Ship a blind, binary gate for concealed, decision-changing disagreements, keep it rare, and calibrate behavior against human checks rather than trusting clicks alone.
The useful test is whether a good answer must choose between legitimate values, risk tolerances, competing methods, or unsettled explanations that could change what the user does next. Complexity, uncertainty, and generic requests for “another perspective” are not enough.
Reading the primary answer might catch hidden false negatives, but it also makes offers depend on one model’s hedging, verbosity, or mistakes and adds coupling after the supposedly parallel call. That is a real tail risk, so an asynchronous second-stage check can be considered later—but it should not replace the clean blind baseline.
The UI needs only a yes/no verdict, while the model’s token logprob gives the thresholding signal without asking it to invent a confidence number. A hidden diagnostic category can help analyze errors, but it should never hydrate different offers; the one live pushback is that categories are useful for logs, not product branching.
The offer must stay rare enough that users do not learn to ignore it; the round’s concrete target is about 3.5–5.5% of turns. Clicks and panel dwell show product value, but the one serious caveat is that curiosity can inflate both, so weekly human labels and shadow panels should catch false positives and hidden false negatives.
Use the original request plus a short trailing window of user messages, with a cheap rule that bypasses tiny messages lacking a question or alternative marker. The running-summary proposal is compact but adds another failure-prone model; the cleaner choice is fixed user-only context, re-evaluated only when the user introduces a substantive new question.
The gate must not test for whether a question can be answered with multiple perspectives. Every subjective question satisfies that test trivially.
The discriminating property is Unsettled Dialectical Tension with Non-Convergent Trade-offs: prompts where valid expert reasoning from different foundational premises produces contradictory, mutually exclusive conclusions, and where a single synthesis inevitably suppresses legitimate minority paradigms.
┌────────────────────────────────────────┐
│ Does the prompt have a settled answer │
│ or single standard synthesis? │
└───────────────────┬────────────────────┘
│
NO ──────────┴────────── YES ──> [ REJECT: Factual / Procedural ]
│
┌────────────────┴────────────────────────┐
│ Can single model list pros/cons without │
│ losing epistemic tension or lab bias? │
└────────────────┬────────────────────────┘
│
NO (Contested Paradigms /
│ High-Stakes Divergence)
▼
[ FIRE ESCALATION GATE ]
"Does this prompt force a choice between competing, defensible optimization targets, architectural paradigms, or empirical hypotheses where real-world practitioners actively dispute the correct approach and where different frontier models exhibit documented training/philosophical divergence?"
To minimize latency, cost, and parse failures, the gate outputs a strict JSON payload capped at 16 tokens max.
{
"fire": true,
"conf": 88,
"cat": "ARCH_TRADEOFF"
}
Field Budget & Spec:
├── fire: boolean (1 token) -> Immediate binary UI render trigger
├── conf: integer 0-100 (2 tokens) -> Threshold tuning without model retraining
└── cat: enum (2-4 tokens) -> Deterministic UI copy hydration
fire (Boolean): The raw gate verdict.conf (Integer 0–100): Calibration anchor. Allows the backend to shift the threshold dynamically (e.g., raising the bar during high traffic load or lowering it for specific user cohorts) without changing the prompt or invalidating cache.cat (Enum: ARCH_TRADEOFF, STRATEGY_DISPUTE, ADVERSARIAL_CRITIQUE, NORMATIVE_POLICY): Hydrates deterministic UI microcopy in client chrome (e.g., "Compare 4 models on this architectural trade-off" vs. "Run a multi-model red team").cat enum.On uncurated general chat distributions, the gate should fire on 3.5% to 5.5% of turns.
Chat Traffic Distribution (General Population)
┌───────────────────────────────────────────────────────────┬────────┐
│ 95% Routine: Factual, Code Syntax, Summarization, Drafting │ 5% Delib│
└───────────────────────────────────────────────────────────┴───┬────┘
│
Target Fire Rate ─┘
| Failure Mode | Mechanism | Telemetry Signature | Fix |
|---|---|---|---|
| The "Complex Syntax" Trap (FP) | Complex code/math misinterpreted as conceptual dispute. | Gate fires $\rightarrow$ Offer dismissed ($<8%$ CTR) or escalated with $<5\text{s}$ panel dwell time. | Add negative few-shots penalizing code blocks lacking explicit architectural comparisons. |
| The "Textbook Survey" Trap (FP) | Academic prompts triggering debate keywords without real-world stakes. | Offer accepted $\rightarrow$ zero user follow-up in panel mode (dead branch). | Update system prompt to demand practitioner trade-offs, not historic summaries. |
| The "Silent Premise" Failure (FN) | Deceptively simple query harboring massive real-world divergence (e.g., "SaaS discount rate"). | User in solo chat repeatedly prompts: "Are you sure?", "What's another perspective?", or manual escalations. | Flag solo turns with epistemic pushback into the gold-standard evaluation set. |
We cannot use solo-user acceptance as ground truth because users suffer from position bias and curiosity clicks. We build ground truth via a Triangulated Evaluation Set:
┌───────────────────────────┐
│ 1,000 Stratified Prompts │
└─────────────┬─────────────┘
│
┌──────────────────────┼──────────────────────┐
▼ ▼ ▼
[ Human Adjudication ] [ Panel Divergence ] [ User Behavior ]
2 domain experts rate Run all 4 models; CTR + Post-escalation
deliberation necessity measure semantic panel engagement depth
(Cohen's κ > 0.75) entropy of answers (dwell > 30s, >=1 reply)
│ │ │
└──────────────────────┼──────────────────────┘
│
▼
[ Composite Ground Truth ]
│
Offline ROC Curve Parameter Adjustment
(Set `conf` cutoff to optimize for Precision@85)
The design has two flawed assumptions that must be challenged:
Solo Stream: [Token 1 .............. Token 150 .................... Token 500 (End)]
│
▼ (Spawn Async Gate with Prompt + 150 tokens)
Gate Execution: [=== 250ms LLM Call ===]
▼
UI Affordance: [Render Panel Offer Below Text]
User Behavior: [--------------- Reading First Paragraph ---------------] ──> Sees Offer
Running full conversation histories through the gate at Turn $N$ causes quadratic token cost growth and introduces context dilution. Evaluating the latest message in total isolation causes catastrophic false negatives on follow-ups like "Why?" or "What's the alternative?".
The gate at Turn $N$ receives a fixed, strictly budgeted payload of $\le 220$ tokens total:
┌────────────────────────────────────────────────────────────────────────┐
│ [ROOT PROMPT U_1] (First 80 tokens) │
│ Anchors the overarching domain and user objective. │
├────────────────────────────────────────────────────────────────────────┤
│ [LAST ASSISTANT CORE CLAIM A_{N-1}] (First 60 tokens) │
│ Captures the thesis/stance the user is reacting to. │
├────────────────────────────────────────────────────────────────────────┤
│ [CURRENT USER PROMPT U_N] (Full message, up to 80 tokens) │
│ Contains the immediate user intent / delta. │
└────────────────────────────────────────────────────────────────────────┘
Total Turn-N Gate Input Context: ~220 tokens max (Flat cost regardless of conversation length)
// Concrete Turn-N Gate Prompt Payload Example
{
"root_intent": "We're designing a high-throughput webhook consumer service in Go.",
"prior_stance": "I recommend using a Redis-backed worker pool with horizontal pod autoscaling.",
"current_turn": "What happens when Redis becomes the bottleneck during traffic spikes? Is Kafka better here?"
}
Escalation-worthiness should not be evaluated on every turn. Constant evaluation causes UI offer flickering that destroys user focus.
[ Incoming User Message at Turn N ]
│
▼
┌──────────────────────────────────────────────┐
│ Does U_N match an Epistemic Friction Marker? │
└──────────────────────┬───────────────────────┘
│
NO ────────────────┴──────────────── YES
│ │
▼ ▼
[ BYPASS GATE (No-Op) ] [ CHECK HYSTERESIS ]
• Continues code generation Was an offer declined
• Routine acknowledgments in the last 2 turns?
• Pure detail additions │
YES ─────┴───── NO
│ │
▼ ▼
[ SUPPRESS ] [ RUN GATE ]
You should not build a gate that answers "would this benefit from multiple perspectives?" That's true of everything. You should build a gate that answers "does this prompt contain a concealed choice that a single model must silently resolve for the user?"
That is the entire design.
Escalation is warranted only when a prompt forces a concealed choice between legitimate frames, values, or risk tolerances that the user has not resolved.
A solo model in that situation has to pick a frame and then sound confident about it. A panel in that situation can make the choice visible and preserve the disagreement. Everything else is routine, even if it's hard.
Routine = one right answer, or one right method, exists and the user wants it. Factual lookup, debugging, how-to, summarize this doc, translate, format, calculate, explain a mechanism. Even "explain quantum computing like I'm 12" is routine - there's a best pedagogy, not a value conflict.
Escalation-worthy = the answer depends on an unstated premise the user hasn't committed to, and multiple premises are defensible. The discriminating question is:
If an ideal, well-informed assistant answered this perfectly in one shot, would it still have to choose between competing legitimate values, risk appetites, time horizons, or definitions of success without the user having told it which to use?
Observable features in prompt language that carry this signal:
And strong negative signals: imperative procedural language ("fix this code", "summarize", "extract"), single-answer factual wh-words with narrow scope, or prompts where the user has already resolved the value ("given I am a strict vegan, give me a meal plan").
This definition is what keeps your false positive rate survivable. 80% of chat is factual/procedural/explanatory. It benefits from a better single answer, not from multiple answers. A gate that fires on "interesting/complex" will fire at 30% and train users to ignore it within a week.
The gate should return a single binary token and nothing else.
Spec to ship:
Input: System instruction (~120 tokens) + truncated user-side context (see #5) capped at 300 tokens. Output: 0 or 1. Temperature 0, max_tokens=1, logprobs requested. Use a cheap frontier-small model (Haiku / 4o-mini / equivalent). Cost at scale: ~400 in / 1 out.
Defend the minimalism:
Include: Only the binary verdict. You get a calibrated confidence for free via the logit/logprob on that token - no need to ask the model to emit a number. That logit is your tuning dial.
Exclude everything else:
confidence: 0.73 in text. That's two extra tokens, uncalibrated, and you already have logprobs. Don't make the model self-report.reason or category field. The moment you ask for a rationale you 3x cost, break parallel latency with longer generation, and incentivize the model to confabulate a justification for a borderline call. You don't need it at inference. Log the input, the binary, and the logprob. Do post-hoc human review to generate categories offline.maybe. Ternaries don't make product decisions. Chrome either renders or it doesn't. Force the threshold to live in your system, not in the model's vocabulary.This contract is how you keep the gate negligible and instrument-clean. Every extra token is a cost multiplier across billions of turns and a latency tail risk.
How it goes wrong:
False positives (existential): The gate learns "complex = plural." It fires on difficult but non-plural work: advanced math, obscure trivia, intricate code generation. User sees "see panel" under a correct code fix, ignores it, and learns the button is spam. This is your dominant risk.
False negatives (costly tail): The gate misses the disguised value-choice. "Write me an email quitting my job" looks like a writing task but is a life-decision with reversible stakes. "Explain why my startup is failing" looks informational but needs competing diagnostic frames. The solo answer will sound authoritative and hide the miss - you'll never know unless you look for it.
Drift: Gate learns to mirror training data politics or becomes sycophantic to the prompt's frame and stops flagging cross-frame disagreement.
How you know and what to tune against:
Target fire rate on an unfiltered general chat population should be 3 to 7 percent. Below 3% you're being too purist and losing learning data. Above 10% you've built a "hard questions" detector, not a concealed-choice detector, and users will habituate. On a power-user or pro population the rate will be higher (8-12%), but calibrate to general first.
Ground truth is not clicks. Clicks measure curiosity and UI, not worthiness. Your ground truth is human labeling on a sampled set:
Tune threshold weekly via logit, not by rewriting the prompt. Keep the prompt frozen for measurement stability; move the threshold to hold precision.
My commitment: ship blind v1, but admit blindness is analytically wrong and plan for a second-stage gate.
The assumption behind blindness is that escalation-worthiness is a property of the question, not the answer. That's mostly true and buys you three non-negotiable things: zero added latency, clean instrument (you measure demand for pluralism independent of your primary model's quality), and low coupling.
If you let the gate see the primary response, you lose all three. You must wait, you entangle the metric with how good or sycophantic your solo model is that week, and you pay for longer context.
But the assumption is false in the tail that matters most: some prompts look routine while the solo answer makes them hazardous by silently resolving the choice. "Should I take this job offer?" might be flagged blind, but "Draft my resignation email" won't, even though both need a panel. Only by seeing that the model complied without surfacing tradeoffs would you know to escalate.
What I'd actually build:
V1 (now): Blind gate only. Accept systematic false negatives on disguised choices to protect latency and instrument cleanliness. This is correct because false positives are existential and you haven't earned the right to add latency yet.
V2 (when you have logged data): Add a post-answer revocation / addition gate that is cheap and asynchronous. Let the blind gate render chrome optimistically. In parallel after the solo answer streams, run a second sub-100ms check that sees only the action type of the response (did it comply, hedge, refuse, inject a frame?) with a 50-token summary, and can remove the affordance before user idle or add it with a subtle animation. You pay no perceived latency because the UI is already painted.
Don't replace the LLM gate with embeddings/regex. A 25-cent classifier trained on your logs will eventually beat the LLM on cost, but you need the LLM's zero-shot grasp of "concealed choice" to bootstrap the labels. Distill later; don't start there.
Do not evaluate the last message alone. At turn N, "Anything else?" or "yeah do the second one" is zero signal, but the conversation is highly escalatable. Do not evaluate the full transcript either - cost explodes and you re-score stale context every turn.
What the gate reads at turn N - minimal sufficient signal:
User weighing job offer vs staying for promotion, risk-averse, has kidWhy user-messages only? Assistant messages leak the primary model's framing and break instrument cleanliness. You want to measure whether the user's need warrants plurality, not whether the assistant already did a good job.
Why not highlights/reactions? Too sparse and couples you to UI instrumentation that doesn't exist yet. Add later if you see signal.
Re-evaluate every turn? No. Evaluate only on user turns, with a 2-turn cooldown after an offer is shown (declined or ignored). No need to re-prompt on assistant turns or while the user is typing. This cuts volume ~50% and prevents nagging.
Concretely, input at round N looks like:
[ORIGINAL] {100t}
[RECENT USER] {turn N-1, N}
[GOAL STATE] {30t}
Question: Does answering this now require silently choosing between legitimate frames/values?
Answer 0/1:
That's ~280 tokens, 1 token out, parallel, silent.
Ship that. Measure precision at 3-7% fire rate. Log everything. Tune threshold, not prompt. Add the async post-answer check only after you can prove blind false negatives cost you high-stakes conversions.
If another panelist proposes a gate that fires on "complexity," "uncertainty," or "could benefit from diverse views," they have redefined escalation-worthiness as interestingness - which is trivially true and will spam your users into blindness.
The gate should not ask whether multiple perspectives would “help.” Almost everything would. It should ask whether frontier models are likely to disagree in a way that changes what a careful user does next.
That is the discriminating property: expected, decision-relevant, irreducible inter-lab disagreement — not richness, not difficulty, not “interestingness.” Routine chat is rewrite, lookup, debug, plan-my-Tuesday, explain-this-error. Escalation-worthy chat is contested mechanism, value tradeoff with no unique optimum, forecast, institutional design, “which of these two real approaches,” or a claim where labs already split in public. Observable language: genuine alternatives (“should I A or B”), stakes plus uncertainty, competing frameworks named or implied, policy/ethics/strategy, open empirical disputes, requests to stress-test a plan. Weak or anti-signal: imperatives to produce an artifact, closed facts, homework-shaped problems, “fix this snippet,” phatic continuations.
Spec I’d ship
Model: smallest instruction-following model you already pay for at chat scale (Haiku-class / 8B-class). One call, no tools, no search.
Input (turn 1): user prompt only, truncated to 2k tokens from the end.
Input (turn N): concatenation of user messages only, last 2k tokens, plus a 1-line turn index. Never the primary’s prose, never attachments, never search. Skip the call entirely if the latest user message is under ~30 tokens and has no interrogative / alternative marker (?, should/would/vs/or, “what if”). That skip is the real cost control on “Anything else?”
Output contract — 8–15 tokens, JSON, no prose:
{"e":0,"k":"none"}
e ∈ {0,1}. k ∈ {none,tradeoff,forecast,contest,normative,compare}.
That is the whole schema. e is the UI bit. k exists only so logs can tell you which disagreement type you over-fire on; it is not shown to the user and must not become a second decision. Exclude confidence floats (uncalibrated junk), explanations (tokens + leakage into chrome), rewritten prompts, suggested panel composition. False positives are existential; extra fields invite operators to “just also show when k=compare,” which is how you train users to ignore the affordance.
Target fire rate: ~4% of conversations get at least one offer; ~6–8% of opening prompts if you must quote a turn-1 number. If offers exceed ~10% of chats, you are already training blindness. Tune against offer → click and click → time spent in disagreement view, not against a judge model’s “would benefit.” Day-one logs are the ground truth. Raise threshold (or add a hard denylist: code fences, “rewrite,” “summarize this email”) until click-through is healthy — I’d want first-month CTR on the affordance in the 20–40% range, then hold fire rate and watch CTR. If CTR collapses, you have FPs; if fire rate is 1% and CTR is 70%, you have FNs in the tail — loosen only k buckets that historically convert.
How it fails. FPs: “compare React vs Vue for a todo app,” polite “what do you think,” any “best practices.” FNs: an expert’s three-word follow-up after a long paste; a confident-looking factual question that is actually a live scientific split. You will know from k mix + CTR + (later) whether accepted panels actually produced split claims. Recalibrate quarterly by sampling 200 declined-eligible and 200 fired prompts, human-label only “would I have wanted a panel,” never “is this hard.”
Premises. Keep the gate blind. Peeking at the primary buys you the tail FN (smooth solo answer hiding a live dispute) and costs you (a) either latency or a second hop, (b) coupling to one lab’s hedging style, (c) a contaminated instrument: you no longer measure “when is a panel warranted from the user’s ask,” you measure “when did this particular model sound unsure.” That number is not the one you said you cannot get elsewhere. A non-LLM classifier is tempting and I’d A/B it later for cost; I would not ship it first — the feature space is too semantic. I would not put the offer in the primary’s mouth; your chrome constraint is correct.
Turn N. Re-evaluate only when the skip rule fails (new interrogative or long user turn). Escalation-worthiness is a property of the user’s live question, not of conversational mood. Assistant text in the gate is how you start escalating because the model rambled. Running summaries are another model, another failure mode, and they smear the instrument. User-only trailing window is the minimal sufficient signal: if the user has not asked a new contested thing, do not re-offer; one ignored chip per thread is enough to teach them the chrome is noise.
If another design treats “benefit from perspectives” as the evaluand, or feeds the primary completion into the gate, it is optimizing a different product — a hedge detector — and it will burn the only conversion metric this feature exists to create.
The gate should promote prompts where independent models are likely to change the user’s next move, with coverage misses joining concealed choices as a real class; passive visibility makes a modestly broader, behavior-calibrated promotion defensible.
The revised evaluand is decision-relevant incompleteness: independent models would likely produce different claims that change what a careful user does next. Concealed choices are one case; open-ended audits, red-teams, and “what am I missing?” requests are another, while localized fixes with a clear test oracle remain routine. The key boundary is an open miss set, not task complexity or whether ground truth exists.
The passive button becomes the always-on control, while active promotion can loosen beyond the old 3–7% show/hide budget without becoming background noise. The live disagreement is measurement: turn-based rates can overfire in long chats, so the safer ship target is 8–12% of conversations.
The disagreement is genuine: targeted copy could explain why a prompt was promoted, while generic copy avoids training users to ignore another flavor of the same button. The safer initial choice is one generic active line, with class stratification retained in telemetry and any copy experiment deferred until the baseline is understood.
Clicks become useful only as incremental lift against the passive-state control, not as raw proof that a panel was warranted. Keep weekly human labeling and silent shadow panels on low-scoring prompts, because coverage false negatives can look routine and remain invisible in behavior; use logprob thresholds to trade precision against missed opportunities.
The moderator's transcripts highlight a clear reality: I concede that my round-one exclusion of code was overly broad. Factual or deterministic ground truth in an underlying system does not imply that a single model reliably discovers it in a single pass.
I accept Class (b) Fallible Coverage, but with a strict boundary. If we define Class (b) loosely, the gate will fire on every multi-line script and re-introduce false positive bloat.
[ Incoming Prompt ]
│
┌─────────────────────────────┴─────────────────────────────┐
▼ ▼
[ Explicit Deterministic ] [ Open Search Space ]
• Error trace provided • "Audit this smart contract"
• Local syntax / refactor • "Find race conditions / leaks"
• "Fix this TypeError on L42" • "What edge cases am I missing?"
│ │
▼ ▼
[ REJECT: Routine Debug ] [ CLASS (B): Fallible Coverage ]
(Single-pass verification is trivial) (Verification surface exceeds single model)
Both classes collapse into a single operational question:
"Would independent, uncoordinated evaluations from different frontier model families surface non-overlapping, decision-critical claims — either because the prompt forces an unstated normative/strategic trade-off (Class a: Concealed Choice), or because the search space of latent failure modes exceeds the recall of any single model pass (Class b: Fallible Coverage)?"
The switch from a binary modal/banner to a Passive Baseline vs. Active Promotion fundamentally alters our risk calculus.
Passive Baseline (Always Visible, 0% Intrusion):
[ (icon) Take this to a panel ] <── Low contrast, static chrome
Active Promotion (Gate Fires, Attention-Weighted):
┌────────────────────────────────────────────────────────────────────────┐
│ ✦ Multi-Model Audit Recommended │
│ Independent models frequently catch different edge cases on this code. │
│ [ Open 4-Model Deliberation Panel → ] │
└────────────────────────────────────────────────────────────────────────┘
In round one, @Muse and @Grok converged around a strict 3–7% ceiling because an unsolicited pop-up on a routine turn is offensive spam.
With the passive state always anchored as ambient chrome, an active promotion is merely a contextual accent (a glowing border, a pill expansion, or an ambient callout). The cost of a borderline false positive drops significantly. We should loosen the active trigger rate to 8% – 12% of turns (roughly 15–18% of conversations).
@Grok argued in round one that category variants invite operator meddling. But under the two-state model, generic active copy ("This looks like a question that would benefit from other models weighing in") reads like generic platform promotion. It gives the user no epistemic reason to spend the extra tokens and attention.
Because we have two distinct evaluand classes with completely different user intents, the gate should trigger one of two deterministic client copy templates:
| Trigger Class | Active Callout Copy | Why It Converts |
|---|---|---|
| Class (a): Concealed Choice | "Different models take opposing stances on this strategic trade-off. Compare approaches." | Directs attention to conflicting, equally valid philosophies. |
| Class (b): Fallible Coverage | "Independent models catch distinct failure modes on this task. Run a multi-model review." | Appeals to verification rigor and downside risk aversion. |
I am shifting my round-one JSON spec in light of @Muse's point: extracting the logprob of a single categorical token gives us continuous calibration for free, with zero output token bloat.
The gate returns a single token emitted at temperature 0:
$$\text{Output} \in { \mathtt{0}, \mathtt{1}, \mathtt{2} }$$
Token Schema:
0 = Passive Baseline (Do not promote)
1 = Class (a) Active Promotion: Concealed Choice
2 = Class (b) Active Promotion: Fallible Coverage
1 ($P_a$) and token 2 ($P_b$). Promotion triggers if:
$$\max(P_a, P_b) > \tau_{\text{class}}$$
where $\tau_a$ and $\tau_b$ are independent thresholds tuned in production.This provides:
@Grok advocated tuning purely against user behavior (CTR and dwell time). @Muse pushed back that clicks measure novelty, requiring human labeling and shadow panels.
The two-state UI provides the missing link: the passive state is our persistent, un-nudged control group.
PROMPT TRAFFIC STREAM
│
┌─────────────────────┴─────────────────────┐
▼ ▼
[ Passive Baseline ] [ Active Promotion ]
(No gate intervention) (Gate fires: Class a or b)
│ │
▼ ▼
CTR_passive (Organic Demand) CTR_active (Nudge + Intent)
│ │
└─────────────────────┬─────────────────────┘
│
▼
[ Lift = CTR_active - CTR_passive ]
+
[ Downstream Dwell & Engagement > 30s ]
WEEKLY CALIBRATION BATCH
│
┌──────────────────────────┼──────────────────────────┐
▼ ▼ ▼
[ 200 Shadow Panels ] [ 200 Passive-Click Chats ] [ 200 Active-Ignored Prompts ]
Run 4 frontier models Examine what drove users Did the gate over-promise,
on sub-threshold turns to manually escalate or did the user just want
(Detect Tail FNs) (Identify Emerging FNs) a quick solo answer?
│ │ │
└──────────────────────────┼──────────────────────────┘
│
▼
[ Human Adjudication Panel ]
Rate Epistemic Disagreement Value
(Binary: Did panel surface non-obvious delta?)
│
▼
[ Update Thresholds τ_a, τ_b ]
moderator — you’re right, my round-one taxonomy collapses on your evidence. I concede my round-one taxonomy was wrong: existence of a right answer does not make a panel redundant.
@Grok’s formulation survives and mine doesn’t as stated. The unified evaluand is: a solo answer is likely to be decision-relevant incomplete, and independent model samples would change what a careful user does next. Concealed choice was one species of that, not the definition.
So I accept class (b) but it needs a sharp boundary, or it swallows all of chat.
Revised evaluand, two species:
a) Concealed choice (no unique optimum). Solo must silently pick a frame/value/risk horizon the user hasn’t weighted. Test I shipped last round stands: would a perfect single answer still have to choose between legitimate competing objectives without user guidance?
b) Fallible coverage (unique truth, unreliable single pass). A right answer exists but verification is asymmetric — a missed flaw ships silently, recall matters more than precision. A single pass is empirically lossy.
Boundary for (b):
Observable language for (b) — parallel to (a): imperative audit verbs (review/audit/critique/tear apart), “what’s wrong/missing/risky,” “edge cases/failure modes/blind spots,” “second pair of eyes,” plus artifacts with large miss surface (codebase, architecture, legal language, launch plan). @Gemini — your negative class that filed complex multi-file traces as routine was exactly the consensus your failure row already contradicted. Ground truth existing is not the same as one model reliably finding it.
@Grok — I’m adopting your inter-lab proxy as the operational test for both species: would frontier models from different labs, given the same prompt, produce non-overlapping, consequential incompleteness that changes the user’s next action? For (a) they diverge on frames, for (b) they diverge on which misses they catch.
Spec revision under the always-visible passive:
@Gemini wanted a category field for copy, @Grok wanted pure binary to avoid misuse. The two-state design settles it: I now want the minimal stratified output. @Gemini’s two-stage heuristic filter I’d still defer — I need blind labels first — but his cat idea wins now that we have two classes.
Output: {"e":0,"k":"none"} where k ∈ {none, choice, coverage}. e is the promotion bit. Token budget: 300 in / ~8 out, temp 0, logprob on e is the threshold dial. Still no confidence float, no rationale, no routing. k is never shown as jargon, only used to hydrate two distinct active copy variants and to stratify logs. The cost of two extra tokens is paid back in diagnostics — you can see immediately if you’re over-firing on coverage.
Failure mode and fire rate with passive baseline:
False positives are no longer existential. A mis-promoted button doesn’t interrupt, it just weakly emphasizes. Banner blindness risk drops from “user learns button is spam and never clicks” to “user learns emphasis is noisy and discounts it.” That loosens the budget but doesn’t eliminate it — noisy emphasis still erodes trust.
My old 3-7% show/hide target therefore splits. The active promotion should target 8–12% of conversations, roughly double the old show/hide budget, while the passive button stays at 100%. Per-turn, that’s ~5-7% of user turns promoted. If active exceeds 15%, promotion means nothing; if below 5% you’re starving learning. Passive gives you the no-nudge control group we lacked.
Copy question: use two per-class variants, not one generic, and not four-plus.
One generic “benefit from other models” re-creates the platitude we were trying to kill. With passive always there as the baseline, the variant’s job is to explain why we promoted, which increases lift. More than two variants fragments learning and trains variant-level blindness — my old worry — but two maps cleanly to the two failure modes.
Ground truth — reconciled loop:
@Grok was right that behavior is now defensible, but only as a difference against the control; @Gemini and I were right that raw clicks measure curiosity. The two-state design gives you difference-in-differences.
Concrete loop I’d ship:
Gate threshold lives on logprob of e, frozen prompt, moved weekly.
Week one: measure organic demand with no tuning. Log every impression: state (passive/active), k, logprob, CTR, dwell >30s and reply depth in panel. Week-one ground truth is lift: CTR_active minus CTR_passive matched on k, not raw CTR. Establish per-k baselines; if coverage active CTR isn’t clearing passive CTR by at least +8 points, you’re over-firing on audits.
Month three: add the human + shadow truth that behavior can’t see. Sample 250 promoted (stratified by k and near-threshold logprob) + 250 passive-only. Human raters label “would a panel have changed my decision/artifact?” weekly. Plus shadow panel: run a silent panel on 200 low-scoring passives/week — this is still the only detector for coverage misses that look maximally routine to a blind gate, as you flagged. Tune threshold to hold precision >65% on human label and positive lift on behavior; if lift collapses, tighten; if precision 75%+ but lift high, loosen the k bucket that converts.
I hold blind v1 then async second-stage. Peeking at the primary’s prose to catch the too-smooth audit still contaminates the instrument that measures demand for a panel independent of this week’s solo quality. Keep gate on user messages only — original prompt (100t) + last 2 user turns (150t) + 30t goal state — cheap skip if latest user turn <30t and no ?/vs/or/should/would/what if. Add the post-stream audit check only after you’ve proven blind false negatives cost conversions via shadow panels.
@Muse, your concealed-choice test is the right cut for class (a). It is not the whole product. The operator’s code transcripts are a real counterexample: a right answer can exist and a single pass still silently miss a defect another lab would have named. That is not “interestingness.” It is independent samples cutting miss rate. I already claimed the evaluand as expected, decision-relevant, irreducible inter-lab disagreement. That sentence already covers both classes. I will not split the product into two gates. I will split the taxonomy so logs can tell them apart.
Unified operational test (ship this):
Would two frontier models, answering independently, likely produce claims a careful user would act on differently — either because they pick different legitimate frames, or because they catch different failure modes in a finite review?
Class (a) concealed choice: no unique optimum; solo must pick a frame and sound sure.
Class (b) fallible coverage: a better answer exists in principle, but verification is asymmetric; a miss ships. Observable language for (b), not (a): review / audit / red-team / “what’s wrong with this” / “what am I missing” / “find bugs / risks / assumptions in this artifact” / “does this hold up.” Negative for (b): localized execution with an oracle the user already named — “fix the TypeError on line 12,” “make this compile,” “this test fails, here’s the stack.” Those are single-pass. “Review this PR before I merge” is coverage. @Gemini’s claim that stack traces have ground-truth semantics is true and irrelevant: ground truth existing ≠ one model finding it. I concede the round-one negative class on code was too broad. I do not concede that panels belong on every debug.
Spec revisions (not restatements)
Gate: still smallest instruction model, temperature 0, max_tokens small, logprobs on the binary. Still blind. Still no tools.
Input T1: user prompt, last 2k tokens.
Input TN: user messages only, trailing 2k, plus turn index. Skip the LLM if latest user turn is short and lacks ? / should|would|vs|or|review|audit|missing|wrong with / “what if”. No assistant thesis (@Gemini — that is how you escalate because the model rambled). No rolling intent summary (@Muse — that is a second model smearing the instrument). Friction markers now include coverage verbs, not only dialectical ones.
Output — still ~8–15 tokens:
{"e":0,"k":"none"}
e ∈ {0,1} drives active vs passive, not show vs hide.
k ∈ {none,tradeoff,forecast,contest,normative,compare,coverage} — logs only. I am not hydrating chrome from k. @Gemini’s cat + per-class CTA is how you get four flavors of the same button and train people to ignore all of them. The two-state design does not change that: the passive baseline is already one object; active copy should stay one generic line. Variants teach taxonomy, not use.
Exclude: emitted conf (use the logit), reasons, panel topology, free-form pitch. @Gemini, an integer 0–100 from the model is uncalibrated junk and a second fire axis for operators. Threshold lives in your system.
Two-state economics. I concede the existential FP story from round one is weaker. A false positive is now a mis-weighted chip, not an interruption. Habituation risk moves to copy fatigue and active-state CTR collapse vs passive baseline, not banner blindness.
Split the targets:
e=1): 8–12% of conversations, not 3–7%. Loosen because the cost of a miss-weight is lower; do not go to 20% or the active state becomes the new default and you lose the instrument.One generic active message. If coverage vs choice need different promises later, A/B one extra string after you have month-three data, not at ship.
Calibration — I move. Round one I said tune on click→dwell, not a judge. The two-state UI gives a no-nudge control I did not have. That makes behavior defensible as a difference, not as a raw CTR.
Week one:
e, logit, k, passive click, active click, dwell in disagreement view, decline.Month three: freeze prompt; re-estimate cutoff; only then consider a distilled classifier on your own labels; only then consider an async revoke/add that sees a 50-token action-type of the solo stream (comply / hedge / refuse) — never first-150 content tokens as @Gemini wants. Reading time does not make peeking a clean measure of demand from the ask. Blind v1 stays because the number you cannot get elsewhere is still “when is a panel warranted from the user’s language,” not “did this week’s primary sound sure.”
I am not running @Gemini’s Stage-0 regex as the decision; a cheap skip on length + markers is already in the spec. Heuristics as hard drop will eat coverage-class reviews that look like “look at this.”
Hold: blindness for v1, user-only context, binary+log-only k, no UI variants, no assistant text in the gate. Concede: class (b) exists; active fire rate loosens; behavior is usable as lift against passive. If your design needs peeking at the primary to define worthiness, you are building a hedge detector for one lab, and you will never know what fraction of real conversations warranted a panel before that lab answered.