{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:VPHUVQIWNZLB3LAGRAZPNLI6OE","short_pith_number":"pith:VPHUVQIW","canonical_record":{"source":{"id":"2505.12370","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-05-18T11:22:04Z","cross_cats_sorted":[],"title_canon_sha256":"cb31d805dd6b4d4fb438e186137f2b4ad0473d1066e586952906e0a9c09073bd","abstract_canon_sha256":"be84a87f998bff2392cb11e102612063669f387c8d48d013f14626677710cb3c"},"schema_version":"1.0"},"canonical_sha256":"abcf4ac1166e561dac068832f6ad1e71034a49df4d32a1f97ea6b40a9693fca1","source":{"kind":"arxiv","id":"2505.12370","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.12370","created_at":"2026-07-05T11:08:41Z"},{"alias_kind":"arxiv_version","alias_value":"2505.12370v2","created_at":"2026-07-05T11:08:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.12370","created_at":"2026-07-05T11:08:41Z"},{"alias_kind":"pith_short_12","alias_value":"VPHUVQIWNZLB","created_at":"2026-07-05T11:08:41Z"},{"alias_kind":"pith_short_16","alias_value":"VPHUVQIWNZLB3LAG","created_at":"2026-07-05T11:08:41Z"},{"alias_kind":"pith_short_8","alias_value":"VPHUVQIW","created_at":"2026-07-05T11:08:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:VPHUVQIWNZLB3LAGRAZPNLI6OE","target":"record","payload":{"canonical_record":{"source":{"id":"2505.12370","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-05-18T11:22:04Z","cross_cats_sorted":[],"title_canon_sha256":"cb31d805dd6b4d4fb438e186137f2b4ad0473d1066e586952906e0a9c09073bd","abstract_canon_sha256":"be84a87f998bff2392cb11e102612063669f387c8d48d013f14626677710cb3c"},"schema_version":"1.0"},"canonical_sha256":"abcf4ac1166e561dac068832f6ad1e71034a49df4d32a1f97ea6b40a9693fca1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:08:41.392694Z","signature_b64":"xCo8NBoyVkF/TqDyH1cYgs7r+/Yp9DyCxmhLo1mONgEsrY6dUPVCPcwA+tH/k7mmPkjLr1uAFeMkvYiR5vyhAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"abcf4ac1166e561dac068832f6ad1e71034a49df4d32a1f97ea6b40a9693fca1","last_reissued_at":"2026-07-05T11:08:41.392227Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:08:41.392227Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.12370","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":"2026-07-05T11:08:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tfYX6UudqRjmnWk6Ji6FUdXZFdLrr9kZ162Mj2fUuZ9cc88gen0jDNesafbYRa4vBOYqWNEUm73Rzz08b7b1AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T15:10:42.527465Z"},"content_sha256":"934d47d6dd6f01847e4a3d55273203ee5e2c43dbd1967dfb00063e5af93cf4f7","schema_version":"1.0","event_id":"sha256:934d47d6dd6f01847e4a3d55273203ee5e2c43dbd1967dfb00063e5af93cf4f7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:VPHUVQIWNZLB3LAGRAZPNLI6OE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Enhancing Visual Grounding for GUI Agents via Self-Evolutionary Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Bo Li, Enguang Wang, Jian Zhang, Jie Chen, Jinwei Chen, Kaixin Li, Lujian Yao, Peng-Tao Jiang, Qibin Hou, Xinbin Yuan, Zhuoxuan Cai","submitted_at":"2025-05-18T11:22:04Z","abstract_excerpt":"Graphical User Interface (GUI) agents have made substantial strides in understanding and executing user instructions across diverse platforms. Yet, grounding these instructions to precise interface elements remains challenging, especially in complex, high-resolution, professional environments. Traditional supervised finetuning (SFT) methods often require large volumes of diverse data and exhibit weak generalization. To overcome these limitations, we introduce a reinforcement learning (RL) based framework that incorporates three core strategies: (1) seed data curation to ensure high quality tra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.12370","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/2505.12370/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":"2026-07-05T11:08:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oDvLXxGvY1Ez+hhmsxx/YDcXwoeIaY8mw4niWovXTrxMGHd1xdRnKWihJ/q3Awhfi4OnRwOl3+5mwNt0llC1CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T15:10:42.527842Z"},"content_sha256":"a5814aa2978d170c4b7a273383cb07abfac12b220c064df1d8d9028e7c888198","schema_version":"1.0","event_id":"sha256:a5814aa2978d170c4b7a273383cb07abfac12b220c064df1d8d9028e7c888198"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VPHUVQIWNZLB3LAGRAZPNLI6OE/bundle.json","state_url":"https://pith.science/pith/VPHUVQIWNZLB3LAGRAZPNLI6OE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VPHUVQIWNZLB3LAGRAZPNLI6OE/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":"2026-07-06T15:10:42Z","links":{"resolver":"https://pith.science/pith/VPHUVQIWNZLB3LAGRAZPNLI6OE","bundle":"https://pith.science/pith/VPHUVQIWNZLB3LAGRAZPNLI6OE/bundle.json","state":"https://pith.science/pith/VPHUVQIWNZLB3LAGRAZPNLI6OE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VPHUVQIWNZLB3LAGRAZPNLI6OE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:VPHUVQIWNZLB3LAGRAZPNLI6OE","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":"be84a87f998bff2392cb11e102612063669f387c8d48d013f14626677710cb3c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-05-18T11:22:04Z","title_canon_sha256":"cb31d805dd6b4d4fb438e186137f2b4ad0473d1066e586952906e0a9c09073bd"},"schema_version":"1.0","source":{"id":"2505.12370","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.12370","created_at":"2026-07-05T11:08:41Z"},{"alias_kind":"arxiv_version","alias_value":"2505.12370v2","created_at":"2026-07-05T11:08:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.12370","created_at":"2026-07-05T11:08:41Z"},{"alias_kind":"pith_short_12","alias_value":"VPHUVQIWNZLB","created_at":"2026-07-05T11:08:41Z"},{"alias_kind":"pith_short_16","alias_value":"VPHUVQIWNZLB3LAG","created_at":"2026-07-05T11:08:41Z"},{"alias_kind":"pith_short_8","alias_value":"VPHUVQIW","created_at":"2026-07-05T11:08:41Z"}],"graph_snapshots":[{"event_id":"sha256:a5814aa2978d170c4b7a273383cb07abfac12b220c064df1d8d9028e7c888198","target":"graph","created_at":"2026-07-05T11:08:41Z","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/2505.12370/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graphical User Interface (GUI) agents have made substantial strides in understanding and executing user instructions across diverse platforms. Yet, grounding these instructions to precise interface elements remains challenging, especially in complex, high-resolution, professional environments. Traditional supervised finetuning (SFT) methods often require large volumes of diverse data and exhibit weak generalization. To overcome these limitations, we introduce a reinforcement learning (RL) based framework that incorporates three core strategies: (1) seed data curation to ensure high quality tra","authors_text":"Bo Li, Enguang Wang, Jian Zhang, Jie Chen, Jinwei Chen, Kaixin Li, Lujian Yao, Peng-Tao Jiang, Qibin Hou, Xinbin Yuan, Zhuoxuan Cai","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-05-18T11:22:04Z","title":"Enhancing Visual Grounding for GUI Agents via Self-Evolutionary Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.12370","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":"sha256:934d47d6dd6f01847e4a3d55273203ee5e2c43dbd1967dfb00063e5af93cf4f7","target":"record","created_at":"2026-07-05T11:08:41Z","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":"be84a87f998bff2392cb11e102612063669f387c8d48d013f14626677710cb3c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-05-18T11:22:04Z","title_canon_sha256":"cb31d805dd6b4d4fb438e186137f2b4ad0473d1066e586952906e0a9c09073bd"},"schema_version":"1.0","source":{"id":"2505.12370","kind":"arxiv","version":2}},"canonical_sha256":"abcf4ac1166e561dac068832f6ad1e71034a49df4d32a1f97ea6b40a9693fca1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"abcf4ac1166e561dac068832f6ad1e71034a49df4d32a1f97ea6b40a9693fca1","first_computed_at":"2026-07-05T11:08:41.392227Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:08:41.392227Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xCo8NBoyVkF/TqDyH1cYgs7r+/Yp9DyCxmhLo1mONgEsrY6dUPVCPcwA+tH/k7mmPkjLr1uAFeMkvYiR5vyhAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:08:41.392694Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.12370","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:934d47d6dd6f01847e4a3d55273203ee5e2c43dbd1967dfb00063e5af93cf4f7","sha256:a5814aa2978d170c4b7a273383cb07abfac12b220c064df1d8d9028e7c888198"],"state_sha256":"1fa1c3686cbf264fbecb9929566951b8ff9e431defce93bbe911ae882b03f390"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"D11NMw0GVMV47+1RMAdS0p4n2pj3OUq31jB+M6CB2vQNl54Kr+SmP0j8RSMJ4mNvc1t3qiJLOXao53+KaEoVAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T15:10:42.530033Z","bundle_sha256":"97f11a1d970a256603f6307e5326363b22ea48c9b81b004e6c3a67c1912356ee"}}