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Paper Citation Record · LEDGER

Omega-S: A Functional Resilience Index for LLM Fine-Tuning

As of 8 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2608.03887.

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

pith.paper-citation-record.v1
2608.03887 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:26:24.633581Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

42 of 42 outbound references displayed

  • verified exact9
  • verified fuzzy17
  • unresolved16
  • parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3e20f671-c0ff-42dc-95f3-aced3bc87b35 · outbound

This paper cites Network Properties of Local Fungal Communities Reveal the Anthropogenic Disturbance Consequences of Farming Practices in Vineyard Soils.mSystems.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Network Properties of Local Fungal Communities Reveal the Anthropogenic Disturbance Consequences of Farming Practices in Vineyard Soils.mSystems

Reference 1

Resolution
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doi, observed 2026-08-05T10:26:24.677796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation de4c8a3a-87da-4895-8362-3b63d3f98fab · outbound

This paper cites Soil Microbial Diversity and Network Organization Respond to Land Use and Agricultural Inputs Worldwide.Global Change Biology.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Soil Microbial Diversity and Network Organization Respond to Land Use and Agricultural Inputs Worldwide.Global Change Biology

Reference 2

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verified exact
doi, observed 2026-08-05T10:26:24.666833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation abd78bbe-925e-45e2-800d-5aa0db9bb7ce · outbound

This paper cites Methods and systems for gen- erating and applying agronomic indices from microbiome-derived parameters.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Methods and systems for gen- erating and applying agronomic indices from microbiome-derived parameters

Reference 3

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 979944e2-6f2b-441f-93db-663a707dedb3 · outbound

This paper cites Methods and systems for evaluating ecological disturbance of an agricultural micro- biome based upon network properties of organism communities.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Methods and systems for evaluating ecological disturbance of an agricultural micro- biome based upon network properties of organism communities

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:26:25.311462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T10:26:24.508285Z digest=sha256:b5d7440f3501b8335f7f0368f22bbbf1867e387ba1950953b438f38b48f47568

Observation 3c177205-ee86-42b1-aef8-ac1b88694b12 · outbound

This paper cites Sharpness-Aware Minimization for Efficiently Improving Generalization.ICLR.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Sharpness-Aware Minimization for Efficiently Improving Generalization.ICLR

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:26:25.302033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T10:26:24.511983Z digest=sha256:025c84e33960cde03356e5cc9bdbf337a534325341aa58c313b9201de9dcdc34

Observation 3eac8890-8ea5-4259-bc80-cb34e1f4894d · outbound

This paper cites Fiedler Regularization: Learning Neural Networks with Graph Sparsity.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Fiedler Regularization: Learning Neural Networks with Graph Sparsity

Reference 6

Resolution
verified exact
local_arxiv, observed 2026-08-05T10:26:25.104134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T10:26:24.515355Z digest=sha256:450ad92c0b76f1c4ae8bf80e673599aaa3f7e8f3b4dc0a4c517471e88d0e4612

Observation 26ac7ee5-f8c6-422e-8b04-67b27bdbc2d0 · outbound

This paper cites A stochastic estimator of the trace of the influence matrix for Lapla- cian smoothing splines.Communications in Statistics — Simulation and Computation.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning A stochastic estimator of the trace of the influence matrix for Lapla- cian smoothing splines.Communications in Statistics — Simulation and Computation

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:26:25.291217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T10:26:24.519216Z digest=sha256:a5f0989b7129c84aaf8117b44b886e7f17990101b943911e9af093ef9928d017

Observation 2e521d1b-b9ad-4b70-a22f-3e2d92571d28 · outbound

This paper cites Decoupled Weight Decay Regularization.ICLR.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Decoupled Weight Decay Regularization.ICLR

Reference 8

Resolution
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raw_fallback, observed 2026-08-05T10:26:25.281227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 535deeb4-f981-4557-ab7f-107082e9db5c · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.ICLR.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning LoRA: Low-Rank Adaptation of Large Language Models.ICLR

Reference 9

Resolution
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raw_fallback, observed 2026-08-05T10:26:25.271179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T10:26:24.525331Z digest=sha256:fbefafbde331e649740d22655a5163276f694b7520750a426041c6ba371cadb4

Observation b5fced87-dfa3-432d-9a4c-905f36636785 · outbound

This paper cites PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel.VLDB.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel.VLDB

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:26:25.261586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T10:26:24.528636Z digest=sha256:40cc682b685cf700903e66405917a81fed49f12315977a2e62bba27defe60e30

Observation 124cfc5c-e3a0-4960-a096-116e633667ae · outbound

This paper cites Model selection and estimation in regression with grouped variables.Journal of the Royal Statistical Society: Series B.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Model selection and estimation in regression with grouped variables.Journal of the Royal Statistical Society: Series B

Reference 11

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raw_fallback, observed 2026-08-05T10:26:25.251891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 03970ac4-470f-4988-8bb8-68a85dcc206b · outbound

This paper cites Emergence of scaling in random networks.Science.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Emergence of scaling in random networks.Science

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation e18fce1d-f8bb-4521-8783-9e34a3925849 · outbound

This paper cites Hutch++: Optimal Stochastic Trace Estima- tion.SIAM Symposium on Simplicity in Algorithms.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Hutch++: Optimal Stochastic Trace Estima- tion.SIAM Symposium on Simplicity in Algorithms

Reference 13

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raw_fallback, observed 2026-08-05T10:26:25.233177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 439dc281-0d6d-4b2f-87ae-a8e357a1c8fc · outbound

This paper cites Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity

Reference 14

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no resolver link, observed 2026-08-05T10:26:24.542193Z

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Unavailable: canonical work link unavailable.

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Observation 4c565b5b-6193-413d-b0a3-005d90c691f6 · outbound

This paper cites The Llama 3 Herd of Models.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning The Llama 3 Herd of Models

Reference 15

Resolution
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no resolver link, observed 2026-08-05T10:26:24.545750Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:26:24.545750Z digest=sha256:1e4e837888f6f7f174341c110bda497a2a9d9abefff05ff5c1af62ca72ec586f

Observation 889fb4b5-23f3-481c-b09d-6bd65db3cbcf · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Evaluating Large Language Models Trained on Code

Reference 16

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Observation e2a57fc2-1cea-4b49-bfee-1a327db1ab70 · outbound

This paper cites Code Alpaca: an instruction-following LLaMA model for code gener- ation.https://github.com/sahil280114/codealpaca.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Code Alpaca: an instruction-following LLaMA model for code gener- ation.https://github.com/sahil280114/codealpaca

Reference 17

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Observation 41a83ff2-b731-4e95-86ba-4afc6989176f · outbound

This paper cites Pointer sentinel mixture models.Interna- tional Conference on Learning Representations.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Pointer sentinel mixture models.Interna- tional Conference on Learning Representations

Reference 18

Resolution
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raw_fallback, observed 2026-08-05T10:26:25.214671Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 1dec3d84-b2dc-4bae-a2f7-a87124a67c0a · outbound

This paper cites LLM.int8(): 8-bit matrix mul- tiplication for transformers at scale.Advances in Neural Information Processing Systems 35:30318–30332.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning LLM.int8(): 8-bit matrix mul- tiplication for transformers at scale.Advances in Neural Information Processing Systems 35:30318–30332

Reference 19

Resolution
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raw_fallback, observed 2026-08-05T10:26:25.205143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation d402ad0d-6e6a-4d28-b70d-ed90bdb7f42c · outbound

This paper cites SmoothQuant: accurate and efficient post-training quantization for large language models.International Conference on Machine Learning, PMLR 202:38087–38099.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning SmoothQuant: accurate and efficient post-training quantization for large language models.International Conference on Machine Learning, PMLR 202:38087–38099

Reference 20

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 0c1556f1-557e-49b1-8056-6c59053467a8 · outbound

This paper cites an unresolved cited work.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Unresolved cited work

Reference 21

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 3fd03ce3-d73d-4723-b0a2-6a6e45c84910 · outbound

This paper cites Muon-OGD: Muon-based Spectral Orthogonal Gradient Projection for LLM Continual Learning.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Muon-OGD: Muon-based Spectral Orthogonal Gradient Projection for LLM Continual Learning

Reference 22

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Unavailable: canonical work link unavailable.

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Observation 96dd2c6a-6f84-4ca3-8372-94fa39a77a91 · outbound

This paper cites Spectral Manifold Regularization for Stable and Modular Routing in Deep MoE Architectures.arXiv:2601.03889.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Spectral Manifold Regularization for Stable and Modular Routing in Deep MoE Architectures.arXiv:2601.03889

Reference 23

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verified exact
raw_fallback, observed 2026-08-05T10:26:25.052897Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 36ae451f-f52e-4756-b9a1-ad8a0e9b6ce8 · outbound

This paper cites Catastrophic Forgetting is Low-Rank: A Function-Space Theory for Continual Adaptation.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Catastrophic Forgetting is Low-Rank: A Function-Space Theory for Continual Adaptation

Reference 24

Resolution
verified exact
local_arxiv, observed 2026-08-05T10:26:24.976044Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 12eea287-66b1-455c-921f-a05adf11afc8 · outbound

This paper cites Non-Determinism in TensorFlow ResNets.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Non-Determinism in TensorFlow ResNets

Reference 25

Resolution
verified exact
local_arxiv, observed 2026-08-05T10:26:24.961766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T10:26:24.578997Z digest=sha256:0fd25f8bc02c56909e7b26cab2842ea0c551892bf55d74f3eb2ab5977908411b

Observation dcd4fc33-e2f7-48eb-824b-6f9f56388f1d · outbound

This paper cites Deterministic Implementations for Reproducibility in Deep Reinforcement Learning.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Deterministic Implementations for Reproducibility in Deep Reinforcement Learning

Reference 26

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no resolver link, observed 2026-08-05T10:26:24.582442Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:26:24.582442Z digest=sha256:6336de5299edfda0f7e3ca6097ea9784feb570ab60ac2f8cb8e2a41d5c66ceac

Observation 7b3b9a17-83ab-43b6-ac22-f5e8712d7325 · outbound

This paper cites On the reproducibility of fully convolutional neural networks for modeling time-space evolving physical systems.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning On the reproducibility of fully convolutional neural networks for modeling time-space evolving physical systems

Reference 27

Resolution
verified exact
local_arxiv, observed 2026-08-05T10:26:24.938453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 9b6ca0dc-c11e-42ed-8a8d-d6daedbfc648 · outbound

This paper cites Subspace Geometry Governs Catastrophic Forgetting in Low-Rank Adap- tation.arXiv:2603.02224.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Subspace Geometry Governs Catastrophic Forgetting in Low-Rank Adap- tation.arXiv:2603.02224

Reference 28

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no resolver link, observed 2026-08-05T10:26:24.589134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:26:24.589134Z digest=sha256:bd0504a10f7b1a90ff67ba9d2b08c805e8dafb586d4eddeae85b4c5304d232ef

Observation a84f72ec-e6fc-4013-a34c-cefd49d7f9ad · outbound

This paper cites From Weights to Features: SAE-Guided Activation Regularization for LLM Continual Learning.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning From Weights to Features: SAE-Guided Activation Regularization for LLM Continual Learning

Reference 29

Resolution
verified exact
local_arxiv, observed 2026-08-05T10:26:24.862242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T10:26:24.592466Z digest=sha256:0fee9e358c3a408c01baabe88cb0cecc12ea659d541dde0f072f58ce8797eac3

Observation fd6dcebe-8e0e-451b-b6c2-8de319cdb07b · outbound

This paper cites Mechanistic Analysis of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning.arXiv:2601.18699.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Mechanistic Analysis of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning.arXiv:2601.18699

Reference 30

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no resolver link, observed 2026-08-05T10:26:24.595800Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:26:24.595800Z digest=sha256:49684cd8eb62d4e636d18edec4f002d5d89e73079e9f317adc047ad107a71ec9

Observation 19e8db7c-b4f9-426c-b28c-9f2998894fd4 · outbound

This paper cites an unresolved cited work.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Unresolved cited work

Reference 31

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raw_fallback, observed 2026-08-05T10:26:25.175488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T10:26:24.598875Z digest=sha256:55f033ed917755d73cda79ac609a95caab332353c03c549942e035ca69744a4d

Observation 805dae23-d667-4e8d-b2fc-55ff2e8b5214 · outbound

This paper cites Learning without forgetting.IEEE TPAMI40(12):2935–2947.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Learning without forgetting.IEEE TPAMI40(12):2935–2947

Reference 32

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raw_fallback, observed 2026-08-05T10:26:25.165673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T10:26:24.602009Z digest=sha256:034f7d777923f7ba04af757d41bf0cc6b07040a89a3ac4f505ae5fdc99318c4c

Observation 1bc4bb28-f81d-455d-b384-bd7bfc09208c · outbound

This paper cites Orthogonal gradient descent for continual learning.AISTATS, PMLR 108:3762–3773.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Orthogonal gradient descent for continual learning.AISTATS, PMLR 108:3762–3773

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:26:25.155622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T10:26:24.605035Z digest=sha256:dc3ce7eb80277a884676173b5e5185a452169339d80bba7c087f9d7d652f275b

Observation 4d0f2c80-17e0-45a2-8ed2-b7d93a4f18b8 · outbound

This paper cites Orthogonal subspace learning for language model continual learning.Findings of EMNLP 2023, 10658–10671.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Orthogonal subspace learning for language model continual learning.Findings of EMNLP 2023, 10658–10671

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:26:25.145067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T10:26:24.608555Z digest=sha256:d28c0b5985f8615f4f95b14e41ed35f76bb3f0db61612d1037bfea6c6979e9ac

Observation 8f910338-c48d-48e9-b6c5-fcaaeac7bb67 · outbound

This paper cites Sculpting Subspaces: Constrained Full Fine-Tuning in LLMs for Continual Learning.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Sculpting Subspaces: Constrained Full Fine-Tuning in LLMs for Continual Learning

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-05T10:26:24.611370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:26:24.611370Z digest=sha256:63c1a5f93f60790f2cf7e6d79c923f8621796543a9d5d223f4bba7a411b1ed3e

Observation e831a52d-b75f-496a-8a05-7ff088fdc8d0 · outbound

This paper cites TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-05T10:26:24.614430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:26:24.614430Z digest=sha256:d39477e5153a3cf3bc75b753ec92a5874e16fef80a18bcc0a58ae5807503d7d8

Observation 608fe6d0-2316-4a45-a739-9e61fecb7bdb · outbound

This paper cites AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models

Reference 37

Resolution
verified exact
local_arxiv, observed 2026-08-05T10:26:24.710725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T10:26:24.617813Z digest=sha256:bf2840873debe53c116402ffc3487033f9ecf11f100e26df9f6f1dd1d05a034c

Observation 4f4e7d1b-230f-41ad-93d6-02a12a112190 · outbound

This paper cites Efficient Shapley Value-based Non-Uniform Pruning of Large Language Models.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Efficient Shapley Value-based Non-Uniform Pruning of Large Language Models

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-05T10:26:24.621117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:26:24.621117Z digest=sha256:85743c82f5907813c8e67bb8479731c4a899e5ab93727626a92b89ce5ef75fd5

Observation 8cd6f8d9-dfc2-4d3a-983f-4a5728b10c99 · outbound

This paper cites DarwinLM: Evolutionary Structured Pruning of Large Language Models.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning DarwinLM: Evolutionary Structured Pruning of Large Language Models

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-05T10:26:24.624287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:26:24.624287Z digest=sha256:8275f759a8fb2f68505cd70c3c76b26af324a864d68d407aafee758dcbdbde0c

Observation 46b58445-028f-458b-a3fc-e18c928addbc · outbound

This paper cites an unresolved cited work.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Unresolved cited work

Reference 40

Resolution
unresolved
raw_fallback, observed 2026-08-05T10:26:25.134839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T10:26:24.627553Z digest=sha256:18e9413305331036309b9362307e311a8637576d722250a91fb4f929c1ab4316

Observation ac4ed349-03fa-4ac9-9061-7fba997d349e · outbound

This paper cites an unresolved cited work.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Unresolved cited work

Reference 41

Resolution
unresolved
raw_fallback, observed 2026-08-05T10:26:25.124728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T10:26:24.630500Z digest=sha256:edf84aef801798fed1f9a3970e537744b282225c468ee0e66aec5d298f25115b

Observation d9671f36-363b-43bf-907c-c49b1a607387 · outbound

This paper cites an unresolved cited work.

Omega-S: A Functional Resilience Index for LLM Fine-Tuning Unresolved cited work

Reference 42

Resolution
unresolved
raw_fallback, observed 2026-08-05T10:26:25.114601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T10:26:24.633581Z digest=sha256:c9fed84b231cd5e1414b341fbbae72237c89c73bf5280bc95b404ea9e821e8f5

Pith citing papers

No inbound Pith citation observations are available.