Paper Citation Record · LEDGER

Limits of Deep Learning: Sequence Modeling through the Lens of Complexity Theory

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2405.16674.

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

pith.paper-citation-record.v1
2405.16674 v3

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 2026-08-19T06:32:44.657259+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T10:52:48.900180Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T22:06:16.206507Z

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 1a90f0c1-0feb-48c2-8b10-e01b428f517e · inbound

The Computational Limits of State-Space Models and Mamba via the Lens of Circuit Complexity cites this paper.

The Computational Limits of State-Space Models and Mamba via the Lens of Circuit Complexity Limits of Deep Learning: Sequence Modeling through the Lens of Complexity Theory

Reference 126

Resolution: unresolved; no resolver link, observed 2026-08-11T20:02:09.856762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2bd99a6f-d2e8-4323-838d-a084d70e3ab5 · inbound

On the Expressiveness and Length Generalization of Selective State-Space Models on Regular Languages cites this paper.

On the Expressiveness and Length Generalization of Selective State-Space Models on Regular Languages Limits of Deep Learning: Sequence Modeling through the Lens of Complexity Theory

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T00:46:47.700522Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7249d233-46d6-4127-b726-431f10b1bec8 · inbound

The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity cites this paper.

The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity Limits of Deep Learning: Sequence Modeling through the Lens of Complexity Theory

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-15T16:10:31.545855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a6423690-7396-4dca-ba8f-a707bb88d458 · inbound

EmoPerso: Enhancing Personality Detection with Self-Supervised Emotion-Aware Modelling cites this paper.

EmoPerso: Enhancing Personality Detection with Self-Supervised Emotion-Aware Modelling Limits of Deep Learning: Sequence Modeling through the Lens of Complexity Theory

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-05T11:40:13.380152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e7115c1-01b0-446d-92b8-e5c20fb144cc · inbound

Rethinking the Role of Positional Encoding: Sliding-Window Transformers without PE Remain Turing Complete cites this paper.

Rethinking the Role of Positional Encoding: Sliding-Window Transformers without PE Remain Turing Complete Limits of Deep Learning: Sequence Modeling through the Lens of Complexity Theory

Reference 46

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T22:06:16.207866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b9a4c5fc-e81a-4988-a904-fabf3eecc8fb · inbound

Static in Frames, Dynamic in Events: Rethinking Features in Event Cameras as Motion Cues cites this paper.

Static in Frames, Dynamic in Events: Rethinking Features in Event Cameras as Motion Cues Limits of Deep Learning: Sequence Modeling through the Lens of Complexity Theory

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-12T10:52:48.900180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.