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Packing Displacement Analysis
x402
Packing Displacement Analysis is a paid API for AI agents from www.mahastrategies.com, paid per call via x402, $0.078/call, status unknown (last checked 2026-10-16).
Compares strict rank-prefix packing, rank-order greedy-fit, and score-density greedy-fit strategies for up to 64 passages under a token budget, reporting evidence displacement and optional selection violations.
Price / call$0.078
Success—
StatusUnknown
How this service works
Compare strict rank-prefix packing with rank-order greedy-fit and score-density greedy-fit for up to 64 passages under a 1–2048 token budget. Report evidence displacement and optional observed selection violations. Uses declared integer costs/scores; no tokenization or causal attribution.
Output
Returns a structured result object containing per-strategy packing outcomes, evidence displacement counts, optional observed selection violation flags, boundary arrays, an offer ID ('packing-displacement-analysis'), version string, amount in base units, and cryptographic input/receipt digests for auditability.
Example use cases
RAG pipeline packing strategy audit
“I have 40 candidate passages with declared token costs and scores for a RAG system running under a 1024-token context budget. Can you compare strict rank-prefix, rank-order greedy, and score-density greedy packing strategies and tell me which one displaces the least required evidence? Data class is public, required evidence IDs are [doc-3, doc-11, doc-22].”
Detecting selection violations in observed picks
“My retrieval system selected passages [p2, p5, p9, p13] from a pool of 30 candidates with a 768-token budget — can you run a packing displacement analysis to see if those observed picks violate what rank-order or score-density greedy-fit would have chosen? Data class is synthetic, required evidence IDs are [p2, p9].”
Optimizing token budget for high-score evidence
“I have 15 passages with explicit integer token costs and relevance scores, a 256-token budget, and I need to know how much required evidence (IDs: [ev-1, ev-2]) gets displaced under each of the three packing strategies — rank-prefix, rank-order greedy, and score-density greedy. Please run the displacement analysis with data class set to public.”
Technical details
Endpoint
POSThttps://www.mahastrategies.com/api/v1/micro/packing-displacement-analysis?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a deterministic, integer-cost comparison of multiple context-window packing strategies without any tokenization or causal inference — specifically when auditing RAG pipelines, verifying that observed passage selections are consistent with optimal strategies, or quantifying evidence displacement across rank-prefix and greedy-fit methods. It is uniquely suited for scenarios where you have explicit declared costs and scores and want a reproducible algorithmic comparison rather than a heuristic or ML-based recommendation.
Side effects
- More than 64 candidates or required evidence IDs submitted — rejected with validation error
- Token budget outside 1–2048 range — rejected
- Missing required fields (dataClass, budget, requiredEvidenceIds, candidates) — 400-level error
- Invalid dataClass enum value (not 'public' or 'synthetic') — schema validation failure
- Payment not processed — x402 payment required error
- observedSelectedPassageIds array exceeds 64 items — rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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