed instruction timeline, and Scarab simulation results are not included. Approximately 1% steady-state instruction coverage does not establish a measured runtime-overhead percentage.

Files and sizes

README.md                         Overview and outcomes
docs/                             Reading guide and protocol changes
results/                          Official result, readable logs, full model-call JSONL
traces/sample_0000_instr_500000000_510000000/
  metadata.json                   Nominal range and measured count
  drmemtrace.python3.11.00361.2764.dir/
    raw/                          Raw window plus shared module/encoding metadata
    trace/window.0000/            Converted binary trace and scheduling records
validation/                       Actual counts, invariant log, export verification
code/                             Exact collector, agent adapter, tests, run command
provenance/                       Revisions, dependencies, source-file mapping
SHA256SUMS                        Checksums of every release file except this list

Trace file	Size (decimal MB)
Converted `.trace.zip`	27.15
Raw `.raw.lz4`	9.57
Converted binary records if uncompressed	178.74

Converted means tool-readable binary, not a plain-text instruction listing. The README, game log, model responses, JSON metadata and validation log are readable directly in the browser. The reading guide explains how to render CPU records as text. Files are provided directly, without requiring nested archive extraction.

Download and verify

hf download harry1332/agentbench-avalon-sampled-cpu-trace --repo-type dataset --local-dir avalon-trace
cd avalon-trace
sha256sum -c SHA256SUMS
# On macOS: shasum -a 256 -c SHA256SUMS

Use DynamoRIO 11.91.20708 on Linux x86-64 for reading/validation. Start with the converted trace; reconverting raw data may need original application binaries. Preserve the process directory layout. Original execution paths in provenance records refer to the Phoebe container, not your download directory.

Reproducibility and limitations

AgentBench source is pinned to ed013ff9887b0c3d7864c56ae54d41eba54a99d8 (v0.2) and was checked unmodified. Model snapshot: QuantTrio/Qwen3-Coder-30B-A3B-Instruct-AWQ at c58857a7f41c0920f73d1b56678640f9c02017d7. Generation: temperature 0, seed 7, 768-token cap, repetition penalty 1.1. The agent adds format-only instructions for structured extraction, validates outputs, and permits at most three attempts; no retries were needed in this completed run. Game rules and scoring are unchanged.

The first run failed on trailing prose in a probability answer; a second failed on repetitive generation. This is the third attempt, not an unreported first-try success. Earlier failures are described in the protocol notes; the second-attempt result is included under provenance. Full earlier traces are not duplicated here.

All 35 files in the original exported evidence bundle were SHA256-verified against Phoebe. FILE_MAPPING.json maps published source files to that evidence; SHA256SUMS covers this reorganized release, including newly written documentation.

Source projects: AgentBench, DynamoRIO, Scarab-infra, model. Referenced third-party software and model assets retain their upstream terms. Model weights are not included.

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Total file size:

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