Paper Citation Record · LEDGER

Inference Scaling Reshapes AI Governance

As of 17 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 2 inbound Pith citation observations for arXiv:2503.05705.

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

pith.paper-citation-record.v1
2503.05705 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T23:38:52.118093Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T11:04:08.234884Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T20:21:08.721459Z

Reference resolution

14 of 14 outbound references displayed

verified exact0
verified fuzzy4
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f9d3efce-334b-48f2-8ff5-67cebf402cec · outbound

This paper cites an unresolved cited work.

Inference Scaling Reshapes AI Governance Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-07T23:38:52.377132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b42ed813-6a73-43ab-b0fc-fd849ff854d3 · outbound

This paper cites This has led to intense speculation that the previous era of scaling pre-training compute could be followed by an era of scaling up inference-compute.

Inference Scaling Reshapes AI Governance This has led to intense speculation that the previous era of scaling pre-training compute could be followed by an era of scaling up inference-compute

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:38:52.363285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation adf4c983-5509-4f24-a5c3-94373b8fa637 · outbound

This paper cites an unresolved cited work.

Inference Scaling Reshapes AI Governance Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-07T23:38:52.229735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1ff3571f-2e6e-4d80-af3e-a59a9e6b57ac · outbound

This paper cites Training Compute Thresholds: Features and Functions in AI Regulation.

Inference Scaling Reshapes AI Governance Training Compute Thresholds: Features and Functions in AI Regulation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T23:38:52.105060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9a320b5-0209-465e-a58e-2ede6394f013 · outbound

This paper cites Nature 550, 354–359.

Inference Scaling Reshapes AI Governance Nature 550, 354–359

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T23:38:52.118093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b3896f5-e41a-4759-a998-40f53ca586f1 · outbound

This paper cites And even if the weights were stolen, the thief would still have to pay the high inference-at-deployment costs.

Inference Scaling Reshapes AI Governance And even if the weights were stolen, the thief would still have to pay the high inference-at-deployment costs

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:38:52.341742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 45accc1a-1780-4842-b0b0-3f3d19dff238 · outbound

This paper cites For example, if you scale up training compute by 1 OOM, that means 0.5 OOMs more parameters and 0.5 OOMs more data.

Inference Scaling Reshapes AI Governance For example, if you scale up training compute by 1 OOM, that means 0.5 OOMs more parameters and 0.5 OOMs more data

Reference 1010

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:38:52.207901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7f5b3ce9-f8bc-49a9-9a90-a0c28d5cb572 · outbound

This paper cites Thinking Fast and Slow with Deep Learning and Tree Search.

Inference Scaling Reshapes AI Governance Thinking Fast and Slow with Deep Learning and Tree Search

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T23:38:52.097617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c7db69c-3849-4fcc-8001-cbd8c0193512 · outbound

This paper cites Scaling Laws for Neural Language Models.

Inference Scaling Reshapes AI Governance Scaling Laws for Neural Language Models

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T23:38:52.114548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 397fba30-a8c5-4cd0-b609-60ba4bafbceb · outbound

This paper cites Scaling Scaling Laws with Board Games.

Inference Scaling Reshapes AI Governance Scaling Scaling Laws with Board Games

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T23:38:52.111251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3655c1d-5b61-48a2-8520-3bad03badbdf · outbound

This paper cites Training Compute-Optimal Large Language Models.

Inference Scaling Reshapes AI Governance Training Compute-Optimal Large Language Models

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T23:38:52.108206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 366d8e1c-d8b4-4240-ab0e-b148173a5719 · outbound

This paper cites an unresolved cited work.

Inference Scaling Reshapes AI Governance Unresolved cited work

Reference 2023

Resolution: unresolved; raw_fallback, observed 2026-08-07T23:38:52.219377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fc0aca0e-effc-4a58-9bb7-842c5ca541fb · outbound

This paper cites A second — and ultimately more important — question concerns the nature of inference-scaling.

Inference Scaling Reshapes AI Governance A second — and ultimately more important — question concerns the nature of inference-scaling

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:38:52.352910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1d2cbe24-f56d-43a8-89ee-f21e0ec56fb9 · outbound

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

Inference Scaling Reshapes AI Governance DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T23:38:52.101704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation dbafce89-44c8-460c-bb99-5a4e71861cf6 · inbound

What Should Frontier AI Developers Disclose About Internal Deployments? cites this paper.

What Should Frontier AI Developers Disclose About Internal Deployments? Inference Scaling Reshapes AI Governance

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:21:08.727090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5b63640c-2956-43da-bfd8-16a3a0e8eb48 · inbound

How to Catch a GPU: A Taxonomy of Verification and Enforcement Mechanisms for International AI Agreements cites this paper.

How to Catch a GPU: A Taxonomy of Verification and Enforcement Mechanisms for International AI Agreements Inference Scaling Reshapes AI Governance

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-02T11:04:08.234884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.