{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:DSQXG63K3QV563B6JZDT74O5QR","short_pith_number":"pith:DSQXG63K","schema_version":"1.0","canonical_sha256":"1ca1737b6adc2bdf6c3e4e473ff1dd844f9d17e6ec9a461869f6028e420bda85","source":{"kind":"arxiv","id":"2607.07391","version":1},"attestation_state":"computed","paper":{"title":"MIRA-Math: A Benchmark for Minimal Information Requesting and Mathematical Reasoning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Charbel Al Bateh, Samer Saab Jr","submitted_at":"2026-07-08T13:23:56Z","abstract_excerpt":"Mathematical reasoning benchmarks typically provide all facts needed to solve each problem, while interactive benchmarks often mix reasoning with tools, retrieval, and long-horizon dialogue. We introduce MIRA-Math, a benchmark for a narrower diagnostic capability: solving mathematical problems whose full latent state has a unique answer, but whose solver-facing view is missing exactly one necessary atomic fact. The solver must request the missing information in natural language under a strict budget and then integrate the returned fact into an exact final answer. A fixed constrained LLM respon"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2607.07391","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-08T13:23:56Z","cross_cats_sorted":[],"title_canon_sha256":"7089724191c97dd4dbf6cc608393e009ca7f6bb089fdc1b4aa1f23f8f56bf077","abstract_canon_sha256":"a1c58767a7f17eeb947ebf5f1a36fdd36d546462c79f41053494a39fefe84f60"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-09T01:20:23.710255Z","signature_b64":"3zeWD6ohYRbc4LAzP0Z8sZ1qBcIwIkgZSappXkqdFGKp7qWqYXTteWlIwAPpPwqHjqcePautpG2I/LeOEIYeAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1ca1737b6adc2bdf6c3e4e473ff1dd844f9d17e6ec9a461869f6028e420bda85","last_reissued_at":"2026-07-09T01:20:23.709800Z","signature_status":"signed_v1","first_computed_at":"2026-07-09T01:20:23.709800Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MIRA-Math: A Benchmark for Minimal Information Requesting and Mathematical Reasoning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Charbel Al Bateh, Samer Saab Jr","submitted_at":"2026-07-08T13:23:56Z","abstract_excerpt":"Mathematical reasoning benchmarks typically provide all facts needed to solve each problem, while interactive benchmarks often mix reasoning with tools, retrieval, and long-horizon dialogue. We introduce MIRA-Math, a benchmark for a narrower diagnostic capability: solving mathematical problems whose full latent state has a unique answer, but whose solver-facing view is missing exactly one necessary atomic fact. The solver must request the missing information in natural language under a strict budget and then integrate the returned fact into an exact final answer. A fixed constrained LLM respon"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.07391","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.07391/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2607.07391","created_at":"2026-07-09T01:20:23.709883+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.07391v1","created_at":"2026-07-09T01:20:23.709883+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.07391","created_at":"2026-07-09T01:20:23.709883+00:00"},{"alias_kind":"pith_short_12","alias_value":"DSQXG63K3QV5","created_at":"2026-07-09T01:20:23.709883+00:00"},{"alias_kind":"pith_short_16","alias_value":"DSQXG63K3QV563B6","created_at":"2026-07-09T01:20:23.709883+00:00"},{"alias_kind":"pith_short_8","alias_value":"DSQXG63K","created_at":"2026-07-09T01:20:23.709883+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DSQXG63K3QV563B6JZDT74O5QR","json":"https://pith.science/pith/DSQXG63K3QV563B6JZDT74O5QR.json","graph_json":"https://pith.science/api/pith-number/DSQXG63K3QV563B6JZDT74O5QR/graph.json","events_json":"https://pith.science/api/pith-number/DSQXG63K3QV563B6JZDT74O5QR/events.json","paper":"https://pith.science/paper/DSQXG63K"},"agent_actions":{"view_html":"https://pith.science/pith/DSQXG63K3QV563B6JZDT74O5QR","download_json":"https://pith.science/pith/DSQXG63K3QV563B6JZDT74O5QR.json","view_paper":"https://pith.science/paper/DSQXG63K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.07391&json=true","fetch_graph":"https://pith.science/api/pith-number/DSQXG63K3QV563B6JZDT74O5QR/graph.json","fetch_events":"https://pith.science/api/pith-number/DSQXG63K3QV563B6JZDT74O5QR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DSQXG63K3QV563B6JZDT74O5QR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DSQXG63K3QV563B6JZDT74O5QR/action/storage_attestation","attest_author":"https://pith.science/pith/DSQXG63K3QV563B6JZDT74O5QR/action/author_attestation","sign_citation":"https://pith.science/pith/DSQXG63K3QV563B6JZDT74O5QR/action/citation_signature","submit_replication":"https://pith.science/pith/DSQXG63K3QV563B6JZDT74O5QR/action/replication_record"}},"created_at":"2026-07-09T01:20:23.709883+00:00","updated_at":"2026-07-09T01:20:23.709883+00:00"}