Pith. sign in

Paper Citation Record · LEDGER

Spectral Filters, Dark Signals, and Attention Sinks

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2402.09221.

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

pith.paper-citation-record.v1
2402.09221 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 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 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:02:17.453309Z

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 9df9a42a-277c-424f-ae19-b008cab31e33 · inbound

When Attention Sink Emerges in Language Models: An Empirical View cites this paper.

When Attention Sink Emerges in Language Models: An Empirical View Spectral Filters, Dark Signals, and Attention Sinks

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-16T17:41:03.793371Z

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.

source=arxiv_source observed=2026-05-16T17:41:03.674759Z digest=sha256:d9c138472fe355c9cc2fe750170ec13864d7a84978ff21e6b90ee46c978c0f3b

Observation 0411558a-34f9-47f4-972b-e806a2042f9a · inbound

What are you sinking? A geometric approach on attention sink cites this paper.

What are you sinking? A geometric approach on attention sink Spectral Filters, Dark Signals, and Attention Sinks

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-06T05:02:17.453309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T05:02:17.453309Z digest=sha256:22919bc960db51e649bcbbad5cedc72a46272655106cc20b7047fe5a576e7776

Observation 03a2aa62-12ee-46cb-994e-2bf7bc33c9d0 · inbound

What Makes Position Zero Special? A Mechanistic Study of Position Zero Attention Sinks in LLMs cites this paper.

What Makes Position Zero Special? A Mechanistic Study of Position Zero Attention Sinks in LLMs Spectral Filters, Dark Signals, and Attention Sinks

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-04T06:15:44.927705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T06:15:44.927705Z digest=sha256:a7aea43bb61ab4b26792c4a935f08889922e2d06d8e094b19626da30abd435d6

Observation fdef788e-530e-45c8-88b3-8e376ca7ddc2 · inbound

When Sinks Help or Hurt: Unified Framework for Attention Sink in Large Vision-Language Models cites this paper.

When Sinks Help or Hurt: Unified Framework for Attention Sink in Large Vision-Language Models Spectral Filters, Dark Signals, and Attention Sinks

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-13T23:23:26.829091Z

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.

source=pdf_text observed=2026-05-13T23:20:51.899127Z digest=sha256:5e0037a797f79e6fa6094658b8be852eda0c53e83dbebbdd42902b33dfcce227

Observation cf2453d3-1367-4fdd-a7d7-d6fe765da7ba · inbound

When Sinks Help or Hurt: Unified Framework for Attention Sink in Large Vision-Language Models cites this paper.

When Sinks Help or Hurt: Unified Framework for Attention Sink in Large Vision-Language Models Spectral Filters, Dark Signals, and Attention Sinks

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-02T17:04:21.630513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T17:04:21.630513Z digest=sha256:726fa6ee05a2ffea80db8203eac8fe306dc0ef76e72d5e20855b199f37703163

Observation 7c3b56a5-1069-4b4e-80e1-13829e13a706 · inbound

The Structural Origin of Attention Sink: Variance Discrepancy, Super Neurons, and Dimension Disparity cites this paper.

The Structural Origin of Attention Sink: Variance Discrepancy, Super Neurons, and Dimension Disparity Spectral Filters, Dark Signals, and Attention Sinks

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-11T19:21:08.591517Z

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.

source=pdf_text observed=2026-05-08T12:11:04.146711Z digest=sha256:2526400b5747a8fe73a494f9030fa8d71934cc5e7e2fb104263e0fc6afd684e8

Observation cb54c0f4-c3b1-4f05-8a2c-fd1553b34198 · inbound

Vocabulary Hijacking in LVLMs: Unveiling Critical Attention Heads by Excluding Inert Tokens to Mitigate Hallucination cites this paper.

Vocabulary Hijacking in LVLMs: Unveiling Critical Attention Heads by Excluding Inert Tokens to Mitigate Hallucination Spectral Filters, Dark Signals, and Attention Sinks

Reference 84

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T05:16:24.204146Z

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.

source=arxiv_source observed=2026-05-12T05:15:34.156717Z digest=sha256:bc6e9f165e2777148f742dfa4a44ecacbbce2787f58c2cd144dbdfc686342efe

Observation 1107a1df-7f6f-4fe5-ada1-a0fcef961f8a · inbound

Contribution Weights: A Geometrical Analysis of Self-Attention Transformers cites this paper.

Contribution Weights: A Geometrical Analysis of Self-Attention Transformers Spectral Filters, Dark Signals, and Attention Sinks

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-06-28T23:32:46.555087Z

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.

source=arxiv_source observed=2026-06-28T23:29:02.457697Z digest=sha256:cf291513ae7e1dbb28d8e817dabef9ac600da5cc381ed1e31eac087aff684810

Observation c389f29d-7e2a-4775-9771-6b0d2a955e01 · inbound

Contribution Weights: A Geometrical Analysis of Self-Attention Transformers cites this paper.

Contribution Weights: A Geometrical Analysis of Self-Attention Transformers Spectral Filters, Dark Signals, and Attention Sinks

Reference 148

Resolution
verified exact
arxiv_id, observed 2026-06-28T23:32:47.327170Z

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.

source=arxiv_source observed=2026-06-28T23:29:02.457697Z digest=sha256:b3a04e62f0a2d8a4c982ecbc49481993ebafcb9b877131d2bacc3d5daf4b4556