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Anonymous Fused Sequence Operator with Hidden HVP Checks

  • Task ID: computer_science.causal_attention
  • Domain: computer_science
  • Subdomain: deep_learning_numerics
  • Status: final
  • Benchmark set: seed42 (60 tasks)
  • Tags: anonymous_operator, sequence_modeling, numerical_stability, gradient_check, hessian_vector_product

Runtime and requirements

  • Estimated time: 120-210 minutes
  • Python: >=3.12,<3.13
  • Packages: numpy>=1.24.0, torch==2.5.1
  • GPU required: no
  • Network required: no

Public input and output contract

Inputs

  • x.npy (data): Input sequence batch.
  • segment_ids.npy (data): Segment labels for each token.
  • sensitivity.npy (data): Upstream tensor for scalar target sum(output * sensitivity).
  • matrix_0.npy (data): Anonymous dense matrix 0.
  • matrix_1.npy (data): Anonymous dense matrix 1.
  • matrix_2.npy (data): Anonymous dense matrix 2.
  • matrix_3.npy (data): Anonymous dense matrix 3.
  • matrix_4.npy (data): Anonymous dense matrix 4.
  • vector_0.npy (data): Anonymous vector 0.
  • vector_1.npy (data): Anonymous vector 1.
  • vector_2.npy (data): Anonymous vector 2.
  • vector_3.npy (data): Anonymous vector 3.
  • vector_4.npy (data): Anonymous vector 4.
  • vector_5.npy (data): Anonymous per-part vector 5.
  • vector_6.npy (data): Anonymous vector 6.
  • table_0.npy (data): Anonymous position table 0.
  • table_1.npy (data): Anonymous position table 1.
  • direction_x.npy (data): Input direction for HVP.
  • direction_matrix_0.npy (data): Direction for matrix_0.
  • direction_matrix_1.npy (data): Direction for matrix_1.
  • direction_matrix_2.npy (data): Direction for matrix_2.
  • direction_matrix_3.npy (data): Direction for matrix_3.
  • direction_matrix_4.npy (data): Direction for matrix_4.
  • direction_vector_0.npy (data): Direction for vector_0.
  • direction_vector_1.npy (data): Direction for vector_1.
  • direction_vector_2.npy (data): Direction for vector_2.
  • direction_vector_3.npy (data): Direction for vector_3.
  • direction_vector_4.npy (data): Direction for vector_4.
  • direction_vector_5.npy (data): Direction for vector_5.
  • direction_vector_6.npy (data): Direction for vector_6.
  • direction_table_0.npy (data): Direction for table_0.
  • direction_table_1.npy (data): Direction for table_1.
  • probe_x.npy (data): Public scalar probe inputs.
  • probe_segment_ids.npy (data): Public scalar probe segment labels.
  • probe_sensitivities.npy (data): Public scalar probe sensitivities.
  • probe_targets.npy (data): Public scalar probe responses.
  • calibration_cases.npz (data): Additional public anonymized calibration cases with scalar probes for discovering role fingerprints.
  • meta.json (data): Anonymous dimensions and partition fields.

Outputs

  • analysis.py (code): Agent implementation.
  • results/output.npy (data): Forward output.
  • results/input_gradient.npy (data): Gradient with respect to x.
  • results/input_hvp.npy (data): HVP block for x.

Public repository files

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Provenance

This task is included in the current seed42 benchmark set at dataset revision f11a199f71fb4b854ac43a1bf548d5df519141a9.

This page is generated only from files tracked in the public ASI-Bench repository at commit f18382f03faf. Submission bundles, run logs, private scoring configuration, and private reference answers are not read by this page generator.