Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2207.08799.
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 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T14:43:14.775196Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
12
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation fbc2d07d-597a-42d0-9b66-0e56c0f3a6b1 · inbound
Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small cites this paper.
Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit
Reference 33
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T17:13:51.494465Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 695b23b0-ae68-418e-9e26-ceb4e62fcf24 · inbound
Progress measures for grokking via mechanistic interpretability cites this paper.
Progress measures for grokking via mechanistic interpretability Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit
Reference 30
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T21:52:56.141440Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 2aaa1d0a-f51b-4518-b377-c470626ae7eb · inbound
Massive Activations in Large Language Models cites this paper.
Massive Activations in Large Language Models Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T07:02:53.826534Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 4382c841-0db5-4bfa-9031-dfca2a9091ca · inbound
Unraveling Token Prediction Refinement and Identifying Essential Layers in Language Models cites this paper.
Unraveling Token Prediction Refinement and Identifying Essential Layers in Language Models Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T14:43:14.775196Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7f726ba0-ece0-4ead-a770-3acad5f9d011 · inbound
Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions cites this paper.
Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T15:34:16.599535Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 19323556-7624-4dab-a6db-53763a74bb8b · inbound
The Long Delay to Arithmetic Generalization: When Learned Representations Outrun Behavior cites this paper.
The Long Delay to Arithmetic Generalization: When Learned Representations Outrun Behavior Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T21:17:58.875355Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 298e738a-b6be-435f-86d9-f592bcc31193 · inbound
The two clocks and the innovation window: When and how generative models learn rules cites this paper.
The two clocks and the innovation window: When and how generative models learn rules Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T03:16:18.120838Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 746933b5-0e7a-4b20-ac76-0a3269ed01ad · inbound
The Benefits of Temporal Correlations: SGD Learns k-Juntas from Random Walks Efficiently cites this paper.
The Benefits of Temporal Correlations: SGD Learns k-Juntas from Random Walks Efficiently Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit
Reference 56
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T05:31:24.123871Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation b17d132e-133a-4163-a9cf-a73a301c60a2 · inbound
Less Data, Faster Training: repeating smaller datasets speeds up learning via sampling biases cites this paper.
Less Data, Faster Training: repeating smaller datasets speeds up learning via sampling biases Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T07:19:46.460873Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 84460bec-87cd-4060-998c-fb2d1b0d334d · inbound
Dead Directions: Geometric Singular Learning cites this paper.
Dead Directions: Geometric Singular Learning Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T11:36:55.206532Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 5da745fc-0311-4d6e-bd9e-a4005e0819f7 · inbound
Tracking Representation Dynamics in Large Language Models with Persistent Homology cites this paper.
Tracking Representation Dynamics in Large Language Models with Persistent Homology Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T00:39:17.222472Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation af373fd4-4fc2-4a8a-88ac-58ab1142c327 · inbound
Dead-Direction Signatures: A Cheap Spectral Reading of Singular Complexity cites this paper.
Dead-Direction Signatures: A Cheap Spectral Reading of Singular Complexity Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T06:19:38.277556Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation d977a711-ba09-4398-9940-0d3116057758 · inbound
Grokking Is Conditional and Fragile: A Fully-Tractable, Multi-Seed Study at 12K Parameters cites this paper.
Grokking Is Conditional and Fragile: A Fully-Tractable, Multi-Seed Study at 12K Parameters Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-07-11T08:46:29.099447Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.