Evidence row 264560

What was observed

{
  "count": 3,
  "tools": [
    {
      "description": "Computes the aggregate entropy-calibrated alpha for a corpus without running a full search -- useful to inspect before committing to a large rank_items_by_nmi_cosine_fusion call. Returns a single aggregate corpus_entropy value, NOT a per-dimension breakdown -- the real logic only exposes the mean marginal entropy across dimensions, not H(X_d) per individual dimension. Do NOT use expecting per-dimension granularity. Requires an x402 payment.",
      "inputSchema": {
        "properties": {
          "corpus_vectors": {
            "description": "List of dense numeric vectors for which to compute the aggregate entropy and calibrated alpha. Each inner array must be the same length. Maximum 500000 entries.",
            "items": {
              "items": {
                "type": "number"
              },
              "type": "array"
            },
            "maxItems": 500000,
            "minItems": 1,
            "title": "Corpus Vectors",
            "type": "array"
          },
          "n_bins": {
            "default": 16,
            "description": "Number of histogram bins for entropy discretization. Must be between 3 and 50; should match the n_bins used in rank_items_by_nmi_cosine_fusion for the profile to be consistent.",
            "maximum": 50,
            "minimum": 3,
            "title": "N Bins",
            "type": "number"
          }
        },
        "required": [
          "corpus_vectors"
        ],
        "title": "estimate_corpus_entropy_profileArguments",
        "type": "object"
      },
      "name": "nexus_similarity_search_api_estimate_corpus_entropy_profile"
    },
    {
      "description": "Ranks a corpus of items against a query vector using a calibrated fusion score (alpha * cosine + (1-alpha) * NMI_normalizado), where alpha is auto-derived from the corpus's marginal entropy unless overridden. Results are identified by their 0-indexed position in corpus_vectors (this tool does not accept explicit item IDs). Use this when you need semantically-calibrated similarity over a stateless corpus of up to 500k items without a vector database. Do NOT use for purely geometric nearest-neighbor search where NMI overhead is unnecessary, nor for corpora larger than 500k items per call. Requires an x402 payment.",
      "inputSchema": {
        "properties": {
          "alpha_override": {
            "default": null,
            "description": "Fixed alpha weight for cosine component in [0.0, 1.0]. If omitted, alpha is auto-calibrated from corpus entropy. Set to 1.0 to use pure cosine; 0.0 for pure NMI.",
            "maximum": 1,
            "minimum": 0,
            "title": "Alpha Override",
            "type": "number"
          },
          "corpus_vectors": {
            "description": "List of dense numeric vectors forming the corpus to rank against. Each inner array must match query_vector dimensionality. Maximum 500000 entries.",
            "items": {
              "items": {
                "type": "number"
              },
              "type": "array"
            },
            "maxItems": 500000,
            "minItems": 1,
            "title": "Corpus Vectors",
            "type": "array"
          },
          "n_bins": {
            "default": 16,
            "description": "Number of histogram bins used to discretize continuous dimensions when estimating NMI. Must be between 3 and 50.",
            "maximum": 50,
            "minimum": 3,
            "title": "N Bins",
            "type": "number"
          },
          "query_vector": {
            "description": "Dense numeric vector representing the query item. Must have the same dimensionality as all corpus_vectors entries.",
            "items": {
              "type": "number"
            },
            "maxItems": 4096,
            "minItems": 2,
            "title": "Query Vector",
            "type": "array"
          },
          "top_k": {
            "default": 10,
            "description": "Number of top-ranked results to return, ordered by descending fusion score. Capped at 1000 by the core service regardless of corpus size.",
            "maximum": 1000,
            "minimum": 1,
            "title": "Top K",
            "type": "number"
          }
        },
        "required": [
          "query_vector",
          "corpus_vectors"
        ],
        "title": "rank_items_by_nmi_cosine_fusionArguments",
        "type": "object"
      },
      "name": "nexus_similarity_search_api_rank_items_by_nmi_cosine_fusion"
    },
    {
      "description": "Computes the NMI-cosine fusion score for exactly one (query, target) vector pair at a fixed alpha. Use for explainability, debugging, or unit-level validation of fusion scores before running full corpus ranking. Unlike corpus-level ranking, alpha is NOT auto-calibrated for a single pair -- the real logic requires a fixed alpha (default 0.5); pass alpha explicitly for a specific blend. Do NOT use in a loop to score many pairs; batch them into rank_items_by_nmi_cosine_fusion instead. Requires an x402 payment.",
      "inputSchema": {
        "properties": {
          "alpha": {
            "default": 0.5,
            "description": "Fixed alpha weight for the cosine component in [0.0, 1.0], applied as-is -- not auto-calibrated. Default 0.5 matches the core service default.",
            "maximum": 1,
            "minimum": 0,
            "title": "Alpha",
            "type": "number"
          },
          "n_bins": {
            "default": 16,
            "description": "Histogram bins for NMI discretization. Must be between 3 and 50.",
            "maximum": 50,
            "minimum": 3,
            "title": "N Bins",
            "type": "number"
          },
          "vector_a": {
            "description": "First dense numeric vector of the pair. Must have the same dimensionality as vector_b.",
            "items": {
              "type": "number"
            },
            "maxItems": 4096,
            "minItems": 2,
            "title": "Vector A",
            "type": "array"
          },
          "vector_b": {
            "description": "Second dense numeric vector of the pair. Must have the same dimensionality as vector_a.",
            "items": {
              "type": "number"
            },
            "maxItems": 4096,
            "minItems": 2,
            "title": "Vector B",
            "type": "array"
          }
        },
        "required": [
          "vector_a",
          "vector_b"
        ],
        "title": "score_pair_nmi_cosineArguments",
        "type": "object"
      },
      "name": "nexus_similarity_search_api_score_pair_nmi_cosine"
    }
  ]
}

What the leaf hashes

{"body":"28ea2a0b702f4372513194be594602beb410f876eb0dacb5607fc1173cb314d3","id":264560,"kind":"mcp.tools","method":"probe.mcp/1","observed_us":1793034258572847,"prev":null,"subject":"b168ac6b-66df-48b3-9c22-d98acb04b4d3","subject_kind":"endpoint"}

Proof

Fold these into the leaf in order, hashing each sorted pair with keccak256, to reach the root of 2026-10-26 (296695 rows).

1. 0x077612e65508b0856cc2bf940c05f34a359dcecc5b45f94c5db9a904a806d32d
2. 0x4fcb2858df006e9b9279b364e2274f9c3861a4d98cffec20d1f796b6dd9aa9de
3. 0xd52f00694eacf0624c8da7c2e4fe8ecfeb02ba97ddfc5cc744f6405933ee1987
4. 0x9c0a1583a3edbfa609522a955482cfb99b829537426b441be7e14c772b6d6cbc
5. 0x888525e8d8416347c53ef7de61a5c520a685264977e94b88707a45ecb0c48044
6. 0x3b3082ba13eadcc9544b4039c79e589481844a135c077ae2145ea63d3d22b573
7. 0x0b785308a90713eafe33651591706def5ec75e36ee5252a0b477e2be96ff0a52
8. 0xaebbe8fb1b8c5decb29f54b13355cc31f020d90808e1581eb084c94ee8a6e574
9. 0xc80af8e272d1cb81ba42182d24905f85e11a9485f6398a240b3a2dc7142dc4c0
10. 0x6c49dc63732a1c853796427378de391ae7c0255d6d855269049c50243c7bc2b5
11. 0xe5919b091405f2537da4d32a97f1520286a61795e2d149438b884ef5190051cc
12. 0x6003e14d04cde1d24b195ac15b568dd483e04047e0f2ff64d9632a51a8f7123d
13. 0xe5a836cf25a8dfc72c9e3b6a69a7561a1f61f50f9cf36707d36f6e926aa15dec
14. 0xa96bb9408ce00a0d40ddaba8736257dfce76d5478b131d69db1afdf8a824312b
15. 0x7ddc2fead128357f881b63e6c7c26d7fbbb84fca6c7c8e0bbfe386de86ca8d39
16. 0xc6a4d7899a0f442958e26505011190b7cff2483611c1b94eba9043e0ba9ac36e
17. 0x8c756542e482157df0c36464d75c937b845e4423dbbcfad08f0cf8d5a16b9a13
18. 0x20704208aa6923d6b2450ef74ede87a6e0265d46447926038fe9506e9e154b2d
19. 0x9b9ce5b74b9bab5121612ef6b64920a8d453cbf311096a423eef9702747e868a

Day root

0xf43e8dcc3d451a4788b26be904c55e8c4c0fa75c9666796c51db38d37e2c0df8

Chained

0x702d5ac508f08b0d1accf36af25822d8e5660367494edd17a642fb61e158a57a

Published

On Arc Testnet, block 69251483: 0x495162f8936e19f0039918feebe6c307f327cd6bf8c37219620b1970041fa9ae