sm-resolver — the corroboration kernel

A tiny, dependency-free kernel for detecting when independent sources disagree about the same subject. Point it at two or more sources, ask them the same question, and it reports any disagreement. It is the machinery under cross-registry / cross-method divergence detection, factored out so any layer can reuse it.

It is the reference implementation of the IETF draft Multi-Source Corroboration for AI Agent Discovery (draft-chandra-agent-registry-corroboration-00).

Five pieces, and only the resolvers know a wire format:

• View — the contract a claim implements: comparable() → {field: value}. Those fields are what gets compared; a None value never participates.
• Resolver[T] — a per-source adapter: a canonical id → (Status, View). It hides one source's format (an HTTP GET, a DID resolve, a DNS lookup) and MUST NOT raise — an unreachable source is error (no claim), never a false absent. It MAY expose a vantage — an observation context, class:value from a controlled vocabulary (SPEC.md §3a).
• Claim — one source's answer from one vantage at one instant.
• diff_claims — the pure diff: a sweep's claims → Finding list. It emits omission (present on one source, positively absent on another), source_equivocation (one source's vantages disagree with each other), and one finding per view field whose values disagree across sources. It never learns its layer.
• Corroborator — resolve every (source, vantage), diff, apply confirmation, and return one SweepResult per subject: a verdict (AGREE / DIVERGENT / INSUFFICIENT), the claims, and the findings. Fewer than two decisive claims → INSUFFICIENT.

Install

pip install sm-resolver        # zero runtime dependencies

Use

Supply a view (its fields are what you compare) and a thin resolver per source:

import asyncio
from collections.abc import Mapping
from dataclasses import dataclass
from sm_resolver import Corroborator, Status

@dataclass(frozen=True)
class RecordView:                       # your layer's view
    endpoint: str | None = None
    def comparable(self) -> Mapping[str, str | None]:
        return {"endpoint": self.endpoint}

class MyResolver:                       # your thin per-source adapter
    def __init__(self, label): self.label = label
    async def resolve(self, agent_id) -> tuple[Status, RecordView | None]:
        ...                             # query this source; normalize to RecordView

results = asyncio.run(
    Corroborator([MyResolver("a"), MyResolver("b")]).check(["agent-1"])
)
for r in results:
    print(r.agent_id, r.verdict)             # agent-1 DIVERGENT
    for f in r.findings:
        print(" ", f.kind, f.confirmation, f.detail)
        # endpoint suspected {'field': 'endpoint', 'values': {'a': 'https://real', 'b': 'https://evil'}}

Long-running? Pass on_finding= — it fires once per distinct finding across the corroborator's lifetime — and staleness_window_s= to promote a re-observed finding from suspected to confirmed.

Who uses it

sm-divergence builds the reference layers on this kernel: a discovery layer (registries — NEST, the NANDA Index) and an identity layer (DID methods — did:key, did:web, Universal Resolver), each supplying its own view and thin resolvers. Capability and evidence layers are the same shape.

Design rules

• A timeout is not a claim. An unreachable or erroring source is excluded, never counted as an omission.
• Only present, comparable values disagree. A None field contributes nothing — an unverifiable value is not a disagreement.
• The diff stays pure and generic — deterministic, no I/O, never raises, never learns its layer. All I/O lives in the resolvers.

Develop

make ci-local   # uv: sync → ruff → format → mypy --strict → pytest

License

MIT

First published: 2026-07-04 | Last modified: 2026-07-04

Personal research contributions aligned with Project NANDA standards. Stellarminds.ai