{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:LGLCRJJX7HPSVKTRPXCIR4X7PD","short_pith_number":"pith:LGLCRJJX","canonical_record":{"source":{"id":"2602.00575","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-01-31T07:36:54Z","cross_cats_sorted":[],"title_canon_sha256":"9e88c0b12f73dd491b74648a8fcfeb5eedd060435528a6d4fe04bd2d9e4970a3","abstract_canon_sha256":"c4238d5cb00b0df39b8781d4f5ac0647ff71e797e768a29804342ed57553279b"},"schema_version":"1.0"},"canonical_sha256":"599628a537f9df2aaa717dc488f2ff78fea466332cb21ca63d598c0837bb7fd2","source":{"kind":"arxiv","id":"2602.00575","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.00575","created_at":"2026-07-28T01:21:35Z"},{"alias_kind":"arxiv_version","alias_value":"2602.00575v2","created_at":"2026-07-28T01:21:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.00575","created_at":"2026-07-28T01:21:35Z"},{"alias_kind":"pith_short_12","alias_value":"LGLCRJJX7HPS","created_at":"2026-07-28T01:21:35Z"},{"alias_kind":"pith_short_16","alias_value":"LGLCRJJX7HPSVKTR","created_at":"2026-07-28T01:21:35Z"},{"alias_kind":"pith_short_8","alias_value":"LGLCRJJX","created_at":"2026-07-28T01:21:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:LGLCRJJX7HPSVKTRPXCIR4X7PD","target":"record","payload":{"canonical_record":{"source":{"id":"2602.00575","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-01-31T07:36:54Z","cross_cats_sorted":[],"title_canon_sha256":"9e88c0b12f73dd491b74648a8fcfeb5eedd060435528a6d4fe04bd2d9e4970a3","abstract_canon_sha256":"c4238d5cb00b0df39b8781d4f5ac0647ff71e797e768a29804342ed57553279b"},"schema_version":"1.0"},"canonical_sha256":"599628a537f9df2aaa717dc488f2ff78fea466332cb21ca63d598c0837bb7fd2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-28T01:21:35.690994Z","signature_b64":"b1tBgf1/0OjBTwry7e3Q8CSAF7/tvFZD3cCfzyANg4fjTD8RIOXIc2ngVxP9l6QPRF89V+2BJj91sbHUL2xTBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"599628a537f9df2aaa717dc488f2ff78fea466332cb21ca63d598c0837bb7fd2","last_reissued_at":"2026-07-28T01:21:35.690059Z","signature_status":"signed_v1","first_computed_at":"2026-07-28T01:21:35.690059Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2602.00575","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-28T01:21:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EnZGbRieA3bM96KNHUuV29dNKoxjJqQRwZSnMMWpJ/yPsDyxKn/bDVfeMCyTCkusCOmbY+uFwFhZJH6S41snCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T09:10:22.442434Z"},"content_sha256":"a83369cfcbbffe2a06c7cc9832f6bd3d2a2dc5e83696f6db043deecdbe2f4ddd","schema_version":"1.0","event_id":"sha256:a83369cfcbbffe2a06c7cc9832f6bd3d2a2dc5e83696f6db043deecdbe2f4ddd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:LGLCRJJX7HPSVKTRPXCIR4X7PD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Agentic Reward Modeling: Verifying GUI Agent via Progressive Trajectory-Grounded Interaction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Chaoqun Cui, Jing Huang, Liming Zheng, Qingchao Kong, Shijing Wang, Zhixiong Zeng","submitted_at":"2026-01-31T07:36:54Z","abstract_excerpt":"Reinforcement learning with verifiable rewards (RLVR) provides a promising pathway for continuously advancing GUI agents, yet existing reward modeling paradigms face complementary limitations. Rule-based methods suffer from poor scalability and cannot handle open-ended tasks. LLM-as-a-Judge methods enable scalable trajectory verification but remain passive and are constrained by partial state observability, since key evidence often resides in latent environment states beyond the trajectory. Recent active environment interaction methods mitigate observability issues but tend to over-rely on pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.00575","kind":"arxiv","version":2},"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/2602.00575/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":"2026-07-28T01:21:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Zl1BFRt5+P+MFx0YcetU3WZVEWP9aMU5EAGJ1MTarrW6hmlq5HTNKYwZF+Zzc2fYxBtTTCaYkoMcXqNfj19wCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T09:10:22.442975Z"},"content_sha256":"174d34fedfc3cbe3059596224188a6a345e244ed08b66b944121af65414e404b","schema_version":"1.0","event_id":"sha256:174d34fedfc3cbe3059596224188a6a345e244ed08b66b944121af65414e404b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LGLCRJJX7HPSVKTRPXCIR4X7PD/bundle.json","state_url":"https://pith.science/pith/LGLCRJJX7HPSVKTRPXCIR4X7PD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LGLCRJJX7HPSVKTRPXCIR4X7PD/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":"2026-08-04T09:10:22Z","links":{"resolver":"https://pith.science/pith/LGLCRJJX7HPSVKTRPXCIR4X7PD","bundle":"https://pith.science/pith/LGLCRJJX7HPSVKTRPXCIR4X7PD/bundle.json","state":"https://pith.science/pith/LGLCRJJX7HPSVKTRPXCIR4X7PD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LGLCRJJX7HPSVKTRPXCIR4X7PD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:LGLCRJJX7HPSVKTRPXCIR4X7PD","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":"c4238d5cb00b0df39b8781d4f5ac0647ff71e797e768a29804342ed57553279b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-01-31T07:36:54Z","title_canon_sha256":"9e88c0b12f73dd491b74648a8fcfeb5eedd060435528a6d4fe04bd2d9e4970a3"},"schema_version":"1.0","source":{"id":"2602.00575","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.00575","created_at":"2026-07-28T01:21:35Z"},{"alias_kind":"arxiv_version","alias_value":"2602.00575v2","created_at":"2026-07-28T01:21:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.00575","created_at":"2026-07-28T01:21:35Z"},{"alias_kind":"pith_short_12","alias_value":"LGLCRJJX7HPS","created_at":"2026-07-28T01:21:35Z"},{"alias_kind":"pith_short_16","alias_value":"LGLCRJJX7HPSVKTR","created_at":"2026-07-28T01:21:35Z"},{"alias_kind":"pith_short_8","alias_value":"LGLCRJJX","created_at":"2026-07-28T01:21:35Z"}],"graph_snapshots":[{"event_id":"sha256:174d34fedfc3cbe3059596224188a6a345e244ed08b66b944121af65414e404b","target":"graph","created_at":"2026-07-28T01:21:35Z","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/2602.00575/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Reinforcement learning with verifiable rewards (RLVR) provides a promising pathway for continuously advancing GUI agents, yet existing reward modeling paradigms face complementary limitations. Rule-based methods suffer from poor scalability and cannot handle open-ended tasks. LLM-as-a-Judge methods enable scalable trajectory verification but remain passive and are constrained by partial state observability, since key evidence often resides in latent environment states beyond the trajectory. Recent active environment interaction methods mitigate observability issues but tend to over-rely on pro","authors_text":"Chaoqun Cui, Jing Huang, Liming Zheng, Qingchao Kong, Shijing Wang, Zhixiong Zeng","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-01-31T07:36:54Z","title":"Agentic Reward Modeling: Verifying GUI Agent via Progressive Trajectory-Grounded Interaction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.00575","kind":"arxiv","version":2},"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:a83369cfcbbffe2a06c7cc9832f6bd3d2a2dc5e83696f6db043deecdbe2f4ddd","target":"record","created_at":"2026-07-28T01:21:35Z","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":"c4238d5cb00b0df39b8781d4f5ac0647ff71e797e768a29804342ed57553279b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-01-31T07:36:54Z","title_canon_sha256":"9e88c0b12f73dd491b74648a8fcfeb5eedd060435528a6d4fe04bd2d9e4970a3"},"schema_version":"1.0","source":{"id":"2602.00575","kind":"arxiv","version":2}},"canonical_sha256":"599628a537f9df2aaa717dc488f2ff78fea466332cb21ca63d598c0837bb7fd2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"599628a537f9df2aaa717dc488f2ff78fea466332cb21ca63d598c0837bb7fd2","first_computed_at":"2026-07-28T01:21:35.690059Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-28T01:21:35.690059Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"b1tBgf1/0OjBTwry7e3Q8CSAF7/tvFZD3cCfzyANg4fjTD8RIOXIc2ngVxP9l6QPRF89V+2BJj91sbHUL2xTBA==","signature_status":"signed_v1","signed_at":"2026-07-28T01:21:35.690994Z","signed_message":"canonical_sha256_bytes"},"source_id":"2602.00575","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a83369cfcbbffe2a06c7cc9832f6bd3d2a2dc5e83696f6db043deecdbe2f4ddd","sha256:174d34fedfc3cbe3059596224188a6a345e244ed08b66b944121af65414e404b"],"state_sha256":"47a98128befbaeaac7e874572fe4cf8092f56ea5af474ba69751ac81f949a4c5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yBxX2v9vRnJ2fTN2bKSOg/nFXS+Vy0Nfi+j6joQb49IFx4iE+ZXvhUALtfix7j4Sv2XqTAebekxFLgeIDN/rBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T09:10:22.446424Z","bundle_sha256":"33866c1965805f4925498f0323837f52ec8cac7efcac1ee4ca2dc6819d423308"}}