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Paper Citation Record · LEDGER

On the Sample Complexity of End-to-end Training vs. Semantic Abstraction Training

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1604.06915.

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

pith.paper-citation-record.v1
1604.06915 v1

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

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:27:41.057081Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-21T20:40:36.237310Z

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 830ee759-6941-415a-89d7-067b2ea4ccce · inbound

From Foresight to Forethought: VLM-In-the-Loop Policy Steering via Latent Alignment cites this paper.

From Foresight to Forethought: VLM-In-the-Loop Policy Steering via Latent Alignment On the Sample Complexity of End-to-end Training vs. Semantic Abstraction Training

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-09T14:27:41.057081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T14:27:41.057081Z digest=sha256:b06c8751cfb25dbfdbf6c392393f53d07fea6e58fc2d4fa9dd96234c166525fb

Observation fdebfc3e-10a6-4f43-88da-5c262bc498d8 · inbound

Deep sequence models tend to memorize geometrically; it is unclear why cites this paper.

Deep sequence models tend to memorize geometrically; it is unclear why On the Sample Complexity of End-to-end Training vs. Semantic Abstraction Training

Reference 162

Resolution
verified exact
local_arxiv, observed 2026-05-21T20:40:36.239104Z

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=2026-05-21T20:38:18.005002Z digest=sha256:8c175ee273a0afb28a10b80fff722d6f95ffb227fdfa31066bc9255f78b99f8e