Cognition · Software component

Adaptive Sample Allocator

Software componentCognitionCognition & Memoryarc:AdaptiveSampleAllocator

A cognition component that sets the number of reasoning samples per query from estimated difficulty and early vote agreement, stopping after a few unanimous samples or requesting more when votes diverge.

Responsibility. Allocates the sampling budget per query according to difficulty.

Also known as: Difficulty-Adaptive Self-Consistency controller, Early-stopping sampler controller

Direct neighbourhood (hover for relationship types)

Relationships

invokes dependency

Query Complexity Classifier Ch5.3

reads dependency

receives data from dynamic

triggers dynamic

Reasoning Path Sampler Ch5.3

Design guidance

SHOULD classify a query as easy after an initial small sample set (e.g., k=3) agrees unanimously with high quality, and escalate to more samples when votes scatter or quality is low.

Quantitative guidance

As stated by the sources; verify before use.

On GSM8K, ~40% easy problems use k=3; average k drops from 40 to 15, cutting cost 63% within 1-2 points of full accuracy (Ch5.3).
Tutoring deployment averaged k=6.5, 40% below uniform k=10 cost; medical coding averaged k=8.7 vs uniform k=10, 13% saving (Ch5.3).

Classification

Patterns: Difficulty-Adaptive Self-ConsistencyEarly-agreement stopping
Quality attributes: Cost efficiencyPerformance efficiency (ISO/IEC 25010)
Risks mitigated: Wasted samples on easy queries

Sources

Ch5.3: T. Nguyen, "Self-Consistency Fundamentals," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.3. ISBN: 9798244538229.