Paper Citation Record · LEDGER

Corrector Sampling in Language Models

As of 17 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2506.06215.

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

pith.paper-citation-record.v1
2506.06215 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:05:46.866034Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

17 of 17 outbound references displayed

verified exact1
verified fuzzy0
unresolved16
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d1784d19-3316-4c90-b7b6-cbade709231d · outbound

This paper cites GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints.

Corrector Sampling in Language Models GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T06:05:45.362156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8ece92b-c42e-482c-b094-1ba049dab7c5 · outbound

This paper cites Iterative Deepening Sampling as Efficient Test-Time Scaling.

Corrector Sampling in Language Models Iterative Deepening Sampling as Efficient Test-Time Scaling

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T06:05:45.782782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e95e6835-b3ac-43f6-81fd-cafb69c26419 · outbound

This paper cites DeepSeek LLM: Scaling Open-Source Language Models with Longtermism.

Corrector Sampling in Language Models DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T06:05:45.882478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41522d2a-988d-4154-b63e-6e2cb30f68de · outbound

This paper cites OLMo: Accelerating the Science of Language Models.

Corrector Sampling in Language Models OLMo: Accelerating the Science of Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T06:05:46.111918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 693a8f52-eee8-47ab-8fe7-2d05aab46dbb · outbound

This paper cites The Curious Case of Neural Text Degeneration.

Corrector Sampling in Language Models The Curious Case of Neural Text Degeneration

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T06:05:46.298256Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea9deae8-0fe1-4c24-a1ec-19bde21174fb · outbound

This paper cites Tulu 3: Pushing Frontiers in Open Language Model Post-Training.

Corrector Sampling in Language Models Tulu 3: Pushing Frontiers in Open Language Model Post-Training

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T06:05:46.428592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be9c0d1d-344f-4388-b425-012c356433bb · outbound

This paper cites DataComp-LM: In search of the next generation of training sets for language models.

Corrector Sampling in Language Models DataComp-LM: In search of the next generation of training sets for language models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T06:05:46.517393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cc38cce-d31b-4a7b-9fce-2d405ca5f9a7 · outbound

This paper cites Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation.

Corrector Sampling in Language Models Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T06:05:46.598527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d6dd5b8-1cc5-42ad-b613-5e18bb63de18 · outbound

This paper cites Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective.

Corrector Sampling in Language Models Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T06:05:46.699573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71998fb7-ba5e-4a8d-be70-12b82ec5c7cd · outbound

This paper cites The RPT improvements are invariant to absolute noise level.

Corrector Sampling in Language Models The RPT improvements are invariant to absolute noise level

Reference 17

Resolution: verified exact; raw_fallback, observed 2026-08-07T06:05:47.111657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3944a524-3751-4d7b-bcc4-48717b7453d3 · outbound

This paper cites Discrete Flow Matching.

Corrector Sampling in Language Models Discrete Flow Matching

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T06:05:45.951184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 317ef725-0509-4222-87ff-e38c6378827c · outbound

This paper cites RoFormer: Enhanced Transformer with Rotary Position Embedding.

Corrector Sampling in Language Models RoFormer: Enhanced Transformer with Rotary Position Embedding

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-07T06:05:46.788940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bc13635-dc3f-448c-be0d-2ead8acb4ec4 · outbound

This paper cites Longformer: The Long-Document Transformer.

Corrector Sampling in Language Models Longformer: The Long-Document Transformer

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T06:05:45.600101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e0bb34a-5386-4735-9206-1a6708fb4072 · outbound

This paper cites Program Synthesis with Large Language Models.

Corrector Sampling in Language Models Program Synthesis with Large Language Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T06:05:45.472027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c0672b8-326a-4f1a-9ea5-f7783833e5d9 · outbound

This paper cites Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design.

Corrector Sampling in Language Models Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T06:05:45.695572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc36729b-f4e6-4700-b3b5-4b4659878540 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Corrector Sampling in Language Models Gemini: A Family of Highly Capable Multimodal Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T06:05:46.043586Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d75b4484-a6ea-4922-98ef-b7e0434e020d · outbound

This paper cites Generator Matching: Generative modeling with arbitrary Markov processes.

Corrector Sampling in Language Models Generator Matching: Generative modeling with arbitrary Markov processes

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T06:05:46.204726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.