Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-13T04:08:39.594367Z
Paper Citation Record · LEDGER
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning
As of 4 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2607.09287.
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 2026-08-04T06:34:03.388597+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
23 of 23 outbound references displayed
- verified exact1
- verified fuzzy0
- unresolved19
- parse uncertain0
- malformed identifier3
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation ad14e144-d21f-4ae8-aec8-dae0ee02fcdb · outbound
This paper cites Scaling Sparse Fine-Tuning to Large Language Models.
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning Scaling Sparse Fine-Tuning to Large Language Models
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T04:08:39.594367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 69a59fa8-6578-4f99-b66e-8686dee62963 · outbound
This paper cites Gallop: Gradient-based sparse learning on low-magnitude parameters.arXiv preprint arXiv:2510.19778,.
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning Gallop: Gradient-based sparse learning on low-magnitude parameters.arXiv preprint arXiv:2510.19778,
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T04:08:39.594367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 46949af6-813f-4d59-8110-6f2e97c88119 · outbound
This paper cites Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al.
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-07-13T04:09:18.631438Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 2d914d76-aefd-4b87-b037-c398446fd5b0 · outbound
This paper cites SLTrain: a sparse plus low-rank approach for parameter and memory efficient pretraining.
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning SLTrain: a sparse plus low-rank approach for parameter and memory efficient pretraining
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T04:08:39.594367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 167fcd97-5dd6-474d-a9e6-6e23631947ef · outbound
This paper cites Learning to solve arithmetic word problems with verb categorization.
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning Learning to solve arithmetic word problems with verb categorization
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T04:08:39.594367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1a7d9844-56aa-4933-97e6-dbf20e0fab69 · outbound
This paper cites LoRA: Low-Rank Adaptation of Large Language Models.
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning LoRA: Low-Rank Adaptation of Large Language Models
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T04:08:39.594367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8a68ca6f-f98b-40c9-a624-23ba4ade3f9c · outbound
This paper cites An Efficient Sparse Fine-Tuning with Low Quantization Error via Neural Network Pruning.
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning An Efficient Sparse Fine-Tuning with Low Quantization Error via Neural Network Pruning
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T04:08:39.594367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0d3738e6-5bf6-4afd-96bd-8c0036ed2584 · outbound
This paper cites Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems.
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T04:08:39.594367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 78d3410a-7aee-47f2-9a1c-686e36e9731f · outbound
This paper cites DoRA: Weight-Decomposed Low-Rank Adaptation.
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning DoRA: Weight-Decomposed Low-Rank Adaptation
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T04:08:39.594367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 093be92d-4679-48e1-93a1-1f90377c6918 · outbound
This paper cites Sparsity-Accelerated Training for Large Language Models.
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning Sparsity-Accelerated Training for Large Language Models
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T04:08:39.594367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 37b5cca5-772e-4a5f-8584-f87d1fe4ee1d · outbound
This paper cites RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation.
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T04:08:39.594367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0f88c719-42cd-43ac-9e97-214c7f146209 · outbound
This paper cites Are NLP Models really able to Solve Simple Math Word Problems?.
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning Are NLP Models really able to Solve Simple Math Word Problems?
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T04:08:39.594367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 22d1c5b2-6e5e-46ad-ab30-960877bc9376 · outbound
This paper cites Solving General Arithmetic Word Problems.
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning Solving General Arithmetic Word Problems
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T04:08:39.594367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0d5e775e-ab87-4672-966f-96573f51e73d · outbound
This paper cites Sparse is Enough in Fine-tuning Pre-trained Large Language Models.
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning Sparse is Enough in Fine-tuning Pre-trained Large Language Models
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T04:08:39.594367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b0f0f5b0-db12-4ef0-8497-96a412f042e7 · outbound
This paper cites A Simple and Effective Pruning Approach for Large Language Models.
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning A Simple and Effective Pruning Approach for Large Language Models
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T04:08:39.594367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b1e6a095-d3e6-4f8a-9e17-8f0c36160bd9 · outbound
This paper cites Let's Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large Language Model.
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning Let's Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large Language Model
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T04:08:39.594367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c9f00262-f308-48fc-8c1f-ed6329db4db5 · outbound
This paper cites S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity.
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T04:08:39.594367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 19775c8f-c5b9-4790-933b-8f75b9c364af · outbound
This paper cites AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning.
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T04:08:39.594367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation db059159-49d4-450f-af3d-46e5f1e1d03b · outbound
This paper cites GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMs.
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMs
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T04:08:39.594367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 71761943-a945-4ba0-a4e6-808937660f68 · outbound
This paper cites All trainable masks use C4 calibration and approximately 5.6M trainable sparse parameters.
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning All trainable masks use C4 calibration and approximately 5.6M trainable sparse parameters
Reference 20
- Resolution
- malformed identifier
- no resolver link, observed 2026-07-13T04:08:39.594367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8cb73a3b-0f5a-4e4e-b7b5-5bf03e12f41e · outbound
This paper cites Full fine-tuning is an unbudgeted reference row separated by rules.
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning Full fine-tuning is an unbudgeted reference row separated by rules
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T04:08:39.594367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dba6f14f-42f3-4146-ba0b-d7b4de02e82b · outbound
This paper cites Each row uses the learning rate selected by the held-out validation split of the fine-tuning set.
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning Each row uses the learning rate selected by the held-out validation split of the fine-tuning set
Reference 22
- Resolution
- malformed identifier
- no resolver link, observed 2026-07-13T04:08:39.594367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 69f67018-2435-45f1-b8d0-149452e377ad · outbound
This paper cites Lower is better.
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning Lower is better
Reference 23
- Resolution
- malformed identifier
- no resolver link, observed 2026-07-13T04:08:39.594367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.