Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing | Paper Citation Record

Observation 6cbcb72f-e269-4481-b8f1-4c689d23b1dd · outbound

This paper cites Online learning from strategic human feedback in llm fine-tuning,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Online learning from strategic human feedback in llm fine-tuning,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.761435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2d90ac0f-1db2-4588-96bf-a8b2dc71f6b9 · outbound

This paper cites Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment

Reference 2

Resolution: malformed identifier; doi_truncated, observed 2026-06-30T16:14:52.714334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e157f391-b81d-4397-9c24-b5d5bbd4327c · outbound

This paper cites Cached model-as-a-resource: Provisioning large language model agents for edge intelligence in space-air-ground integrated networks,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Cached model-as-a-resource: Provisioning large language model agents for edge intelligence in space-air-ground integrated networks,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.757764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 825428b7-7b39-4b0b-ba0a-fa9a29461cf7 · outbound

This paper cites an unresolved cited work.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-07-08T14:35:02.748275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 37113cf3-d3a6-41fe-b222-d97738783908 · outbound

This paper cites Crowdsensing-based urban traﬀic monitoring using mobile social media,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Crowdsensing-based urban traﬀic monitoring using mobile social media,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.688589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0c04cfb8-f921-43a5-97a6-aeb93b74de2f · outbound

This paper cites Training language models to follow instructions with human feedback.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Training language models to follow instructions with human feedback

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.750270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5233e9f5-afa5-4035-8e1c-311006792fbc · outbound

This paper cites Gemini apps privacy hub,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Gemini apps privacy hub,

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-06-30T16:14:53.417032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0c66b43e-cb58-4d12-999f-b9ad9b77a4b4 · outbound

This paper cites Mechanism design for llm ﬁne- tuning with multiple reward models.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Mechanism design for llm ﬁne- tuning with multiple reward models

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-06-30T16:14:53.434552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 71795b9b-0e15-4a5c-94b2-891af9805b1a · outbound

This paper cites Truthful Aggregation of LLMs with an Application to Online Advertising.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Truthful Aggregation of LLMs with an Application to Online Advertising

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T16:14:53.437475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6bc42dd0-aa8a-4c56-bfae-a0d76058c293 · outbound

This paper cites Rlhf from heterogeneous feedback via personalization and preference aggregation,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Rlhf from heterogeneous feedback via personalization and preference aggregation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.694616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c510d379-40a2-4c5b-a724-60b8b3fbf594 · outbound

This paper cites Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-06-30T16:14:53.440566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d488413c-d1d4-47b8-aa7d-6cab3183a29e · outbound

This paper cites Online prediction with selfish experts,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Online prediction with selfish experts,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.692069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 78a7e61b-5671-4677-b194-1fe57497515a · outbound

This paper cites Lie for a dime: When most prescreening responses are honest but most study participants are impostors,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Lie for a dime: When most prescreening responses are honest but most study participants are impostors,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.746605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b1f164a0-80df-45d9-b22c-8c6985103ce5 · outbound

This paper cites The shape of and solutions to the mturk quality crisis,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing The shape of and solutions to the mturk quality crisis,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.700139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5f408fb6-8035-4a62-8bcb-516d9ef1aae2 · outbound

This paper cites Online detection and snr estimation in cooperative spectrum sensing,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Online detection and snr estimation in cooperative spectrum sensing,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.776521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0f8794dc-9207-4d50-a612-ecea99db0b8a · outbound

This paper cites Adaptive em-based al- gorithm for cooperative spectrum sensing in mobile environ- ments,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Adaptive em-based al- gorithm for cooperative spectrum sensing in mobile environ- ments,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.686819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 53977ff5-794d-4cf7-9c6c-a8ea8a0af6a7 · outbound

This paper cites On the universal near optimality of hedge in combinatorial settings,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing On the universal near optimality of hedge in combinatorial settings,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.781805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 96d099bd-c1d9-4055-b355-85977ea51ab3 · outbound

This paper cites Eﬀicient online learning with memory via frank-wolfe optimization: Algorithms with bounded dynamic regret and applications to control,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Eﬀicient online learning with memory via frank-wolfe optimization: Algorithms with bounded dynamic regret and applications to control,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.783669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9131592b-0a04-4762-ae04-b64149d4711a · outbound

This paper cites Auctions with LLM Summaries.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Auctions with LLM Summaries

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-06-30T16:14:53.422582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d0a60e7c-09c3-4f17-b5b7-d9a58f87d02a · outbound

This paper cites Epvisa: Eﬀicient auction design for real-time physical-virtual synchronization in the human-centric 16 metaverse,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Epvisa: Eﬀicient auction design for real-time physical-virtual synchronization in the human-centric 16 metaverse,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.768902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 259c5c69-a3fd-4853-8aea-1b06de1a54e9 · outbound

This paper cites Rlhf workflow: From reward modeling to online rlhf,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Rlhf workflow: From reward modeling to online rlhf,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.772846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 03dc38ed-3efd-4ec2-a59d-d4ba8e4aad21 · outbound

This paper cites Online iterative reinforcement learning from human feedback with general preference model,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Online iterative reinforcement learning from human feedback with general preference model,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.763321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5dc021bf-b111-43f2-bc8d-ba8cf453c13b · outbound

This paper cites Iterative preference learning from human feed- back: Bridging theory and practice for rlhf under kl-constraint,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Iterative preference learning from human feed- back: Bridging theory and practice for rlhf under kl-constraint,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.759567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ad49bda5-7638-4478-b1f1-1387c6062a8c · outbound

This paper cites Col- laborative algorithms for online personalized mean estimation,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Col- laborative algorithms for online personalized mean estimation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.765155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0a48a4ec-67ac-4844-90ce-7e383835ec87 · outbound

This paper cites Mechanism design for collaborative normal mean estimation,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Mechanism design for collaborative normal mean estimation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.770798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0ab6c217-a0aa-4127-89b8-2b0dd746fa6e · outbound

This paper cites Strategyproof mechanisms for group- fair obnoxious facility location problems,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Strategyproof mechanisms for group- fair obnoxious facility location problems,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.753950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b41a11b0-b381-4bff-b87b-c1e572ce6aef · outbound

This paper cites Positive intra-group externalities in facility location,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Positive intra-group externalities in facility location,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.744787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b6f838a6-38ff-42dd-9bea-1f3ec750cb33 · outbound

This paper cites Reinforcement Learning from Human Feedback.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Reinforcement Learning from Human Feedback

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-06-30T16:14:53.419913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bfd47f0b-e664-405c-9d2b-eea96d9d665a · outbound

This paper cites On strategy-proofness and single peakedness.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing On strategy-proofness and single peakedness

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.752101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f87e86ea-4f55-4e6c-ad8a-deeb1106e93a · outbound

This paper cites A Bayesian truth serum for subjective data,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing A Bayesian truth serum for subjective data,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.690323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f6b45111-9ffa-40b0-bc71-7c7b2b838905 · outbound

This paper cites Eliciting informative feedback: The peer-prediction method,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Eliciting informative feedback: The peer-prediction method,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.739186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2ded82ef-a453-4596-976c-29f83c87ead5 · outbound

This paper cites Machine-learning aided peer prediction,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Machine-learning aided peer prediction,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.735597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bcda5051-e289-43d0-a44e-9e456c7254a9 · outbound

This paper cites Incentive-compatible forecasting competitions,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Incentive-compatible forecasting competitions,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.774762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 92b55817-2078-41b8-93ff-fc91f7807adc · outbound

This paper cites Maximum likelihood estimation of observer error-rates using the EM algorithm,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Maximum likelihood estimation of observer error-rates using the EM algorithm,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.737406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f685c66e-cfa7-493c-b0cb-80bbe54c967b · outbound

This paper cites Slowly changing adversarial bandit algorithms are eﬀicient for discounted mdps,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Slowly changing adversarial bandit algorithms are eﬀicient for discounted mdps,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.740960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b13b09eb-806d-4e68-9989-20a31a72ed43 · outbound

This paper cites Meta-learning adversarial bandit algo- rithms,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Meta-learning adversarial bandit algo- rithms,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.730057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4f8bf21d-df99-4c59-80d2-414bb8d047fe · outbound

This paper cites Double or nothing: Multiplicative incentive mechanisms for crowdsourcing,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Double or nothing: Multiplicative incentive mechanisms for crowdsourcing,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.731802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1cfe049a-99c4-4656-ad16-928907b959f9 · outbound

This paper cites No-regret and incentive-compatible online learning,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing No-regret and incentive-compatible online learning,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.728302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 904199f3-3684-4460-8be4-f2edfef3dabd · outbound

This paper cites Incentivized truthful communication for federated bandits,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Incentivized truthful communication for federated bandits,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.726506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 63c88e2e-0f07-4ada-9d11-6cd8a1f00486 · outbound

This paper cites Large language models and their applications in roadway safety and mobility enhancement: A comprehensive review,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Large language models and their applications in roadway safety and mobility enhancement: A comprehensive review,

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-06-30T16:14:53.425933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0ac2bbca-ea46-462f-ab73-7afc4b3aa26a · outbound

This paper cites Towards explainable road nav- igation systems,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Towards explainable road nav- igation systems,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.724648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4f5f1291-7749-42b0-9492-798de0453e83 · outbound

This paper cites Machine learning- based spectrum occupancy prediction: A comprehensive sur- vey,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Machine learning- based spectrum occupancy prediction: A comprehensive sur- vey,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.721646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4c953c93-7b82-4adc-a3ba-593eabac8ba9 · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 43

Resolution: malformed identifier; local_arxiv, observed 2026-06-30T16:14:53.431514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d070f191-1287-4098-a4d5-8c96690a9027 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-06-30T16:14:53.428951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7b6d76ba-baa8-4562-b547-7f19e62059a9 · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Direct preference optimization: Your language model is secretly a reward model,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.733610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b532d84e-3f1f-484f-b045-3666585197f2 · outbound

This paper cites Deep reinforcement learning from human pref- erences.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Deep reinforcement learning from human pref- erences

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.742714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 41141610-2a86-4819-8554-b7fe520ef7da · outbound

This paper cites Lora: Low-rank adaptation of large language models,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Lora: Low-rank adaptation of large language models,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.767016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 42cd20cd-a1f2-49bd-9c6a-bd405f0be175 · outbound

This paper cites Freeway performance measurement system: Mining loop de- tector data,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Freeway performance measurement system: Mining loop de- tector data,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.780069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c36ec281-68c1-4c3a-8f37-a72d4f685d23 · outbound

This paper cites SHARP: Spectrum harvesting with ARQ retransmission and probing in cognitive radio,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing SHARP: Spectrum harvesting with ARQ retransmission and probing in cognitive radio,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.716158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9bffc0b0-61b7-4c83-a81b-972fa22a43b7 · outbound

This paper cites Indexability of restless bandit problems and optimality of whittle index for dynamic multichannel access,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Indexability of restless bandit problems and optimality of whittle index for dynamic multichannel access,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.778260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cedfb1b6-9677-45cf-a72e-e480ef0c00fc · outbound

This paper cites Decentralized cognitive mac for dynamic spectrum access,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Decentralized cognitive mac for dynamic spectrum access,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.717889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 20b10718-b214-4f72-84f3-54499ae7eb4e · outbound

This paper cites Amendment of the Commission’s Rules with Regard to Commercial Operations in the 3550-3650 MHz Band,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Amendment of the Commission’s Rules with Regard to Commercial Operations in the 3550-3650 MHz Band,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.709494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fac084c3-ff9a-4374-ae8c-aee38b5c5634 · outbound

This paper cites Marshall, Three-Tier Shared Spectrum, Shared Infrastruc- ture, and a Path to 5G.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Marshall, Three-Tier Shared Spectrum, Shared Infrastruc- ture, and a Path to 5G

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.705373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 50cad737-5ac0-4438-9108-6e89aee2ea7c · outbound

This paper cites an unresolved cited work.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-07-08T14:35:02.755711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6a1572c4-3049-4741-801d-03a0c3fe0f84 · outbound

This paper cites Reputation-based incentive protocols in crowdsourcing applications,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Reputation-based incentive protocols in crowdsourcing applications,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.701914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 39d6f21e-da0a-467d-8de4-9282d1bb07de · outbound

This paper cites Design and analysis of incentive and reputation mechanisms for online crowdsourcing systems,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Design and analysis of incentive and reputation mechanisms for online crowdsourcing systems,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.703644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f9357dd5-a902-4417-a423-2591f48c18e2 · outbound

This paper cites FedAB: Truthful federated learning with auction-based combi- natorial multi-armed bandit,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing FedAB: Truthful federated learning with auction-based combi- natorial multi-armed bandit,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.707441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2ab43dc0-0046-496b-8abc-4baf62f7a66b · outbound

This paper cites Truthful incentive mechanism for federated learning with crowdsourced data labeling,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Truthful incentive mechanism for federated learning with crowdsourced data labeling,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.719861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d258520a-5fd1-4048-99de-3783e06e6b22 · outbound

This paper cites Deepsense: Fast wideband spectrum sensing through real-time in-the-loop deep learning,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Deepsense: Fast wideband spectrum sensing through real-time in-the-loop deep learning,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.713376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1807fd80-d13b-45d0-a60e-b80ec562f7a5 · outbound

This paper cites Online mixture of experts: No-regret learning for optimal collective decision-making,.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Online mixture of experts: No-regret learning for optimal collective decision-making,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.698416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2aaeb83b-cc9a-4c9f-a0db-6e2dcdf52c95 · outbound

This paper cites an unresolved cited work.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-07-08T14:35:02.696577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c610be0c-0ec2-484a-be9a-4bb081f35c7f · outbound

This paper cites Therefore, E[w2 k,lie] − E[w2 k,truth] =(1 − q) · γ1 + γ0 − 1 α + β + 1.

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing Therefore, E[w2 k,lie] − E[w2 k,truth] =(1 − q) · γ1 + γ0 − 1 α + β + 1

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T14:35:02.711508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers