Paper Citation Record · LEDGER

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs

As of 8 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2607.21291.

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

pith.paper-citation-record.v1
2607.21291 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T07:57:57.442345Z

measured 29 of 29 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 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

29 of 29 outbound references displayed

verified exact0
verified fuzzy0
unresolved29
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2a95d8a5-51f7-41a6-b3b1-1742456fd0df · outbound

This paper cites In: Proceedings of the AAAI conference on artificial intelligence.

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs In: Proceedings of the AAAI conference on artificial intelligence

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:55.895306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb810ab3-2adf-435b-aea3-cbf45c518c39 · outbound

This paper cites GPT-NeoX-20B: An Open-Source Autoregressive Language Model.

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs GPT-NeoX-20B: An Open-Source Autoregressive Language Model

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:55.950137Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd01ba4a-ad7d-482a-ac80-f0abae54a201 · outbound

This paper cites In: Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition.

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs In: Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:56.010466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27522345-587d-4c15-a6e2-51a5c70d6338 · outbound

This paper cites GenQA: Generating Millions of Instructions from a Handful of Prompts.

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs GenQA: Generating Millions of Instructions from a Handful of Prompts

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:56.074171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed721b9a-c3f0-44b7-8fde-daf38d8f6deb · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:56.132129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f74a2898-3986-4144-91bb-af1adfd5d508 · outbound

This paper cites In: In- ternational Conference on Learning Representations (2021),https://openreview.

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs In: In- ternational Conference on Learning Representations (2021),https://openreview

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:56.193591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c7d41c1-8d83-405c-a52d-ba66718bd77e · outbound

This paper cites Journal of Machine Learning Research 23(120), 1–39 (2022).

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs Journal of Machine Learning Research 23(120), 1–39 (2022)

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:56.244092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e73633ca-4411-4d06-bcc3-885d674d71e5 · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:56.277529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20f07b8d-bc24-43fb-a09c-fbe0eefcbc56 · outbound

This paper cites Advances in Neural Information Processing Systems37, 1725–1749 (2024).

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs Advances in Neural Information Processing Systems37, 1725–1749 (2024)

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:56.359553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bfe89af3-cfae-49c7-b7b6-7dc52c121e1a · outbound

This paper cites In: Cohn, T., He, Y., Liu, Y.

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs In: Cohn, T., He, Y., Liu, Y

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:56.389790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb7f1877-f29f-44b4-8bc7-c255f9f3a5df · outbound

This paper cites In: International conference on machine learning.

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs In: International conference on machine learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:56.450763Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70cf3784-ab73-4044-baa3-6050e6167f44 · outbound

This paper cites an unresolved cited work.

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs Unresolved cited work

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:56.481225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c73cf897-1e86-4461-9e6e-7a0fa90bb896 · outbound

This paper cites Infinity Instruct: Scaling Instruction Selection and Synthesis to Enhance Language Models.

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs Infinity Instruct: Scaling Instruction Selection and Synthesis to Enhance Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:56.538301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8371fdc8-4896-44ee-a03a-4b7ed91c8c79 · outbound

This paper cites In: ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs In: ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:56.574763Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ed43d25-dd50-4e90-80f7-79f6bdca0aaf · outbound

This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:56.605765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7ac9581-3e99-491d-82ad-7dcdfbd24b6a · outbound

This paper cites In: International confer- ence on machine learning.

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs In: International confer- ence on machine learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:56.664732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9516767b-71e1-486b-85d6-4e67771d65e3 · outbound

This paper cites Mixture-of-Depths: Dynamically allocating compute in transformer-based language models.

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs Mixture-of-Depths: Dynamically allocating compute in transformer-based language models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:56.720492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85ea7d5d-7914-4d2b-858d-11b07b114619 · outbound

This paper cites In: Proceedings of the AAAI Conference on Artificial Intelligence.

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs In: Proceedings of the AAAI Conference on Artificial Intelligence

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:56.775213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4199456-375f-4c06-b9af-4e3558a71b61 · outbound

This paper cites Frontiers in Marine Science10, 1174347 (2023).

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs Frontiers in Marine Science10, 1174347 (2023)

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:56.834084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61b45701-3762-48bc-ba56-35af7094c8e2 · outbound

This paper cites an unresolved cited work.

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs Unresolved cited work

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:56.889457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2652e1ef-ecfb-4ae8-bfd9-cab7632d3f35 · outbound

This paper cites In: First Workshop on Scalable Optimization for Efficient and Adaptive Foundation Models (2025), https://openreview.net/forum?id=E9Jw3IHuDH.

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs In: First Workshop on Scalable Optimization for Efficient and Adaptive Foundation Models (2025), https://openreview.net/forum?id=E9Jw3IHuDH

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:56.951988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f1ca8f5-bfd4-4427-a1bc-0b33a977614f · outbound

This paper cites In: Proceedings of the IEEE/CVF International Conference on Computer Vision.

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs In: Proceedings of the IEEE/CVF International Conference on Computer Vision

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:57.009194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8509ac4e-0a05-4d4e-b7af-1e5e7252c8e6 · outbound

This paper cites Qwen2 Technical Report.

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs Qwen2 Technical Report

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:57.068645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c209ef8-2f8f-4862-9434-aa2ba6b667c9 · outbound

This paper cites an unresolved cited work.

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs Unresolved cited work

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:57.156106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af8a9ccc-53a2-4e69-84d3-2c3a71009dd3 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs LLaMA: Open and Efficient Foundation Language Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:57.217363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5cc7fcd4-8fe0-48c2-b108-cafaf3898151 · outbound

This paper cites Preprints (March 2026).https://doi.org/10.20944/preprints202603.2262.v1,https:// doi.org/10.20944/preprints202603.2262.v1.

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs Preprints (March 2026).https://doi.org/10.20944/preprints202603.2262.v1,https:// doi.org/10.20944/preprints202603.2262.v1

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:57.274141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 348e6bd5-e86a-4e6f-b6a0-91ecf78037d2 · outbound

This paper cites an unresolved cited work.

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs Unresolved cited work

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:57.356407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45cb2122-f4b3-4942-aeea-fd6513563964 · outbound

This paper cites A Survey on Knowledge Distillation of Large Language Models.

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs A Survey on Knowledge Distillation of Large Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:57.386320Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5f760b4-795e-43d9-bec9-e95101d714d4 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-01T07:57:57.442345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.