{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:IWHFYRJXW722EKC25VFBFAUTQP","short_pith_number":"pith:IWHFYRJX","canonical_record":{"source":{"id":"2605.26046","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-25T17:08:55Z","cross_cats_sorted":["cs.AI","cs.LG","cs.MA","cs.SE"],"title_canon_sha256":"13144c49a3290aab8d7123fa6eb503b3f10d14eb59e225140b5580d22967fbce","abstract_canon_sha256":"7af6a8dc00856265b991c0001794cc13c2477077c4a1780608816604b68aa571"},"schema_version":"1.0"},"canonical_sha256":"458e5c4537b7f5a2285aed4a12829383cf73e41c5e7b4750ee609e4a34a4e4ef","source":{"kind":"arxiv","id":"2605.26046","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.26046","created_at":"2026-05-26T02:05:24Z"},{"alias_kind":"arxiv_version","alias_value":"2605.26046v1","created_at":"2026-05-26T02:05:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.26046","created_at":"2026-05-26T02:05:24Z"},{"alias_kind":"pith_short_12","alias_value":"IWHFYRJXW722","created_at":"2026-05-26T02:05:24Z"},{"alias_kind":"pith_short_16","alias_value":"IWHFYRJXW722EKC2","created_at":"2026-05-26T02:05:24Z"},{"alias_kind":"pith_short_8","alias_value":"IWHFYRJX","created_at":"2026-05-26T02:05:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:IWHFYRJXW722EKC25VFBFAUTQP","target":"record","payload":{"canonical_record":{"source":{"id":"2605.26046","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-25T17:08:55Z","cross_cats_sorted":["cs.AI","cs.LG","cs.MA","cs.SE"],"title_canon_sha256":"13144c49a3290aab8d7123fa6eb503b3f10d14eb59e225140b5580d22967fbce","abstract_canon_sha256":"7af6a8dc00856265b991c0001794cc13c2477077c4a1780608816604b68aa571"},"schema_version":"1.0"},"canonical_sha256":"458e5c4537b7f5a2285aed4a12829383cf73e41c5e7b4750ee609e4a34a4e4ef","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T02:05:24.830124Z","signature_b64":"PUA+RZzlE0vj10zNSQiZtTNR/lmBSFT4iEc5Yx5abdVSzFPmCI/ZD6jymwcZvtLOK3brL7LIN/6Lc71FLRCuCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"458e5c4537b7f5a2285aed4a12829383cf73e41c5e7b4750ee609e4a34a4e4ef","last_reissued_at":"2026-05-26T02:05:24.829291Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T02:05:24.829291Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.26046","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":"2026-05-26T02:05:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5tr487GbkO5LRLUuFlIRgBwZ7bK3/K63Sv5ZtC/fPUG6zwwbdLaJ54EqcRAJtoKCXyTni0Nn5UhK3+JQJjWiCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T14:04:15.821744Z"},"content_sha256":"9d6be74223f31f7b2d2e20826e8da6768745b3aec9d7f3c626f1b5d96102f29d","schema_version":"1.0","event_id":"sha256:9d6be74223f31f7b2d2e20826e8da6768745b3aec9d7f3c626f1b5d96102f29d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:IWHFYRJXW722EKC25VFBFAUTQP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"When Gradients Collide: Failure Modes of Multi-Objective Prompt Optimization for LLM Judges","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.MA","cs.SE"],"primary_cat":"cs.CL","authors_text":"Abhishek Divekar, Parth Darshan","submitted_at":"2026-05-25T17:08:55Z","abstract_excerpt":"Customizing an LLM judge to a specific task or domain often involves optimizing its prompt across multiple evaluation criteria simultaneously. Textual gradient methods automate this for a single judge criterion, however they produce natural-language critiques, not numerical vectors. Thus, the conflict-resolution toolkit of multi-task learning (PCGrad, MGDA) doesn't apply to the multi-objective textual gradient setting. We test five decomposition modes of textual gradient optimizers by varying how much cross-task information the loss, gradient and optimizer LLMs share. In 6 of 10 configurations"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.26046","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/2605.26046/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":"2026-05-26T02:05:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KOD7sLn6I/rMLb//jF5D5vO+/1P+/hX2xfPnEiMUtoKjdxIn07zd8m6FOJTwG6/wAaJSU2cPDfyA8HNBRd76BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T14:04:15.822119Z"},"content_sha256":"4370758e0821c0456b63c797393a4e5ac4ada3af0ed360043e940e10272d0573","schema_version":"1.0","event_id":"sha256:4370758e0821c0456b63c797393a4e5ac4ada3af0ed360043e940e10272d0573"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IWHFYRJXW722EKC25VFBFAUTQP/bundle.json","state_url":"https://pith.science/pith/IWHFYRJXW722EKC25VFBFAUTQP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IWHFYRJXW722EKC25VFBFAUTQP/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":"2026-06-30T14:04:15Z","links":{"resolver":"https://pith.science/pith/IWHFYRJXW722EKC25VFBFAUTQP","bundle":"https://pith.science/pith/IWHFYRJXW722EKC25VFBFAUTQP/bundle.json","state":"https://pith.science/pith/IWHFYRJXW722EKC25VFBFAUTQP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IWHFYRJXW722EKC25VFBFAUTQP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:IWHFYRJXW722EKC25VFBFAUTQP","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":"7af6a8dc00856265b991c0001794cc13c2477077c4a1780608816604b68aa571","cross_cats_sorted":["cs.AI","cs.LG","cs.MA","cs.SE"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-25T17:08:55Z","title_canon_sha256":"13144c49a3290aab8d7123fa6eb503b3f10d14eb59e225140b5580d22967fbce"},"schema_version":"1.0","source":{"id":"2605.26046","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.26046","created_at":"2026-05-26T02:05:24Z"},{"alias_kind":"arxiv_version","alias_value":"2605.26046v1","created_at":"2026-05-26T02:05:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.26046","created_at":"2026-05-26T02:05:24Z"},{"alias_kind":"pith_short_12","alias_value":"IWHFYRJXW722","created_at":"2026-05-26T02:05:24Z"},{"alias_kind":"pith_short_16","alias_value":"IWHFYRJXW722EKC2","created_at":"2026-05-26T02:05:24Z"},{"alias_kind":"pith_short_8","alias_value":"IWHFYRJX","created_at":"2026-05-26T02:05:24Z"}],"graph_snapshots":[{"event_id":"sha256:4370758e0821c0456b63c797393a4e5ac4ada3af0ed360043e940e10272d0573","target":"graph","created_at":"2026-05-26T02:05:24Z","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/2605.26046/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Customizing an LLM judge to a specific task or domain often involves optimizing its prompt across multiple evaluation criteria simultaneously. Textual gradient methods automate this for a single judge criterion, however they produce natural-language critiques, not numerical vectors. Thus, the conflict-resolution toolkit of multi-task learning (PCGrad, MGDA) doesn't apply to the multi-objective textual gradient setting. We test five decomposition modes of textual gradient optimizers by varying how much cross-task information the loss, gradient and optimizer LLMs share. In 6 of 10 configurations","authors_text":"Abhishek Divekar, Parth Darshan","cross_cats":["cs.AI","cs.LG","cs.MA","cs.SE"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-25T17:08:55Z","title":"When Gradients Collide: Failure Modes of Multi-Objective Prompt Optimization for LLM Judges"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.26046","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":"sha256:9d6be74223f31f7b2d2e20826e8da6768745b3aec9d7f3c626f1b5d96102f29d","target":"record","created_at":"2026-05-26T02:05:24Z","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":"7af6a8dc00856265b991c0001794cc13c2477077c4a1780608816604b68aa571","cross_cats_sorted":["cs.AI","cs.LG","cs.MA","cs.SE"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-25T17:08:55Z","title_canon_sha256":"13144c49a3290aab8d7123fa6eb503b3f10d14eb59e225140b5580d22967fbce"},"schema_version":"1.0","source":{"id":"2605.26046","kind":"arxiv","version":1}},"canonical_sha256":"458e5c4537b7f5a2285aed4a12829383cf73e41c5e7b4750ee609e4a34a4e4ef","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"458e5c4537b7f5a2285aed4a12829383cf73e41c5e7b4750ee609e4a34a4e4ef","first_computed_at":"2026-05-26T02:05:24.829291Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-26T02:05:24.829291Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PUA+RZzlE0vj10zNSQiZtTNR/lmBSFT4iEc5Yx5abdVSzFPmCI/ZD6jymwcZvtLOK3brL7LIN/6Lc71FLRCuCw==","signature_status":"signed_v1","signed_at":"2026-05-26T02:05:24.830124Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.26046","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9d6be74223f31f7b2d2e20826e8da6768745b3aec9d7f3c626f1b5d96102f29d","sha256:4370758e0821c0456b63c797393a4e5ac4ada3af0ed360043e940e10272d0573"],"state_sha256":"b22327ddd5a5f4f41cb319257614b633f556667b64bb13e01d050da76e2da6b7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DAlaoCrgV7IjC+BL2/HnfWufS5soYptldLRDkN/LpcNPK17erSN5Sdxtp66GW0mJPts3nRPu5FHGflYWfQRcAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-30T14:04:15.824095Z","bundle_sha256":"74394a55b4a0243baca4e10e5c98b99fc58b026cf901699b938a5ccd976f8fe4"}}