Chain of Thought Variance Clamp: Guardrails and Fix Pattern

Reduce random drift in planning and multi step reasoning. This page gives a clamp recipe so your plan length, tool order, and citations stay stable across seeds and paraphrases.

Open these first

Visual map and recovery
→ rag-architecture-and-recovery.md
End to end knobs
→ retrieval-playbook.md
Traceability and payload schema
→ retrieval-traceability.md
→ data-contracts.md
Related failures
→ logic-collapse.md · entropy-overload.md · recursive-loop.md · hallucination-reentry.md · context-stitching-and-window-joins.md
Prompt fences
→ PromptAssembly memory fences & state keys
Eval
→ eval_semantic_stability.md

Symptoms

Symptom	What you see
Same inputs, different plans	Tool order or step count changes by run
Paraphrase flips the answer	Harmless wording changes cause new chain or conclusion
JSON plan reshuffles	Fields reorder or optional fields go missing
Intermittent tool loops	One seed calls tools twice, another once
Cite then explain breaks	Citations disappear in long chains or only appear sometimes

Why variance explodes

Unpinned headers. Role and policy text move around between runs.
Loose schemas. Plans allow free text where enums should exist.
No state keys. Chains cannot carry plan_rev, seed_id, or λ_target.
Ranking variance. Inputs to the chain are not deterministically ordered.
No bridges. Cross window steps lack an anchor restatement.
High entropy. Overlong prompts and mixed analyzers amplify randomness.

Acceptance targets

λ remains convergent across three paraphrases and two seeds
ΔS(question, plan_header) ≤ 0.45 and flat across seeds
Plan length variance ≤ 10 percent across two seeds
Tool call sequence identical for the same evidence set
Coverage of target section ≥ 0.70 with cite then explain intact

Fix in 60 seconds

Lock the header order and schema
Pin system header segments and require cite then explain.
→ data-contracts.md · retrieval-traceability.md
Attach state keys
Carry {plan_rev, seed_id, λ_target, index_hash, context_hash} through each step.
→ memory_fences_and_state_keys.md
Apply BBAM variance clamp
Two stage plan. Stage A generates the plan at low temperature with enumerated options and a deterministic tie break. Stage B executes the plan with normal temperature but cannot change step count or tool order unless it emits a structured re plan request.
Deterministic ordering in inputs
Sort snippets by (doc_id, section_id, win_idx) after rerank.
→ rerankers.md
Add BBCR micro bridges at joins
Restate the active anchor and the current step goal across window boundaries.
→ anchoring-and-bridge-proofs.md

Minimal clamp contract

Add this struct to your plan steps. Enforce it in tools and in the LLM planner.

{
  "plan_rev": 3,
  "λ_target": "convergent",
  "seed_id": "s1",
  "index_hash": "faiss:7c91...",
  "context_hash": "sha1:b2ae...",
  "steps": [
    {"idx": 1, "tool": "retrieve", "args_schema": "strict", "may_branch": false},
    {"idx": 2, "tool": "analyze_snippets", "args_schema": "strict", "may_branch": false},
    {"idx": 3, "tool": "answer", "args_schema": "strict", "may_branch": false}
  ],
  "tie_break": "doc_id,section_id,win_idx"
}

Rules

Stage A can only choose among enumerated step templates.
Stage B cannot insert or remove steps. To change, it must emit {replan:true, reason:"..."} and stop.
Tool args must be strict JSON with enums where applicable.

Verification playbook

Three paraphrase run with two seeds. λ stays convergent, plan length variance ≤ 10 percent.
ΔS(question, plan_header) ≤ 0.45 on both seeds.
Citations appear before explanation in every run.
Tie break yields the same snippet order across seeds.

If ΔS is flat and high, suspect index or metric mismatch. → embedding-vs-semantic.md · chunking-checklist.md

Copy paste prompt

You have TXT OS and the WFGY Problem Map loaded.

Goal: clamp chain-of-thought variance.

Inputs:
- question: "{q}"
- snippets: [{doc_id, section_id, win_idx, ΔS_to_question, source_url}]
- constraints: cite_then_explain=true, args_schema="strict"

Do:
1) Stage A (planner, low temperature 0.2–0.4):
   - Produce a fixed-length plan using the step templates {retrieve, analyze_snippets, answer}.
   - Order inputs deterministically by (doc_id, section_id, win_idx).
   - Output:
     {
       "plan_rev": n,
       "λ_target": "convergent",
       "seed_id": "{seed}",
       "steps": [{"idx":1,"tool":"retrieve"}, ...],
       "tie_break": "doc_id,section_id,win_idx"
     }

2) Stage B (executor):
   - Execute the plan without changing step count or order.
   - If a change is needed, stop and emit {"replan": true, "reason": "..."}.

3) Always return JSON:
   {
     "plan_rev": n,
     "answer": "... cite then explain ...",
     "λ_state": "convergent|divergent",
     "ΔS_plan_header": 0.xx,
     "coverage": 0.xx
   }
If λ is divergent or ΔS ≥ 0.60, include the exact fix page to open.

Common gotchas

Planner runs with a different header than executor. Keep a single pinned header block.
Rerank uses a different analyzer than indexing. Normalize, then tie break deterministically.
Tool schemas accept free text. Replace with enums and strict JSON.
Bridges omitted at window boundaries. Re cite the anchor before continuing.
Prompt injection or role drift unlocks free form steps. Lock system text and schema. → prompt-injection.md

When to escalate

λ remains divergent after clamp and bridges → inspect long chain stability and collapse patterns. Open: logic-collapse.md · entropy-overload.md
Live flip flops only in production → add live probes and slow ramp with backoff. Open: ops/live_monitoring_rag.md · ops/debug_playbook.md

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