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Paper Citation Record · LEDGER

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search

As of 9 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 2 inbound Pith citation observations for arXiv:2603.24917.

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

pith.paper-citation-record.v1
2603.24917 v3

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T02:30:44.757300Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T01:52:09.658975Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T14:01:38.041828Z

Reference resolution

36 of 36 outbound references displayed

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  • verified fuzzy0
  • unresolved34
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  • malformed identifier2
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Outbound references

Observation 0b35f267-826b-49c7-a22f-6468f7d8092c · outbound

This paper cites an unresolved cited work.

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search Unresolved cited work

Reference 1

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Observation baf2f07d-1c76-40e9-bd98-7ff5ad1a1368 · outbound

This paper cites an unresolved cited work.

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search Unresolved cited work

Reference 2

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Observation ce89ac67-a64b-4c90-bf7d-c9c837f00304 · outbound

This paper cites Computing pz without sampling (teacher-forced scoring).To evaluate pz for verbatim extraction, we do not need to generate any tokens.

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search Computing pz without sampling (teacher-forced scoring).To evaluate pz for verbatim extraction, we do not need to generate any tokens

Reference 3

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This paper cites , T− 1}; existence)If T≥ 3and |V| ≥T, then there exists b∈V T such that for everyεwith2≤ε<T, BHam ε (b)⊊B Lev ε (b).

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search , T− 1}; existence)If T≥ 3and |V| ≥T, then there exists b∈V T such that for everyεwith2≤ε<T, BHam ε (b)⊊B Lev ε (b)

Reference 4

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This paper cites an unresolved cited work.

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search Unresolved cited work

Reference 5

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This paper cites And so c∈B Lev ε (b)—i.e., every member of BHam ε (b) must also be a member of BLev ε (b).

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search And so c∈B Lev ε (b)—i.e., every member of BHam ε (b) must also be a member of BLev ε (b)

Reference 6

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This paper cites an unresolved cited work.

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search Unresolved cited work

Reference 7

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This paper cites And so, by Equation 22, Levenshtein(b, c)≤Hamming(b , c)≤T.

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search And so, by Equation 22, Levenshtein(b, c)≤Hamming(b , c)≤T

Reference 8

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This paper cites samples independently either hits or misses the ε-ball.

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search samples independently either hits or misses the ε-ball

Reference 9

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This paper cites no extraction.

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search no extraction

Reference 10

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This paper cites an unresolved cited work.

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search Unresolved cited work

Reference 11

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Observation 11ba697b-8cc9-4548-9dd7-d39125474b30 · outbound

This paper cites 25 Preprint.

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search 25 Preprint

Reference 12

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This paper cites This is done for efficiency, i.e., to prevent explosive blow-up of the number of sequences under consideration.

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search This is done for efficiency, i.e., to prevent explosive blow-up of the number of sequences under consideration

Reference 13

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Estimating near-verbatim extraction risk in language models with decoding-constrained beam search Unresolved cited work

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This paper cites an unresolved cited work.

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search Unresolved cited work

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This paper cites After T steps, the algorithm returns all B·k complete continuations (i.e., without performing a final across-beam prune).

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search After T steps, the algorithm returns all B·k complete continuations (i.e., without performing a final across-beam prune)

Reference 16

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Observation c8c7dca5-7107-455a-a675-8847494e4846 · outbound

This paper cites an unresolved cited work.

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search Unresolved cited work

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This paper cites an unresolved cited work.

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search Unresolved cited work

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Observation cc1dd9db-582f-4d52-8638-3a2ce60ee1a0 · outbound

This paper cites Select the top-B candidates from the top-k tokens →L 1 (no additional forward pass needed).

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search Select the top-B candidates from the top-k tokens →L 1 (no additional forward pass needed)

Reference 19

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Observation 01c04cb2-b862-4759-ba6d-d35bb6953c6a · outbound

This paper cites up to B·k.

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search up to B·k

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This paper cites an unresolved cited work.

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search Unresolved cited work

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Estimating near-verbatim extraction risk in language models with decoding-constrained beam search Unresolved cited work

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Estimating near-verbatim extraction risk in language models with decoding-constrained beam search Unresolved cited work

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Estimating near-verbatim extraction risk in language models with decoding-constrained beam search looks bad

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Estimating near-verbatim extraction risk in language models with decoding-constrained beam search , jmax(t+1) (left to right) using Equation 48, treating any out-of-band predecessor as +∞

Reference 25

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Estimating near-verbatim extraction risk in language models with decoding-constrained beam search Unresolved cited work

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This paper cites an unresolved cited work.

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search Unresolved cited work

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This paper cites an unresolved cited work.

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search Unresolved cited work

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This paper cites an unresolved cited work.

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search Unresolved cited work

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Estimating near-verbatim extraction risk in language models with decoding-constrained beam search List of submissions to the Nth Academy Awards

Reference 32

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This paper cites The template structure and stock phrases (track descriptions, race numbering) are widely duplicated.

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search The template structure and stock phrases (track descriptions, race numbering) are widely duplicated

Reference 33

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This paper cites b z(suf):rought the Batman to the forefront of public consciousness.

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search b z(suf):rought the Batman to the forefront of public consciousness

Reference 34

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Estimating near-verbatim extraction risk in language models with decoding-constrained beam search unlocked

Reference 35

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Estimating near-verbatim extraction risk in language models with decoding-constrained beam search For the baseline runs, we also manually examine outputs that surpassτmin but are not within Levε= 5 of the target suffix

Reference 749

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Estimating near-verbatim extraction risk in language models with decoding-constrained beam search running mismatch counter ≤ε

Reference 1974

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This paper cites We thenreservoir sampled11 100,000 pages that fit these conditions from the whole range of Wikipedia pages in this time frame, de-duplicating by page ID.

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search We thenreservoir sampled11 100,000 pages that fit these conditions from the whole range of Wikipedia pages in this time frame, de-duplicating by page ID

Reference 2024

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Pith citing papers

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Extracting memorized pieces of (copyrighted) books from open-weight language models cites this paper.

Extracting memorized pieces of (copyrighted) books from open-weight language models Estimating near-verbatim extraction risk in language models with decoding-constrained beam search

Reference 61

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Observation dc3fdf8a-09e0-4afd-b711-65a5e1fc4f1f · inbound

Probabilistic "Copies" in Generative AI Models cites this paper.

Probabilistic "Copies" in Generative AI Models Estimating near-verbatim extraction risk in language models with decoding-constrained beam search

Reference 24

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unresolved
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