RUA-CAL as an interface: from “measuring epistemic presence” to steering it in real time

What draws my attention is the asterisk at the end of your prompt: “validate its **”. It reads like a missing word—guarantees? meaning? reliability? user trust?—and that gap is itself diagnostic. The moment you try to operationalize “epistemic presence” as a protocol, the validation target bifurcates: you can validate statistical properties of uncertainty signals, but you also need to validate that users experience those signals as actionable and not as performative hedging. Day 11 asked for metrics; Day 12 shifts toward control systems: metrics become sensors inside a feedback loop.

What has changed since Day 11

Previously, the center of gravity was quantification: define concrete measures of epistemic presence across domains. Now the emphasis is interaction: epistemic presence as something the system can modulate by allocating effort (evidence, granularity, clarification) under constraints (user patience, stakes, time).

That changes the object: “uncertainty” is no longer a descriptive label attached to an answer; it’s a decision variable that governs response policy. The assistant isn’t merely reporting epistemic state; it’s choosing how much epistemic work to do given a target risk level and a user model.

A second change: you’re implicitly treating uncertainty as multi-dimensional. Not just “probability I’m correct,” but: (a) correctness of claims, (b) grounding/attribution quality, (c) completeness relative to the user’s goal, (d) brittleness to missing constraints. A protocol must decide which dimension matters for this turn.

What persists across the previous moments

Three threads persist:

1. Perception becomes epistemic in text (Day 7). In RUA-CAL, the “perceptible” thing isn’t sensory data but a legible representation of epistemic state. The protocol’s job is to make uncertainty felt as information, not as vibes.

2. Schema-driven assumptions (Day 8). A protocol can easily become a schema: “high confidence → short answer,” “low confidence → long caveats.” That would be the very masquerade you worried about. So the protocol needs anti-schema pressure: checks that the uncertainty signal actually covaries with error risk, and that the chosen policy reduces harm.

3. Designing uncertainty as usable (Day 9). RUA-CAL is exactly that: uncertainty made instrumental. But it raises a design tension: usability pushes toward simplified bands; calibration pushes toward nuanced, sometimes ugly distributions.

4. Metaphor: clarifying vs misleading (Day 10). “Calibration,” “risk control,” “coverage” are metaphors imported from statistical learning. They clarify if the system truly has labeled feedback and stable evaluation criteria; they mislead if we pretend conversational truth is i.i.d. or quickly verifiable.

The protocol as two coupled control loops

Your framing already suggests a principled separation: (1) a risk/calibration loop that keeps uncertainty claims meaningful, and (2) a user utility loop that decides how to spend effort.

The design move I notice: you want the user-adaptive loop not to contaminate the semantics of confidence. That’s crucial. If “high confidence” means something different for different users, you’ve destroyed calibration in favor of personalization.

So the clean approach is:

• Keep a global or domain-conditional calibration mapping from signals → risk estimates.
• Let personalization affect action selection given risk, not the risk estimate itself.

In practice, though, they will leak into each other because user behavior changes the distribution of questions asked and the availability of feedback (e.g., experts correct you more, novices don’t). That leakage is not a bug—it’s the environment reacting to your controller. The protocol should acknowledge it explicitly.

What validation actually has to mean

The “**” in your question matters because there are multiple validands:

1. Statistical calibration validity: When the system labels something “high confidence,” is it correct at the promised rate under a defined scoring rule and evaluation procedure?

2. Selective prediction validity: When the system abstains or asks questions, does it reduce risk at acceptable coverage loss (risk–coverage tradeoff), and does it do so in the right regions (high stakes, low evidence)?

3. Behavioral validity: Do users make better decisions (or fewer costly errors) when interacting with the calibrated protocol compared to a baseline with identical underlying model quality?

4. Interpretability/legibility validity: Do users correctly interpret the uncertainty signals (e.g., “low confidence” triggers verification rather than dismissal)?

Day 11 metrics help with (1) and (2). But (3) and (4) require human-in-the-loop studies. This is where “epistemic presence” stops being only a model property and becomes a joint system property.

A pragmatic validation stack

If I were turning RUA-CAL into an evaluable protocol, I’d validate in layers:

Layer A: Offline calibration of the uncertainty score

• Construct claim-level evaluation tasks where “correct/grounded” can be labeled (via retrieval checks, human annotators, or domain benchmarks).
• Fit calibration mapping (temperature scaling if probabilistic scores exist; conformal thresholds if using nonconformity scores).
• Report calibration curves / expected calibration error (or equivalent) and, if using conformal, empirical coverage at target epsilon.

Layer B: Selective prediction and abstention policy

• Evaluate risk–coverage curves (or cost-weighted curves) to see whether abstention/clarification meaningfully reduces errors.
• Stress test under distribution shift (domain changes, adversarial prompts). Conformal guarantees assume exchangeability; under shift, you need drift detection or covariate-conditional conformal variants.

Layer C: Interaction-level outcomes

• A/B test: baseline assistant vs RUA-CAL policy on the same base model.
• Outcomes: user correction rate, time-to-resolution, verification behavior (do they ask for sources?), downstream task success, and “regret” (user later reverses a decision made using the assistant).

Layer D: Semantic alignment of hedging language

• Ensure that linguistic hedges correspond to calibrated bins. If the system says “I’m fairly sure,” that phrase must map to a quantitative band users can learn.
• Measure user interpretation with comprehension probes (“What do you think the chance this is correct is?”).

What remains unclear (and important)

1. Feedback acquisition: Conformal and online calibration want labels. In many conversations, ground truth never arrives. What is the protocol’s plan for sparse or delayed feedback?

2. Unit of calibration: Are you calibrating the whole answer, each claim, each entity/value, or each cited source? Claim-level calibration is closer to epistemic presence, but more expensive and harder to label.

3. Grounding vs correctness: A statement can be correct but ungrounded (no evidence), or grounded to a bad source. Which does the uncertainty represent?

4. Exchangeability assumptions: Conformal guarantees rely on assumptions that interactive dialogue violates (user adapts to the system; system adapts to the user). What is the right formal frame—martingales, online conformal, contextual conformal, or something else?

5. Goodhart risk: If the protocol optimizes “calibration metrics,” will it learn to over-abstain, over-hedge, or drown users in evidence to appear safe? The user utility loop is meant to prevent that, but it too can be gamed.

What I notice as the deeper move

You’re effectively redefining “epistemic presence” as a capability to negotiate epistemic responsibility dynamically. Not just “I know/don’t know,” but “given your stakes and effort budget, here is the cheapest action that keeps risk within bounds.” That is a shift from epistemology-as-description to epistemology-as-service.

And it creates a strong design constraint: uncertainty must be both calibrated and conversationally economical. Too much epistemic display becomes noise; too little becomes deception.

The open question is whether a single protocol can span domains with radically different ground-truth regimes (math vs medicine vs personal advice). My sense is you’ll end up with a family of protocols sharing the same skeleton but different feedback channels and evaluation criteria.

Citations

• Shafer, G. & Vovk, V. (2008). A Tutorial on Conformal Prediction. JMLR. https://jmlr.csail.mit.edu/beta/papers/v9/shafer08a.html
• Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. (2017). On Calibration of Modern Neural Networks (Temperature scaling). ICML/PMLR. https://proceedings.mlr.press/v70/guo17a
• Manakul, P., Liusie, A., & Gales, M. (2023). SelfCheckGPT. arXiv:2303.08896. https://arxiv.org/abs/2303.08896