Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T16:26:58.655031Z
Paper Citation Record · LEDGER
Apple Intelligence Foundation Language Models: Tech Report 2025
As of 15 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 7 inbound Pith citation observations for arXiv:2507.13575.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-15T12:17:47.123519Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T16:59:59.011934Z
Reference resolution
20 of 20 outbound references displayed
- verified exact2
- verified fuzzy3
- unresolved15
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 1030b647-c9d1-4893-b1b6-7d7d65700e0a · outbound
This paper cites Revisiting MoE and Dense Speed-Accuracy Comparisons for LLM Training.
Apple Intelligence Foundation Language Models: Tech Report 2025 Revisiting MoE and Dense Speed-Accuracy Comparisons for LLM Training
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:26:58.571348Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation afe6b634-b2a1-4f08-ba14-063b67a85fe5 · outbound
This paper cites MegaBlocks: Efficient Sparse Training with Mixture-of-Experts.
Apple Intelligence Foundation Language Models: Tech Report 2025 MegaBlocks: Efficient Sparse Training with Mixture-of-Experts
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:26:58.583406Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b71d98ad-623e-40ac-b628-ee4c427294bd · outbound
This paper cites Kingma and Jimmy Ba.
Apple Intelligence Foundation Language Models: Tech Report 2025 Kingma and Jimmy Ba
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:26:58.589157Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0678f9b1-c802-4438-82ce-60172d14a2fe · outbound
This paper cites 21 Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, and Zhifeng Chen.
Apple Intelligence Foundation Language Models: Tech Report 2025 21 Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, and Zhifeng Chen
Reference 9
- Resolution
- verified exact
- raw_fallback, observed 2026-08-06T16:26:59.229205Z
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.
Observation fcadb4a1-0cab-478f-b7c2-4e6c346d5a29 · outbound
This paper cites From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline.
Apple Intelligence Foundation Language Models: Tech Report 2025 From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:26:58.604148Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a1da3105-5b7d-4721-b756-7ac1cee4b7e9 · outbound
This paper cites ParetoQ: Scaling laws in extremely low-bit LLM quantization.CoRR, abs/2502.02631,.
Apple Intelligence Foundation Language Models: Tech Report 2025 ParetoQ: Scaling laws in extremely low-bit LLM quantization.CoRR, abs/2502.02631,
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:26:58.613304Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f0a1092a-9c2c-434e-a99f-671f78c85b7f · outbound
This paper cites URLhttps://doi.org/ 10.48550/arXiv.2502.02631.
Apple Intelligence Foundation Language Models: Tech Report 2025 URLhttps://doi.org/ 10.48550/arXiv.2502.02631
Reference 13
- Resolution
- verified exact
- doi, observed 2026-08-06T16:26:58.796273Z
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.
Observation 9c5b7e91-0ef3-44f7-ba03-612cb3886353 · outbound
This paper cites ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities.
Apple Intelligence Foundation Language Models: Tech Report 2025 ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:26:58.622125Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8410a8e6-e9ff-481d-b0e2-f8a50450711a · outbound
This paper cites Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al.
Apple Intelligence Foundation Language Models: Tech Report 2025 Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:26:59.449157Z
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.
Observation 2a3b746a-998d-4931-803d-0187f2f6957a · outbound
This paper cites an unresolved cited work.
Apple Intelligence Foundation Language Models: Tech Report 2025 Unresolved cited work
Reference 16
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T16:26:59.425788Z
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.
Observation a1f48685-11cf-4250-9dbe-b3ce12df8f55 · outbound
This paper cites Yutao Sun, Li Dong, Yi Zhu, Shaohan Huang, Wenhui Wang, Shuming Ma, Quanlu Zhang, Jianyong Wang, and Furu Wei.
Apple Intelligence Foundation Language Models: Tech Report 2025 Yutao Sun, Li Dong, Yi Zhu, Shaohan Huang, Wenhui Wang, Shuming Ma, Quanlu Zhang, Jianyong Wang, and Furu Wei
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:26:59.404759Z
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.
Observation 955c31a0-ba0d-4abb-bc29-6ff3401cfe39 · outbound
This paper cites Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, and Le Hou.
Apple Intelligence Foundation Language Models: Tech Report 2025 Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, and Le Hou
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:26:58.648228Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 85bd6396-169b-49bf-b005-41efc2ba5aa0 · outbound
This paper cites ST-MoE: Designing Stable and Transferable Sparse Expert Models.
Apple Intelligence Foundation Language Models: Tech Report 2025 ST-MoE: Designing Stable and Transferable Sparse Expert Models
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:26:58.655031Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 700c6f25-df48-4c7c-ab11-c4978dd2a239 · outbound
This paper cites Adam: A Method for Stochastic Optimization.
Apple Intelligence Foundation Language Models: Tech Report 2025 Adam: A Method for Stochastic Optimization
Reference 2015
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:26:58.594207Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 73651ff4-cc98-4532-9bdf-807e7af3c866 · outbound
This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.
Apple Intelligence Foundation Language Models: Tech Report 2025 Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Reference 2017
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:26:58.637768Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 65e3fd72-42bd-4643-af44-2f4bf7cbb5e3 · outbound
This paper cites Longformer: The Long-Document Transformer.
Apple Intelligence Foundation Language Models: Tech Report 2025 Longformer: The Long-Document Transformer
Reference 2020
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:26:58.556559Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3cef134b-8794-431d-9533-d59c13455cff · outbound
This paper cites GLaM: Efficient Scaling of Language Models with Mixture-of-Experts.
Apple Intelligence Foundation Language Models: Tech Report 2025 GLaM: Efficient Scaling of Language Models with Mixture-of-Experts
Reference 2022
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:26:58.562199Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2b95effb-6fd7-487a-8064-afd2ebaf7eba · outbound
This paper cites Let's Verify Step by Step.
Apple Intelligence Foundation Language Models: Tech Report 2025 Let's Verify Step by Step
Reference 2023
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:26:58.608507Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d943f4eb-1a45-4146-b990-81f7c764f798 · outbound
This paper cites Iz Beltagy, Matthew E.
Apple Intelligence Foundation Language Models: Tech Report 2025 Iz Beltagy, Matthew E
Reference 2024
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T16:26:59.483747Z
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.
Observation 1baa63de-ea50-49ce-b194-b3eef2599eca · outbound
This paper cites Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators.
Apple Intelligence Foundation Language Models: Tech Report 2025 Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators
Reference 2025
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:26:58.577802Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
Observation 5398ef00-189e-43c5-9fec-603c2000b1f2 · inbound
FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation cites this paper.
FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation Apple Intelligence Foundation Language Models: Tech Report 2025
Reference 37
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T14:25:55.519452Z
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.
Observation ecd78edc-2baf-490f-b6c7-1e2b149138c1 · inbound
FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation cites this paper.
FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation Apple Intelligence Foundation Language Models: Tech Report 2025
Reference 37
- Resolution
- unresolved
- no resolver link, observed 2026-07-15T12:17:47.123519Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7b090a95-e557-4e7c-a477-774897339a1b · inbound
Peak-Detector: Explainable Peak Detection via Instruction-Tuned Large Language Models in Physiological Sign cites this paper.
Peak-Detector: Explainable Peak Detection via Instruction-Tuned Large Language Models in Physiological Sign Apple Intelligence Foundation Language Models: Tech Report 2025
Reference 40
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T19:53:42.878553Z
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.
Observation dd531b98-6dae-41c4-8180-61f9a1ee724e · inbound
Unlocking Apple's Private Cloud Compute: An Analysis of Privacy-Preserving Artificial Intelligence cites this paper.
Unlocking Apple's Private Cloud Compute: An Analysis of Privacy-Preserving Artificial Intelligence Apple Intelligence Foundation Language Models: Tech Report 2025
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T15:44:48.404302Z
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.
Observation 943a05fe-9f01-4c3e-b3d3-a01e2399edff · inbound
FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation cites this paper.
FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation Apple Intelligence Foundation Language Models: Tech Report 2025
Reference 37
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T11:39:46.618747Z
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.
Observation 5973fb76-c1ce-4d99-943e-fdb39c2e5562 · inbound
Forget to Improve: On-Device LLM-Agent Continual Learning via Budget-Curated Memory cites this paper.
Forget to Improve: On-Device LLM-Agent Continual Learning via Budget-Curated Memory Apple Intelligence Foundation Language Models: Tech Report 2025
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T16:59:59.013677Z
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.
Observation 9b066353-cca8-4b3a-b4a0-7b0868bc952c · inbound
Punching Above Their Weight: Classification-Head Fine-Tuning of Tiny Language Models (TLMs) for Verifiable Multiple-Choice Tasks cites this paper.
Punching Above Their Weight: Classification-Head Fine-Tuning of Tiny Language Models (TLMs) for Verifiable Multiple-Choice Tasks Apple Intelligence Foundation Language Models: Tech Report 2025
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-07-11T23:52:16.201593Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.