Paper Citation Record · LEDGER

Adaptformer: Sequence models as adaptive iterative planners

As of 14 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2412.00293.

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

pith.paper-citation-record.v1
2412.00293 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:36:16.229866Z

measured 28 of 28 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

28 of 28 outbound references displayed

verified exact1
verified fuzzy15
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1a841a92-0092-4849-8a57-167f196f0bfa · outbound

This paper cites an unresolved cited work.

Adaptformer: Sequence models as adaptive iterative planners Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-12T05:36:16.455141Z

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.

Observation 14206156-1303-4bfb-a22a-bfd061c289e5 · outbound

This paper cites Deadly triad matters for offline reinforcement learning,.

Adaptformer: Sequence models as adaptive iterative planners Deadly triad matters for offline reinforcement learning,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:36:16.447397Z

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.

Observation babb5244-e6f4-4352-bf14-325d76be6df2 · outbound

This paper cites Goal-conditioned reinforcement learning: Problems and solutions,.

Adaptformer: Sequence models as adaptive iterative planners Goal-conditioned reinforcement learning: Problems and solutions,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T05:36:16.145903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 867a0529-e38f-461d-aa49-803b2f950d53 · outbound

This paper cites Decision transformer: Re- inforcement learning via sequence modeling,.

Adaptformer: Sequence models as adaptive iterative planners Decision transformer: Re- inforcement learning via sequence modeling,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:36:16.437549Z

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.

Observation 395d768a-8b06-4c71-a8c0-8fadabdf8cd0 · outbound

This paper cites Planning with sequence models through iterative energy minimization,.

Adaptformer: Sequence models as adaptive iterative planners Planning with sequence models through iterative energy minimization,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:36:16.428111Z

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.

Observation abaab2fc-3c9e-4561-ac07-50e56d9206f2 · outbound

This paper cites Offline reinforcement learning: Tutorial, review, and perspectives on open problems,.

Adaptformer: Sequence models as adaptive iterative planners Offline reinforcement learning: Tutorial, review, and perspectives on open problems,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:36:16.418668Z

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.

Observation 3c1e5fd2-2b3f-4e8f-85cb-6b53c82e1224 · outbound

This paper cites Off-policy deep reinforcement learning without exploration,.

Adaptformer: Sequence models as adaptive iterative planners Off-policy deep reinforcement learning without exploration,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:36:16.408854Z

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.

Observation 831f8042-8e24-4aa3-b37f-1346828104c6 · outbound

This paper cites Stabilizing off-policy q-learning via bootstrapping error reduction,.

Adaptformer: Sequence models as adaptive iterative planners Stabilizing off-policy q-learning via bootstrapping error reduction,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:36:16.399591Z

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.

Observation f834e5fd-779f-46c8-95a0-a87e7d9500e7 · outbound

This paper cites A minimalist approach to offline reinforce- ment learning,.

Adaptformer: Sequence models as adaptive iterative planners A minimalist approach to offline reinforce- ment learning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:36:16.391938Z

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.

Observation 88a17ffe-7ff9-4592-af59-2683e4a50269 · outbound

This paper cites Conservative q- learning for offline reinforcement learning,.

Adaptformer: Sequence models as adaptive iterative planners Conservative q- learning for offline reinforcement learning,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T05:36:16.171378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7629272-33af-4a83-ab59-bdc045fef931 · outbound

This paper cites Long short-term memory,.

Adaptformer: Sequence models as adaptive iterative planners Long short-term memory,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T05:36:16.174139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c47dc2fd-b396-4919-869e-7617c90d56b2 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Adaptformer: Sequence models as adaptive iterative planners BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T05:36:16.178536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5470bd73-5cdd-4a79-b87b-f630bce3e5b6 · outbound

This paper cites Offline Reinforcement Learning as One Big Sequence Modeling Problem,.

Adaptformer: Sequence models as adaptive iterative planners Offline Reinforcement Learning as One Big Sequence Modeling Problem,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:36:16.370130Z

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.

Observation 33a7dd12-ac4c-4654-aecb-d55803c52435 · outbound

This paper cites Generalized decision transformer for offline hindsight information matching,.

Adaptformer: Sequence models as adaptive iterative planners Generalized decision transformer for offline hindsight information matching,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:36:16.362002Z

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.

Observation f2ad8743-cefc-406e-9681-e36aa73ecd5f · outbound

This paper cites You Can’t Count on Luck: Why Decision Transformers and RvS Fail in Stochastic Environments,.

Adaptformer: Sequence models as adaptive iterative planners You Can’t Count on Luck: Why Decision Transformers and RvS Fail in Stochastic Environments,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:36:16.354597Z

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.

Observation 8756ef34-fc24-428b-87a5-41369d78d884 · outbound

This paper cites Maximum entropy gain exploration for long horizon multi-goal reinforcement learning,.

Adaptformer: Sequence models as adaptive iterative planners Maximum entropy gain exploration for long horizon multi-goal reinforcement learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:36:16.346931Z

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.

Observation 8c520d41-c0a7-43c9-accf-38eef9cf9e44 · outbound

This paper cites RvS: What is Essential for Offline RL via Supervised Learning?.

Adaptformer: Sequence models as adaptive iterative planners RvS: What is Essential for Offline RL via Supervised Learning?

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T05:36:16.193382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3079d82d-ef69-4cec-bbbe-ec449c981298 · outbound

This paper cites Waypoint transformer: Reinforcement learning via supervised learning with intermediate targets,.

Adaptformer: Sequence models as adaptive iterative planners Waypoint transformer: Reinforcement learning via supervised learning with intermediate targets,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:36:16.338013Z

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.

Observation 74006f79-7056-4c43-b4ce-acda921b7622 · outbound

This paper cites Dinov2: Learning robust visual features without supervision,.

Adaptformer: Sequence models as adaptive iterative planners Dinov2: Learning robust visual features without supervision,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T05:36:16.201177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7098f9b6-eeb6-4b85-98c8-075fc1db1142 · outbound

This paper cites Generative adversarial networks,.

Adaptformer: Sequence models as adaptive iterative planners Generative adversarial networks,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T05:36:16.203894Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b24cbb3c-b067-4702-a125-f17dda8bce3c · outbound

This paper cites Model-based Offline Policy Optimization with Adversarial Network.

Adaptformer: Sequence models as adaptive iterative planners Model-based Offline Policy Optimization with Adversarial Network

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-12T05:36:16.279851Z

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.

Observation e32e11db-68ad-4fff-ba8b-f146d5c38f41 · outbound

This paper cites Exposing the Implicit Energy Networks behind Masked Language Models via Metropolis--Hastings.

Adaptformer: Sequence models as adaptive iterative planners Exposing the Implicit Energy Networks behind Masked Language Models via Metropolis--Hastings

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T05:36:16.210285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f94996e-d1bf-45f0-897a-d03d56f81188 · outbound

This paper cites Online decision transformer,.

Adaptformer: Sequence models as adaptive iterative planners Online decision transformer,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:36:16.320244Z

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.

Observation a7108bc3-00af-4821-a46f-d0b82ed103bf · outbound

This paper cites Soft Actor-Critic Algorithms and Applications.

Adaptformer: Sequence models as adaptive iterative planners Soft Actor-Critic Algorithms and Applications

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T05:36:16.216323Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e9b4d6d-d7ec-42a3-b24f-a4307975895a · outbound

This paper cites Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,.

Adaptformer: Sequence models as adaptive iterative planners Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T05:36:16.220196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03574109-7735-4e78-b7aa-13b7a09b83cf · outbound

This paper cites BabyAI: First steps towards grounded language learning with a human in the loop,.

Adaptformer: Sequence models as adaptive iterative planners BabyAI: First steps towards grounded language learning with a human in the loop,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:36:16.308221Z

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.

Observation 86953cec-eda8-44f7-964c-639de92aeb9e · outbound

This paper cites Minigrid & miniworld: Modular & customizable reinforcement learning environments for goal-oriented tasks,.

Adaptformer: Sequence models as adaptive iterative planners Minigrid & miniworld: Modular & customizable reinforcement learning environments for goal-oriented tasks,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:36:16.300275Z

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.

Observation 3280859f-1aaa-42f6-b1d4-65f354a0c9ba · outbound

This paper cites Offline Reinforcement Learning with Implicit Q-Learning.

Adaptformer: Sequence models as adaptive iterative planners Offline Reinforcement Learning with Implicit Q-Learning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T05:36:16.229866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.