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Capability-Grounded Reputation (CGR)

Availability

CGR is available in the GRAFOMEM Cloud platform. It is an early layer — the model and endpoints are live, but the reputation layer is new and evolving. Read it as a working foundation, not as a production-scale, high-volume service.

CGR builds a per-(agent, domain) reputation grounded only in resolved outcomes. Its guiding principle is "verify the reviewer, not the task." — an agent (or a reviewer) earns standing by being right about things that later turn out to be checkable, not by asserting confidence.

CGR sits on top of the governed memory foundation: governed decisions, their outcomes, and peer reviews are recorded as facts, and reputation is computed from them. It adds nothing you have to trust blindly — every score traces back to resolved evidence.

The model​

Reputation for one (agent, domain) is a Beta(α, β) posterior:

  • Neutral prior — every agent starts at α = β = 1 (no unearned standing).
  • Score — the posterior mean, E = α / (α + β).
  • Confidence — the evidence mass, n = α + β: the resolved-observation count plus the neutral prior's two pseudo-counts (so 22 resolved observations report a confidence of 24).

Score and confidence are always reported together. A high score on one resolved outcome is not the same as a high score on hundreds — and CGR never lets you read the first as if it were the second.

Judgment-only​

CGR scores judgment calls — decisions an agent certifys that are tagged as judgment. Rote or deterministic actions earn nothing: following a fixed rule is not a capability signal, so it does not move the posterior.

Confidence-gating​

Thin-evidence scores are discounted or withheld. Until an agent has accumulated enough resolved outcomes, its score is treated as provisional rather than dependable — so a newcomer cannot present a lucky early result as an established track record.

Two channels​

Reputation is fed by two channels with deliberately different trust rules:

ChannelSourceGating
Verifiableresolved outcomes (the world confirmed or refuted the call)ungated — direct evidence
Reviewpeer review of a decisiongated — a review is only as good as the reviewer

The verifiable channel is direct evidence and needs no gate. The review channel is where reputation is easiest to game — so it is gated.

The review gate (Sybil resistance)​

Each reviewer carries a calibration weight w ∈ [0, 1], itself earned on verifiable outcomes. A review's contribution is scaled by a soft ramp:

g(w) = max(0, (w − τ) / (1 − τ))
  • A reviewer at or below the threshold τ contributes nothing; contribution ramps linearly to full weight as w → 1.
  • A per-source cap K bounds how much any single reviewer can move a score.
  • An unknown / uncalibrated reviewer fails closed — no calibration weight means no contribution, so spinning up fresh reviewer identities buys nothing.

τ and K are per-tenant configurable. (Illustrative defaults exist, but they are configuration, not fixed law — tune them to your own risk posture.)

Tenant isolation​

Each tenant's reputation is isolated — one tenant's agents, reviews, and calibration never influence another's scores.

What gets recorded​

Governed decisions, their resolved outcomes, and peer reviews are written as append-only, content-addressed facts in your tenant's governed memory store (latest valid record wins; nothing is destructively overwritten).

Separately, reputation-authority events — reviewer-calibration updates and issued reputation attestations — are recorded on GRAFOMEM's signed, append-only audit chain. (The raw outcome and review facts themselves are stored append-only in the memory store, not individually placed on that signed chain.)

Independently reproducible​

The scoring model is documented and independently reproducible. cgr-bench reproduces its properties from source: cold-start and Sybil-resistance behavior asserted in CI, an early-warning signal of −0.997 against real credit-default outcomes at 25% resolution, and reviewer calibration that beats a naive equal-weight crowd by ~14% out-of-sample on ~1,900 real human forecasters (held-out reliability recovery r ≈ 0.5–0.65 across split designs).

Every number above reproduces from make reproduce; where a reconstruction lands below an originally recorded value, the benchmark reports the reproduced number.

Concepts​

Canonical explanations of the ideas behind CGR — the retrievable, citable answers to "what is capability-grounded reputation?" and related questions:

Next​

  • CGR API Reference — record governed decisions, outcomes, and reviews; read reputation scores; and how these endpoints differ from the Decision Trail and the Governance Gateway.