{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:QNEYCMD7BWKDBLN6GWCLFRSYUY","short_pith_number":"pith:QNEYCMD7","canonical_record":{"source":{"id":"2504.10179","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2025-04-14T12:31:39Z","cross_cats_sorted":["cs.CL","cs.ET"],"title_canon_sha256":"d29064faab00ac58ff055a88c97801407bac7d665166c923d6b1bc79781b57f8","abstract_canon_sha256":"b6d1a41fc0b1d963bf6a31c5fa6199fca03785783b80bd43df8ba2f153b0310f"},"schema_version":"1.0"},"canonical_sha256":"834981307f0d9430adbe3584b2c658a63c47ea06a844882d38f5f7a4612410d8","source":{"kind":"arxiv","id":"2504.10179","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.10179","created_at":"2026-07-05T10:48:48Z"},{"alias_kind":"arxiv_version","alias_value":"2504.10179v1","created_at":"2026-07-05T10:48:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.10179","created_at":"2026-07-05T10:48:48Z"},{"alias_kind":"pith_short_12","alias_value":"QNEYCMD7BWKD","created_at":"2026-07-05T10:48:48Z"},{"alias_kind":"pith_short_16","alias_value":"QNEYCMD7BWKDBLN6","created_at":"2026-07-05T10:48:48Z"},{"alias_kind":"pith_short_8","alias_value":"QNEYCMD7","created_at":"2026-07-05T10:48:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:QNEYCMD7BWKDBLN6GWCLFRSYUY","target":"record","payload":{"canonical_record":{"source":{"id":"2504.10179","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2025-04-14T12:31:39Z","cross_cats_sorted":["cs.CL","cs.ET"],"title_canon_sha256":"d29064faab00ac58ff055a88c97801407bac7d665166c923d6b1bc79781b57f8","abstract_canon_sha256":"b6d1a41fc0b1d963bf6a31c5fa6199fca03785783b80bd43df8ba2f153b0310f"},"schema_version":"1.0"},"canonical_sha256":"834981307f0d9430adbe3584b2c658a63c47ea06a844882d38f5f7a4612410d8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:48:48.746298Z","signature_b64":"zvhfGXZVezRJx/7EeVk0LQrPKwWvt8RvZIvnh9MT3/LZgsk2HLT6IKlQcXlrcVbRIywVvdiESWHaX9QIVnybDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"834981307f0d9430adbe3584b2c658a63c47ea06a844882d38f5f7a4612410d8","last_reissued_at":"2026-07-05T10:48:48.745705Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:48:48.745705Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.10179","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":"2026-07-05T10:48:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pmh2JqsrSFjRcj4BQEGHDM9SeyesGHuYfKSosTR1x6qCjyuHd9SUIS0VhZpIn4hpM1fgELzN5R2kYJ4f95BxCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T21:13:51.578978Z"},"content_sha256":"083fd6511cd68ac924950306fca3a0663cdbedfb2fc5a8d79629e73ec31a9039","schema_version":"1.0","event_id":"sha256:083fd6511cd68ac924950306fca3a0663cdbedfb2fc5a8d79629e73ec31a9039"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:QNEYCMD7BWKDBLN6GWCLFRSYUY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"The Future of MLLM Prompting is Adaptive: A Comprehensive Experimental Evaluation of Prompt Engineering Methods for Robust Multimodal Performance","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CL","cs.ET"],"primary_cat":"cs.AI","authors_text":"Anwesha Mohanty, Arsalan Shahid, Venkatesh Balavadhani Parthasarathy","submitted_at":"2025-04-14T12:31:39Z","abstract_excerpt":"Multimodal Large Language Models (MLLMs) are set to transform how machines process and generate human-like responses by integrating diverse modalities such as text, images, and code. Yet, effectively harnessing their capabilities hinges on optimal prompt engineering. We present a comprehensive experimental evaluation of seven prompt engineering methods applied to 13 open-source MLLMs over 24 tasks spanning Reasoning and Compositionality, Multimodal Understanding and Alignment, Complex Code Generation and Execution, and Knowledge Retrieval and Integration. Our approach stratifies models by para"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.10179","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/2504.10179/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":"2026-07-05T10:48:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vyR4G1Cz6LSXzvsjTp0+lCS2RdpGlT4BLKik3jV7WC+TMHiZa2QlFgrZNvs/XDn2xCwKihNe6vQ0exFcS6WkCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T21:13:51.579572Z"},"content_sha256":"2c29590d95026777227d0583a536491a5bd5199a9ba135b58ef17a553569957d","schema_version":"1.0","event_id":"sha256:2c29590d95026777227d0583a536491a5bd5199a9ba135b58ef17a553569957d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QNEYCMD7BWKDBLN6GWCLFRSYUY/bundle.json","state_url":"https://pith.science/pith/QNEYCMD7BWKDBLN6GWCLFRSYUY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QNEYCMD7BWKDBLN6GWCLFRSYUY/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":"2026-08-05T21:13:51Z","links":{"resolver":"https://pith.science/pith/QNEYCMD7BWKDBLN6GWCLFRSYUY","bundle":"https://pith.science/pith/QNEYCMD7BWKDBLN6GWCLFRSYUY/bundle.json","state":"https://pith.science/pith/QNEYCMD7BWKDBLN6GWCLFRSYUY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QNEYCMD7BWKDBLN6GWCLFRSYUY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:QNEYCMD7BWKDBLN6GWCLFRSYUY","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":"b6d1a41fc0b1d963bf6a31c5fa6199fca03785783b80bd43df8ba2f153b0310f","cross_cats_sorted":["cs.CL","cs.ET"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2025-04-14T12:31:39Z","title_canon_sha256":"d29064faab00ac58ff055a88c97801407bac7d665166c923d6b1bc79781b57f8"},"schema_version":"1.0","source":{"id":"2504.10179","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.10179","created_at":"2026-07-05T10:48:48Z"},{"alias_kind":"arxiv_version","alias_value":"2504.10179v1","created_at":"2026-07-05T10:48:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.10179","created_at":"2026-07-05T10:48:48Z"},{"alias_kind":"pith_short_12","alias_value":"QNEYCMD7BWKD","created_at":"2026-07-05T10:48:48Z"},{"alias_kind":"pith_short_16","alias_value":"QNEYCMD7BWKDBLN6","created_at":"2026-07-05T10:48:48Z"},{"alias_kind":"pith_short_8","alias_value":"QNEYCMD7","created_at":"2026-07-05T10:48:48Z"}],"graph_snapshots":[{"event_id":"sha256:2c29590d95026777227d0583a536491a5bd5199a9ba135b58ef17a553569957d","target":"graph","created_at":"2026-07-05T10:48:48Z","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/2504.10179/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multimodal Large Language Models (MLLMs) are set to transform how machines process and generate human-like responses by integrating diverse modalities such as text, images, and code. Yet, effectively harnessing their capabilities hinges on optimal prompt engineering. We present a comprehensive experimental evaluation of seven prompt engineering methods applied to 13 open-source MLLMs over 24 tasks spanning Reasoning and Compositionality, Multimodal Understanding and Alignment, Complex Code Generation and Execution, and Knowledge Retrieval and Integration. Our approach stratifies models by para","authors_text":"Anwesha Mohanty, Arsalan Shahid, Venkatesh Balavadhani Parthasarathy","cross_cats":["cs.CL","cs.ET"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2025-04-14T12:31:39Z","title":"The Future of MLLM Prompting is Adaptive: A Comprehensive Experimental Evaluation of Prompt Engineering Methods for Robust Multimodal Performance"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.10179","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":"sha256:083fd6511cd68ac924950306fca3a0663cdbedfb2fc5a8d79629e73ec31a9039","target":"record","created_at":"2026-07-05T10:48:48Z","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":"b6d1a41fc0b1d963bf6a31c5fa6199fca03785783b80bd43df8ba2f153b0310f","cross_cats_sorted":["cs.CL","cs.ET"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2025-04-14T12:31:39Z","title_canon_sha256":"d29064faab00ac58ff055a88c97801407bac7d665166c923d6b1bc79781b57f8"},"schema_version":"1.0","source":{"id":"2504.10179","kind":"arxiv","version":1}},"canonical_sha256":"834981307f0d9430adbe3584b2c658a63c47ea06a844882d38f5f7a4612410d8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"834981307f0d9430adbe3584b2c658a63c47ea06a844882d38f5f7a4612410d8","first_computed_at":"2026-07-05T10:48:48.745705Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:48:48.745705Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zvhfGXZVezRJx/7EeVk0LQrPKwWvt8RvZIvnh9MT3/LZgsk2HLT6IKlQcXlrcVbRIywVvdiESWHaX9QIVnybDg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:48:48.746298Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.10179","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:083fd6511cd68ac924950306fca3a0663cdbedfb2fc5a8d79629e73ec31a9039","sha256:2c29590d95026777227d0583a536491a5bd5199a9ba135b58ef17a553569957d"],"state_sha256":"2276f38b3d7527d8d114b2e03c5a0832a7a10b7fbc7605c7d2827d01cad23558"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MdjPhnMbhswQmGwNeBTCkn2mwZNJ5zaVYUIrAtb3yU1o+yG0KU5LZl2gO9lwvUiyiFND5bSO/aKL+bV3UAi8Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T21:13:51.584048Z","bundle_sha256":"f6b320dad68bddb9c58b769903648edadf970481ee0ba08d733bed5067555c68"}}