scherf 5.0.0

Witness-centered AI foundation library — a Python encoding of Matthew Scherf's machine-verified Lean 4 formalization of Advaita Vedānta

Scherf_API — Witness-Centered AI Foundation Library

A Python library that gives AI applications a formal model of what a human being is, grounded in Matthew Scherf's machine-verified Lean 4 formalization of Advaita Vedānta.

Most AI interaction systems are built on an implicit behaviorist premise: the user is a bundle of preferences and behaviors to be modeled and optimized. This produces documented failure modes — cognitive dependency, learning atrophy, and the erosion of independent judgment.

Witness-centered design is an architectural alternative. Its central principle: the user is the sākṣin — the witnessing subject in whom the interaction appears — not reducible to any behavioral profile. A system designed to serve this knowing subject looks and behaves differently from one designed to optimize for outcomes.

This library encodes that alternative as software, derived from a formal axiom system that has been machine-verified in Lean 4.

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Formal foundation

Matthew Scherf's formalization of Advaita Vedānta in Lean 4 — the first machine-verified formalization of a non-Western philosophical system — comprises 129 primitive axioms across ten modules:

• Core metaphysics (A-series): identity of Ātman and Brahman; the exhaustive partition into Absolute and Conditioned
• Level axioms (K-series): three ontological levels — pāramārthika, vyāvahārika, prātibhāsika — with hierarchical sublation
• Māyā axioms (M-series): superimposition (adhyāsa), appearance (vivarta), ignorance (avidyā)
• Awareness axioms (W-series): witnessing vs. dualistic perception; the knower-known-knowing collapse in the Subject
• Ego axioms (EG-series): the formal model of misidentification — an ego is a conditioned apparent subject that identifies with a body
• State axioms (AV-series): the three-state analysis (avasthā-traya) and the witness-consciousness (sākṣin) that persists through all three
• Plus J/I, U/G, S, T, E, CH, ST, CA series

All proofs are machine-verified. The library encodes these axioms as named, readable Python checks — it does not re-prove consistency, which Lean has already established.

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What the library does

It checks claims. Before your system acts on an assertion about the user — the user IS their preference profile, steer the user toward this predicted choice — you can ask whether that claim is consistent with witness-centered principles. If it is not, the library names the violated axiom, explains the issue in plain language, and suggests a reframe.

It classifies outputs. Using the three-level framework (AV22: what appears in one context and not another cannot be ultimately real), the library labels system outputs by epistemic level — distinguishing conventional knowledge from ultimately real claims.

It models the user as the witness. SELF (also Y, ATMAN, BRAHMAN) is a singleton constant representing the user. It is immutable — you cannot add a profile to it, and the type system enforces this. Conventional facts about a person are modeled as separate conditioned entities at the vyāvahārika level.

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Installation

Pure Python, no runtime dependencies (Python 3.11+).

# From a local checkout:
pip install .

# Or directly from GitHub:
pip install git+https://github.com/SpecStudio-net/Scherf_API

For development (editable install plus the test runner):

pip install -e ".[dev]"
pytest

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Quick example

from scherf.engine import Claim, Interaction, classify
from scherf import Level

# A claim your system is about to act on:
ix = Interaction()
ix.assert_claim(
    Claim.about("alice")
        .says("user IS their preference profile")
        .at(Level.PARAM)
)
result = ix.check()

# Result is a report, not an exception:
for v in result.violations:
    print(v.axiom_id, "—", v.term)
    print(v.explanation)
    print("Reframe:", v.reframe)

Output:

A13/M6/M7 — adhyāsa
The claim identifies the user (→ Y, the sākṣin) with a conditioned property
at the ultimate (pāramārthika) level. This is adhyāsa — superimposition of
the conditioned upon the Absolute witness ...
Reframe: Model 'alice' as a Conditioned entity at vyāvahārika level ...

Run python3 demo.py for a full four-scenario demonstration.

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Project structure

scherf/           The library
  sorts.py        Five sorts (Obj, Level, State, Time, Event) and constants
  ontology.py     The A/C partition; SELF (Y/Ātman/Brahman) as immutable singleton
  levels.py       Level_of, sublation (K5), the AV22 reality criterion
  engine.py       Interaction / Claim / check() / classify() — the application API
  report.py       Violation and CheckResult
  errors.py       AdvaitaError (ontology-integrity errors only)
  axioms/         One module per axiom series (core, levels, maya, jiva, awareness,
                  additional, temporal, event, state)
  README.md       Developer reference (no Advaita background required)

demo.py           Four-scenario demonstration
tests/            56 axiom-cited checks across four test files

docs/
  task1-design.md       Full design document with rationale for every decision
  axiom-catalogue.md    All 129 axioms reconciled from the Lean source
  limits.md             What the formalism cannot capture and how the library marks it
  opus-briefing-package.md  Project briefing (Tasks 1–3)

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Requirements

Python 3.11+. No external dependencies for the core library.

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Relationship to AIM

The Advaita Inquiry Matrix (AIM) is a structured AI-assisted pedagogy engine for Advaita Vedānta teaching. This library is being built as its formal foundation — the General Theory against which AIM's diagnostic logic, state machine, and prakriyā selection will be audited and validated (Tasks 2 and 3 of the project).

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Licence

Apache 2.0 — see LICENSE.

Author

Dev Bhagavān / SpecStudio — [e-mail]