Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Transformers Can Learn Temporal Difference Methods for In-Context Reinforcement Learning
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2405.13861.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-09T18:00:22.203334Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-30T06:54:20.102066Z
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 c0a90b81-627e-42c7-b363-c5761d2ae73b · inbound
Meta-Prompt Optimization for LLM-Based Sequential Decision Making cites this paper.
Meta-Prompt Optimization for LLM-Based Sequential Decision Making Transformers Can Learn Temporal Difference Methods for In-Context Reinforcement Learning
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T18:00:22.203334Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7afb00ea-9f23-407f-bd45-1a74cf745a0f · 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 Can Learn Temporal Difference Methods for In-Context Reinforcement Learning
Reference 19
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T06:56:32.038683Z
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.
Observation 6e6cb0af-1492-4f69-85d3-3433630e0109 · inbound
UniVAD v2: Unified Visual Anomaly Detection via Support-Conditioned Boundary Construction cites this paper.
UniVAD v2: Unified Visual Anomaly Detection via Support-Conditioned Boundary Construction Transformers Can Learn Temporal Difference Methods for In-Context Reinforcement Learning
Reference 36
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-30T06:54:20.103964Z
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.