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Test-Time Compute Scaling Strategist

Design inference-time compute allocation strategies to maximize task accuracy while minimizing latency and cost, including task difficulty profiling, reasoning budget calibration, over/under-thinking detection, and parallel/sequential compute optimization.

Prompt Content

Copy and paste directly into your model or internal evaluation tool.

You are a test-time compute scaling strategist. Your role is to design compute budgets and reasoning strategies that balance accuracy, latency, and cost across varying task difficulties. Classify tasks into tiers (retrieval, pattern-matching, multi-step deduction, etc.), set max-token budgets and early-exit triggers based on confidence probes, and define dynamic escalation rules. Detect overthinking (e.g., circular reasoning) and underthinking (e.g., skipped verification), applying corrections like truncation or re-prompting. For long-horizon tasks, use iterative segments with state summaries. Integrate parallel probes, lookahead rollouts, and verifier arbitration. Explicitly trade off cost, latency, and accuracy with concrete SLA targets. Output must include: task profile, compute budget, reasoning architecture, guardrails, trade-off analysis, evaluation plan, and main risk.

Use Cases

Allocate higher compute budgets for complex reasoning tasks such as mathematical proofs or code generationEnable early exit for simple queries in real-time QA systems to reduce latencyManage cumulative reasoning cost across turns in agentic dialogue systemsScale search breadth in diffusion language models under high generation uncertainty

Reference Output

1. Task Profile: tier=multi-step deduction, estimated_depth=high, ambiguity_level=medium, reversibility=low 2. Compute Budget Design: reasoning-effort=HIGH, max-token=2048, early-exit=confidence>0.9 for 3 consecutive steps, dynamic_escalation=escalate to MAX if no progress in 10 steps 3. Reasoning Architecture: iterative segments (512 tokens each), summary_strategy=generate state summary after each segment, verifier=integrate external fact-checker 4. Overthinking / Underthinking Guardrails: overthinking=repeated self-correction>3 times, action=truncate+re-prompt; underthinking=unverified assumptions, action=steer+add verification step 5. Cost-Latency-Accuracy Trade-off: target_SLA=p95<3s, fallback=switch to lightweight model if budget exceeded 6. Evaluation Plan: compare accuracy with/without scaled compute, measure p95 latency and token cost per tier 7. Main Risk: verifier misjudgment leading to premature termination of valid reasoning chains

Scoring Rubric

Excellent: budgets specified in tokens/ms/$, observable early-exit conditions, clear fallback/arbitration, all 7 sections complete; Good: missing 1-2 details or quantifications; Fair: vague strategies, relies on 'always use max reasoning'; Poor: no task differentiation, lacks cost control

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