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Paper Citation Record · LEDGER

Does learning the right latent variables necessarily improve in-context learning?

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2405.19162.

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

pith.paper-citation-record.v1
2405.19162 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:48:41.127955Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T19:33:22.386221Z

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 a7a968ba-088e-42e7-88eb-6634513736ba · inbound

Amortized Inference of Causal Models via Conditional Fixed-Point Iterations cites this paper.

Amortized Inference of Causal Models via Conditional Fixed-Point Iterations Does learning the right latent variables necessarily improve in-context learning?

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-23T19:33:22.388380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-23T19:30:30.965348Z digest=sha256:7b65f7524cbe2c964304076ad7cb10a91523a35b566f15e46d79773c6af4f249

Observation 5763f258-be75-44b1-9827-cc292dce71bb · inbound

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective cites this paper.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Does learning the right latent variables necessarily improve in-context learning?

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-06T23:48:41.127955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:48:41.127955Z digest=sha256:b4de785fbaa403f6008953b5f5bbd0a87c4fbef13a9bbfefe5339d86bb02e903