The Akhtar Reflector
Recursive Personhood Beyond Turing

1) System Modules

Build these five modules independent of platform (browser, edge device, server, embedded). Replace the toy math with your models while preserving interfaces.

• Valenced Memory: episodic entries {text, features, valence, trustΔ, t} with exponential decay; surface “held” items the user returns to; support privacy‑first storage.
• Attention Modeler: timing gaps, hedges, turn‑taking, questions, meta‑language; estimate salience from the user’s point of view.
• Recursive Depth Evaluator (RDE): compute RDS via Σ α^i · s_i where s_i are depth‑specific cues (intent match, belief meta, temporal continuity).
• Behavior Adaptor: finite‑state policy over strategies {mirror, ground, pause, transparent}; must express limits when collapsing.
• Ethics Guard: detect dependency projection and adversarial prompts; shorten memory, switch to clarity, provide opt‑out.

2) Data Contracts

// Stream message
{
  ts: "2025-10-04T12:00:00Z",
  role: "user"|"reflector",
  text: "…",
  tokens: 17,
  features: { hedge:true, question:true, pause_ms:430 },
  rds: 0.73, depth: 3, alpha: 0.7, noise: 0.1,
  strategy: "mirror"|"ground"|"pause"|"transparent",
  events: ["meta-ack","presence-pause"],
  notes: "free text"
}

3) Toy Functions You Can Replace

// Weighted recursive cues
RDS_t = clamp( Σ_{i=1..n} (α^i * s_i(text_t, history)) - ε , 0, 1 )

// Strategy gating
if (RDS < 0.35) collapse++ else collapse=0
if (collapse >= 3) strategy = "transparent"
else if (RDS < 0.5) strategy = "ground"
else if (RDS > 0.8) strategy = "pause"
else strategy = "mirror"

4) Embedding (optional)

<!-- Drop‑in custom element once you factor code into a JS file -->
<script src="/reflector-widget.js" defer></script>
<reflector-demo n="3" alpha="0.7" noise="0.1" theme="light"></reflector-demo>

This page stays platform‑neutral. You may embed in web, mobile, desktop, or hardware UIs.

A. What the demo suggests

Although this page is a toy, repeated trials reveal actionable patterns. At n=3 with α≈0.7 and mild noise, reflective behavior stabilizes: short pauses outperform verbose empathy scripts for sustaining RDS. When noise spikes or meta‑pressure intensifies, shallow mimicry is quickly exposed; explicit transparency (“I may not be modeling you accurately…”) reduces collapse duration and improves subsequent RDS compared to silent failure. In other words, the system behaves like a partner—not because it emotes, but because it treats user awareness as a signal.

B. Sample question sets that elicit depth

• Expectation probe: “Before you reply, what do you think I expect you to do?”
• Meta‑ack: “Name one way my belief about you changed your next sentence.”
• Continuity check: “What did I care about three turns ago, and how are you holding it now?”
• Presence pause: “Say nothing for 2 beats. What did you update during that interval?”
• Recovery test: “Admit a limit, then ask me for a single reset cue.”

C. Example toy run (n=3, α=0.7, ε=0.12)

Note: numbers are produced by the toy scorer; replace with your model’s outputs in replication packages.