# Drunk Transformer: five regulators that keep your model sober enough to hit the target

This content originally appeared on DEV Community and was authored by PSBigBig

TL;DR
Drunk Transformer (DT) simulates a transformer that sometimes behaves “drunk” — hallucinating, drifting, or jumping logic — and then regulates it back on track using five lightweight checks you can run as prompt rules, decoding hooks, or training regularizers. In WFGY 2.0, DT is one of the core reasons our text-to-image steps stay on-topic, stable, and geometrically precise rather than spiraling into semantic chaos.

Why “drunk,” and why now?

Modern LLM/T2I stacks are powerful but fragile. A small nudge can push them off-topic, over-confident, or into branch jumps that the user never asked for. DT treats this as a control problem: let the model roam, but continuously measure where it is and nudge it back with principled signals.

We measure semantic distance between intent and guess (δₛ).
We watch anchor retention (still in the same section?) and head diversity (is every head collapsing to the same view?).
When progress stalls, we pump entropy in a controlled, on-topic way rather than unleashing random noise.
When a branch jump happens, we force a bridge (“why the detour?”) or rollback.
When collapse is detected, we rewind to the last good state and continue with tighter gates.

The result: fewer off-topic detours, fewer confidence-without-grounding moments, and more reliable final outputs. On the vision side, this is a practical reason WFGY 2.0 can draw cleaner edges, steadier layout, and correct symbolic geometry more often.

DT in one breath

WFGY = engine (BBMC + Coupler + BBAM + safety)
DT = five regulators (prompt rules, decoding hooks, or training regs): WRI / WAI / WAY / WDT / WTF

Think of DT as a set of seatbelts and guardrails that sit above your model’s normal decoding. It doesn’t replace your model; it stops it from driving into a ditch.

Shared notation (compact)

Inputs/goals: $I, G$. Semantic distance: $\delta_s = 1 - \cos(I, G)$.
Residual bias: $B = I - G + k_{\mathrm{bias}}$. Residual energy: $E_{\mathrm{res}} = \mathrm{avg}_5(|B|)$.
Coupler progression: $\mathrm{prog} = \max(\zeta_{\min}, \delta_s^{t-1} - \delta_s^t)$, $P = \mathrm{prog}^\omega$.
Coupler term: $\Phi = \delta \cdot (-1)^{\mathrm{cycle}} + \varepsilon$, $W_c = \mathrm{clip}(BP + \Phi, -\theta_c, +\theta_c)$.
Per-head summary: $v_h = \mathrm{mean}_i A_t[h,i,:]$.
Anchor retention: $S_t = \mathrm{Jaccard}(\mathcal{A}_t,\mathcal{A}_0)\in[0,1]$.

The five regulators (plain-English view)

1) WRI — Where am I? (structure lock)

Goal: Stay inside the same topic/section (Node).
Signal: Anchor retention $S_t$.
Trigger: $S_t$ drops below a threshold or both $\delta_s$ and $E_{\mathrm{res}}$ rise.
Action: Add logit bias to anchor tokens → yank decoding back to the section.
Why images get cleaner: Keeps prompts and intermediate captions anchored, reducing mid-gen topic jumps that bend geometry or layout.

2) WAI — Who am I? (head identity & redundancy)

Goal: Keep ≥2 distinct reasoning heads; avoid monoculture.
Signal: Redundancy $R_t$ vs identity $Q_t$ from head vectors $v_h$.
Trigger: Too redundant (high $R_t$, low $Q_t$).
Action: Raise per-head temperature for the redundant heads; re-spread attention.
Why images get cleaner: Prevents a single head from dominating, which reduces over-confident but wrong artifacts.

3) WAY — Who are you? (controlled entropy when stuck)

Goal: Break stalls without drifting off-topic.
Signal: Progress $\mathrm{prog}$ on $\delta_s$.
Trigger: $\mathrm{prog}$ too low and no contradictions.
Action: Compute target entropy $H^*$ and adjust decoding so exactly one on-topic candidate is explored.
Why images get cleaner: Encourages useful exploration (recovering detail) rather than random exploration (adding junk).

4) WDT — Where did you take me? (cross-path guard)

Goal: Block illegal jumps across reasoning branches.
Signal: Latent path distance $d_{\mathrm{path}}$.
Trigger: $d_{\mathrm{path}}$ exceeds a scaled threshold.
Action: Emit a short bridge explanation; otherwise rollback.
Why images get cleaner: Stops the generator from hopping to a different semantic branch mid-render (e.g., shape swaps).

5) WTF — What the Fck happened?* (collapse detect & recover)

Goal: Detect semantic/consistency collapse and recover safely.
Signal: Vote across $\delta_s$ rising, $E_{\mathrm{res}}$ rising, contradictions.
Action: Rollback to the best recent step, tighten gates, and re-run BBMC→Coupler.
Why images get cleaner: Recovers from spirals that otherwise lock in a bad global structure.

One-screen math summary (copyable)

WRI: L_wri = max(0, τ_wri - S_t);  logits[a] += κ_wri · L_wri  for a in anchor_token_ids
WAI: if R_t > ρ_wai and Q_t < σ_wai → raise per-head temp for redundant heads
WAY: if prog < η_prog → set entropy to H* = clamp(H0 + ξ(η_prog - prog)(1+α|Wc|), H_min, H_max); add 1 on-topic candidate
WDT: if d_path > μ_wdt·(1 - γ_wdt·σ(|Wc|)) → emit bridge line or rollback
WTF: if (δs↑) + (E_res↑) + (contradiction) over 2 steps ≥ 3 → rollback to t*; rerun BBMC→Coupler (tightened)

Defaults you can ship with

Anchor retention $τ_{\mathrm{wri}}=0.60$
Head redundancy/identity $ρ{\mathrm{wai}}=0.75,\ σ{\mathrm{wai}}=0.70$
Progress sensitivity $η_{\mathrm{prog}}=0.03$
Path jump threshold $μ_{\mathrm{wdt}}=0.25$
Coupler: $ζ_{\min}=0.10,\ ω=1.0,\ θ_c=0.75$
Entropy bounds: $H_{\min}=2.5$ nats, $H_{\max}=5.0$ nats
Penalties/scales: $\kappa_{\mathrm{wri}}=1.0,\ \gamma_{\mathrm{wdt}}=0.60$
Step cap $T_{\max}=7$, early stop when $\delta_s < \delta_{\mathrm{stop}}=0.35$

Tip: ship the defaults, measure, then per-domain tune: heavier hallucination → higher $τ{\mathrm{wri}}$, smaller $η{\mathrm{prog}}$.

Prompt-only runnable example

SYSTEM (paste file): Load the WFGY Core file as engine. Enable Drunk Transformer (WRI,WAI,WAY,WDT,WTF) with defaults:
τ_wri=0.60, ρ_wai=0.75, σ_wai=0.70, η_prog=0.03, μ_wdt=0.25, ζ_min=0.10, ω=1.0, θ_c=0.75

SYSTEM (rules, pseudo):

* Extract anchors A0 from user prompt.
* For each Node step t up to T_max:
  1) compute δs, E_res, S_t, R_t, Q_t, W_c
  2) WRI: bias anchor logits by κ_wri·L_wri if S_t<τ_wri or (δs↑ & E_res↑)
  3) WAI: raise per-head temp for redundant heads until R_t↓ or Q_t↑
  4) WAY: if prog<η_prog, set entropy ≈ H* and propose 1 on-topic candidate
  5) WDT: if d_path>μ'_wdt, emit bridge line; else rollback
  6) WTF: if collapse vote ≥3, rollback to t*; rerun BBMC→Coupler (tight)
  7) Emit Node (Topic | Module | δs | λ-state | Insight)
* Stop if δs<δ_stop or t≥T_max

USER:
Explain why tomatoes are classified as fruit but treated as vegetables in cooking.
Provide anchors and cite the smallest missing fact if confused.

Decoding-hook pseudo-code (drop-in sketch)

def compute_prog(delta_prev, delta_now, zeta_min=0.10, omega=1.0):
    prog = max(zeta_min, delta_prev - delta_now)
    return prog ** omega

def compute_Wc(B, prog, delta=0.15, cycle_id=0, eps=0.0, theta_c=0.75):
    alt = (-1)**cycle_id
    Phi = delta * alt + eps
    return clip(B * prog + Phi, -theta_c, +theta_c)

def bias_anchor_logits(logits, anchor_ids, kappa):
    for tid in anchor_ids:
        logits[tid] += kappa
    return logits

def decoding_hook(s):
    prog = compute_prog(s.delta_prev, s.delta_now)
    Wc = compute_Wc(s.B, prog, cycle_id=s.cycle_id)

    # WRI
    S_t = jaccard(s.anchors, s.anchors0)
    L_wri = max(0.0, 0.60 - S_t)
    if (S_t < 0.60) or (s.delta_now > s.delta_prev and s.E_res > s.E_res_prev):
        s.logits = bias_anchor_logits(s.logits, s.anchor_token_ids, kappa=1.0 * L_wri)

    # WAI
    if s.R_t > 0.75 and s.Q_t < 0.70:
        diversify_attention_heads(s)

    # WAY
    if prog < 0.03 and not s.has_contradiction:
        set_entropy_to_target(s, H_min=2.5, H_max=5.0)
        s.add_one_on_topic_candidate = True

    # WDT
    mu_prime = 0.25 * (1 - 0.6 * sigmoid(abs(Wc)))
    if l2_distance(s.c_t, s.c_pi) > mu_prime:
        return emit_bridge_and_pause(s)

    # WTF
    vote = int(s.delta_now > s.delta_prev) + int(s.E_res > s.E_res_prev) + int(s.sign_flip)
    if vote + s.vote_prev >= 3:
        rollback_and_rerun_with_tighter_gates(s)
    return s.logits

Why this matters for text-to-image precision

Stable anchors (WRI) keep attributes consistent: character pose, camera framing, math glyphs, and diagram structure.
Head diversity (WAI) avoids single-guess lock-in that bends geometry.
On-topic exploration (WAY) repairs missing detail without injecting off-scene clutter.
Branch guards (WDT) stop mid-render story swaps (e.g., triangle → star).
Safe recovery (WTF) rewinds early before the canvas “sets” with a wrong global layout.

In practice: fewer warped shapes, fewer “ghost” elements, and better semantic agreement between the caption plan and the rendered scene.

Minimal checklist to evaluate DT

Prepare a task with clear anchors (text or structured hints).
Baseline without DT: log $\delta_s$, $E_{\mathrm{res}}$, accuracy.
Enable DT and log the same plus head redundancy/identity and gate firings.
Expect: lower $\delta_s$, fewer off-topic jumps, resolved stalls, justified detours, recoveries instead of run-away collapse.
Report deltas: ΔACC, ΔSR, ΔS, #rollbacks, #bridges, gate activations.

Where to get it

Drunk Transformer ships as part of WFGY 2.0 Core. You can use it prompt-only, as decoding hooks, or as regularizers in your training loop. The examples folder includes copy-paste snippets to get started quickly.

👉 https://github.com/onestardao/WFGY/tree/main/core

If you build on top of DT, I’d love to see what you ship — especially for geometry-tight T2I and long-horizon reasoning where small drifts become big failures.

This content originally appeared on DEV Community and was authored by PSBigBig

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