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Paper Citation Record · LEDGER

A Latent Space Theory for Emergent Abilities in Large Language Models

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2304.09960.

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

pith.paper-citation-record.v1
2304.09960 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:09:34.949524Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T06:57:27.809927Z

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 d3a02ee1-3937-426a-b3fa-ddb9522938c6 · inbound

A Survey on In-context Learning cites this paper.

A Survey on In-context Learning A Latent Space Theory for Emergent Abilities in Large Language Models

Reference 6

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T12:58:27.514920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-12T12:58:27.430374Z digest=sha256:bad5741272369e42a442bcc7c2a2a75895a33a4ee6db30265fd41d719ec19168

Observation 819664b4-7710-4a21-a097-e6626c574c4d · inbound

Curse of Attention: A Kernel-Based Perspective for Why Transformers Fail to Generalize on Time Series Forecasting and Beyond cites this paper.

Curse of Attention: A Kernel-Based Perspective for Why Transformers Fail to Generalize on Time Series Forecasting and Beyond A Latent Space Theory for Emergent Abilities in Large Language Models

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-11T20:09:34.949524Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T20:09:34.949524Z digest=sha256:30ada3056708552bb5f5c772795d5e613e6d10a0d74fc241f098e9b739c288e0

Observation 1138a40f-56d7-45f9-aaa3-3df80e5f49cb · inbound

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading cites this paper.

Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading A Latent Space Theory for Emergent Abilities in Large Language Models

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-10T15:19:49.628482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:19:49.628482Z digest=sha256:7efa36befda80b2c6181caab77813b6dd0e8c4ad474ea3af3d116740fb3f73cb

Observation 6e068fc1-440d-4087-ae2a-866705288b5b · inbound

Video Latent Flow Matching: Optimal Polynomial Projections for Video Interpolation and Extrapolation cites this paper.

Video Latent Flow Matching: Optimal Polynomial Projections for Video Interpolation and Extrapolation A Latent Space Theory for Emergent Abilities in Large Language Models

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-09T18:51:12.494508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T18:51:12.494508Z digest=sha256:2b827c43167ff3e8925f1cd83d048eca2be7db3e7cc2235a3468065c1f079c3f

Observation 08280556-9764-4d4d-b0a9-b52218e1f21e · inbound

The Role of Diversity in In-Context Learning for Large Language Models cites this paper.

The Role of Diversity in In-Context Learning for Large Language Models A Latent Space Theory for Emergent Abilities in Large Language Models

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-07T14:21:37.098271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:21:37.098271Z digest=sha256:8eaf9d404b905ba45abb329b5eeb085991d2aeb90dcb26e0da5a3cabc27852ac

Observation 04cfe5c6-48d7-47bf-9ad2-f2da8a8e07b9 · inbound

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models cites this paper.

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models A Latent Space Theory for Emergent Abilities in Large Language Models

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-07T11:40:55.929001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:40:55.929001Z digest=sha256:5235d0ce23421801354bab87fee0f00355e8018e42d78d27ae75872c3cfa0c1d

Observation 3badab03-2211-481b-94bd-d1beadc38d01 · inbound

Autoregressive Learning in Joint KL: Sharp Oracle Bounds and Lower Bounds cites this paper.

Autoregressive Learning in Joint KL: Sharp Oracle Bounds and Lower Bounds A Latent Space Theory for Emergent Abilities in Large Language Models

Reference 62

Resolution
verified exact
arxiv_id, observed 2026-05-13T06:57:27.812818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-13T06:55:33.934110Z digest=sha256:1d3e844f5033c804cc76b5be31a1e0f823886ca85c17d1bbedf8a9f50d3a1541

Observation 5ef71018-6a83-4667-91ca-2e08960f1dab · inbound

Training with (Swap) Regret Loss in a Single-Layer Self-Attention Model: A Case Study on the Probability Simplex cites this paper.

Training with (Swap) Regret Loss in a Single-Layer Self-Attention Model: A Case Study on the Probability Simplex A Latent Space Theory for Emergent Abilities in Large Language Models

Reference 124

Resolution
unresolved
no resolver link, observed 2026-07-31T23:52:00.268987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-31T23:52:00.268987Z digest=sha256:99df65d140f8c130d25f128db7ef9a2af465126b4655234e0087173b1e62ba89