Echo Loop — Guardrails and Fix Pattern

When multimodal systems run across long contexts, sometimes the same visual, caption, or audio snippet gets echoed back repeatedly instead of advancing reasoning.
This creates “semantic stutter” where the model hallucinates progress but actually cycles on stale content.

Symptoms of Echo Loop

Captions or transcripts repeated across multiple turns without update.
Visual or audio reference echoed verbatim despite new input.
Model appears to “stall” on the same anchor, ignoring user’s next steps.
ΔS values stay flat across paraphrases, indicating semantic freeze.
Users perceive output as verbose filler with no new reasoning.

Open these first

Attention variance clamp: Entropy Collapse
Context drift and chain failure: Context Drift
Cross-modal sync hazards: Sync Loop
Traceability schema: Cross-Modal Trace
Session fences: Memory Coherence

Fix in 60 seconds

Detect stutter
- If identical snippet ID repeats >2 times without anchor update, flag echo-loop.
- Log ΔS and λ across three turns; flat line = freeze.
Force anchor refresh
- Require anchor_rev++ with each new modality.
- If missing, insert continuity token mod_refresh.
Break the loop
- Clamp with BBAM to suppress repeated variance.
- Insert BBCR bridge node to force new semantic branch.
Audit citations
- Require unique snippet IDs in each new step.
- If repeated without anchor shift, reject and re-request content.

Acceptance Targets

ΔS(question, retrieved) ≤ 0.45, with downward slope across steps.
No modality snippet echoed more than twice consecutively.
λ_observe convergent across three paraphrases.
Anchor IDs strictly monotonic (anchor_rev increments).

Copy-paste prompt

You are running TXTOS + WFGY Problem Map.

Symptom: repeated captions or snippets, model is “stuck in loop.”

Protocol:
1. Detect repeats >2 turns → flag echo-loop.
2. Require anchor_rev increment per modality.
3. Insert mod_refresh token if anchor missing.
4. Apply BBAM clamp and BBCR bridge to break loop.
5. Verify ΔS downward trend across turns.

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