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Paper Citation Record · LEDGER

A Theory of Usable Information Under Computational Constraints

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2002.10689.

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

pith.paper-citation-record.v1
2002.10689 v1

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 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:26:09.945249Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T05:10:21.948130Z

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 1557a7cb-8939-4676-8e91-3d26f9c25a62 · inbound

Laplace Sample Information: Data Informativeness Through a Bayesian Lens cites this paper.

Laplace Sample Information: Data Informativeness Through a Bayesian Lens A Theory of Usable Information Under Computational Constraints

Reference 70

Resolution
unresolved
no resolver link, observed 2026-08-07T15:26:09.945249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:26:09.945249Z digest=sha256:c5f7ff97687f000874c3de5db4072ac7427d60e08907d5aeab895d0a47f2688d

Observation 47911f31-5f6f-4971-b1a1-c16b5621bfa8 · inbound

How much do language models memorize? cites this paper.

How much do language models memorize? A Theory of Usable Information Under Computational Constraints

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-07T12:35:41.368427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:41.368427Z digest=sha256:60e99ebe905b624db5a99df8d0687d8f067aa4c2c157daf3066ade22e9e06a8c

Observation ccc34d92-69f0-4757-ae50-eb8e762be252 · inbound

Aligning Multimodal Representations through an Information Bottleneck cites this paper.

Aligning Multimodal Representations through an Information Bottleneck A Theory of Usable Information Under Computational Constraints

Reference 74

Resolution
unresolved
no resolver link, observed 2026-08-07T10:43:53.975431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:43:53.975431Z digest=sha256:7eb1c9ed110650f961261adb00185212508bc84edf82e0939a596a243cc6068e

Observation 758f968f-13d4-4e3b-bcb8-88eb9031e3b4 · inbound

Hessian-Enhanced Token Attribution (HETA): Interpreting Autoregressive LLMs cites this paper.

Hessian-Enhanced Token Attribution (HETA): Interpreting Autoregressive LLMs A Theory of Usable Information Under Computational Constraints

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-05-11T10:41:02.188710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-10T15:23:53.906870Z digest=sha256:10049c61f82037aebe431e143654b5f57eb53ed43faeb0a2ba5c3536920682d0

Observation 60e19352-28ee-41ec-a964-f5c94f9d4f84 · inbound

Parametric Prior Mapping Framework for Non-stationary Probabilistic Time Series Forecasting cites this paper.

Parametric Prior Mapping Framework for Non-stationary Probabilistic Time Series Forecasting A Theory of Usable Information Under Computational Constraints

Reference 48

Resolution
verified exact
arxiv_id, observed 2026-05-25T05:10:21.950819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-25T05:09:06.410581Z digest=sha256:c3123d86abbe3b6a474e4d5baeacf1d6d5016af8f3ec980bbf1a6273a73091f7

Observation 24557a6d-177f-462f-b870-b310d96bb688 · inbound

Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics Breakthrough cites this paper.

Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics Breakthrough A Theory of Usable Information Under Computational Constraints

Reference 82

Resolution
unresolved
no resolver link, observed 2026-07-14T00:50:24.285797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T00:50:24.285797Z digest=sha256:f234f95158bc69900ece26deae8777721076b6c38a698764ea340846fc1cf4f1