{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:HXW43BMRZU5YBCELP7S3J4FDUP","short_pith_number":"pith:HXW43BMR","canonical_record":{"source":{"id":"2605.24900","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-24T06:56:06Z","cross_cats_sorted":[],"title_canon_sha256":"1b6697b795b56104fb896cca991cdba62d86e7790709dde75d275a55cb510a34","abstract_canon_sha256":"40959a06ce8e5559a35d8e2ac53e7d2dd2d6c9435319898293ec3f76f859ddf1"},"schema_version":"1.0"},"canonical_sha256":"3dedcd8591cd3b80888b7fe5b4f0a3a3d93fa400ac9a3d4b5fa6087f73ac92fa","source":{"kind":"arxiv","id":"2605.24900","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.24900","created_at":"2026-05-26T01:04:04Z"},{"alias_kind":"arxiv_version","alias_value":"2605.24900v1","created_at":"2026-05-26T01:04:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.24900","created_at":"2026-05-26T01:04:04Z"},{"alias_kind":"pith_short_12","alias_value":"HXW43BMRZU5Y","created_at":"2026-05-26T01:04:04Z"},{"alias_kind":"pith_short_16","alias_value":"HXW43BMRZU5YBCEL","created_at":"2026-05-26T01:04:04Z"},{"alias_kind":"pith_short_8","alias_value":"HXW43BMR","created_at":"2026-05-26T01:04:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:HXW43BMRZU5YBCELP7S3J4FDUP","target":"record","payload":{"canonical_record":{"source":{"id":"2605.24900","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-24T06:56:06Z","cross_cats_sorted":[],"title_canon_sha256":"1b6697b795b56104fb896cca991cdba62d86e7790709dde75d275a55cb510a34","abstract_canon_sha256":"40959a06ce8e5559a35d8e2ac53e7d2dd2d6c9435319898293ec3f76f859ddf1"},"schema_version":"1.0"},"canonical_sha256":"3dedcd8591cd3b80888b7fe5b4f0a3a3d93fa400ac9a3d4b5fa6087f73ac92fa","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T01:04:04.497815Z","signature_b64":"hd4WPCdNhaH7PBmnOHrnrMflFfn/pGyCfiqWXeQHaj0nQg4Q3ag7he0Ntr0VtqwtY6lOtRZSiID4uICdyNgSBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3dedcd8591cd3b80888b7fe5b4f0a3a3d93fa400ac9a3d4b5fa6087f73ac92fa","last_reissued_at":"2026-05-26T01:04:04.496955Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T01:04:04.496955Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.24900","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":"2026-05-26T01:04:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wlHy5Tp/rzEyCm3eaPx6weQ8C7qEwhGeoXEMh3pYMYz7UU76n6JM9ma3Iytd9YE6bAh3VYOU+ESU54hn2avlAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T17:23:12.184876Z"},"content_sha256":"0f2919b446c7151ad232a3f33384ee44e40ad1241f81332d5ae7693c846dfb47","schema_version":"1.0","event_id":"sha256:0f2919b446c7151ad232a3f33384ee44e40ad1241f81332d5ae7693c846dfb47"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:HXW43BMRZU5YBCELP7S3J4FDUP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Bin He, Chenguang Wang, Lei Ding, Yang Liu","submitted_at":"2026-05-24T06:56:06Z","abstract_excerpt":"Proactive task-oriented agents must autonomously anticipate user needs, identify actionable opportunities, and trigger software actions at appropriate moments - fundamentally shifting from reactive systems that await explicit instructions. However, existing approaches lack generalizable end-to-end solutions for measuring and optimizing such anticipatory behaviors.\n  This paper introduces ProActor, a unified framework for conversational task scheduling that integrates: (1) a domain-agnostic automated annotation methodology that enables scalable proactiveness reinforcement learning (RL) by gener"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.24900","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/2605.24900/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":"2026-05-26T01:04:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"591ZQwDOLQB3Qg5C5ZAp2jJ4Q9NWOcP5yFGEyp3bRdmJZm8EGNqsaoKVVBXHyE7horgP/+GDDJB3JLxrEIH0Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T17:23:12.185267Z"},"content_sha256":"745fc7feb5ab7fb53ba215b79c48f438a39aa2a0aa5175bafc773498073a64aa","schema_version":"1.0","event_id":"sha256:745fc7feb5ab7fb53ba215b79c48f438a39aa2a0aa5175bafc773498073a64aa"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HXW43BMRZU5YBCELP7S3J4FDUP/bundle.json","state_url":"https://pith.science/pith/HXW43BMRZU5YBCELP7S3J4FDUP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HXW43BMRZU5YBCELP7S3J4FDUP/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":"2026-05-28T17:23:12Z","links":{"resolver":"https://pith.science/pith/HXW43BMRZU5YBCELP7S3J4FDUP","bundle":"https://pith.science/pith/HXW43BMRZU5YBCELP7S3J4FDUP/bundle.json","state":"https://pith.science/pith/HXW43BMRZU5YBCELP7S3J4FDUP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HXW43BMRZU5YBCELP7S3J4FDUP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:HXW43BMRZU5YBCELP7S3J4FDUP","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":"40959a06ce8e5559a35d8e2ac53e7d2dd2d6c9435319898293ec3f76f859ddf1","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-24T06:56:06Z","title_canon_sha256":"1b6697b795b56104fb896cca991cdba62d86e7790709dde75d275a55cb510a34"},"schema_version":"1.0","source":{"id":"2605.24900","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.24900","created_at":"2026-05-26T01:04:04Z"},{"alias_kind":"arxiv_version","alias_value":"2605.24900v1","created_at":"2026-05-26T01:04:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.24900","created_at":"2026-05-26T01:04:04Z"},{"alias_kind":"pith_short_12","alias_value":"HXW43BMRZU5Y","created_at":"2026-05-26T01:04:04Z"},{"alias_kind":"pith_short_16","alias_value":"HXW43BMRZU5YBCEL","created_at":"2026-05-26T01:04:04Z"},{"alias_kind":"pith_short_8","alias_value":"HXW43BMR","created_at":"2026-05-26T01:04:04Z"}],"graph_snapshots":[{"event_id":"sha256:745fc7feb5ab7fb53ba215b79c48f438a39aa2a0aa5175bafc773498073a64aa","target":"graph","created_at":"2026-05-26T01:04:04Z","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/2605.24900/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Proactive task-oriented agents must autonomously anticipate user needs, identify actionable opportunities, and trigger software actions at appropriate moments - fundamentally shifting from reactive systems that await explicit instructions. However, existing approaches lack generalizable end-to-end solutions for measuring and optimizing such anticipatory behaviors.\n  This paper introduces ProActor, a unified framework for conversational task scheduling that integrates: (1) a domain-agnostic automated annotation methodology that enables scalable proactiveness reinforcement learning (RL) by gener","authors_text":"Bin He, Chenguang Wang, Lei Ding, Yang Liu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-24T06:56:06Z","title":"ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.24900","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":"sha256:0f2919b446c7151ad232a3f33384ee44e40ad1241f81332d5ae7693c846dfb47","target":"record","created_at":"2026-05-26T01:04:04Z","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":"40959a06ce8e5559a35d8e2ac53e7d2dd2d6c9435319898293ec3f76f859ddf1","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-24T06:56:06Z","title_canon_sha256":"1b6697b795b56104fb896cca991cdba62d86e7790709dde75d275a55cb510a34"},"schema_version":"1.0","source":{"id":"2605.24900","kind":"arxiv","version":1}},"canonical_sha256":"3dedcd8591cd3b80888b7fe5b4f0a3a3d93fa400ac9a3d4b5fa6087f73ac92fa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3dedcd8591cd3b80888b7fe5b4f0a3a3d93fa400ac9a3d4b5fa6087f73ac92fa","first_computed_at":"2026-05-26T01:04:04.496955Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-26T01:04:04.496955Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hd4WPCdNhaH7PBmnOHrnrMflFfn/pGyCfiqWXeQHaj0nQg4Q3ag7he0Ntr0VtqwtY6lOtRZSiID4uICdyNgSBA==","signature_status":"signed_v1","signed_at":"2026-05-26T01:04:04.497815Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.24900","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0f2919b446c7151ad232a3f33384ee44e40ad1241f81332d5ae7693c846dfb47","sha256:745fc7feb5ab7fb53ba215b79c48f438a39aa2a0aa5175bafc773498073a64aa"],"state_sha256":"7227f405d19d8b2f2dd4e773332aa9db127301cadda735934a3db7e9679ed69e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cc+kKq6eJAxigO93baMgR/CMi2Xj/bovJP6LoYKUxy0FD5JvWw48Jvf6J42Hp0utoSsECIdDx/4W6TwbnH11Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T17:23:12.187936Z","bundle_sha256":"b64a5de6564eac55e63ce05c72778875c200e7db505a36d71279b3cb19747829"}}