ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents | Paper Citation Record

Observation 7459392c-beb7-40a1-8f3a-0bc2d670f53b · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents LoRA: Low-Rank Adaptation of Large Language Models

Reference 1

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ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 2

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ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 3

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ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 4

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ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 5

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Observation 973f68d7-52a1-4dab-95e0-64a9abbd49a2 · outbound

This paper cites To derive this, we employ a heuristic approach that generates automa- tion samples from system interactions and evaluates parameter presence across all cases.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents To derive this, we employ a heuristic approach that generates automa- tion samples from system interactions and evaluates parameter presence across all cases

Reference 6

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ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 7

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ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 8

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ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents catalog_metadata

Reference 9

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ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 10

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This paper cites an unresolved cited work.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 11

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ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 12

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ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 13

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Observation 73ae5b67-03c7-4c7c-b492-38314dc36a6c · outbound

This paper cites gpt-4.1-mini.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents gpt-4.1-mini

Reference 14

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ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 15

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ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 17

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Observation 40833913-925f-4967-b7a4-7d176b0e6607 · outbound

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ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 18

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ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents {current_text}

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T08:16:07.022870Z

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ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 20

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ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 21

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This paper cites an unresolved cited work.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-07-09T08:16:06.935735Z

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Observation 29384520-e34b-41e1-bfca-5fe3151b7f28 · outbound

This paper cites an unresolved cited work.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 23

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Observation 851137b9-454d-457a-9743-d5cbc509ea76 · outbound

This paper cites an unresolved cited work.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-07-09T08:16:06.933427Z

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Observation 9620b8e1-9b15-4e34-a70c-ee27daad84cd · outbound

This paper cites As reflected by Table 4, the annotation agent with future-viewing outperforms the setting skip- ping the future turn information.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents As reflected by Table 4, the annotation agent with future-viewing outperforms the setting skip- ping the future turn information

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T08:16:06.881515Z

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Observation 05d70eeb-6f7d-44dc-bd98-b3463247fb4f · outbound

This paper cites C.1 Design Rationale Prompting Strategy Design.Our three prompt- ing strategies progressively externalize reasoning and memory.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents C.1 Design Rationale Prompting Strategy Design.Our three prompt- ing strategies progressively externalize reasoning and memory

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T08:16:06.884695Z

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Observation cba89c0b-5bf7-4e58-a880-14beb0cd7d16 · outbound

This paper cites an unresolved cited work.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-07-09T08:16:07.002845Z

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Observation ac8c0518-0315-406a-8304-9faf598f069d · outbound

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ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 28

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Observation fe4bb8e3-629f-49ff-997d-b74caa9e9915 · outbound

This paper cites The agent then op- erates in a turn-by-turn manner, generating predictions while collecting performance statistics.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents The agent then op- erates in a turn-by-turn manner, generating predictions while collecting performance statistics

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T08:16:06.953896Z

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Observation 4ae14f89-46f7-4252-bc78-8601a6185105 · outbound

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ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 30

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Observation 7f6d7a73-b122-4668-8010-35b0ee850ad8 · outbound

This paper cites an unresolved cited work.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 31

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Observation 308bb666-3286-4bb4-af35-1758f2cb5d04 · outbound

This paper cites think about specific aspects.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents think about specific aspects

Reference 32

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Observation 444772e1-fbbc-419a-a5e9-95e19bafbf1d · outbound

This paper cites an unresolved cited work.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 33

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Observation 55624ddb-5d8f-4148-9e36-eb46e6e61a2b · outbound

This paper cites an unresolved cited work.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 34

Resolution: unresolved; raw_fallback, observed 2026-07-09T08:16:06.942718Z

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Observation d2c8d3e2-4dcf-490f-b097-930b44eaf4c2 · outbound

This paper cites openai/gpt-4.1-mini.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents openai/gpt-4.1-mini

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T08:16:06.938402Z

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Observation 63e4ac72-a2d3-43bb-9f8d-ee3948728612 · outbound

This paper cites an unresolved cited work.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 36

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Observation 3042414a-73d3-457f-ab43-9fcce380a662 · outbound

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ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 37

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This paper cites an unresolved cited work.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-07-09T08:16:07.000276Z

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Observation 059dd35a-e354-44cd-87a3-07ec645cc11a · outbound

This paper cites an unresolved cited work.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 39

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Observation 421dd35b-8c28-46a8-b2a5-540d94e53669 · outbound

This paper cites proactive_action_opportunities.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents proactive_action_opportunities

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T08:16:06.890493Z

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Observation 8a254acb-cc3f-4aad-bac7-8e4fb00196cf · outbound

This paper cites openai/gpt-4.1-mini.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents openai/gpt-4.1-mini

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T08:16:06.895126Z

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Observation 7085cb16-40f8-4e45-a0a4-cd7939d4d8b1 · outbound

This paper cites proactiveness_evaluator.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents proactiveness_evaluator

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T08:16:06.897219Z

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Observation 555bb3c3-8714-4058-98d9-ef79ce9e9f26 · outbound

This paper cites an unresolved cited work.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-07-09T08:16:06.899439Z

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Observation aff9aae3-87a4-4404-aa9c-76f00af4fc6a · outbound

This paper cites reasoning.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents reasoning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T08:16:06.892879Z

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Observation 480298c5-9d79-4ea7-93b6-d4e8fecbf495 · outbound

This paper cites After the training comple- tion, we resume monitoring for the leftover server and scale up back to N server and pre- pare for next rollout cycle.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents After the training comple- tion, we resume monitoring for the leftover server and scale up back to N server and pre- pare for next rollout cycle

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T08:16:06.931294Z

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Observation 34aa65b5-58e5-4e9d-a9d1-e243eac5be25 · outbound

This paper cites Once the training is completed, we restart the whole in- ference cluster and enter the next rollout cycle.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Once the training is completed, we restart the whole in- ference cluster and enter the next rollout cycle

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T08:16:06.887879Z

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Observation dd3e29c2-db22-4cb8-af85-6810072c5e32 · outbound

This paper cites Specifically, we initialize the process con- text using mp.get_context("spawn"), and use mp.Process to launch N worker processes and cre- ate the shared M Q within the same context.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Specifically, we initialize the process con- text using mp.get_context("spawn"), and use mp.Process to launch N worker processes and cre- ate the shared M Q within the same context

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T08:16:06.992101Z

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Observation c43ddb1a-1c7f-40b0-a6e1-020830a285da · outbound

This paper cites We allow developers to extend the exist- ing MlflowSystemMonitor to enables more customized monitoring.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents We allow developers to extend the exist- ing MlflowSystemMonitor to enables more customized monitoring

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T08:16:06.981202Z

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Observation 0baf42cc-3979-4d42-a1af-8156f017b581 · outbound

This paper cites Trajectory saving via W&B Weave (Weights & Biases, 2025a) is made optional to mitigate potential sensitive data leakage during training.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Trajectory saving via W&B Weave (Weights & Biases, 2025a) is made optional to mitigate potential sensitive data leakage during training

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T08:16:06.878339Z

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Observation 8a537ebf-fe64-4cdb-bfc1-39514e35c25a · outbound

This paper cites avalanche.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents avalanche

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T08:16:06.875395Z

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Observation 8fe16d89-b717-4500-a53f-5b101e70432e · outbound

This paper cites Dataset splitting into training, validation, and test sets is also performed at this stage.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Dataset splitting into training, validation, and test sets is also performed at this stage

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T08:16:06.978581Z

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Observation e52203a1-6b7b-460a-982f-98886605e5fc · outbound

This paper cites an unresolved cited work.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-07-09T08:16:06.983472Z

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Observation d935ff61-060f-47eb-ab98-24f236e0231a · outbound

This paper cites <Your project Name>.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents <Your project Name>

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T08:16:06.986254Z

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Observation b700ed95-34b2-4d5d-a370-f7d32b2e860b · outbound

This paper cites Assis- tant.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Assis- tant

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T08:16:06.971379Z

Source-reported events for the cited work

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Observation 56446b8f-8c99-476c-8393-7a0d7f3ff9b9 · outbound

This paper cites content":.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents content":

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-06-30T11:54:38.278690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dd087a41-8875-4e61-80bd-fe90f72e2810 · outbound

This paper cites an unresolved cited work.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-07-09T08:16:06.973656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7f9ee03a-c287-4f88-9ac2-f6c608317423 · outbound

This paper cites an unresolved cited work.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-07-09T08:16:06.975934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e17e2e33-bcda-4342-98d9-f569e85d76ac · outbound

This paper cites an unresolved cited work.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-07-09T08:16:06.988956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 338f06be-27f9-4b22-988a-79b64e2ea0d8 · outbound

This paper cites an unresolved cited work.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-07-09T08:16:06.961380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7d315cb8-a61c-4118-9d7c-8e42aff52af9 · outbound

This paper cites an unresolved cited work.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-07-09T08:16:06.946911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5dbe4bf1-15f9-4910-a3a6-14dd4459c117 · outbound

This paper cites when to act.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents when to act

Reference 61

Resolution: malformed identifier; raw_fallback, observed 2026-07-09T08:16:06.969049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b612cb58-0724-4761-aa45-429e4e3c1c62 · outbound

This paper cites an unresolved cited work.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-07-09T08:16:06.928855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation afa1cf6d-6a54-4871-8da5-a7f5e5f9857a · outbound

This paper cites RL’s composite reward (RAC + RULER) explic- itly optimizes both dimensions simultaneously, en- abling a calibrated trade-off that static supervised learning cannot discover.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents RL’s composite reward (RAC + RULER) explic- itly optimizes both dimensions simultaneously, en- abling a calibrated trade-off that static supervised learning cannot discover

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T08:16:06.913182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a7b5cbd8-a113-4170-9b07-2db92dfc8d0c · outbound

This paper cites an unresolved cited work.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-07-09T08:16:06.917409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fe353d2e-a361-4c32-b9e3-bdaa00a66c7d · outbound

This paper cites an unresolved cited work.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents Unresolved cited work

Reference 65

Resolution: malformed identifier; arxiv_id, observed 2026-06-30T11:54:38.275832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 53d7cbf5-f7b3-490b-9c9e-14765b658254 · outbound

This paper cites These may be pre-suggested by the automated pipeline or identified by the annotator during review.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents These may be pre-suggested by the automated pipeline or identified by the annotator during review

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T08:16:06.926405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f0ae0d74-533d-42c7-b00c-048ae6f35689 · outbound

This paper cites • READY_TO_TRIGGER: All required parameters for a are available and confirmed—the action can be appropri- ately triggered at turnt.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents • READY_TO_TRIGGER: All required parameters for a are available and confirmed—the action can be appropri- ately triggered at turnt

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T08:16:06.906193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation adcecfc3-5989-490a-ad13-282277aeb8fc · outbound

This paper cites • (Optional, if annotation budget per- mits) The fullvalid triggering window [t∗ a, tend a ], capturing the range of turns over which triggering remains appropri- ate.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents • (Optional, if annotation budget per- mits) The fullvalid triggering window [t∗ a, tend a ], capturing the range of turns over which triggering remains appropri- ate

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T08:16:07.011398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 787c91f6-4225-413e-bac5-1a86cf2449d6 · outbound

This paper cites missing required authorization,.

ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents missing required authorization,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T08:16:06.908823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers