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Paper Citation Record · LEDGER

Floor, Ceiling, and the Fusion Gap: How Much of Crowd Reading Attention Can Machines Predict?

As of 14 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2608.01704.

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

pith.paper-citation-record.v1
2608.01704 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T22:38:10.202648Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

12 of 12 outbound references displayed

  • verified exact3
  • verified fuzzy0
  • unresolved9
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 590ebf92-5e13-49b6-95b9-b58a8b9ffdd3 · outbound

This paper cites Language Models Agree With Each Other, Not With Readers.

Floor, Ceiling, and the Fusion Gap: How Much of Crowd Reading Attention Can Machines Predict? Language Models Agree With Each Other, Not With Readers

Reference 1

Resolution
verified exact
local_arxiv, observed 2026-08-04T22:38:10.341865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-04T22:38:10.161728Z digest=sha256:e4680212f79e766ccbce0d85f68a9edaafcf676bee22cb6434ddf17b72d8765b

Observation f58f7d54-77d8-45a2-8027-689a4f434b17 · outbound

This paper cites Personal Salience: Highlighting Is Social, but Individuality Lives in Selection.

Floor, Ceiling, and the Fusion Gap: How Much of Crowd Reading Attention Can Machines Predict? Personal Salience: Highlighting Is Social, but Individuality Lives in Selection

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-04T22:38:10.166218Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:38:10.166218Z digest=sha256:091e4be0fbca4351ca907476b21c5b09a71c819d08a9ef23b493a10c033fdb39

Observation b8be6a00-f513-41cc-970f-b639c130b3f3 · outbound

This paper cites The Long Tail, Not the Front Page: Cold-Start Prediction of Crowd Highlight Salience.

Floor, Ceiling, and the Fusion Gap: How Much of Crowd Reading Attention Can Machines Predict? The Long Tail, Not the Front Page: Cold-Start Prediction of Crowd Highlight Salience

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-04T22:38:10.170180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:38:10.170180Z digest=sha256:ca48277113d49dc056f24b0701f52d4b16ca44887dd136bd8e9cd76eb5d53ea9

Observation f6bd52b7-2819-4e15-b7b2-690456ea129f · outbound

This paper cites an unresolved cited work.

Floor, Ceiling, and the Fusion Gap: How Much of Crowd Reading Attention Can Machines Predict? Unresolved cited work

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-08-04T22:38:10.310457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-04T22:38:10.173555Z digest=sha256:79b43ebf73160deb0dbaa7df2f92c20f2632439e8d27c0d60ae8d2f3357ef2f1

Observation cfed68b7-2298-426d-a868-017c01fedc31 · outbound

This paper cites Denisov-Blanch, J.

Floor, Ceiling, and the Fusion Gap: How Much of Crowd Reading Attention Can Machines Predict? Denisov-Blanch, J

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-04T22:38:10.176499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:38:10.176499Z digest=sha256:1369872237fd35d997757d557d6f5b2ed089097fa4f9ebcc8424b07be7e44b98

Observation 50af255d-e041-4cdb-a160-dd4f9b4a8654 · outbound

This paper cites Wisdom from Diversity: Bias Mitigation Through Hybrid Human-LLM Crowds.

Floor, Ceiling, and the Fusion Gap: How Much of Crowd Reading Attention Can Machines Predict? Wisdom from Diversity: Bias Mitigation Through Hybrid Human-LLM Crowds

Reference 6

Resolution
verified exact
local_arxiv, observed 2026-08-04T22:38:10.286291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-04T22:38:10.180555Z digest=sha256:79c3e47c597ca3524c534a6cf4f95148735b72bdcaf9abfeda06b4865be1709f

Observation 41bdb891-3502-4a91-92ec-d5d299d47bce · outbound

This paper cites Great Models Think Alike and this Undermines AI Oversight.

Floor, Ceiling, and the Fusion Gap: How Much of Crowd Reading Attention Can Machines Predict? Great Models Think Alike and this Undermines AI Oversight

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-04T22:38:10.185108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:38:10.185108Z digest=sha256:f060a8fecb36b2584d57c248fb62916beb1a56d890968e8960f3820c20f3f7c4

Observation d15fd6a5-32d9-48b5-a2ac-443aad570904 · outbound

This paper cites LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression.

Floor, Ceiling, and the Fusion Gap: How Much of Crowd Reading Attention Can Machines Predict? LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-04T22:38:10.188571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:38:10.188571Z digest=sha256:e0396d345d9830032bca1a0553db3d1bfec063e095ec7dcd13a7d5dec4583d16

Observation 4d78e7b5-35ea-4d24-8624-ad9ab2d01459 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Floor, Ceiling, and the Fusion Gap: How Much of Crowd Reading Attention Can Machines Predict? Distilling the Knowledge in a Neural Network

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-04T22:38:10.192012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:38:10.192012Z digest=sha256:306419e13da5b252e0fb09dfa97626c7c99ec8c0c38fc1235af75a6cfa0ae903

Observation 4ede2da6-0f21-4cac-9d77-a69eac232420 · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

Floor, Ceiling, and the Fusion Gap: How Much of Crowd Reading Attention Can Machines Predict? QLoRA: Efficient Finetuning of Quantized LLMs

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-04T22:38:10.195806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:38:10.195806Z digest=sha256:41ad24e5b64ba6633002080500e9af30ea47eca26277053fb7e423f77f7f24b4

Observation 7605ad79-bafe-4cc7-a8f8-a0d8beaca872 · outbound

This paper cites Qwen3 Technical Report.

Floor, Ceiling, and the Fusion Gap: How Much of Crowd Reading Attention Can Machines Predict? Qwen3 Technical Report

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-04T22:38:10.199570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:38:10.199570Z digest=sha256:9ecdb4cab75633ce691ceaab4effbace30fb0be41d72e9b44e9e9235c704bb33

Observation bf995f93-9d4b-4d25-b750-c83e81c94a9b · outbound

This paper cites Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference.

Floor, Ceiling, and the Fusion Gap: How Much of Crowd Reading Attention Can Machines Predict? Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-04T22:38:10.202648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:38:10.202648Z digest=sha256:ed602240e87abb9d0a0854e9f59add7bd50a1d0f898a0b00d5e394e33010ffee

Pith citing papers

No inbound Pith citation observations are available.