Research Program

BLISP Research Program

Research on admissibility, deterministic execution, provenance, and capability-grounded AI systems.

Author: Thomas Dionysopoulos
Program: 9 papers
Status: All 9 papers published
Version: v1.0
BLISP Research Program Overview
Overview

Research Program Structure

The BLISP research program develops a formal framework for AI systems that generate and execute computational pipelines. Papers 1–5 establish the foundation: admissibility (grounding gate), canonical execution semantics, quotient categories, provenance algebra, and fiber structure under stochastic generation. Papers 6–7 show that a single semantic coordinate predicts optimizer behavior at 99.6% accuracy and generalizes to unseen operations at 100%. Paper 8 tests cross-system transfer: the frozen taxonomy predicts execution behavior in Polars and DuckDB at 91.1% combined accuracy. Paper 9 demonstrates that independent agents reconstruct structurally equivalent execution-identity primitives under task pressure, with 7/8 primitives converging above 0.90 across three model families.

Program DOI

Cite the Program

The complete nine-paper program is archived as a single citable record on Zenodo. Each paper also has its own DOI (listed below).

Program DOI 10.5281/zenodo.20459958

Program DOI: 10.5281/zenodo.20459958

Papers

Published and Forthcoming

PAPER 1
The Grounding Gate: Admissibility and Replay Guarantees for AI-Driven Research
Published Draft
Thomas Dionysopoulos
DOI: 10.5281/zenodo.20456984
AI systems that generate computational pipelines from natural language may propose operations that are structurally valid but semantically unwarranted. Schema validation catches malformed proposals; it does not catch valid-but-wrong ones.

We present a grounding gate: a mandatory admissibility boundary between AI-proposed operations and deterministic execution. The system discovers which capabilities match the user's terms by querying a live registry (236 capabilities), and a deterministic grounding function verifies that every name in the proposal has evidence in the discovery result. Names lacking evidence are rejected before execution, even if they name real capabilities. Admitted proposals execute deterministically, producing an 8-layer execution hash that decomposes provenance into semantic layers for fault localization without re-execution.

The capability registry separates description from identity: three layers (semantic, algebraic, implementation) determine a capability's hash, while discovery metadata (aliases, tags) does not, allowing the registry to improve discoverability without invalidating prior execution hashes.

We evaluate on 30 prompts across 5 categories (4 strategy families, 9 metrics, 36 valid combinations). An unconstrained pipeline executes valid-but-unwarranted capabilities at 23.3%; the grounded pipeline reduces this to 10.0% (Fisher exact p = 0.027), eliminating them entirely on undiscoverable prompts (100% to 0%). On adversarial prompts exploiting discovery-alias gaps, the grounded pipeline has a higher failure rate -- a tradeoff inherent to constraining the admissible set. Repeated executions produce bit-identical hashes across 50 runs. Grounding overhead is under 14 ms.
PDF Source (.tar.gz) Artifacts Zenodo 
@article{dionysopoulos2026grounding,
  title   = {The Grounding Gate: Admissibility and Replay Guarantees
             for AI-Driven Research},
  author  = {Dionysopoulos, Thomas},
  year    = {2026},
  doi     = {10.5281/zenodo.20456984},
  note    = {Published draft, BLISP Research Program Paper 1},
  url     = {https://blisp.ai/papers/paper1.pdf}
}
PAPER 2
Canonical Execution Semantics for Stochastic Program Generators
Published Draft
Thomas Dionysopoulos
DOI: 10.5281/zenodo.20457255
When programs are generated by stochastic systems, independently generated programs that represent the same intended computation arrive in different surface forms, producing different hashes, different provenance records, and failed replay comparisons. We argue that execution systems for stochastic generators require a canonical execution boundary: an architectural invariant that partitions the pipeline into a stochastic upstream and a deterministic downstream. Four mechanisms enforce the boundary: typed specifications, a canonicalization pipeline (278 surface forms to 235 canonical operations), 8-layer execution hashing, and description/identity separation. Evaluated on 1,200 stochastic LLM generations with 50-run replay determinism.
PDF Zenodo Artifacts
PAPER 3
Execution Categories for Stochastic Program Generators: Quotient Semantics for Deterministic Executable Identity
Published Draft
Thomas Dionysopoulos
DOI: 10.5281/zenodo.20457403
We define a registry-indexed execution category whose objects are typed executable artifacts and whose morphisms are admissible pipeline transformations. The operational equivalence generated by the system's rewrite rules forms a congruence: equivalent subexpressions remain equivalent under arbitrary well-typed pipeline composition. The resulting quotient category gives precise meaning to deterministic execution identity. Content-addressed hashing serves as a computable operational witness of quotient membership.
PDF Zenodo Artifacts
PAPER 4
Provenance Algebra for Deterministic AI Execution: Replay Semantics for Stochastic Program Generators
Published Draft
Thomas Dionysopoulos
DOI: 10.5281/zenodo.20457667
Provenance for deterministic execution systems is not metadata but a semantic factorization of execution identity. We define a provenance map over the quotient execution category, assigning to each execution equivalence class an 8-layer hash record that decomposes execution identity into semantic dependency boundaries. A dependency-indexed composition law establishes that pipeline provenance is determined by stage provenance and the declared dependency map. Enables replay equivalence, divergence localization, partial replay, and provenance-preserving registry evolution.
PDF Zenodo Artifacts
PAPER 5
Proposal Collapse and Execution Fibers in Stochastic Program Generation
Published Draft
Thomas Dionysopoulos
DOI: 10.5281/zenodo.20457990
Two distinct kinds of variation emerge when stochastic generators propose executable specifications. Surface-form variation is absorbed by canonicalization (intra-fiber). Execution ambiguity creates clean transitions between execution classes (inter-fiber). Across 2,200 proposals with controlled perturbations: synonym rewording stays within fibers (rho = 0.985), metric/family substitutions produce zero same-fiber mass (rho = 0.000) with perfect stability (sigma = 1.000). The adjacency graph is sparse (density = 0.095).
PDF Zenodo Artifacts
PAPER 6
The Semantic Structure of Execution: An Empirical Study of Predictive Coordinates in Computational Operations
Published Draft
Thomas Dionysopoulos
DOI: 10.5281/zenodo.20612709
A single 7-valued coordinate (DependencyClass) classifies operations by data-dependency shape and predicts four independent optimizer behaviors—fusion eligibility, window semantics, pipeline position, and state management—with 99.6% accuracy (243/244 behavior predictions, z = 13.0, p < 10−38 vs random baseline). The coordinate is not a descriptive label; it is a predictive object that determines execution behavior from semantic structure alone. Conditional mutual information analysis confirms that DependencyClass provides information about optimizer behavior beyond what operation name alone provides.
Zenodo Artifacts
PAPER 7
Semantic Coordinates as Predictive Objects in Time-Series Computation
Published Draft
Thomas Dionysopoulos
DOI: 10.5281/zenodo.20706294
A frozen taxonomy trained on 61 operations generalizes to 25 unseen operations at 100% accuracy (100/100 holdout predictions) with zero recalibration. Coordinate ablation confirms that the full coordinate is minimal—removing any single dimension degrades prediction. Random baselines with equivalent cardinality achieve chance accuracy. The result establishes semantic coordinates as predictive objects: they predict optimizer behavior, not merely describe it.
PDF Zenodo Artifacts
PAPER 8
Dependency Shape Predicts Execution Behavior Across Independent Data Processing Systems
Published Draft
Thomas Dionysopoulos
DOI: 10.5281/zenodo.20706086
A frozen 8-valued dependency-shape taxonomy, built without inspecting either target system, predicts three execution behaviors (streaming eligibility, buffering requirements, warmup) in Polars (Rust, morsel-driven parallelism) and DuckDB (C++, push-based pipelines). Buffering predictions reach 96.7% accuracy in both systems, with the single shared error (filter) reflecting a classification boundary. Combined accuracy across 180 predictions is 91.1%, with zero errors from incorrect dependency-shape assignments. All errors trace to architectural choices and API conventions, not to the taxonomy itself.
PDF Zenodo Artifacts
PAPER 9
Agents Reconstruct Execution Identity Algebra Under Task Pressure
Published Draft
Thomas Dionysopoulos
DOI: 10.5281/zenodo.20706156
Independent frontier model families (Anthropic, OpenAI, Google), working on independent domains (finance, SQL, build/CI), reconstruct structurally equivalent execution-identity primitives under task pressure. Nine question tiers of increasing difficulty elicit eight primitives: normalization, canonical identity, equivalence classes, grouping, composite rewriting, replay mappings, computation DAGs, and policy checking. 7/8 primitives converge above 0.90 across 55 runs. Reconstruction is convergent, staged, and expensive (~178,000 tokens per reconstruction). A reference implementation materializes the same eight primitives as persistent, composable, domain-portable infrastructure at zero marginal query cost.
PDF Zenodo Artifacts
Program Timeline

Research Dependency Graph

Paper 1
Grounding Gate
-->
Paper 2
Execution
-->
Paper 3
Categories
-->
Paper 4
Provenance
-->
Paper 5
Fibers
-->
Paper 6
Semantic Struct.
-->
Paper 7
Predictive Obj.
-->
Paper 8
Cross-System
-->
Paper 9
Convergence
Each paper builds on the formal foundation of its predecessor.
Reproducibility

Build Provenance

Every published paper is pinned to a specific commit, tag, and artifact hash. Source and compiled artifacts are independently verifiable.

Paper 1 — The Grounding Gate

ArtifactValue
Git commit3d634ee
Git tagpaper1-grounding-gate-arxiv-v1
Registry snapshot236 capabilities (DIC at tag)
Tarballpaper1-source.tar.gz
SHA-256e0f12745f09f68c006fdd21e1bcf2cbd41372cf0bcebaae39e9d1f7c8fad0be0
Pages13
Prompts evaluated30 (5 categories, 4 families, 9 metrics)
Hash stability50 runs, bit-identical
Citation

How to Cite

If you reference the BLISP research program or any individual paper, please use the following.

Paper 1

@article{dionysopoulos2026grounding,
  title   = {The Grounding Gate: Admissibility and Replay Guarantees
             for AI-Driven Research},
  author  = {Dionysopoulos, Thomas},
  year    = {2026},
  doi     = {10.5281/zenodo.20456984},
  note    = {Published draft, BLISP Research Program Paper 1},
  url     = {https://blisp.ai/papers/paper1.pdf}
}

Paper 2

@article{dionysopoulos2026canonical,
  title   = {Canonical Execution Semantics for Stochastic Program
             Generators},
  author  = {Dionysopoulos, Thomas},
  year    = {2026},
  doi     = {10.5281/zenodo.20457255},
  note    = {Published draft, BLISP Research Program Paper 2},
  url     = {https://blisp.ai/papers/paper2.pdf}
}

Paper 3

@article{dionysopoulos2026categories,
  title   = {Execution Categories for Stochastic Program Generators:
             Quotient Semantics for Deterministic Executable Identity},
  author  = {Dionysopoulos, Thomas},
  year    = {2026},
  doi     = {10.5281/zenodo.20457403},
  note    = {Published draft, BLISP Research Program Paper 3},
  url     = {https://blisp.ai/papers/paper3.pdf}
}

Paper 4

@article{dionysopoulos2026provenance,
  title   = {Provenance Algebra for Deterministic {AI} Execution:
             Replay Semantics for Stochastic Program Generators},
  author  = {Dionysopoulos, Thomas},
  year    = {2026},
  doi     = {10.5281/zenodo.20457667},
  note    = {Published draft, BLISP Research Program Paper 4},
  url     = {https://blisp.ai/papers/paper4.pdf}
}

Paper 5

@article{dionysopoulos2026fibers,
  title   = {Proposal Collapse and Execution Fibers in Stochastic
             Program Generation},
  author  = {Dionysopoulos, Thomas},
  year    = {2026},
  doi     = {10.5281/zenodo.20457990},
  note    = {Published draft, BLISP Research Program Paper 5},
  url     = {https://blisp.ai/papers/paper5.pdf}
}

Paper 6

@article{dionysopoulos2026semantic,
  title   = {The Semantic Structure of Execution: An Empirical Study of
             Predictive Coordinates in Computational Operations},
  author  = {Dionysopoulos, Thomas},
  year    = {2026},
  doi     = {10.5281/zenodo.20612709},
  note    = {Published draft, BLISP Research Program Paper 6},
  url     = {https://doi.org/10.5281/zenodo.20612709}
}

Paper 7

@article{dionysopoulos2026predictive,
  title   = {Semantic Coordinates as Predictive Objects in Time-Series
             Computation},
  author  = {Dionysopoulos, Thomas},
  year    = {2026},
  doi     = {10.5281/zenodo.20706294},
  note    = {Published draft, BLISP Research Program Paper 7},
  url     = {https://doi.org/10.5281/zenodo.20706294}
}

Paper 8

@article{dionysopoulos2026transfer,
  title   = {Dependency Shape Predicts Execution Behavior Across
             Independent Data Processing Systems},
  author  = {Dionysopoulos, Thomas},
  year    = {2026},
  doi     = {10.5281/zenodo.20706086},
  note    = {Published draft, BLISP Research Program Paper 8},
  url     = {https://doi.org/10.5281/zenodo.20706086}
}

Paper 9

@article{dionysopoulos2026convergence,
  title   = {Agents Reconstruct Execution Identity Algebra Under
             Task Pressure},
  author  = {Dionysopoulos, Thomas},
  year    = {2026},
  doi     = {10.5281/zenodo.20706156},
  note    = {Published draft, BLISP Research Program Paper 9},
  url     = {https://doi.org/10.5281/zenodo.20706156}
}

Research Program

@misc{blisp2026research,
  title        = {BLISP Research Program: Admissibility, Execution,
                  Provenance, and Capability-Grounded AI Systems},
  author       = {Dionysopoulos, Thomas},
  year         = {2026},
  howpublished = {\url{https://blisp.ai/papers}},
  note         = {9-paper program; all papers published}
}
Downloads

Paper Assets

AssetFormatLink
Paper 1 — PDF PDF, 13 pages paper1.pdf
Paper 1 — Source LaTeX tarball paper1-source.tar.gz
Paper 1 — Artifacts Prompts, verification scripts artifacts/
Paper 2 — PDF PDF, 23 pages paper2.pdf
Paper 3 — PDF PDF, 14 pages paper3.pdf
Paper 4 — PDF PDF, 15 pages paper4.pdf
Paper 5 — PDF PDF, 12 pages paper5.pdf
Paper 6 — Metadata Results via cargo test paper6-zenodo/
Paper 7 — PDF PDF, 14 pages paper7.pdf
Paper 8 — PDF PDF, 17 pages paper8.pdf
Paper 9 — PDF PDF, 17 pages paper9.pdf
All Papers — Artifacts Zenodo packages + source blisp-research
Research Impact

Readership

--
Unique readers
--
Paper downloads
--
Source downloads