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Paper Citation Record · LEDGER

Concentration bounds on response-based vector embeddings of black-box generative models

As of 8 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 2 inbound Pith citation observations for arXiv:2511.08307.

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

pith.paper-citation-record.v1
2511.08307 v2

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T23:05:54.863973Z

measured 14 of 14 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T23:28:47.530333Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-06-30T23:35:07.655438Z

Reference resolution

12 of 12 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved12
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  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ffd5e01c-ed24-4fc3-860a-588a4220e8bc · outbound

This paper cites Consistent estimation of generative model representations in the data kernel perspective space.

Concentration bounds on response-based vector embeddings of black-box generative models Consistent estimation of generative model representations in the data kernel perspective space

Reference 1

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unresolved
no resolver link, observed 2026-08-03T23:05:53.950141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T23:05:53.950141Z digest=sha256:978ad3ed7e1d69f8f2a4f00372027a11abe74b9d9b9dc0d4115c2caa787ba2c7

Observation b4a55bd9-a7fa-4fb0-a5da-951b74d3cf5e · outbound

This paper cites Proposition A.1.In our setting, supposer=ω(n 3).

Concentration bounds on response-based vector embeddings of black-box generative models Proposition A.1.In our setting, supposer=ω(n 3)

Reference 2

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no resolver link, observed 2026-08-03T23:05:54.685528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T23:05:54.685528Z digest=sha256:a67327f04bd72f962ad1669d17dc8926cea22bc93409cca26f1c55e46788ad1f

Observation ff99f637-1299-4e68-8a22-775a92d80266 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

Concentration bounds on response-based vector embeddings of black-box generative models Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 4

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unresolved
no resolver link, observed 2026-08-03T23:05:54.166497Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T23:05:54.166497Z digest=sha256:dd747e564d2199eddbc7e2c5d55ad527b087fa166c5a833a4b58441bbf026b75

Observation d48be3ca-a6a6-48e9-930f-30c9c2fa77f6 · outbound

This paper cites Statistical inference on black-box generative models in the data kernel perspective space.

Concentration bounds on response-based vector embeddings of black-box generative models Statistical inference on black-box generative models in the data kernel perspective space

Reference 7

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unresolved
no resolver link, observed 2026-08-03T23:05:54.402909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T23:05:54.402909Z digest=sha256:7e0b20e1ce36747231a2cc7def1b06313768afa1e4130f460cbb73586b23e1ec

Observation 77dccd6b-28ef-4979-98a3-4fc0443ec515 · outbound

This paper cites Multitask Prompted Training Enables Zero-Shot Task Generalization.

Concentration bounds on response-based vector embeddings of black-box generative models Multitask Prompted Training Enables Zero-Shot Task Generalization

Reference 8

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unresolved
no resolver link, observed 2026-08-03T23:05:54.497681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T23:05:54.497681Z digest=sha256:1a19b6b8d85d12ea17bf92712dd57850db9ad81c82c750645fc7ee8ddfc34cda

Observation 65b460ba-b2fb-4c09-abc0-fcdcc41ccaf3 · outbound

This paper cites 12 Proposition A.6.In our setting, supposer=ω(n 3).

Concentration bounds on response-based vector embeddings of black-box generative models 12 Proposition A.6.In our setting, supposer=ω(n 3)

Reference 11

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unresolved
no resolver link, observed 2026-08-03T23:05:54.776363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T23:05:54.776363Z digest=sha256:f051380b0508ec4c3076f0035b420228de6c81bec72a1fa494f3189054ca1643

Observation c8c06047-5d9e-4e30-8bb7-f615e016b0d0 · outbound

This paper cites Proof.First note that (from proof ofLemma C.4in Agterberg et al.

Concentration bounds on response-based vector embeddings of black-box generative models Proof.First note that (from proof ofLemma C.4in Agterberg et al

Reference 12

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no resolver link, observed 2026-08-03T23:05:54.863973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T23:05:54.863973Z digest=sha256:c6a5231db8d49cead6351242f2d1558eac1c967b0882688aee59489e53005025

Observation 25b8fa40-a5a2-40f6-9391-3e07277ee9da · outbound

This paper cites Planning with Large Language Models for Code Generation.

Concentration bounds on response-based vector embeddings of black-box generative models Planning with Large Language Models for Code Generation

Reference 2015

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unresolved
no resolver link, observed 2026-08-03T23:05:54.598627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T23:05:54.598627Z digest=sha256:9b92b6b1cb31742951cedc12dcb7e80e5a39e993b54642e7b606a95ca41e7677

Observation d48a6ad8-14be-4874-adcc-ca1b7bd6bb9e · outbound

This paper cites Comparing Foundation Models using Data Kernels.

Concentration bounds on response-based vector embeddings of black-box generative models Comparing Foundation Models using Data Kernels

Reference 2019

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unresolved
no resolver link, observed 2026-08-03T23:05:54.233256Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T23:05:54.233256Z digest=sha256:10b2c7fa2a8b46a877b8ff4a34abfda74bfd5cb9164b3768565529362f6683e6

Observation 661c251b-715a-4fc9-84c3-51b0ee7fc804 · outbound

This paper cites Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901,.

Concentration bounds on response-based vector embeddings of black-box generative models Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901,

Reference 2022

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unresolved
no resolver link, observed 2026-08-03T23:05:54.094465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T23:05:54.094465Z digest=sha256:3170bbf880c54389dfa8f6e5e1bc59fe6eb40f7592e3411a5297e38e7dfd1e5f

Observation c29c8036-57bc-4dc1-a76c-edb43759226c · outbound

This paper cites Perspectives on large language models for relevance judgment.

Concentration bounds on response-based vector embeddings of black-box generative models Perspectives on large language models for relevance judgment

Reference 2023

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unresolved
no resolver link, observed 2026-08-03T23:05:54.318594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T23:05:54.318594Z digest=sha256:967dd229b8660e386e30b2e6ad268eff4de3c0887307e4d21112789f2718d67e

Observation 34fe2092-0cfa-4a12-a6a3-222dce8b541b · outbound

This paper cites Joint Spectral Clustering in Multilayer Degree-Corrected Stochastic Blockmodels.

Concentration bounds on response-based vector embeddings of black-box generative models Joint Spectral Clustering in Multilayer Degree-Corrected Stochastic Blockmodels

Reference 2024

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unresolved
no resolver link, observed 2026-08-03T23:05:54.015552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T23:05:54.015552Z digest=sha256:ed0281c8d3751c6764aacbc6be25bd471d7a697facfb8493946e90b2dac093de

Pith citing papers

Observation 313b2b72-616e-4d07-af42-007b78b2d70b · inbound

Recovering manifold structure in LLM responses through a joint Euclidean mirror cites this paper.

Recovering manifold structure in LLM responses through a joint Euclidean mirror Concentration bounds on response-based vector embeddings of black-box generative models

Reference 1

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verified exact
arxiv_id, observed 2026-06-30T02:16:11.168432Z

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.

source=arxiv_source observed=2026-05-10T18:27:08.866645Z digest=sha256:f3cbb2a12f79dd9906ce46398460aeee96f9d5cf933f3850081fe288d40922bb

Observation decfb28e-53a2-4237-aa3a-470ce083d8f5 · inbound

Query-efficient model evaluation using cached responses cites this paper.

Query-efficient model evaluation using cached responses Concentration bounds on response-based vector embeddings of black-box generative models

Reference 2

Resolution
verified exact
local_arxiv, observed 2026-06-30T23:35:07.657084Z

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.

source=pdf_text observed=2026-06-30T23:28:47.530333Z digest=sha256:e9e2fd8a8975a1dd4db5e54b80d36b830e6486b2a13dc36cdb7a9fa6e94ea578