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Paper Citation Record · LEDGER

UCB Exploration via Q-Ensembles

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:1706.01502.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1706.01502 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:54:15.024862Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-07-04T03:09:30.514483Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 3bc65c9a-76bd-44ee-bba8-a9a87a2fa851 · inbound

Group-Agent Reinforcement Learning with Heterogeneous Agents cites this paper.

Group-Agent Reinforcement Learning with Heterogeneous Agents UCB Exploration via Q-Ensembles

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-10T17:54:15.024862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:54:15.024862Z digest=sha256:d9a72911bbb75a3689a63c73439be42a0479c910cdf1b702239ebe28d3a7a8fc

Observation ebf614f8-4bed-4c10-94df-94b442431c4e · inbound

Universal Value-Function Uncertainties cites this paper.

Universal Value-Function Uncertainties UCB Exploration via Q-Ensembles

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-07T13:49:54.352169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:49:54.352169Z digest=sha256:5ee627b75dcc84dd2eb4194a2be5f6bdddf0d75de082233cbee3b10a250c84fe

Observation 01cb9d14-27c9-49de-90a8-9cb70d15ebfb · inbound

Application of LLMs to Multi-Robot Path Planning and Task Allocation cites this paper.

Application of LLMs to Multi-Robot Path Planning and Task Allocation UCB Exploration via Q-Ensembles

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-06T18:48:17.824172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:48:17.824172Z digest=sha256:6ff244ae13d56659bf93bf5a863a6239b700a7e878c3227ff52aa370eed149ad

Observation beacb836-2396-408a-8dd2-a0c82240a4db · inbound

Adaptive Ensemble Aggregation for Actor-Critics cites this paper.

Adaptive Ensemble Aggregation for Actor-Critics UCB Exploration via Q-Ensembles

Reference 9

Resolution
verified exact
local_arxiv, observed 2026-05-19T02:16:59.346904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-19T02:14:26.752215Z digest=sha256:7782e04298ed39d7c53fb786c60660857c03757851af6e5a0798a3f3bcd4de6d

Observation f1ea7b3f-1edb-49f9-93a2-677f9a47c633 · inbound

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies cites this paper.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies UCB Exploration via Q-Ensembles

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-06T04:39:01.982238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:39:01.982238Z digest=sha256:f637b965e9bf041e20ac1033fb61355be23e53ed47688227cb674b18f1ecec81

Observation 6311f24f-1c9e-4748-822f-db37fd559411 · inbound

Divide, Discover, Deploy: Factorized Skill Learning with Symmetry and Style Priors cites this paper.

Divide, Discover, Deploy: Factorized Skill Learning with Symmetry and Style Priors UCB Exploration via Q-Ensembles

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-05T15:25:43.886708Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:25:43.886708Z digest=sha256:1b86011c93ca1ead0052d5530ebe2c26143e1d990fc94658ac6675e319701277

Observation c2a29e33-de26-42e6-82cb-2e8a6753f5a4 · inbound

Learning to Plan, Planning to Learn: Adaptive Hierarchical RL-MPC for Sample-Efficient Decision Making cites this paper.

Learning to Plan, Planning to Learn: Adaptive Hierarchical RL-MPC for Sample-Efficient Decision Making UCB Exploration via Q-Ensembles

Reference 2

Resolution
verified exact
local_arxiv, observed 2026-05-16T21:11:16.782360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-16T21:10:38.484582Z digest=sha256:7b504bb5cb3f472632b461327dd288d0b58a63146f79289dfc0b6eb7f5dd5c21

Observation 6c613cfc-5f2e-49a9-91c1-5bc3e63113d6 · inbound

Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution cites this paper.

Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution UCB Exploration via Q-Ensembles

Reference 6

Resolution
verified exact
local_arxiv, observed 2026-05-16T06:37:28.527496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-16T06:35:30.479542Z digest=sha256:405a4e49a83c6c13c7301a02c103f0a20bb362d5a679374ed9c801af589377a9

Observation 0474db11-b85a-48a1-aa17-e388e357b7f4 · inbound

Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution cites this paper.

Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution UCB Exploration via Q-Ensembles

Reference 6

Resolution
verified exact
local_arxiv, observed 2026-05-21T13:10:10.484697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-21T13:06:54.002248Z digest=sha256:471084b0212ff953107cf7011b3d2cac97443aa86fb330278856bd85b05c234b

Observation ce4ed17f-4f93-45ad-b01a-f0e928ca33ee · inbound

Distributional Off-Policy Evaluation with Deep Quantile Process Regression cites this paper.

Distributional Off-Policy Evaluation with Deep Quantile Process Regression UCB Exploration via Q-Ensembles

Reference 30

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T12:06:03.831550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-05-10T04:10:14.476158Z digest=sha256:a065a79dbee25b443e46f170fb553bfc61e773bce0c1868c513eeb4e9017c139

Observation 367f215f-0a50-456b-b926-aa4b488e5ac2 · inbound

DF-ExpEnse: Diffusion Filtered Exploration for Sample Efficient Finetuning cites this paper.

DF-ExpEnse: Diffusion Filtered Exploration for Sample Efficient Finetuning UCB Exploration via Q-Ensembles

Reference 4

Resolution
metadata mismatch
local_arxiv, observed 2026-07-04T01:29:22.907085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-26T20:19:15.766523Z digest=sha256:e61e10438d9edaecedaca540b918788faea83ab15859f5a7a922aed93951f5f9

Observation 7ae7c894-47f4-4c60-b83c-99c6921cf039 · inbound

Quantile of Means: A Bonus-Free Ensemble Method for Minimax Optimal Reinforcement Learning cites this paper.

Quantile of Means: A Bonus-Free Ensemble Method for Minimax Optimal Reinforcement Learning UCB Exploration via Q-Ensembles

Reference 32

Resolution
metadata mismatch
local_arxiv, observed 2026-07-04T03:09:30.515885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-06-26T18:19:02.314185Z digest=sha256:88428c13aad3396cb063fa6014e1c9f3fa3bd4202c1902ed5b1a42628a2f0ab4