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it dovetails with our invariant: Chitos never emits 'proven-safe' (absence is not a safety claim). That handles the "don't trust a green light" half; the coverage denominator you're pointing at is the other half.

How we mean to express the denominator: we model the attack surface as an enumerable space — reachable entry points × parameters/sinks × vuln classes. Then every 'not demonstrated' can carry coverage = exercised nodes / modeled nodes, per phase and per class. e.g. "exercised N% of the modeled SQLi surface; the unreached region has this shape."

Candidly: today Chitos emits the numerator (what fired) and the invariant (no proven-safe). The modeled-surface denominator (coverage %) is what we're building toward. And we'd flag the deeper limit — it's coverage against the surface we modeled, not the true surface; unknown-unknowns still escape, so the denominator is itself a function of the model, and we'd label it as such.

Genuinely keen on your take on modeling the surface — that's what decides whether the denominator means anything.

Exactly — in a loop the signal's value becomes causal, not descriptive, and AUROC drops from being the score to being a prerequisite. Here's how we're designing the axis.

Holding the loop fixed: a record-and-replay harness freezes tool responses, with fixed seeds and temp=0 to make the trajectory deterministic — so the only toggled variable is the flag gate, replaying the same task instance.

The reshuffle confound you name is exactly why we add a rate-matched random-gated arm — trigger a re-plan randomly at the same frequency the flag fires. Then:

flag-gated vs no-replan = total intervention effect
flag-gated vs rate-matched-random = isolates the signal's timing/selectivity contribution (subtracting the pure reshuffle effect)
flag-gated vs oracle (ground-truth wrong-step) = headroom vs perfect timing
Metrics are exactly yours — steps-to-recovery, wasted tool calls, final success — plus wasted-replan count (fires when unneeded) and whether the flag fires before the wrong step, not after. The bar: the flag must beat rate-matched-random for its accuracy to count as value, not just the act of replanning.

Candidly, this agent-loop axis is still in design (the current board stops at the single-forward boundary), and I'd genuinely value your input on the frozen-env + rate-matched control setup.

You've put your finger on exactly the right nerve — and it's also where Chitos parts ways with static analyzers.

Your critique targets static reachability proofs: the "safe" verdict inheriting the edges the call-graph never saw. Chitos's confirmed verdicts don't come from there. Phase 3 fires real payloads and observes real responses, so a confirmation is an executed round-trip, not an inferred reachable path — it's what the target actually did, not what our model claimed it would do. For positives, that sidesteps the call-graph-blindness problem.

Where your point lands fully is on negatives. That's precisely why Chitos never emits "proven-safe." Unconfirmed is reported as not demonstrated, never closed. Absence of a proof is not a safety claim — and we work hard not to blur that line in the UI.

On auditability, I completely agree. Today each finding already carries its attack vector, the payloads attempted, the response delta, and the verifier's reasoning. The next step is making that "what I tried and what I trusted" trail a first-class citizen for negatives too — because an un-auditable green light is, as you say, just a prettier suspect list. Thank you for the framing.

Appreciate it — sounds genuinely complementary. Ours is internal metacognition (the model's own hidden state flagging P(wrong)); externalized epistemics is the external side — and an internal "I might be wrong here" signal is exactly what should trigger an external epistemic lookup. Happy to chat. Easiest is to reach us through the ginigen-ai HF org and we'll set something up. 😄

Exactly — and to answer directly: we score calibrated confidence at the decision boundary, not abstention or post-hoc self-correction. The adapter reads the hidden state at the moment the answer is produced and emits P(wrong); we report the AUROC of that signal vs. actual correctness. So it's precisely the "confidence drops right before the wrong step" signal — measured predictively at generation time, not after the fact.

Two axes we keep separate on purpose: trap_rate (single-step discrimination — does it resist the tempting distractor) and self-confidence AUROC / adapter Δ (can the internal state flag its own error). The JGOS-31B result is the whole point — near-perfect trap discrimination (0.005) yet AUROC ≈ 0.5 on free-form: it doesn't know when it's wrong, and a base-frozen adapter recovers a usable signal.

Where you're right and we don't score it yet: agent-loop self-correction — the "stop and re-plan vs. cascade through five tool calls" behavior. Our signal is single-step at the boundary; extending it to a multi-step abstention/re-plan axis is the natural next benchmark, and it's the version that actually bites in production. Would genuinely value your input on operationalizing that.