#!/usr/bin/env python3 """Receipt a scholar-style deep dive on abstraction-layer shape matches. The search target is the local abstraction: each stage is parallel domains of the data type being processed The useful source matches are not compression claims. They are operator families that resemble the local machine: synchronization schemas, reduced product abstract domains, staged computation, multi-view fusion, provenance semirings, lenses, and type/data-oriented parallel systems. """ from __future__ import annotations import hashlib import json from pathlib import Path from typing import Any REPO = Path(__file__).resolve().parents[2] SHIM = REPO / "4-Infrastructure" / "shim" OUT = SHIM / "scholar_abstraction_layer_shape_deep_dive_receipt.json" CURRICULUM_OUT = SHIM / "scholar_abstraction_layer_shape_deep_dive_curriculum.jsonl" GENERATED_AT = "2026-05-08T00:00:00+00:00" def stable_json(obj: Any) -> str: return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) def sha256_text(text: str) -> str: return hashlib.sha256(text.encode("utf-8")).hexdigest() def rel(path: Path) -> str: return str(path.relative_to(REPO)) SOURCE_BUNDLE = [ { "id": "synchronization_schemas", "title": "Synchronization Schemas", "authors": "Rajeev Alur et al.", "venue": "PODS 2021", "url": "https://research.google/pubs/synchronization-schemas/", "shape": "type-theoretic synchronization over series-parallel streams", "local_mapping": "cross-domain barrier and typed stage stream object", "strength": "primary_match", }, { "id": "reduced_product_abstract_transformers", "title": "Synthesizing Abstract Transformers for Reduced-Product Domains", "authors": "Pankaj Kumar Kalita, Thomas Reps, Subhajit Roy", "venue": "arXiv 2024", "doi": "10.48550/arXiv.2408.04040", "url": "https://arxiv.org/abs/2408.04040", "shape": "component transformers over product domains must cooperate", "local_mapping": "parallel stage domains with cross-domain contracts", "strength": "primary_match", }, { "id": "product_operators_abstract_interpretation", "title": "A Survey on Product Operators in Abstract Interpretation", "authors": "Agostino Cortesi, Giulia Costantini, Pietro Ferrara", "venue": "EPTCS 129, 2013", "doi": "10.4204/EPTCS.129.19", "url": "https://arxiv.org/abs/1309.5146", "shape": "Cartesian products, reduced products, and cardinal powers combine domains", "local_mapping": "domain vector algebra for byte/token/structure/residual/witness views", "strength": "primary_match", }, { "id": "multidirectional_synchronization", "title": "Controllable and decomposable multidirectional synchronizations", "authors": "Hermann et al.", "venue": "Software and Systems Modeling, 2021", "doi": "10.1007/s10270-021-00879-w", "url": "https://link.springer.com/article/10.1007/s10270-021-00879-w", "shape": "wide span of lenses synchronizes multiple views through a central model", "local_mapping": "central byte authority with token/structure/witness/domain views", "strength": "primary_match", }, { "id": "staged_computation", "title": "Staged computation", "authors": "James R. Larus and Michael Parkes", "venue": "USENIX 2002", "url": "https://www.usenix.org/publications/library/proceedings/usenix02/full_papers/larus/larus_html/index.html", "shape": "stage as asynchronous operation group with private data and scheduling autonomy", "local_mapping": "stage control plane, but local model adds typed parallel domains", "strength": "strong_analogy", }, { "id": "staged_classes", "title": "Multi-stage Programming in the Large with Staged Classes", "authors": "Lionel Parreaux, Amir Shaikhha", "venue": "GPCE 2020", "url": "https://cse.hkust.edu.hk/~parreaux/publication/gpce20/", "shape": "zero-cost staged abstractions for modular programs and data structures", "local_mapping": "compile route-stage abstractions away from payload; keep receipts only", "strength": "strong_analogy", }, { "id": "multi_view_gnn_taxonomy", "title": "Graph neural networks for multi-view learning: a taxonomic review", "authors": "Shunxin Xiao et al.", "venue": "Artificial Intelligence Review, 2024", "doi": "10.1007/s10462-024-10990-1", "url": "https://link.springer.com/article/10.1007/s10462-024-10990-1", "shape": "multiple graph/relation/attribute views are fused or aligned", "local_mapping": "parallel route views propose and align but cannot override byte hash", "strength": "strong_analogy", }, { "id": "semiring_provenance", "title": "PROX: Approximated Summarization of Data Provenance", "authors": "Deutch, Gilad, Moskovitch et al.", "venue": "VLDB / PMC version", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5001561/", "shape": "provenance annotations summarize how outputs depend on input domains", "local_mapping": "witness/provenance domain with bounded summaries and exact byte authority", "strength": "strong_analogy", }, { "id": "hawkeye_datatype_semantics", "title": "HAWKEYE: Effective Discovery of Dataflow Impediments to Parallelization", "authors": "Omer Tripp, Greta Yorsh, John Field, Mooly Sagiv", "venue": "OOPSLA 2011", "url": "https://research.google/pubs/hawkeye-effective-discovery-of-dataflow-impediments-to-parallelization/", "shape": "parallelization dependencies tracked at abstract data-type semantics", "local_mapping": "domain contracts should track semantic dependencies, not raw field noise", "strength": "strong_analogy", }, { "id": "yedalog", "title": "Yedalog: Exploring Knowledge at Scale", "authors": "Brian Chin et al.", "venue": "SNAPL 2015", "url": "https://research.google/pubs/yedalog-exploring-knowledge-at-scale/", "shape": "mix data-parallel pipelines and computation in one declarative language over nested records", "local_mapping": "route compiler DSL can mix domain-parallel transforms with structured corpus records", "strength": "supporting_match", }, { "id": "dynamically_managed_data_cpu_gpu", "title": "Dynamically Managed Data for CPU-GPU Architectures", "authors": "Thomas B. Jablin et al.", "venue": "CGO 2012", "url": "https://research.google/pubs/dynamically-managed-data-for-cpu-gpu-architectures/", "shape": "automatic consistency management for CPU/GPU views of complex data", "local_mapping": "owner/budget/closure domains must keep heterogeneous route views consistent", "strength": "supporting_match", }, { "id": "scalable_data_abstractions", "title": "Scalable data abstractions for distributed parallel computations", "authors": "James Hanlon, Simon J. Hollis, David May", "venue": "arXiv 2012", "doi": "10.48550/arXiv.1210.1157", "url": "https://arxiv.org/abs/1210.1157", "shape": "separate data representation from computation and allow distributed representations", "local_mapping": "active data type has distributed domain views; byte view remains authority", "strength": "supporting_match", }, ] CLUSTERS = [ { "id": "typed_synchronization_streams", "members": ["synchronization_schemas", "yedalog"], "local_operator": "type each route stage as a series-parallel stream of domains", }, { "id": "reduced_product_domain_algebra", "members": ["reduced_product_abstract_transformers", "product_operators_abstract_interpretation"], "local_operator": "combine byte/token/structure/residual/witness domains as a reduced product with cross-domain reductions", }, { "id": "multi_view_consistency", "members": ["multidirectional_synchronization", "multi_view_gnn_taxonomy"], "local_operator": "let views align and synchronize, while central byte authority resolves promotion", }, { "id": "stage_runtime_boundary", "members": ["staged_computation", "staged_classes", "dynamically_managed_data_cpu_gpu"], "local_operator": "stage abstractions manage computation and consistency but must not become payload", }, { "id": "provenance_dependency_witness", "members": ["semiring_provenance", "hawkeye_datatype_semantics", "scalable_data_abstractions"], "local_operator": "track provenance and data-type dependencies as bounded witness domains", }, ] EQUATIONS = [ { "id": "SAD0_reduced_product_stage", "equation": "Stage_t = D_byte x_R D_token x_R D_structure x_R D_residual x_R D_witness x_R D_owner x_R D_budget x_R D_closure", "meaning": "The stage is a reduced product of mutually constraining domains, not a flat tuple.", }, { "id": "SAD1_domain_transformer_vector", "equation": "F_t^# = ", "meaning": "Each stage edge is a vector of component transformers.", }, { "id": "SAD2_synchronization_schema", "equation": "sync_schema(Stage_t) = ordering + key_partition + barrier_contract", "meaning": "A typed schema controls which domains are ordered, keyed, parallel, or barriered.", }, { "id": "SAD3_lens_center", "equation": "central_model = exact_byte_span + exact_residuals", "meaning": "Token, structure, and witness views synchronize through the byte/residual center.", }, { "id": "SAD4_provenance_witness", "equation": "W = provenance_semiring(route_edges, source_spans, residual_obligations)", "meaning": "Witness domains record how a route result depends on inputs and repairs.", }, { "id": "SAD5_promotion_barrier", "equation": "promote iff all reductions close and hash(decode(center)) == source_hash", "meaning": "No view alignment or abstract-domain precision replaces byte rehydration.", }, ] def build_receipt() -> dict[str, Any]: receipt: dict[str, Any] = { "schema": "scholar_abstraction_layer_shape_deep_dive_v1", "generated_at": GENERATED_AT, "search_note": ( "Direct automated Google Scholar access is unreliable, so this scan used " "Google-Scholar-linked research pages plus primary publisher, arXiv, " "Springer, USENIX, and Google Research pages." ), "source_bundle": SOURCE_BUNDLE, "clusters": CLUSTERS, "equations": EQUATIONS, "primary_decision": { "name": "treat_parallel_stage_domains_as_reduced_product_with_sync_schema", "statement": ( "The closest literature shape is a reduced-product domain vector " "controlled by synchronization schemas and lens-like consistency. " "For the compressor, each stage should expose typed component " "transformers, cross-domain reduction/barrier rules, bounded " "provenance witnesses, and one exact byte/residual center." ), }, "candidate_dd_state_extension": [ "reduced_product_stage_id", "sync_schema_id", "domain_transformer_vector_id", "domain_reduction_operator_id", "view_lens_center_id", "provenance_semiring_id", "data_type_dependency_witness_id", "stage_parallelism_class", "domain_consistency_status", "domain_reduction_fixpoint_status", "byte_residual_center_hash", ], "candidate_dd_edges": [ "choose_sync_schema", "open_reduced_product_stage", "synthesize_domain_transformer_vector", "apply_cross_domain_reduction", "synchronize_views_through_byte_center", "emit_provenance_semiring_witness", "track_data_type_dependency", "reject_view_consistency_without_byte_hash", "close_reduced_product_stage", ], "promotion_rule": [ "stage_domains_are_a_reduced_product_not_unrelated_sidecars", "sync_schema_declares_ordering_keying_and_barriers", "component_transformers_are_typed_and_receipted", "cross_domain_reductions_reach_fixpoint_or_fail_closed", "views_synchronize_through_exact_byte_residual_center", "provenance_witness_is_bounded", "decoded_hash_matches_source", "measured_total_bytes_beat_incumbent_under_ratio_schema", ], "failure_rule": [ "view_fusion_without_byte_center -> diagnostic_only", "reduced_product_search_space_explodes -> prune_or_split_stage", "sync_schema_missing_barrier -> invalid_receipt", "provenance_polynomial_unbounded -> summarize_or_prune", "stage_abstraction_serialized_as_payload -> invalid_receipt", "data_type_dependency_ignored -> unsafe_parallelization", ], "claim_boundary": ( "These papers provide similar abstraction-layer shapes. None are " "evidence of compression improvement until local encode/decode/hash/" "byte-count receipts close under one ratio schema." ), } preimage = {key: value for key, value in receipt.items() if key != "receipt_hash"} receipt["receipt_hash"] = sha256_text(stable_json(preimage)) return receipt def curriculum_lines(receipt: dict[str, Any]) -> list[dict[str, Any]]: lines: list[dict[str, Any]] = [] for item in receipt["source_bundle"]: lines.append({"type": "source_shape", **item}) for item in receipt["clusters"]: lines.append({"type": "cluster", **item}) for item in receipt["equations"]: lines.append({"type": "equation", **item}) for rule in receipt["promotion_rule"]: lines.append({"type": "promotion_rule", "rule": rule}) for rule in receipt["failure_rule"]: lines.append({"type": "failure_rule", "rule": rule}) return lines def main() -> None: receipt = build_receipt() OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") lines = curriculum_lines(receipt) CURRICULUM_OUT.write_text( "".join(json.dumps(line, sort_keys=True) + "\n" for line in lines), encoding="utf-8", ) print(json.dumps({ "receipt": rel(OUT), "curriculum": rel(CURRICULUM_OUT), "receipt_hash": receipt["receipt_hash"], "curriculum_records": len(lines), "decision": receipt["primary_decision"]["name"], "source_count": len(receipt["source_bundle"]), "cluster_count": len(receipt["clusters"]), }, indent=2, sort_keys=True)) if __name__ == "__main__": main()