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Paper Citation Record · LEDGER

Saffron-1: Safety Inference Scaling

As of 15 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 1 inbound Pith citation observation for arXiv:2506.06444.

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

pith.paper-citation-record.v1
2506.06444 v2

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:02:25.916469Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:39:06.998121Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

30 of 30 outbound references displayed

  • verified exact2
  • verified fuzzy5
  • unresolved23
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5d02b4c5-1624-42d6-aff7-f504e9300542 · outbound

This paper cites Large Language Monkeys: Scaling Inference Compute with Repeated Sampling.

Saffron-1: Safety Inference Scaling Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 4

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no resolver link, observed 2026-08-07T06:02:25.813428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T06:02:25.813428Z digest=sha256:2e757489d246dd3a2412cfcb5fd79049aeeb2944bd1dc6a04b9ff9b5ab9946d1

Observation 24b8f4dd-b16e-42ec-9539-ea095a1b0517 · outbound

This paper cites Chan, Jui-Hung Cheng, Mao Xun Huang, Chao-Ting Chen, and Hen-Hsen Huang.

Saffron-1: Safety Inference Scaling Chan, Jui-Hung Cheng, Mao Xun Huang, Chao-Ting Chen, and Hen-Hsen Huang

Reference 5

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no resolver link, observed 2026-08-07T06:02:25.817677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T06:02:25.817677Z digest=sha256:a5460c37073443ba53735ac3e9e9d1ce4334f77b756c3a228ff458e6c8aa8ba2

Observation dd5cc94e-9825-4a77-835d-6d9122f0e074 · outbound

This paper cites Group fairness via group consensus.

Saffron-1: Safety Inference Scaling Group fairness via group consensus

Reference 6

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verified fuzzy
raw_fallback, observed 2026-08-07T06:02:26.755608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-07T06:02:25.821751Z digest=sha256:8b1ce4dbbb8182587ceb07ec97540405797edd3bc682f308f5b465dc455a9f30

Observation b1d870e0-da28-42a6-829b-a9028a492b00 · outbound

This paper cites WAPITI: A Watermark for Finetuned Open-Source LLMs.

Saffron-1: Safety Inference Scaling WAPITI: A Watermark for Finetuned Open-Source LLMs

Reference 7

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verified exact
local_arxiv, observed 2026-08-07T06:02:26.541186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-07T06:02:25.825533Z digest=sha256:6ec4141c2c0ae0d4da3fb16397aa6833d311babdd617ae7462e03912cd42b9c8

Observation ca556af4-5b6f-43f9-905c-a985acb323db · outbound

This paper cites Rm-r1: Reward modeling as reasoning.arXiv preprint arXiv:2505.02387,.

Saffron-1: Safety Inference Scaling Rm-r1: Reward modeling as reasoning.arXiv preprint arXiv:2505.02387,

Reference 8

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T06:02:25.830083Z digest=sha256:a6c3820e3010c0580f291452af6e0b23e15c37f5c5a3add4679844b9c033600b

Observation c109cc7b-338a-48e4-8cda-b61376d2f70f · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Saffron-1: Safety Inference Scaling DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 9

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no resolver link, observed 2026-08-07T06:02:25.833832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T06:02:25.833832Z digest=sha256:bd08d880e46d6e3cc7fdef50a0704e501cafe1ed3777bef365b9e7c0808815c5

Observation bb77d116-b274-4552-b351-e310acc6a5e7 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Saffron-1: Safety Inference Scaling Distilling the Knowledge in a Neural Network

Reference 10

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source=pdf_text observed=2026-08-07T06:02:25.837826Z digest=sha256:0652adaff9e6630f56b4e34395dfe07ada2f197f195111c251784ef9918e0beb

Observation 50dbe9f6-cc3c-4606-a1ce-937fccbd5681 · outbound

This paper cites Huang, Sailik Sengupta, Daniele Bonadiman, Yi-an Lai, Arshit Gupta, Nikolaos Pappas, Saab Mansour, Katrin Kirchhoff, and Dan Roth.

Saffron-1: Safety Inference Scaling Huang, Sailik Sengupta, Daniele Bonadiman, Yi-an Lai, Arshit Gupta, Nikolaos Pappas, Saab Mansour, Katrin Kirchhoff, and Dan Roth

Reference 12

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T06:02:25.845838Z digest=sha256:dd1863f7260f16b9d426ca740c321662081c75c395ecc5d7b93cb3cf64bcf6c3

Observation 59ae1677-fd09-432f-a926-d582bf0fa8d3 · outbound

This paper cites Model-free graph data selection under distribution shift.arXiv preprint arXiv:2505.17293, 2025a.

Saffron-1: Safety Inference Scaling Model-free graph data selection under distribution shift.arXiv preprint arXiv:2505.17293, 2025a

Reference 14

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T06:02:25.853332Z digest=sha256:1b83df20adcc546abbee4215a32a5faf52b5e7e2f7d73a358297091da396e750

Observation 31f44421-3ffd-404a-a582-d439fb0c3376 · outbound

This paper cites Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs.

Saffron-1: Safety Inference Scaling Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs

Reference 15

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source=pdf_text observed=2026-08-07T06:02:25.856630Z digest=sha256:632db43c82e98d1dbec1cc6126afeba57ab4d8e2160eb9ff2b8cd5ebd2aec030

Observation 9cf43577-ad33-4b59-8585-f8bc50c62ee6 · outbound

This paper cites Class-imbalanced graph learning without class rebalancing.

Saffron-1: Safety Inference Scaling Class-imbalanced graph learning without class rebalancing

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-07T06:02:25.860322Z digest=sha256:2b95c4690a42c6a9f987e8c6eeaa333772af4e900f25e0864f91bbed248b9a6c

Observation ad1c8170-f1f3-4b98-b887-a95067ce60f6 · outbound

This paper cites Rule Based Rewards for Language Model Safety.

Saffron-1: Safety Inference Scaling Rule Based Rewards for Language Model Safety

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T06:02:25.867442Z digest=sha256:146c35c1a9058238704f3578f7ce533c10137ee3f02427b9c546469d3a4de371

Observation 001fc83e-79c6-404a-80d5-5202aedabe33 · outbound

This paper cites GPT-4 Technical Report.

Saffron-1: Safety Inference Scaling GPT-4 Technical Report

Reference 19

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no resolver link, observed 2026-08-07T06:02:25.872021Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T06:02:25.872021Z digest=sha256:b442cc3f9b84b9d96c501ff455cf3cbf6c598829e0a6230f398c5f6775095d76

Observation 29506041-8e66-498b-9629-265f86f062d8 · outbound

This paper cites OpenAI o1 System Card.

Saffron-1: Safety Inference Scaling OpenAI o1 System Card

Reference 20

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T06:02:25.875537Z digest=sha256:729818cfcc583817ee7f3729b51e38942e3a32da7fbf6f9ec23d145145466162

Observation d056f852-f55b-46ae-ad7d-fc106b8bcbd1 · outbound

This paper cites Reconstructing graph diffusion history from a single snapshot.

Saffron-1: Safety Inference Scaling Reconstructing graph diffusion history from a single snapshot

Reference 21

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raw_fallback, observed 2026-08-07T06:02:26.729428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-07T06:02:25.879114Z digest=sha256:dae120de5c2ca5df9013c79aa5260ff7303305025efe1d3dc4c9f045f5f76ffa

Observation 756daa71-4fdb-40ec-a3be-f8490c98192b · outbound

This paper cites do anything now.

Saffron-1: Safety Inference Scaling do anything now

Reference 22

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verified fuzzy
raw_fallback, observed 2026-08-07T06:02:26.716829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-07T06:02:25.882667Z digest=sha256:5da859a15f8bd35322758a0618d88e3184b61d079b5c685c3520c31c8a8ecfb4

Observation b81a66a0-0446-435f-92da-f2479f9723c9 · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Saffron-1: Safety Inference Scaling Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 23

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T06:02:25.886327Z digest=sha256:ce0ad2f3f330e469d861695b0bb362790ec05db7a86797df7a2e3b3ac916e701

Observation 72be71cf-0c7e-4576-a367-8697020cb15d · outbound

This paper cites Bypassing the Safety Training of Open-Source LLMs with Priming Attacks.

Saffron-1: Safety Inference Scaling Bypassing the Safety Training of Open-Source LLMs with Priming Attacks

Reference 24

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source=pdf_text observed=2026-08-07T06:02:25.890123Z digest=sha256:bf735140df2c9cf50eb0b285a25d91023a196f09ee38ffcb7b519b6445f1003d

Observation 3032f056-8384-4bfe-8e22-04848bb30f25 · outbound

This paper cites Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations.

Saffron-1: Safety Inference Scaling Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations

Reference 25

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no resolver link, observed 2026-08-07T06:02:25.893745Z

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source=pdf_text observed=2026-08-07T06:02:25.893745Z digest=sha256:2fe0426c6051126a8b8e2e7ffaa33ac5dc3af8cc5a9e3ddb2ea2dcc0ba66f992

Observation 1d1e9ce5-fb57-4d22-a606-eeac6cec649a · outbound

This paper cites Fair Anomaly Detection For Imbalanced Groups.

Saffron-1: Safety Inference Scaling Fair Anomaly Detection For Imbalanced Groups

Reference 26

Resolution
verified exact
local_arxiv, observed 2026-08-07T06:02:26.142501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-07T06:02:25.897311Z digest=sha256:96c878fe2731e02ef0a3130a2ad4c675fe4943100d29da79cb14424853ba169d

Observation 2077d018-7f44-4198-876e-d60717390b84 · outbound

This paper cites Ensuring user-side fairness in dynamic recommender systems.

Saffron-1: Safety Inference Scaling Ensuring user-side fairness in dynamic recommender systems

Reference 27

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raw_fallback, observed 2026-08-07T06:02:26.704565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-07T06:02:25.901099Z digest=sha256:a32a566e82b29ea6fcb477b2ff5b3a9f0b35c382b9aa5b396031a31b01efdc74

Observation 57fd0848-0280-4069-9452-33e34040186a · outbound

This paper cites Abdelza- her, Jiawei Han, and Hanghang Tong.

Saffron-1: Safety Inference Scaling Abdelza- her, Jiawei Han, and Hanghang Tong

Reference 28

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source=pdf_text observed=2026-08-07T06:02:25.905900Z digest=sha256:0ddad3d219ec6e196d8f5d43ba112df0bbcac01ec92a0f0d3c59561f7b12ad1d

Observation 29171856-4ebc-4e54-82ae-c81cd49d84c9 · outbound

This paper cites The Lessons of Developing Process Reward Models in Mathematical Reasoning.

Saffron-1: Safety Inference Scaling The Lessons of Developing Process Reward Models in Mathematical Reasoning

Reference 29

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source=pdf_text observed=2026-08-07T06:02:25.912510Z digest=sha256:26f5446c35ffbe818978e07935b1e4f721153f1240e241b5517d8ee6a9094912

Observation 582b2241-230b-4985-be30-928542f3a0cb · outbound

This paper cites Transformer copilot: Learning from the mistake log in LLM fine-tuning.arXiv preprint arXiv:2505.16270,.

Saffron-1: Safety Inference Scaling Transformer copilot: Learning from the mistake log in LLM fine-tuning.arXiv preprint arXiv:2505.16270,

Reference 30

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no resolver link, observed 2026-08-07T06:02:25.916469Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T06:02:25.916469Z digest=sha256:54a0bfeb4cfd7b33828c5a87e24f0c670a9f3c598ea0bfcea5446ed9c9a74620

Observation 62869168-c707-46cb-b891-5d413d74a27c · outbound

This paper cites The Curious Case of Neural Text Degeneration.

Saffron-1: Safety Inference Scaling The Curious Case of Neural Text Degeneration

Reference 2015

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source=pdf_text observed=2026-08-07T06:02:25.841775Z digest=sha256:e368fe7d6b04158b2c3c537a03ec7e98d64d7e745dce0087d9aea57e327577d1

Observation f9e09cf3-21fe-4d34-a920-f96f687101a1 · outbound

This paper cites The Llama 3 Herd of Models.

Saffron-1: Safety Inference Scaling The Llama 3 Herd of Models

Reference 2019

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source=pdf_text observed=2026-08-07T06:02:25.863931Z digest=sha256:ea4e3acb3585977a778727fc299285cb112aa18e54a87aa40041c6716b5ec045

Observation 972c1835-d789-4249-9d43-16e6bb3a105c · outbound

This paper cites InfAlign: Inference-aware language model alignment.

Saffron-1: Safety Inference Scaling InfAlign: Inference-aware language model alignment

Reference 2022

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source=pdf_text observed=2026-08-07T06:02:25.803951Z digest=sha256:60c1e5697f6fd219313d8deb700b16ab72ae1af4d0f388e9d795de3739d9bb1e

Observation 572c1b5b-3620-4b18-9e08-805d655e69bd · outbound

This paper cites RewardBench: Evaluating Reward Models for Language Modeling.

Saffron-1: Safety Inference Scaling RewardBench: Evaluating Reward Models for Language Modeling

Reference 2023

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source=pdf_text observed=2026-08-07T06:02:25.849371Z digest=sha256:b53b192da75c9fc7f5a732b2358be1af0ba3c97034d3256f23f454248804b2d0

Observation 3d2422cc-5f7a-453c-9cf4-7df4670ccb21 · outbound

This paper cites Theoretical guarantees on the best-of-n alignment policy.

Saffron-1: Safety Inference Scaling Theoretical guarantees on the best-of-n alignment policy

Reference 2024

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T06:02:25.808950Z digest=sha256:d505d5be76a3d3a6fa07f14199c9878e91263d6ab19e2832335f479678c754f8

Observation aacac039-34e5-475c-a7a9-bfe02ccb3abd · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Saffron-1: Safety Inference Scaling Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 2025

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source=pdf_text observed=2026-08-07T06:02:25.798807Z digest=sha256:39b8deb96bc75e91d3ca40ef705fe56ccba63344a37438d4306bdda83d60ffb4

Pith citing papers

Observation caad090c-df54-44b2-a7c2-2331bec8d32b · inbound

A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models cites this paper.

A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models Saffron-1: Safety Inference Scaling

Reference 141

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source=pdf_text observed=2026-08-05T10:39:06.998121Z digest=sha256:1c2a72c580ed7b6c059552e9a85b772c3268d4089298451d66c75d9f00bc0e92