Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
schema_version: string
description: string
usage: string
basis: string
total_experts: int64
policies: list<item: struct<percent: int64, path: string, selected_experts: int64, cumulative_mean_importance_ (... 186 chars omitted)
child 0, item: struct<percent: int64, path: string, selected_experts: int64, cumulative_mean_importance_share: doub (... 174 chars omitted)
child 0, percent: int64
child 1, path: string
child 2, selected_experts: int64
child 3, cumulative_mean_importance_share: double
child 4, cumulative_scicode_importance_share: double
child 5, cumulative_terminal_bench_hard_agent_importance_share: double
child 6, keep_experts: list<item: int64>
child 0, item: int64
child 7, sha256: string
child 8, bytes: int64
top100_layer_expert_by_mean_importance: list<item: struct<layer: int64, expert_id: int64, mean_importance_share: double, min_importance_shar (... 92 chars omitted)
child 0, item: struct<layer: int64, expert_id: int64, mean_importance_share: double, min_importance_share: double, (... 80 chars omitted)
child 0, layer: int64
child 1, expert_id: int64
child 2, mean_importance_share: double
child 3, min_importance_share: double
child 4, scicode_importance_score_share: double
child 5, terminal_importance_score_share: double
correlations: struct<expert_importance_pearson: double, expert_importance_spearman: double, expert_activation_pear (... 131 chars omitted)
ch
...
tivation_share: double
child 5, top64_by_robust_min_importance: struct<n: int64, experts: list<item: int64>, scicode_importance_share: double, terminal_bench_import (... 50 chars omitted)
child 0, n: int64
child 1, experts: list<item: int64>
child 0, item: int64
child 2, scicode_importance_share: double
child 3, terminal_bench_importance_share: double
child 4, mean_importance_share: double
child 6, top77_by_robust_min_importance: struct<n: int64, experts: list<item: int64>, scicode_importance_share: double, terminal_bench_import (... 50 chars omitted)
child 0, n: int64
child 1, experts: list<item: int64>
child 0, item: int64
child 2, scicode_importance_share: double
child 3, terminal_bench_importance_share: double
child 4, mean_importance_share: double
overlap: list<item: struct<cohort: string, scicode_n: int64, terminal_bench_n: int64, intersection_n: int64, (... 139 chars omitted)
child 0, item: struct<cohort: string, scicode_n: int64, terminal_bench_n: int64, intersection_n: int64, union_n: in (... 127 chars omitted)
child 0, cohort: string
child 1, scicode_n: int64
child 2, terminal_bench_n: int64
child 3, intersection_n: int64
child 4, union_n: int64
child 5, jaccard: double
child 6, scicode_containment: double
child 7, terminal_bench_containment: double
child 8, intersection_experts: list<item: int64>
child 0, item: int64
to
{'basis': Value('string'), 'datasets': {'scicode': {'label': Value('string'), 'trace_rows': Value('int64'), 'heatmap_rows': Value('int64'), 'questions': Value('int64'), 'layers': Value('int64'), 'experts': Value('int64'), 'importance_total': Value('float64'), 'importance_gini': Value('float64'), 'importance_hhi': Value('float64'), 'importance_effective_experts': Value('float64'), 'top20_importance_share': Value('float64'), 'top64_importance_share': Value('float64'), 'top77_importance_share': Value('float64')}, 'terminal_bench_hard_agent': {'label': Value('string'), 'trace_rows': Value('int64'), 'heatmap_rows': Value('int64'), 'questions': Value('int64'), 'layers': Value('int64'), 'experts': Value('int64'), 'importance_total': Value('float64'), 'importance_gini': Value('float64'), 'importance_hhi': Value('float64'), 'importance_effective_experts': Value('float64'), 'top20_importance_share': Value('float64'), 'top64_importance_share': Value('float64'), 'top77_importance_share': Value('float64')}}, 'correlations': {'expert_importance_pearson': Value('float64'), 'expert_importance_spearman': Value('float64'), 'expert_activation_pearson': Value('float64'), 'expert_activation_spearman': Value('float64'), 'layer_expert_importance_pearson': Value('float64'), 'layer_expert_importance_spearman': Value('float64')}, 'overlap': List({'cohort': Value('string'), 'scicode_n': Value('int64'), 'terminal_bench_n': Value('int64'), 'intersection_n': Value('int64'), 'union_n': Value('int64'), 'jac
...
, 'terminal_bench_importance_share': Value('float64'), 'mean_importance_share': Value('float64'), 'scicode_activation_share': Value('float64'), 'terminal_bench_activation_share': Value('float64')}, 'top64_by_robust_min_importance': {'n': Value('int64'), 'experts': List(Value('int64')), 'scicode_importance_share': Value('float64'), 'terminal_bench_importance_share': Value('float64'), 'mean_importance_share': Value('float64')}, 'top77_by_robust_min_importance': {'n': Value('int64'), 'experts': List(Value('int64')), 'scicode_importance_share': Value('float64'), 'terminal_bench_importance_share': Value('float64'), 'mean_importance_share': Value('float64')}}, 'top100_by_mean_importance': List({'expert_id': Value('int64'), 'mean_importance_rank': Value('int64'), 'scicode_importance_score_rank': Value('int64'), 'terminal_importance_score_rank': Value('int64'), 'mean_importance_share': Value('float64'), 'min_importance_share': Value('float64'), 'scicode_importance_score_share': Value('float64'), 'terminal_importance_score_share': Value('float64'), 'scicode_activation_count_share': Value('float64'), 'terminal_activation_count_share': Value('float64'), 'importance_share_delta': Value('float64')}), 'top100_layer_expert_by_mean_importance': List({'layer': Value('int64'), 'expert_id': Value('int64'), 'mean_importance_share': Value('float64'), 'min_importance_share': Value('float64'), 'scicode_importance_score_share': Value('float64'), 'terminal_importance_score_share': Value('float64')})}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
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 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 299, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
schema_version: string
description: string
usage: string
basis: string
total_experts: int64
policies: list<item: struct<percent: int64, path: string, selected_experts: int64, cumulative_mean_importance_ (... 186 chars omitted)
child 0, item: struct<percent: int64, path: string, selected_experts: int64, cumulative_mean_importance_share: doub (... 174 chars omitted)
child 0, percent: int64
child 1, path: string
child 2, selected_experts: int64
child 3, cumulative_mean_importance_share: double
child 4, cumulative_scicode_importance_share: double
child 5, cumulative_terminal_bench_hard_agent_importance_share: double
child 6, keep_experts: list<item: int64>
child 0, item: int64
child 7, sha256: string
child 8, bytes: int64
top100_layer_expert_by_mean_importance: list<item: struct<layer: int64, expert_id: int64, mean_importance_share: double, min_importance_shar (... 92 chars omitted)
child 0, item: struct<layer: int64, expert_id: int64, mean_importance_share: double, min_importance_share: double, (... 80 chars omitted)
child 0, layer: int64
child 1, expert_id: int64
child 2, mean_importance_share: double
child 3, min_importance_share: double
child 4, scicode_importance_score_share: double
child 5, terminal_importance_score_share: double
correlations: struct<expert_importance_pearson: double, expert_importance_spearman: double, expert_activation_pear (... 131 chars omitted)
ch
...
tivation_share: double
child 5, top64_by_robust_min_importance: struct<n: int64, experts: list<item: int64>, scicode_importance_share: double, terminal_bench_import (... 50 chars omitted)
child 0, n: int64
child 1, experts: list<item: int64>
child 0, item: int64
child 2, scicode_importance_share: double
child 3, terminal_bench_importance_share: double
child 4, mean_importance_share: double
child 6, top77_by_robust_min_importance: struct<n: int64, experts: list<item: int64>, scicode_importance_share: double, terminal_bench_import (... 50 chars omitted)
child 0, n: int64
child 1, experts: list<item: int64>
child 0, item: int64
child 2, scicode_importance_share: double
child 3, terminal_bench_importance_share: double
child 4, mean_importance_share: double
overlap: list<item: struct<cohort: string, scicode_n: int64, terminal_bench_n: int64, intersection_n: int64, (... 139 chars omitted)
child 0, item: struct<cohort: string, scicode_n: int64, terminal_bench_n: int64, intersection_n: int64, union_n: in (... 127 chars omitted)
child 0, cohort: string
child 1, scicode_n: int64
child 2, terminal_bench_n: int64
child 3, intersection_n: int64
child 4, union_n: int64
child 5, jaccard: double
child 6, scicode_containment: double
child 7, terminal_bench_containment: double
child 8, intersection_experts: list<item: int64>
child 0, item: int64
to
{'basis': Value('string'), 'datasets': {'scicode': {'label': Value('string'), 'trace_rows': Value('int64'), 'heatmap_rows': Value('int64'), 'questions': Value('int64'), 'layers': Value('int64'), 'experts': Value('int64'), 'importance_total': Value('float64'), 'importance_gini': Value('float64'), 'importance_hhi': Value('float64'), 'importance_effective_experts': Value('float64'), 'top20_importance_share': Value('float64'), 'top64_importance_share': Value('float64'), 'top77_importance_share': Value('float64')}, 'terminal_bench_hard_agent': {'label': Value('string'), 'trace_rows': Value('int64'), 'heatmap_rows': Value('int64'), 'questions': Value('int64'), 'layers': Value('int64'), 'experts': Value('int64'), 'importance_total': Value('float64'), 'importance_gini': Value('float64'), 'importance_hhi': Value('float64'), 'importance_effective_experts': Value('float64'), 'top20_importance_share': Value('float64'), 'top64_importance_share': Value('float64'), 'top77_importance_share': Value('float64')}}, 'correlations': {'expert_importance_pearson': Value('float64'), 'expert_importance_spearman': Value('float64'), 'expert_activation_pearson': Value('float64'), 'expert_activation_spearman': Value('float64'), 'layer_expert_importance_pearson': Value('float64'), 'layer_expert_importance_spearman': Value('float64')}, 'overlap': List({'cohort': Value('string'), 'scicode_n': Value('int64'), 'terminal_bench_n': Value('int64'), 'intersection_n': Value('int64'), 'union_n': Value('int64'), 'jac
...
, 'terminal_bench_importance_share': Value('float64'), 'mean_importance_share': Value('float64'), 'scicode_activation_share': Value('float64'), 'terminal_bench_activation_share': Value('float64')}, 'top64_by_robust_min_importance': {'n': Value('int64'), 'experts': List(Value('int64')), 'scicode_importance_share': Value('float64'), 'terminal_bench_importance_share': Value('float64'), 'mean_importance_share': Value('float64')}, 'top77_by_robust_min_importance': {'n': Value('int64'), 'experts': List(Value('int64')), 'scicode_importance_share': Value('float64'), 'terminal_bench_importance_share': Value('float64'), 'mean_importance_share': Value('float64')}}, 'top100_by_mean_importance': List({'expert_id': Value('int64'), 'mean_importance_rank': Value('int64'), 'scicode_importance_score_rank': Value('int64'), 'terminal_importance_score_rank': Value('int64'), 'mean_importance_share': Value('float64'), 'min_importance_share': Value('float64'), 'scicode_importance_score_share': Value('float64'), 'terminal_importance_score_share': Value('float64'), 'scicode_activation_count_share': Value('float64'), 'terminal_activation_count_share': Value('float64'), 'importance_share_delta': Value('float64')}), 'top100_layer_expert_by_mean_importance': List({'layer': Value('int64'), 'expert_id': Value('int64'), 'mean_importance_share': Value('float64'), 'min_importance_share': Value('float64'), 'scicode_importance_score_share': Value('float64'), 'terminal_importance_score_share': Value('float64')})}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Qwen3.6 MoE Coding Expert Saliency Analysis
This repository contains the combined coding-task expert-saliency report for local Qwen3.6-35B-A3B MoE tracing work.
Primary artifact:
combined_coding_expert_analysis.html: combined expert activation, router-weight, contribution-proxy, correlation, and residency-candidate analysis across the SciCode MVP trace set and the full Terminal-Bench Hard real-agent trace run.
Companion artifacts:
combined_coding_expert_residency_candidates.json: machine-readable observational residency candidates.reports/scicode_expert_saliency_analysis.html: source SciCode report.reports/terminal_bench_hard_agent_saliency_analysis.html: source Terminal-Bench Hard agent report.summaries/scicode_saliency_summary.json: SciCode summary metrics.summaries/terminal_bench_hard_agent_saliency_summary.json: Terminal-Bench Hard agent summary metrics.recommendations/atx_expert_residency_policy.json: local ATX residency policy artifact, when available.upload_manifest.json: file hashes, source paths, and upload metadata.
Portable ATX Residency Policies
The policies/ directory contains four portable --moe-residency-policy JSON files generated from the combined coding saliency data:
| File | Selected layer-expert cells | Combined importance share |
|---|---|---|
policies/combined_top_05pct_layer_experts.atx.json |
512 | 66.1% |
policies/combined_top_10pct_layer_experts.atx.json |
1,024 | 80.0% |
policies/combined_top_15pct_layer_experts.atx.json |
1,536 | 87.1% |
policies/combined_top_20pct_layer_experts.atx.json |
2,048 | 91.2% |
Example usage with the ATX fork:
./build-atx-metal/bin/llama-server \
-m /path/to/Qwen3.6-35B-A3B-UD-Q4_K_XL.gguf \
--moe-residency-policy policies/combined_top_10pct_layer_experts.atx.json \
--moe-residency-stats stats.json \
--ctx-size 4096 \
--parallel 1
These policies rank layer-expert cells by mean normalized importance across SciCode MVP and Terminal-Bench Hard Agent traces. They recommend which cells to duplicate/cache hot in VRAM; they are not yet throughput-validated as globally optimal.
Global Expert Policies
The policies/ directory also includes expert-only policies using keep_experts, which keeps selected expert IDs hot across all MoE layers:
| File | Selected experts | Combined importance share |
|---|---|---|
policies/combined_top_05pct_global_experts.atx.json |
13 | 16.2% |
policies/combined_top_10pct_global_experts.atx.json |
26 | 27.3% |
policies/combined_top_15pct_global_experts.atx.json |
39 | 36.6% |
policies/combined_top_20pct_global_experts.atx.json |
52 | 44.4% |
These are less targeted than layer-expert policies, but easier to transfer across runtime experiments because each object is just a global expert-ID set.
Policy Pareto Comparison
For throughput/offload analysis, activation capture is the more direct signal than importance capture: a RAM miss is triggered when a routed expert is selected. Importance remains useful as a quality-risk proxy.
| Policy family | 5% | 10% | 15% | 20% |
|---|---|---|---|---|
| Layer-expert cells, importance | 66.1% | 80.0% | 87.1% | 91.2% |
| Layer-expert cells, activation | 20.2% | 32.5% | 41.5% | 48.9% |
| Global experts, importance | 16.2% | 27.3% | 36.6% | 44.4% |
| Global experts, activation | 6.5% | 12.8% | 19.1% | 24.8% |
See policies/policy_pareto_comparison.json and policies/policy_pareto_comparison.csv.
Important Caveats
These results are observational routing/contribution-proxy measurements. They are useful for proposing which experts or layer-expert cells should remain VRAM-resident, but they are not a causal proof that those policies preserve quality or maximize throughput. Final residency recommendations should be validated with forced-residency/offload runs on the same prompts or agent tasks.
This upload intentionally excludes raw trace files and large parquet intermediates. It is a shareable analysis bundle, not the complete trace corpus.
The reports are not official Artificial Analysis benchmark reproductions. They use public/reconstructable tasks and local instrumentation to study MoE routing behavior.
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