Paper Citation Record · LEDGER

Why are Sensitive Functions Hard for Transformers?

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2402.09963.

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

pith.paper-citation-record.v1
2402.09963 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:21:01.050664Z

measured 1 of 1 external citation measurements

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

0
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 206e2655-941d-4e3c-a85b-05d3558c2bba · inbound

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions cites this paper.

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions Why are Sensitive Functions Hard for Transformers?

Reference 1963

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:01.050664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9213f9c7-cd0f-4d46-b13c-3943507de087 · inbound

Rethinking Memorization Measures and their Implications in Large Language Models cites this paper.

Rethinking Memorization Measures and their Implications in Large Language Models Why are Sensitive Functions Hard for Transformers?

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T15:54:33.958427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0143d74-35b4-480d-b1a4-8816abcc7a9f · inbound

Parity Requires Unified Input Dependence and Negative Eigenvalues in SSMs cites this paper.

Parity Requires Unified Input Dependence and Negative Eigenvalues in SSMs Why are Sensitive Functions Hard for Transformers?

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T22:16:08.033402Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26f08f07-4f28-4dbc-a335-14808e6b6ea5 · inbound

Transformers with RL or SFT Provably Learn Sparse Boolean Functions, But Differently cites this paper.

Transformers with RL or SFT Provably Learn Sparse Boolean Functions, But Differently Why are Sensitive Functions Hard for Transformers?

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T20:57:10.449801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8469ea8-0a52-42c9-a083-b0609de949c6 · inbound

On the Spatiotemporal Dynamics of Generalization in Neural Networks cites this paper.

On the Spatiotemporal Dynamics of Generalization in Neural Networks Why are Sensitive Functions Hard for Transformers?

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-16T09:00:46.760016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0b00372b-7f7e-4e1e-a327-8ca3ee50b862 · inbound

On the Mirage of Long-Range Dependency, with an Application to Integer Multiplication cites this paper.

On the Mirage of Long-Range Dependency, with an Application to Integer Multiplication Why are Sensitive Functions Hard for Transformers?

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:07:57.635708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation de99a489-cdad-4841-b6b2-735a41683e37 · inbound

A framework for analyzing concept representations in neural models cites this paper.

A framework for analyzing concept representations in neural models Why are Sensitive Functions Hard for Transformers?

Reference 242

Resolution: verified exact; arxiv_id, observed 2026-05-09T22:18:59.236359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5f3bf837-6fd9-4802-aed5-da8e946d2c70 · 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 Why are Sensitive Functions Hard for Transformers?

Reference 128

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T05:31:24.361198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 73e62de4-01ae-4aa0-952d-e7d2277d1c28 · inbound

Agentic Transformers Provably Learn to Search via Reinforcement Learning cites this paper.

Agentic Transformers Provably Learn to Search via Reinforcement Learning Why are Sensitive Functions Hard for Transformers?

Reference 40

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T23:42:49.979338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c958545a-b265-4aa5-840a-fd489240a1bd · inbound

From Expressivity to Sample Complexity: Narrow Teachers for Transformers via C-RASP cites this paper.

From Expressivity to Sample Complexity: Narrow Teachers for Transformers via C-RASP Why are Sensitive Functions Hard for Transformers?

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-14T03:23:31.727605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 199a613a-a6b5-4214-a5c4-e1cfda8ad1a8 · inbound

Can Transformers Really Do It All? On the Compatibility of Inductive Biases Across Tasks cites this paper.

Can Transformers Really Do It All? On the Compatibility of Inductive Biases Across Tasks Why are Sensitive Functions Hard for Transformers?

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T17:31:46.982657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 666d89c3-8a28-41cc-9e99-fb96b37bac7e · inbound

When Does Recurrence Become an Algorithm? Convergence Selection in Weight-Tied Looped Transformers cites this paper.

When Does Recurrence Become an Algorithm? Convergence Selection in Weight-Tied Looped Transformers Why are Sensitive Functions Hard for Transformers?

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T10:14:11.203822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.