{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:5QXRITSOMTOKTVKOEF7PEDQZRL","short_pith_number":"pith:5QXRITSO","canonical_record":{"source":{"id":"2607.22621","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-17T02:43:58Z","cross_cats_sorted":[],"title_canon_sha256":"5dc2fb73adafa2819d722a73329382b36474a32e0393718d2eff8b2027dbf6b9","abstract_canon_sha256":"7d48ad374dae2eff8c2248abc16b26b0155495198386394e56e812b9d9991b0c"},"schema_version":"1.0"},"canonical_sha256":"ec2f144e4e64dca9d54e217ef20e198ae30a5eb7413fe96b11c2a3b2d450e45c","source":{"kind":"arxiv","id":"2607.22621","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.22621","created_at":"2026-07-28T00:21:45Z"},{"alias_kind":"arxiv_version","alias_value":"2607.22621v1","created_at":"2026-07-28T00:21:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.22621","created_at":"2026-07-28T00:21:45Z"},{"alias_kind":"pith_short_12","alias_value":"5QXRITSOMTOK","created_at":"2026-07-28T00:21:45Z"},{"alias_kind":"pith_short_16","alias_value":"5QXRITSOMTOKTVKO","created_at":"2026-07-28T00:21:45Z"},{"alias_kind":"pith_short_8","alias_value":"5QXRITSO","created_at":"2026-07-28T00:21:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:5QXRITSOMTOKTVKOEF7PEDQZRL","target":"record","payload":{"canonical_record":{"source":{"id":"2607.22621","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-17T02:43:58Z","cross_cats_sorted":[],"title_canon_sha256":"5dc2fb73adafa2819d722a73329382b36474a32e0393718d2eff8b2027dbf6b9","abstract_canon_sha256":"7d48ad374dae2eff8c2248abc16b26b0155495198386394e56e812b9d9991b0c"},"schema_version":"1.0"},"canonical_sha256":"ec2f144e4e64dca9d54e217ef20e198ae30a5eb7413fe96b11c2a3b2d450e45c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-28T00:21:45.530005Z","signature_b64":"G+67qfALB0/I5ICiXbtCPUisEK65d0TxpVIoaQUWbW7mUChEmVCSkpG5Je3GtxLG0gwRNUFawO1Usw1Mx4tzBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ec2f144e4e64dca9d54e217ef20e198ae30a5eb7413fe96b11c2a3b2d450e45c","last_reissued_at":"2026-07-28T00:21:45.529078Z","signature_status":"signed_v1","first_computed_at":"2026-07-28T00:21:45.529078Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.22621","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-28T00:21:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ncFpTKOce5DXdfEmNbe5smhnoxg+gLjoIM9KYmbASBGsnUAhNLWQiYik3AYNKaBvYJ+FK9zirIMqImcuycPwCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T01:03:09.536799Z"},"content_sha256":"7575847f1eaf15ab76bac94a7799934e3094a753e0e37f8c5f5c72d33d2879e6","schema_version":"1.0","event_id":"sha256:7575847f1eaf15ab76bac94a7799934e3094a753e0e37f8c5f5c72d33d2879e6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:5QXRITSOMTOKTVKOEF7PEDQZRL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Opti-Q: A Constraint-Based Optimization Framework for Multi-LLM Question Planning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Aamir Hamid, Bharg Barot, Primal Pappachan, Roberto Yus, Satvik Racharla, Tim Finin","submitted_at":"2026-06-17T02:43:58Z","abstract_excerpt":"While large language models (LLMs) enable strong question answering (QA), budgeted deployment is complicated by nondeterminism and heterogeneous resource profiles (cost, latency, and energy). We present OPTI-Q, a database-inspired, cost-based optimizer that implements a plan-before-execute paradigm for multi-LLM orchestration. OPTI-Q models LLM invocations as physical operators in an execution DAG and, for each question, searches for plans that optimize answer quality (QoA) while trading off financial cost, latency, and energy under user-specified resource constraints. Plans can include sequen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.22621","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/2607.22621/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-28T00:21:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HiU6cQhBb0wdsN39gASmOrOKLZtJpF00I8n+KsLokCfUccBgXtlUergGNZU1i0aXncvedFH8+pDmUHvMmxEFAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T01:03:09.537314Z"},"content_sha256":"11b855d0b50d9d881b8c0b3e91ad03d3bde204d673d00ea7c00493dae6bd88cc","schema_version":"1.0","event_id":"sha256:11b855d0b50d9d881b8c0b3e91ad03d3bde204d673d00ea7c00493dae6bd88cc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5QXRITSOMTOKTVKOEF7PEDQZRL/bundle.json","state_url":"https://pith.science/pith/5QXRITSOMTOKTVKOEF7PEDQZRL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5QXRITSOMTOKTVKOEF7PEDQZRL/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-04T01:03:09Z","links":{"resolver":"https://pith.science/pith/5QXRITSOMTOKTVKOEF7PEDQZRL","bundle":"https://pith.science/pith/5QXRITSOMTOKTVKOEF7PEDQZRL/bundle.json","state":"https://pith.science/pith/5QXRITSOMTOKTVKOEF7PEDQZRL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5QXRITSOMTOKTVKOEF7PEDQZRL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:5QXRITSOMTOKTVKOEF7PEDQZRL","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"7d48ad374dae2eff8c2248abc16b26b0155495198386394e56e812b9d9991b0c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-17T02:43:58Z","title_canon_sha256":"5dc2fb73adafa2819d722a73329382b36474a32e0393718d2eff8b2027dbf6b9"},"schema_version":"1.0","source":{"id":"2607.22621","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.22621","created_at":"2026-07-28T00:21:45Z"},{"alias_kind":"arxiv_version","alias_value":"2607.22621v1","created_at":"2026-07-28T00:21:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.22621","created_at":"2026-07-28T00:21:45Z"},{"alias_kind":"pith_short_12","alias_value":"5QXRITSOMTOK","created_at":"2026-07-28T00:21:45Z"},{"alias_kind":"pith_short_16","alias_value":"5QXRITSOMTOKTVKO","created_at":"2026-07-28T00:21:45Z"},{"alias_kind":"pith_short_8","alias_value":"5QXRITSO","created_at":"2026-07-28T00:21:45Z"}],"graph_snapshots":[{"event_id":"sha256:11b855d0b50d9d881b8c0b3e91ad03d3bde204d673d00ea7c00493dae6bd88cc","target":"graph","created_at":"2026-07-28T00:21:45Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2607.22621/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While large language models (LLMs) enable strong question answering (QA), budgeted deployment is complicated by nondeterminism and heterogeneous resource profiles (cost, latency, and energy). We present OPTI-Q, a database-inspired, cost-based optimizer that implements a plan-before-execute paradigm for multi-LLM orchestration. OPTI-Q models LLM invocations as physical operators in an execution DAG and, for each question, searches for plans that optimize answer quality (QoA) while trading off financial cost, latency, and energy under user-specified resource constraints. Plans can include sequen","authors_text":"Aamir Hamid, Bharg Barot, Primal Pappachan, Roberto Yus, Satvik Racharla, Tim Finin","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-17T02:43:58Z","title":"Opti-Q: A Constraint-Based Optimization Framework for Multi-LLM Question Planning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.22621","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:7575847f1eaf15ab76bac94a7799934e3094a753e0e37f8c5f5c72d33d2879e6","target":"record","created_at":"2026-07-28T00:21:45Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"7d48ad374dae2eff8c2248abc16b26b0155495198386394e56e812b9d9991b0c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-17T02:43:58Z","title_canon_sha256":"5dc2fb73adafa2819d722a73329382b36474a32e0393718d2eff8b2027dbf6b9"},"schema_version":"1.0","source":{"id":"2607.22621","kind":"arxiv","version":1}},"canonical_sha256":"ec2f144e4e64dca9d54e217ef20e198ae30a5eb7413fe96b11c2a3b2d450e45c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ec2f144e4e64dca9d54e217ef20e198ae30a5eb7413fe96b11c2a3b2d450e45c","first_computed_at":"2026-07-28T00:21:45.529078Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-28T00:21:45.529078Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"G+67qfALB0/I5ICiXbtCPUisEK65d0TxpVIoaQUWbW7mUChEmVCSkpG5Je3GtxLG0gwRNUFawO1Usw1Mx4tzBg==","signature_status":"signed_v1","signed_at":"2026-07-28T00:21:45.530005Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.22621","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7575847f1eaf15ab76bac94a7799934e3094a753e0e37f8c5f5c72d33d2879e6","sha256:11b855d0b50d9d881b8c0b3e91ad03d3bde204d673d00ea7c00493dae6bd88cc"],"state_sha256":"bf22537ce5acac958ee899060fd828f56bd7e034449781341f6ffd34ea1be45c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WNi5AwlIdRoaTP7o7L431rz3EA6aS+W08ktqdX4vOKPpgkzcYiZqWqV7MIP/4pUi5NFtTUti4B5UoEHeMG2YAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T01:03:09.542337Z","bundle_sha256":"826bbce66041fe96fafa82957f23c915d3b83de36d2e12042308fd0b33627a10"}}