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Paper Citation Record · LEDGER

Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2310.08566.

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

pith.paper-citation-record.v1
2310.08566 v2

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 2026-08-10T06:31:04.303077+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T22:34:21.362732Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T23:39:05.062745Z

Reference resolution

0 of 0 outbound references displayed

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  • verified fuzzy0
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  • malformed identifier0
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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 b7f25448-aaed-4adf-8b41-21cbe5c5195f · inbound

OT-Transformer: A Continuous-time Transformer Architecture with Optimal Transport Regularization cites this paper.

OT-Transformer: A Continuous-time Transformer Architecture with Optimal Transport Regularization Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-09T22:34:21.362732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T22:34:21.362732Z digest=sha256:a946f71ad3af156b4e5b6514ecab997cb7e7d277ae79d6a2fe82fa6a7ad6fd06

Observation 253d1545-1299-4b93-9269-aff5e5d781cb · inbound

Transformers and Their Roles as Time Series Foundation Models cites this paper.

Transformers and Their Roles as Time Series Foundation Models Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-09T05:01:32.335815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T05:01:32.335815Z digest=sha256:481c3d7771b84cfc9039681d48b979d5bb1f03a0d8ca09cffb837ffaeaba41e2

Observation 541f0503-2ac9-4743-a858-254dd076fde4 · inbound

Filtering Learning Histories Enhances In-Context Reinforcement Learning cites this paper.

Filtering Learning Histories Enhances In-Context Reinforcement Learning Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-07T15:27:54.037626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:27:54.037626Z digest=sha256:975bb854d5858605b51b829cd7a319de0ed023cdf22b21dbc4f57064e8af3161

Observation 50c28daa-a967-4087-85c5-2fc111ff7716 · inbound

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models cites this paper.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-07T11:40:56.046409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:40:56.046409Z digest=sha256:64d6d09dcdf9943a40e00b381a7933b2626a1da4879f11c0f681041a1dca7c35

Observation c84e3eb6-9af2-4682-a82d-4f7318704b19 · inbound

Sample Complexity and Representation Ability of Test-time Scaling Paradigms cites this paper.

Sample Complexity and Representation Ability of Test-time Scaling Paradigms Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-07T10:35:36.202620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:35:36.202620Z digest=sha256:f72dde543e04087598dc14c8f6f391d200be0349c23654f1b60b5e63bf9cb4a7

Observation 627131a0-eb13-4e91-838c-1ba57d615dbd · inbound

Interaction as Intelligence: Deep Research With Human-AI Partnership cites this paper.

Interaction as Intelligence: Deep Research With Human-AI Partnership Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-06T15:30:40.997628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T15:30:40.997628Z digest=sha256:443748f27683bcc3c6b09bdb6ec338e59a363f184a50c1415e24223d37c17bca

Observation 8c60488e-d1e0-4a74-9e90-5374a3e4abe7 · inbound

Learning-To-Measure: In-Context Active Feature Acquisition cites this paper.

Learning-To-Measure: In-Context Active Feature Acquisition Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-04T09:58:08.178667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T09:58:08.178667Z digest=sha256:2b7a0aef62f7247a88087f0290102a67b8cc06453253b20ec2ebae16489fcdda

Observation ebf6068c-72f4-48a4-85fb-f1e9f78abac9 · inbound

Convergent Stochastic Training of Attention and Understanding LoRA cites this paper.

Convergent Stochastic Training of Attention and Understanding LoRA Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining

Reference 37

Resolution
verified exact
arxiv_id, observed 2026-05-11T03:05:55.072521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-11T02:48:35.907351Z digest=sha256:0f351cd63ba3bd68b294dd2330ea1f8a161e62857316ed4532b7698752037ad6

Observation 0a4ae872-dfc1-4ca1-8eec-3aa057f95ca3 · inbound

One for All: A Non-Linear Transformer can Enable Cross-Domain Generalization for In-Context Reinforcement Learning cites this paper.

One for All: A Non-Linear Transformer can Enable Cross-Domain Generalization for In-Context Reinforcement Learning Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-12T06:56:31.956466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-12T03:46:21.786972Z digest=sha256:aba3aefd7bd98d8ee91efe02cb103d7fae2e1d27d06b1f5a89928def73f6e7ad

Observation a545a2ba-50f9-4a1c-a6b9-1a5908caabdd · inbound

Multi-Objective Learning for Diffusion Models: A Statistical Theory under Semi-Supervised Learning cites this paper.

Multi-Objective Learning for Diffusion Models: A Statistical Theory under Semi-Supervised Learning Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining

Reference 9

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T12:34:39.412832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-30T12:09:27.409746Z digest=sha256:53e60f2ac1213a94e77775d9cecef8db8004b078e5b8817049a6e15c28125808

Observation cb66579a-6707-412b-a64f-bb9f26450594 · inbound

Reinforcement Learning Foundation Models Should Already Be A Thing cites this paper.

Reinforcement Learning Foundation Models Should Already Be A Thing Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining

Reference 12

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T23:39:05.065765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-26T21:50:00.974590Z digest=sha256:61451025ca44ceec94559130b771073fa225ad1d533dcbf7ce87add1dfdc38fa

Observation 04ef0b51-afca-40b5-b359-5390aa85266e · inbound

Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation cites this paper.

Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining

Reference 18

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T06:45:29.285229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-01T06:44:18.631601Z digest=sha256:c4c2e8ee7b35b9f410eaf40d1c31034ee81f212a792974575e8ba2983e51bbe1