decision-pga 0.1.0

Decision-PGA diagnostics for probability clouds on the categorical simplex.

Decision-PGA

Decision-PGA is a synthetic-first prototype for agent-facing diagnostics on model decision states. Version 1 analyzes clouds of categorical probability vectors with Fisher-Rao/square-root geometry:

probabilities -> sqrt embedding on the positive sphere -> intrinsic mean
-> tangent vectors -> principal geodesic dispersion tensor

The immediate goal is to distinguish decision geometries that ordinary entropy summaries blur together:

• stable confident decisions;
• coherent binary ambiguity;
• diffuse uncertainty;
• perturbation-sensitive boundary cases;
• sliding-window regime shifts.

Status

This is an initial public research release. It is intended for collaborators, agent-tooling experiments, synthetic benchmarks, and critique. It is not a production safety layer, not clinical validation, and not a medical device or clinical decision support product. The examples are synthetic or public-facing fixtures; the package does not call model APIs or require credentials.

Quick Start

Use the public repo directly:

git clone https://github.com/zmichels/Decision-PGA.git
cd Decision-PGA
python3 -m venv .venv
. .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -e ".[mcp]"
python -m unittest discover -s tests -v

Run the JSON CLI:

decision-pga diagnose examples/model_outputs.json
cat examples/model_outputs.json | decision-pga diagnose -
decision-pga diagnose --pretty examples/provider_scores.json
decision-pga diagnose --pretty examples/agent/tool_action_ambiguity.json
decision-pga diagnose --pretty examples/agent/rag_evidence_conflict.json
decision-pga evaluate --config examples/evaluation_config.json --output reports/latest
decision-pga evaluate --suite application --output reports/application-latest
decision-pga evaluate --suite document-extraction --output reports/document-extraction-latest

For the shortest agent-builder path, start with docs/agent-toolkit.md. It walks through CLI diagnosis, Python API diagnosis, local MCP launch, and copy-paste examples for tool ambiguity, RAG evidence conflict, document extraction routing, agent drift, and stable abstention.

Open or execute:

notebooks/01_fisher_rao_probability_clouds.ipynb

Minimal API

from decision_pga import (
    diagnose_probability_cloud,
    diagnose_model_outputs,
    diagnose_sampled_responses,
    ModelOutputObservation,
    observation_from_provider_scores,
    SampledResponse,
    pga_probability_cloud,
    synthetic_probability_cloud,
)

probs = synthetic_probability_cloud("binary_ambiguity", 80, 5, seed=7)
result = pga_probability_cloud(probs, label="binary ambiguity")

print(result.pc1_fraction)
print(result.anisotropy_ratio)
print(result.mean_margin)

diagnostic = diagnose_probability_cloud(
    probs,
    labels=["alpha", "beta", "gamma", "delta", "epsilon"],
)

print(diagnostic.state)
print(diagnostic.recommended_action)
print(diagnostic.to_dict())

observations = [
    ModelOutputObservation([0.88, 0.08, 0.04], kind="probabilities"),
    ModelOutputObservation([0.86, 0.10, 0.04], kind="probabilities"),
    ModelOutputObservation([0.89, 0.07, 0.04], kind="probabilities"),
    ModelOutputObservation([0.87, 0.09, 0.04], kind="probabilities"),
]

model_result = diagnose_model_outputs(
    observations,
    labels=["approve", "reject", "defer"],
)

print(model_result.to_dict())

sampled_result = diagnose_sampled_responses(
    [
        SampledResponse("approve"),
        SampledResponse("approve"),
        SampledResponse("reject"),
        SampledResponse("reject"),
    ],
    labels=["approve", "reject", "defer"],
    window_size=2,
)

print(sampled_result.to_dict())

provider_observation = observation_from_provider_scores(
    {
        "output": {
            "scores": {
                "approve": -0.13,
                "reject": -2.53,
                "defer": -3.10,
            }
        }
    },
    score_path=("output", "scores"),
    candidates=["approve", "reject", "defer"],
    kind="logprobs",
)

The first agent-facing diagnostic states and recommended actions are:

State	Recommended action
stable	proceed
binary_ambiguity	clarify_between_top_labels
diffuse_uncertainty	gather_more_evidence
boundary_sensitive	inspect_sensitivity
regime_shift	segment_or_replan

See docs/model-output-adapters.md for the provider-neutral model output adapter boundary and docs/source-adapters.md for sampled-response and trajectory adapters. Provider-shaped response extraction is documented in docs/provider-bridges.md. The process-level JSON contract is documented in docs/cli.md. The synthetic benchmark harness is documented in docs/evaluation.md. The application-gap bridge and review article are in docs/application-gap-review.md, docs/decision-pga-application-scenarios.md, and docs/articles/decision-pga-gap-review.md. The separate document-extraction gap bridge is documented in docs/document-extraction-gap-review.md, docs/decision-pga-document-extraction-scenarios.md, and docs/articles/decision-pga-document-extraction-gap-review.md. The healthcare-focused publication draft is docs/articles/decision-pga-healthcare-decision-state-diagnostics.md, with a one-week publication checklist in docs/healthcare-publication-plan.md.

Local MCP Server

Install the optional MCP dependency and launch the local stdio server:

python -m pip install -e ".[mcp]"
decision-pga-mcp

The MCP server is local, deterministic, and read-only. It exposes the same diagnostic contract as the Python API and CLI. See docs/mcp-server.md.

Draft MCP Registry metadata is prepared in docs/mcp-registry/server.json, but it has not been submitted. The first supported MCP surface is local stdio.

Tester Path

For a short collaborator trial, start with docs/tester-guide.md and capture comments with docs/tester-feedback-template.md.

For release and adoption preparation, see docs/release-checklist.md, docs/community-engagement.md, and docs/outreach/launch-posts.md.

Notes

This prototype is deliberately model-free. It does not call the OpenAI API, run a local LLM, or inspect hidden activations. Real model adapters should come after the synthetic geometry is stable and tested.

License And Citation

Decision-PGA is released under the MIT License. See LICENSE.

If you use this prototype in research, demos, or internal evaluation, please cite the repository metadata in CITATION.cff.