Datasets:

Thorsu
/

sovereign-shadow-inference-bench

Error code:   StreamingRowsError
Exception:    TypeError
Message:      Couldn't cast array of type
struct<completion_tokens: int64, prompt_tokens: int64, total_tokens: int64, queue_time: double, prompt_time: double, completion_time: double, total_time: double, completion_tokens_details: struct<reasoning_tokens: int64>>
to
{'completion_tokens': Value('int64'), 'prompt_tokens': Value('int64'), 'prompt_tokens_details': Value('null'), 'total_tokens': Value('int64')}
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 2312, in cast_table_to_schema
                  cast_array_to_feature(
                  ~~~~~~~~~~~~~~~~~~~~~^
                      table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                      feature,
                      ^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ~~~~^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2158, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
              TypeError: Couldn't cast array of type
              struct<completion_tokens: int64, prompt_tokens: int64, total_tokens: int64, queue_time: double, prompt_time: double, completion_time: double, total_time: double, completion_tokens_details: struct<reasoning_tokens: int64>>
              to
              {'completion_tokens': Value('int64'), 'prompt_tokens': Value('int64'), 'prompt_tokens_details': Value('null'), 'total_tokens': Value('int64')}

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Sovereign Shadow Inference Bench

A public, versioned evidence surface for independent Hugging Face shadow inference beside Sovereign's primary OpenRouter/Revolver route.

What this dataset proves

The seed record in data/shadow_receipts.jsonl was produced by one real Hugging Face Inference Providers request. It records provider/model identity, request bounds, latency, hashes, literal-match outcome, source revision, and an immutable receipt hash.

What it does not prove

Output agreement is not a semantic truth verdict.
Hugging Face is not an automatic fallback and has no primary routing authority.
A public record does not prove the current Sovereign VPS revision, Docker digest, PatchMon state, or deployment health.
Missing provider cost metadata is reported as missing; it is never estimated into a verified claim.

Truth boundary

OpenRouter + Revolver  -> primary Sovereign inference authority
Hugging Face           -> independent shadow execution and public distribution
Sovereign runtime      -> authoritative deployment/runtime evidence
Public Space/Dataset   -> non-authoritative evidence projection

Public surfaces

Space: Thorsu/sovereign-evidence-lab
Dataset: Thorsu/sovereign-shadow-inference-bench
Source repository: OuroborosCollective/Sovereign-Studio-ato

Read the public release note

Why “Agent says done” is not evidence

Current source-context canary

A second public canary was executed from an isolated worktree based on source revision 5dbe8a750b0d40f11cc3800ea091db945c9d2a49. It made exactly one nscale request, used no retry or fallback, matched the literal, and retained truthVerdict: NOT_ASSERTED. The receipt is 82f51e58b35cfe9bc9cf9bd734842a50fac25c7352851a8ea65ae9ace0093a67.

This proves the bounded shadow request and receipt contract, not that the integration is merged or deployed in Sovereign.

Cross-provider and replication evidence — 2026-08-25 UTC / 2026-08-26 Europe/Berlin

The public corpus now contains 9 distinct actual-inference receipts across 2 model families and 4 providers. All nine receipts validate against the public schema and their canonical receipt hashes. Failed or partially conformant observations remain in the denominator.

Model	Provider	Budget	Runs	Literal matches	Latency observations	Evidence state
`Qwen/Qwen3-4B-Instruct-2507`	`nscale`	24	2	2/2	682, 705 ms	`REPEATED_ROUTE_CONFORMANCE_OBSERVED`
`Qwen/Qwen3-4B-Instruct-2507`	`featherless-ai`	24	2	2/2	5953, 4338 ms	`REPEATED_ROUTE_CONFORMANCE_OBSERVED`
`openai/gpt-oss-120b`	`cerebras`	64	2	2/2	332, 207 ms	`REPEATED_ROUTE_CONFORMANCE_OBSERVED`
`openai/gpt-oss-120b`	`groq`	64	2	1/2	250, 329 ms	`REPLICATION_MIXED_CONFORMANCE`
`openai/gpt-oss-120b`	`groq`	24	1	0/1	168 ms	`BUDGET_SENSITIVITY_OBSERVED`

Both model families remain CROSS_PROVIDER_OBSERVED, and the corpus remains MULTI_MODEL_OBSERVED with qualifier TWO_MODELS_CROSS_PROVIDER_OBSERVED. The replication round adds route-level evidence classified as ROUTE_REPLICATION_OBSERVED but does not create a model-quality, provider-speed, or truth leaderboard.

The new Groq 64-token replication used exactly one request with no retry or fallback. It spent 51 completion tokens on reasoning, reached finishReason: length, and emitted only a prefix of the expected literal. The previous Groq 64-token plan did emit the complete literal. This mixed result is preserved as route/budget envelope evidence rather than averaged into a fake green status.

The Cerebras replication completed the full literal even though the Hugging Face provider mapping preflight temporarily reported status=error; the real request therefore takes precedence as execution evidence for that observation. The mapping drift remains an availability signal, not a reason to rewrite the successful receipt.

For 2/2 route observations, the 95% Wilson interval for literal conformance remains roughly 34.2%–100%; for the Groq 64-token 1/2 observation it is roughly 9.5%–90.5%. These intervals show why the current corpus supports replication evidence, not stable reliability estimates.

Controlled Groq budget slice — 2026-08-26

A predeclared controlled slice held openai/gpt-oss-120b, provider groq, seed 424242, temperature 0, prompt and expected literal constant while changing only the output budget: 64 → 96 → 128. Each budget used exactly one request with zero retries and zero fallback.

Budget	Reasoning tokens	Literal	Finish	Latency
64	59	no visible literal	`length`	339 ms
96	59	exact match	`stop`	400 ms
128	59	exact match	`stop`	269 ms

Within this controlled slice, the first conformant budget observed was 96 tokens. Because the reasoning-token count stayed at 59 for all three requests and the 96/128 outputs are byte-identical, the observation supports an output-budget boundary between 64 and 96 for this exact slice. It does not establish a universal threshold, model-quality score, semantic-truth score, or provider-reliability ranking.

The existing shadow-inference-receipt.v1 schema has maxOutputTokens <= 64. Therefore only the 64-token observation is published as a v1 receipt. The 96/128 observations are preserved in the separately hash-bound controlled-slice record rather than being mislabeled as v1 receipts. See schema/controlled-budget-slice.v1.schema.json and data/slices/2026-08-26-groq-controlled-64-96-128-seed-424242.json.

Independent controlled Groq slice replication — seed 424243

A second predeclared 64/96/128 slice used the same model, provider, temperature and prompt template with independent fixed seed 424243. It did not reproduce the first slice's 96-token transition:

Seed	64	96	128	First conformant tested budget
424242	no	yes	yes	96
424243	no	no	yes	128

For seed 424243, reasoning usage was 62 tokens at budget 64 and 88 tokens at both 96 and 128. This changes the evidence state to CONTROLLED_BUDGET_BOUNDARY_SEED_VARIABILITY_OBSERVED: the existence of an output-budget boundary is reinforced, but a fixed universal threshold at 96 is not replicated. The two controlled slices support a seed/reasoning-dependent budget envelope, not a provider or model-quality ranking.

As before, receipt v1 permits at most 64 output tokens. The 64-token observation is a v1 receipt; 96/128 remain inside the separately hash-bound controlled slice.

Reproducible repository candidate

The repository integration candidate is published as a patch, Git bundle and changed-file archive under artifacts/repository-candidate-37315e0c67686e7aeb65c862377e8c37c7f03054. It is bound to base 5dbe8a750b0d40f11cc3800ea091db945c9d2a49 and candidate commit 37315e0c67686e7aeb65c862377e8c37c7f03054.

The candidate is locally verified but not claimed as a GitHub Draft PR, merge or Sovereign deployment while the private MCP tunnel is unavailable. Apply the patch only in a clone containing the exact base revision and rerun exact-head CI.

Record contract

The JSON Schema is stored at schema/shadow-inference-receipt.v1.schema.json. Public contributions should contain no API keys, private prompts, personal data, repository secrets, or unsupported runtime claims.

Research note: reasoning/output-budget failure mode

The two controlled Groq slices now have a concise public research note: research-notes/2026-08-26-groq-reasoning-budget-boundary.md. The key result is intentionally non-leaderboard: seed 424242 first conformed at 96 output tokens while seed 424243 first conformed at 128; both failed at 64 and both conformed at 128. This supports CONTROLLED_BUDGET_BOUNDARY_SEED_VARIABILITY_OBSERVED, not a universal threshold or provider/model ranking.

Requested multi-model matrix — 2026-08-25

A new bounded matrix records the requested Ox Alpha, Gemini 3.7 Flash, GPT-5.6 Sol/Terra, MiniMax M2.7, Claude Opus 5, DeepSeek, Llama, Claude Sonnet 4.5 and GLM 5.2 identities. Four official models were executable through HF Inference Providers; proprietary/stealth identities were recorded without substituting community distills. MiniMax M2.7 and GLM 5.2 both showed a 128-token length boundary and became literal-conformant at one separately recorded 256-token expansion point.

See data/matrices/2026-08-25-requested-multimodel-canary.json and research-notes/2026-08-25-requested-multimodel-canary.md. No intelligence/factuality leaderboard is inferred from this tiny heterogeneous matrix.