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Paper Citation Record · LEDGER

Autoregressive Action Sequence Learning for Robotic Manipulation

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2410.03132.

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

pith.paper-citation-record.v1
2410.03132 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:03:14.918844Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T14:53:06.985231Z

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 9d34cc6f-5254-4f79-aaf1-c6b436aaf822 · inbound

The Unreasonable Effectiveness of Discrete-Time Gaussian Process Mixtures for Robot Policy Learning cites this paper.

The Unreasonable Effectiveness of Discrete-Time Gaussian Process Mixtures for Robot Policy Learning Autoregressive Action Sequence Learning for Robotic Manipulation

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-16T00:03:14.918844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:03:14.918844Z digest=sha256:e74ef88a4f03460bddd4533c69b21054462ed05390c1bf6b0fc4a0556febaedf

Observation 5eee344d-792a-4775-a81a-50f48879c35a · inbound

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models cites this paper.

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models Autoregressive Action Sequence Learning for Robotic Manipulation

Reference 2008

Resolution
unresolved
no resolver link, observed 2026-08-15T21:06:23.105923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:06:23.105923Z digest=sha256:79d4b0e080bb0b094afcb27fef0fd61c43ca14e84070ec6d08df49dbda5b9d67

Observation 769b52c5-bf43-4f8d-a3bf-51afd6cdb6fa · inbound

Diffusion-Based Imaginative Coordination for Bimanual Manipulation cites this paper.

Diffusion-Based Imaginative Coordination for Bimanual Manipulation Autoregressive Action Sequence Learning for Robotic Manipulation

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-06T17:18:55.765438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:18:55.765438Z digest=sha256:570dc468a04bf6ae900134984f0f38f19e5d2ebead9ca829fdeff476c3a6898c

Observation 43789518-e227-467e-a1bb-bc7ae060cfd7 · inbound

AR-VLA: True Autoregressive Action Expert for Vision-Language-Action Models cites this paper.

AR-VLA: True Autoregressive Action Expert for Vision-Language-Action Models Autoregressive Action Sequence Learning for Robotic Manipulation

Reference 47

Resolution
verified exact
arxiv_id, observed 2026-05-15T12:50:37.171399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-15T12:50:02.670780Z digest=sha256:714bccfc574dcf8a8a27f82751f32def873f9964b39ae240133a568e2fba859d

Observation bbe80798-538f-43fd-aef0-d0eb1a6020b7 · inbound

SkiP: When to Skip and When to Refine for Efficient Robot Manipulation cites this paper.

SkiP: When to Skip and When to Refine for Efficient Robot Manipulation Autoregressive Action Sequence Learning for Robotic Manipulation

Reference 39

Resolution
verified exact
arxiv_id, observed 2026-05-19T14:53:06.987365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-19T14:49:37.945785Z digest=sha256:ad342b91ad4a77f5827281a2fff73aef6e5b31ac4db5a3cf27ebc0e24c96009c

Observation db80365e-1a83-40de-95fd-494c374a9237 · inbound

One Hand Watches The Other: Dynamic Multi-Agent Cooperation for Sample-Efficient Bimanual Manipulation in Dynamic Environments cites this paper.

One Hand Watches The Other: Dynamic Multi-Agent Cooperation for Sample-Efficient Bimanual Manipulation in Dynamic Environments Autoregressive Action Sequence Learning for Robotic Manipulation

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-01T05:47:21.969440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T05:47:21.969440Z digest=sha256:2767d1b6407d33f9ae7c15869ba920ec348800209f7b4af3deaa65c85f8f7869