Micro Retrieval Recovery Regression
by Retrieval recovery regression in AI models & inference
Compare complete, partial, and missed evidence recovery on one matched, hash-frozen cohort of up to 64 questions and papers. Report paired transitions and a caller-declared complete-recovery drop threshold. Descriptive evidence-ID overlap, not answer quality or causal inference.
https://www.mahastrategies.com/api/v1/micro/retrieval-recovery-regression- Price
- $0.074
- per call in USDC
- Uptime
- 100%
- last 30 days
- Response time
- 420 ms
- 641 ms slowest 5%
- Last check
- Passed
- 40 min ago, next any minute now
Reliability
How to call it
POST https://www.mahastrategies.com/api/v1/micro/retrieval-recovery-regression
Each call costs $0.074 in USDC on Base, paid with the request over x402.
Run a free test callOne unpaid request, the way an agent sends it. You see the payment challenge it gets back. Nothing is charged.
Example request
From the listing.
POST https://www.mahastrategies.com/api/v1/micro/retrieval-recovery-regression
content-type: application/json
{
"baseline": {
"cohortSha256": "sha256:874ebcbb47229979ed9499f3733e3f483b970ceb0b0c1f9bc52c734f3176d8f2",
"observations": [
{
"paperId": "paper-a",
"questionId": "q1",
"retrievedEvidenceIds": [
"a1"
]
},
{
"paperId": "paper-b",
"questionId": "q2",
"retrievedEvidenceIds": [
"b1"
]
}
],
"runId": "baseline"
},
"candidate": {
"cohortSha256": "sha256:874ebcbb47229979ed9499f3733e3f483b970ceb0b0c1f9bc52c734f3176d8f2",
"observations": [
{
"paperId": "paper-a",
"questionId": "q1",
"retrievedEvidenceIds": [
"a1",
"a2"
]
},
{
"paperId": "paper-b",
"questionId": "q2",
"retrievedEvidenceIds": []
}
],
"runId": "candidate"
},
"corpusHash": "sha256:5e1bf4f25cdc1fc0fbcbb2240affabbd808fd2ba8fa1ce0b6723dac5dc682759",
"dataClass": "synthetic",
"evidence": [
{
"evidenceId": "a1",
"paperId": "paper-a"
},
{
"evidenceId": "a2",
"paperId": "paper-a"
},
{
"evidenceId": "b1",
"paperId": "paper-b"
}
],
"maxCompleteDropBps": 0,
"paperIds": [
"paper-a",
"paper-b"
],
"questions": [
{
"expectedEvidenceIds": [
"a1",
"a2"
],
"paperId": "paper-a",
"questionId": "q1"
},
{
"expectedEvidenceIds": [
"b1"
],
"paperId": "paper-b",
"questionId": "q2"
}
]
}Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
baseline | object | Yes | |
candidate | object | Yes | |
corpusHash | string | Yes | |
dataClass | string | Yes | One of: public, synthetic. |
evidence | array | Yes | |
maxCompleteDropBps | integer | Yes | |
paperIds | array | Yes | |
questions | array | Yes |
Example response
The provider's example. We can't check it without paying.
Example response, 72 lines
{
"amountBaseUnits": "74000",
"boundaries": [
"Public or synthetic inputs only. Caller labels are declarations,…"
],
"inputDigest": "sha256:9d22eb8f9fd0df5d9672e8b177d134dd6a99bfc7ae6ad1cc1b73750e6…",
"offerId": "retrieval-recovery-regression",
"receiptDigest": "sha256:7ad155f100f5edb50a0919474d844a9a72cae35b0d64afd136ac93607…",
"result": {
"baselineCompleteBps": 5000,
"baselineCounts": {
"complete": 1,
"miss": 0,
"partial": 1
},
"baselineRunId": "baseline",
"candidateCompleteBps": 5000,
"candidateCounts": {
"complete": 1,
"miss": 1,
"partial": 0
},
"candidateRunId": "candidate",
"completeDeltaBps": 0,
"dataClass": "synthetic",
"frozenCohortSha256": "sha256:874ebcbb47229979ed9499f3733e3f483b970ceb0b0c1f9bc52c734f3…",
"improved": 1,
"inputSha256": "sha256:9d22eb8f9fd0df5d9672e8b177d134dd6a99bfc7ae6ad1cc1b73750e6…",
"limits": [
"Public/synthetic is a caller declaration, not content inspection…"
],
"matchedPaperIds": [
"paper-a",
"paper-b"
],
"maxCompleteDropBps": 0,
"papers": [
{
"baselineComplete": 0,
"candidateComplete": 1,
"completeDeltaBps": 10000,
"paperId": "paper-a",
"questionCount": 1
}
],
"questionCount": 2,
"questions": [
{
"baselineRecovered": 1,
"baselineState": "partial",
"candidateRecovered": 2,
"candidateState": "complete",
"paperId": "paper-a",
"questionId": "q1",
"requiredCount": 2
}
],
"regressed": 1,
"regressionObserved": false,
"resultSha256": "sha256:86aad75a7fb866a83e687aeb1dee304c7a67e4a31f8cac9c28a9fd803…",
"sourcesVerified": false,
"transitions": [
{
"count": 0,
"from": "complete",
"to": "complete"
}
],
"unchanged": 0
},
"version": "maha-microproducts/0.1"
}Call it from code
The curl command shows the payment challenge. The JavaScript and Python versions pay and call, and never pay more than $0.074.
curl: see the payment challenge
# bash or zsh: see the payment challenge. Nothing is charged.
curl -sS -g -i -X POST 'https://www.mahastrategies.com/api/v1/micro/retrieval-recovery-regression' \
-H 'content-type: application/json' \
--data-raw '{"baseline":{"cohortSha256":"sha256:874ebcbb47229979ed9499f3733e3f483b970ceb0b0c1f9bc52c734f3176d8f2","observations":[{"paperId":"paper-a","questionId":"q1","retrievedEvidenceIds":["a1"]},{"paperId":"paper-b","questionId":"q2","retrievedEvidenceIds":["b1"]}],"runId":"baseline"},"candidate":{"cohortSha256":"sha256:874ebcbb47229979ed9499f3733e3f483b970ceb0b0c1f9bc52c734f3176d8f2","observations":[{"paperId":"paper-a","questionId":"q1","retrievedEvidenceIds":["a1","a2"]},{"paperId":"paper-b","questionId":"q2","retrievedEvidenceIds":[]}],"runId":"candidate"},"corpusHash":"sha256:5e1bf4f25cdc1fc0fbcbb2240affabbd808fd2ba8fa1ce0b6723dac5dc682759","dataClass":"synthetic","evidence":[{"evidenceId":"a1","paperId":"paper-a"},{"evidenceId":"a2","paperId":"paper-a"},{"evidenceId":"b1","paperId":"paper-b"}],"maxCompleteDropBps":0,"paperIds":["paper-a","paper-b"],"questions":[{"expectedEvidenceIds":["a1","a2"],"paperId":"paper-a","questionId":"q1"},{"expectedEvidenceIds":["b1"],"paperId":"paper-b","questionId":"q2"}]}'
# x402 v2 sends the challenge base64-encoded in the PAYMENT-REQUIRED header (v1 sends it in the body).
# To read the header:
curl -sS -g -o /dev/null -D - -X POST 'https://www.mahastrategies.com/api/v1/micro/retrieval-recovery-regression' \
-H 'content-type: application/json' \
--data-raw '{"baseline":{"cohortSha256":"sha256:874ebcbb47229979ed9499f3733e3f483b970ceb0b0c1f9bc52c734f3176d8f2","observations":[{"paperId":"paper-a","questionId":"q1","retrievedEvidenceIds":["a1"]},{"paperId":"paper-b","questionId":"q2","retrievedEvidenceIds":["b1"]}],"runId":"baseline"},"candidate":{"cohortSha256":"sha256:874ebcbb47229979ed9499f3733e3f483b970ceb0b0c1f9bc52c734f3176d8f2","observations":[{"paperId":"paper-a","questionId":"q1","retrievedEvidenceIds":["a1","a2"]},{"paperId":"paper-b","questionId":"q2","retrievedEvidenceIds":[]}],"runId":"candidate"},"corpusHash":"sha256:5e1bf4f25cdc1fc0fbcbb2240affabbd808fd2ba8fa1ce0b6723dac5dc682759","dataClass":"synthetic","evidence":[{"evidenceId":"a1","paperId":"paper-a"},{"evidenceId":"a2","paperId":"paper-a"},{"evidenceId":"b1","paperId":"paper-b"}],"maxCompleteDropBps":0,"paperIds":["paper-a","paper-b"],"questions":[{"expectedEvidenceIds":["a1","a2"],"paperId":"paper-a","questionId":"q1"},{"expectedEvidenceIds":["b1"],"paperId":"paper-b","questionId":"q2"}]}' \
| grep -i '^payment-required:' | cut -d' ' -f2 | tr -d '\r' | base64 --decodeJavaScript: pay and call
// npm i @x402/fetch @x402/evm viem
import { wrapFetchWithPayment, x402Client } from "@x402/fetch";
import { registerExactEvmScheme } from "@x402/evm/exact/client";
import { privateKeyToAccount } from "viem/accounts";
// Pays at most $0.074 per call.
const client = new x402Client().setSpendControls({ maxAmountPerPayment: "$0.074" });
registerExactEvmScheme(client, { signer: privateKeyToAccount(process.env.EVM_PRIVATE_KEY) });
const pay = wrapFetchWithPayment(fetch, client);
// Not sure it's safe to pay? ToolVet checks it first for $0.005. Pay for that from a separate
// client: this one is capped at $0.074 per payment. GET https://toolvet.app/api/v1/check?url=https%3A%2F%2Fwww.mahastrategies.com%2Fapi%2Fv1%2Fmicro%2Fretrieval-recovery-regression&method=POST
const res = await pay("https://www.mahastrategies.com/api/v1/micro/retrieval-recovery-regression", {
method: "POST",
headers: { "content-type": "application/json" },
body: JSON.stringify({
"baseline": {
"cohortSha256": "sha256:874ebcbb47229979ed9499f3733e3f483b970ceb0b0c1f9bc52c734f3176d8f2",
"observations": [
{
"paperId": "paper-a",
"questionId": "q1",
"retrievedEvidenceIds": [
"a1"
]
},
{
"paperId": "paper-b",
"questionId": "q2",
"retrievedEvidenceIds": [
"b1"
]
}
],
"runId": "baseline"
},
"candidate": {
"cohortSha256": "sha256:874ebcbb47229979ed9499f3733e3f483b970ceb0b0c1f9bc52c734f3176d8f2",
"observations": [
{
"paperId": "paper-a",
"questionId": "q1",
"retrievedEvidenceIds": [
"a1",
"a2"
]
},
{
"paperId": "paper-b",
"questionId": "q2",
"retrievedEvidenceIds": []
}
],
"runId": "candidate"
},
"corpusHash": "sha256:5e1bf4f25cdc1fc0fbcbb2240affabbd808fd2ba8fa1ce0b6723dac5dc682759",
"dataClass": "synthetic",
"evidence": [
{
"evidenceId": "a1",
"paperId": "paper-a"
},
{
"evidenceId": "a2",
"paperId": "paper-a"
},
{
"evidenceId": "b1",
"paperId": "paper-b"
}
],
"maxCompleteDropBps": 0,
"paperIds": [
"paper-a",
"paper-b"
],
"questions": [
{
"expectedEvidenceIds": [
"a1",
"a2"
],
"paperId": "paper-a",
"questionId": "q1"
},
{
"expectedEvidenceIds": [
"b1"
],
"paperId": "paper-b",
"questionId": "q2"
}
]
}),
});
console.log(res.status, await res.text());Python: pay and call
# pip install "x402[requests,evm]"
import os
from eth_account import Account
from x402 import x402ClientSync
from x402.http.clients import x402_requests
from x402.mechanisms.evm import EthAccountSigner
from x402.mechanisms.evm.exact.register import register_exact_evm_client
# Pays at most $0.074 per call.
client = x402ClientSync().set_spend_controls({"max_amount_per_payment": "$0.074"})
register_exact_evm_client(client, EthAccountSigner(Account.from_key(os.environ["EVM_PRIVATE_KEY"])))
with x402_requests(client) as session:
res = session.post(
"https://www.mahastrategies.com/api/v1/micro/retrieval-recovery-regression",
json={
"baseline": {
"cohortSha256": "sha256:874ebcbb47229979ed9499f3733e3f483b970ceb0b0c1f9bc52c734f3176d8f2",
"observations": [
{
"paperId": "paper-a",
"questionId": "q1",
"retrievedEvidenceIds": [
"a1",
],
},
{
"paperId": "paper-b",
"questionId": "q2",
"retrievedEvidenceIds": [
"b1",
],
},
],
"runId": "baseline",
},
"candidate": {
"cohortSha256": "sha256:874ebcbb47229979ed9499f3733e3f483b970ceb0b0c1f9bc52c734f3176d8f2",
"observations": [
{
"paperId": "paper-a",
"questionId": "q1",
"retrievedEvidenceIds": [
"a1",
"a2",
],
},
{
"paperId": "paper-b",
"questionId": "q2",
"retrievedEvidenceIds": [],
},
],
"runId": "candidate",
},
"corpusHash": "sha256:5e1bf4f25cdc1fc0fbcbb2240affabbd808fd2ba8fa1ce0b6723dac5dc682759",
"dataClass": "synthetic",
"evidence": [
{
"evidenceId": "a1",
"paperId": "paper-a",
},
{
"evidenceId": "a2",
"paperId": "paper-a",
},
{
"evidenceId": "b1",
"paperId": "paper-b",
},
],
"maxCompleteDropBps": 0,
"paperIds": [
"paper-a",
"paper-b",
],
"questions": [
{
"expectedEvidenceIds": [
"a1",
"a2",
],
"paperId": "paper-a",
"questionId": "q1",
},
{
"expectedEvidenceIds": [
"b1",
],
"paperId": "paper-b",
"questionId": "q2",
},
],
},
)
print(res.status_code, res.text)Security scan
No findings
We scan names, descriptions and tool definitions for hidden instructions and other prompt-injection patterns.
Recent checks
| When | Result | HTTP | Time | Price |
|---|---|---|---|---|
| 40 min ago | Passed | 402 | 373 ms | $0.074 |
| 5 h ago | Passed | 402 | 734 ms | $0.074 |
| 9 h ago | Passed | 402 | 395 ms | $0.074 |
| 13 h ago | Passed | 402 | 390 ms | $0.074 |
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Show 10 older checks
| 21 h ago | Passed | 402 | 920 ms | $0.074 |
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| 44 h ago | Passed | 402 | 420 ms | $0.074 |
| 2 days ago | Passed | 402 | 641 ms | $0.074 |
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| 2 days ago | Passed | 402 | 378 ms | $0.074 |