{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:JAJOILQXIZOWSVYRGI6R2HB6QB","short_pith_number":"pith:JAJOILQX","canonical_record":{"source":{"id":"2506.08727","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-10T12:23:02Z","cross_cats_sorted":["cs.AI","cs.CY","cs.SE"],"title_canon_sha256":"7a114d072b7adf29d48b1cab15ffe212dedb7bebc37b95af53c503bdad9526f9","abstract_canon_sha256":"294fe9e78c80f173ce1018ffb783155e3bd19be35fa192670d13dfc647384e29"},"schema_version":"1.0"},"canonical_sha256":"4812e42e17465d695711323d1d1c3e804542c731b52ef51e640ce8a983eb6337","source":{"kind":"arxiv","id":"2506.08727","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.08727","created_at":"2026-07-05T12:06:14Z"},{"alias_kind":"arxiv_version","alias_value":"2506.08727v1","created_at":"2026-07-05T12:06:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.08727","created_at":"2026-07-05T12:06:14Z"},{"alias_kind":"pith_short_12","alias_value":"JAJOILQXIZOW","created_at":"2026-07-05T12:06:14Z"},{"alias_kind":"pith_short_16","alias_value":"JAJOILQXIZOWSVYR","created_at":"2026-07-05T12:06:14Z"},{"alias_kind":"pith_short_8","alias_value":"JAJOILQX","created_at":"2026-07-05T12:06:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:JAJOILQXIZOWSVYRGI6R2HB6QB","target":"record","payload":{"canonical_record":{"source":{"id":"2506.08727","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-10T12:23:02Z","cross_cats_sorted":["cs.AI","cs.CY","cs.SE"],"title_canon_sha256":"7a114d072b7adf29d48b1cab15ffe212dedb7bebc37b95af53c503bdad9526f9","abstract_canon_sha256":"294fe9e78c80f173ce1018ffb783155e3bd19be35fa192670d13dfc647384e29"},"schema_version":"1.0"},"canonical_sha256":"4812e42e17465d695711323d1d1c3e804542c731b52ef51e640ce8a983eb6337","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:06:14.756985Z","signature_b64":"0BAtal7pBffSWSrV1CXcjVY8LuCpLHv1htDDq3AKEnmoUlsIogzoG8HsAw5vUtymApoovWcFEp+tEOtsiXZQDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4812e42e17465d695711323d1d1c3e804542c731b52ef51e640ce8a983eb6337","last_reissued_at":"2026-07-05T12:06:14.756482Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:06:14.756482Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.08727","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":"2026-07-05T12:06:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wGooehXKmxoZXNI/yDY/GkUgtvj/8FKJRlxXdmLL3kracaAW0hI8HK4d8FM5MHxBuRvUlG1mvuNJmeHaODdeDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T21:01:44.781339Z"},"content_sha256":"542688d5e6e6f8bca14f3e1f9ca5f02236a51344a369d6d288aea3a5358cd35e","schema_version":"1.0","event_id":"sha256:542688d5e6e6f8bca14f3e1f9ca5f02236a51344a369d6d288aea3a5358cd35e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:JAJOILQXIZOWSVYRGI6R2HB6QB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CY","cs.SE"],"primary_cat":"cs.LG","authors_text":"Adam P. Burden, Nikhil Bamby, Priyavanshi Pathania, Rohit Mehra, Samarth Sikand, Sanjay Podder, Vibhu Saujanya Sharma, Vikrant Kaulgud","submitted_at":"2025-06-10T12:23:02Z","abstract_excerpt":"While Generative AI stands to be one of the fastest adopted technologies ever, studies have made evident that the usage of Large Language Models (LLMs) puts significant burden on energy grids and our environment. It may prove a hindrance to the Sustainability goals of any organization. A crucial step in any Sustainability strategy is monitoring or estimating the energy consumption of various components. While there exist multiple tools for monitoring energy consumption, there is a dearth of tools/frameworks for estimating the consumption or carbon emissions. Current drawbacks of both monitorin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.08727","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/2506.08727/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":"2026-07-05T12:06:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ssvOZbREFCqYCjv7mm62Hjau6VX7IUfQMtPSMF83NyTZg4A724wKVtZ270HVpGAT/H7waadHPzjtb+rG9LckAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T21:01:44.782449Z"},"content_sha256":"c67ca55ce2007728c7f3c148fdd787241787a94588416c5ec73b9f984b785e14","schema_version":"1.0","event_id":"sha256:c67ca55ce2007728c7f3c148fdd787241787a94588416c5ec73b9f984b785e14"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JAJOILQXIZOWSVYRGI6R2HB6QB/bundle.json","state_url":"https://pith.science/pith/JAJOILQXIZOWSVYRGI6R2HB6QB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JAJOILQXIZOWSVYRGI6R2HB6QB/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":"2026-08-07T21:01:44Z","links":{"resolver":"https://pith.science/pith/JAJOILQXIZOWSVYRGI6R2HB6QB","bundle":"https://pith.science/pith/JAJOILQXIZOWSVYRGI6R2HB6QB/bundle.json","state":"https://pith.science/pith/JAJOILQXIZOWSVYRGI6R2HB6QB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JAJOILQXIZOWSVYRGI6R2HB6QB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:JAJOILQXIZOWSVYRGI6R2HB6QB","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":"294fe9e78c80f173ce1018ffb783155e3bd19be35fa192670d13dfc647384e29","cross_cats_sorted":["cs.AI","cs.CY","cs.SE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-10T12:23:02Z","title_canon_sha256":"7a114d072b7adf29d48b1cab15ffe212dedb7bebc37b95af53c503bdad9526f9"},"schema_version":"1.0","source":{"id":"2506.08727","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.08727","created_at":"2026-07-05T12:06:14Z"},{"alias_kind":"arxiv_version","alias_value":"2506.08727v1","created_at":"2026-07-05T12:06:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.08727","created_at":"2026-07-05T12:06:14Z"},{"alias_kind":"pith_short_12","alias_value":"JAJOILQXIZOW","created_at":"2026-07-05T12:06:14Z"},{"alias_kind":"pith_short_16","alias_value":"JAJOILQXIZOWSVYR","created_at":"2026-07-05T12:06:14Z"},{"alias_kind":"pith_short_8","alias_value":"JAJOILQX","created_at":"2026-07-05T12:06:14Z"}],"graph_snapshots":[{"event_id":"sha256:c67ca55ce2007728c7f3c148fdd787241787a94588416c5ec73b9f984b785e14","target":"graph","created_at":"2026-07-05T12:06:14Z","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/2506.08727/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While Generative AI stands to be one of the fastest adopted technologies ever, studies have made evident that the usage of Large Language Models (LLMs) puts significant burden on energy grids and our environment. It may prove a hindrance to the Sustainability goals of any organization. A crucial step in any Sustainability strategy is monitoring or estimating the energy consumption of various components. While there exist multiple tools for monitoring energy consumption, there is a dearth of tools/frameworks for estimating the consumption or carbon emissions. Current drawbacks of both monitorin","authors_text":"Adam P. Burden, Nikhil Bamby, Priyavanshi Pathania, Rohit Mehra, Samarth Sikand, Sanjay Podder, Vibhu Saujanya Sharma, Vikrant Kaulgud","cross_cats":["cs.AI","cs.CY","cs.SE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-10T12:23:02Z","title":"Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.08727","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":"sha256:542688d5e6e6f8bca14f3e1f9ca5f02236a51344a369d6d288aea3a5358cd35e","target":"record","created_at":"2026-07-05T12:06:14Z","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":"294fe9e78c80f173ce1018ffb783155e3bd19be35fa192670d13dfc647384e29","cross_cats_sorted":["cs.AI","cs.CY","cs.SE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-10T12:23:02Z","title_canon_sha256":"7a114d072b7adf29d48b1cab15ffe212dedb7bebc37b95af53c503bdad9526f9"},"schema_version":"1.0","source":{"id":"2506.08727","kind":"arxiv","version":1}},"canonical_sha256":"4812e42e17465d695711323d1d1c3e804542c731b52ef51e640ce8a983eb6337","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4812e42e17465d695711323d1d1c3e804542c731b52ef51e640ce8a983eb6337","first_computed_at":"2026-07-05T12:06:14.756482Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:06:14.756482Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0BAtal7pBffSWSrV1CXcjVY8LuCpLHv1htDDq3AKEnmoUlsIogzoG8HsAw5vUtymApoovWcFEp+tEOtsiXZQDg==","signature_status":"signed_v1","signed_at":"2026-07-05T12:06:14.756985Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.08727","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:542688d5e6e6f8bca14f3e1f9ca5f02236a51344a369d6d288aea3a5358cd35e","sha256:c67ca55ce2007728c7f3c148fdd787241787a94588416c5ec73b9f984b785e14"],"state_sha256":"b15f6f21188b843cfe49dbc1d735d102c4188c81d8e1cf1ece589634e2010112"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pz3m2ktZELzKjIaqe4QwFgJv3HzBi7JwkbejlR84MGakF+5m/0ez3HA1tnCNq8yrup+aeqIzDaYvZHwKOdahCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T21:01:44.793393Z","bundle_sha256":"07fbc54bc8bb23338feeff9ab329dcb01289a081e0d5fbd9f00b32fed0f3e800"}}