Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning | Paper Citation Record

Observation 9ebd430f-e568-428e-a6e8-af8a5cfddb96 · inbound

Prefix-Tuning: Optimizing Continuous Prompts for Generation cites this paper.

Prefix-Tuning: Optimizing Continuous Prompts for Generation Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:57:25.130831Z

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Observation 09f407dd-8bc8-4250-ade5-b1a3f63ac589 · inbound

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

LoRA: Low-Rank Adaptation of Large Language Models Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-09T05:01:40.870617Z

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Observation 4ae294c0-dbf4-4331-a97d-36fcee26a0fe · inbound

Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey cites this paper.

Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 75

Resolution: verified exact; arxiv_id, observed 2026-05-13T11:32:36.949559Z

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Observation f86e1917-3d17-4b67-93a3-1b6b18f82a8d · inbound

Small Language Models (SLMs) Can Still Pack a Punch: A survey (updated 2026) cites this paper.

Small Language Models (SLMs) Can Still Pack a Punch: A survey (updated 2026) Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-23T05:52:37.538773Z

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Observation af6ac63d-9843-42d8-82a8-1ced34bac69d · inbound

Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models cites this paper.

Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 3

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Observation f400d333-3e30-48f7-8d56-4699437eded5 · inbound

ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential Recommendation cites this paper.

ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential Recommendation Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 3

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Observation c656a7c6-1b45-4e4e-b8e9-2ed8b62022ce · inbound

LoRA-TTT: Low-Rank Test-Time Training for Vision-Language Models cites this paper.

LoRA-TTT: Low-Rank Test-Time Training for Vision-Language Models Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 1

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Observation 2d67a427-911d-4180-82ba-808941a99a23 · inbound

Dynamic Rank Adjustment in Diffusion Policies for Efficient and Flexible Training cites this paper.

Dynamic Rank Adjustment in Diffusion Policies for Efficient and Flexible Training Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 1

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Observation 3818e60a-cff0-4246-b839-04b28c61f2ec · inbound

TruthFlow: Truthful LLM Generation via Representation Flow Correction cites this paper.

TruthFlow: Truthful LLM Generation via Representation Flow Correction Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 2

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Observation 6d527b2f-bf43-41e5-81b6-596e8173d85a · inbound

Low Tensor-Rank Adaptation of Kolmogorov--Arnold Networks cites this paper.

Low Tensor-Rank Adaptation of Kolmogorov--Arnold Networks Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 45

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Observation 54d53c3d-4ecc-4400-83dd-217b2b2a99e4 · inbound

Document Retrieval Augmented Fine-Tuning (DRAFT) for safety-critical software assessments cites this paper.

Document Retrieval Augmented Fine-Tuning (DRAFT) for safety-critical software assessments Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-22T17:11:49.715631Z

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Observation 5658877c-8b24-4641-be9b-f57e8843d9f6 · inbound

Transformers for Learning on Noisy and Task-Level Manifolds: Approximation and Generalization Insights cites this paper.

Transformers for Learning on Noisy and Task-Level Manifolds: Approximation and Generalization Insights Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-22T16:01:46.053959Z

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Observation fac4bddb-0480-4d44-8798-60e2bf00ee42 · inbound

ReqBrain: Task-Specific Instruction Tuning of LLMs for AI-Assisted Requirements Generation cites this paper.

ReqBrain: Task-Specific Instruction Tuning of LLMs for AI-Assisted Requirements Generation Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 47

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Observation 47a8bf3b-365a-4475-bd6c-9aa8ea6f0876 · inbound

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models cites this paper.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 43

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Observation 614b927c-bac2-480d-bd63-147f6848bdab · inbound

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation cites this paper.

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T12:56:49.026580Z

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Observation 305bed03-0915-4917-ad85-997ed049fba9 · inbound

Weight Spectra Induced Efficient Model Adaptation cites this paper.

Weight Spectra Induced Efficient Model Adaptation Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 1

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Observation 66ed174c-812f-446a-b1f5-bea44fb1fabb · inbound

Gradient-Based Model Fingerprinting for LLM Similarity Detection and Family Classification cites this paper.

Gradient-Based Model Fingerprinting for LLM Similarity Detection and Family Classification Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 26

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Observation a017da4f-df26-49d9-aa76-4beeda337956 · inbound

Backbone Augmented Training for Adaptations cites this paper.

Backbone Augmented Training for Adaptations Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 28

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Observation b24f5cad-fbf4-4111-89a9-6f7730b111b9 · inbound

Slimming Down LLMs Without Losing Their Minds cites this paper.

Slimming Down LLMs Without Losing Their Minds Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 1

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Observation 47218487-b391-4fa6-9710-2c135d10332c · inbound

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence cites this paper.

Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T00:43:48.811606Z

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Observation 00e37b73-2621-4e45-a100-dcd97eba557f · inbound

Generalizing vision-language models to novel domains: A comprehensive survey cites this paper.

Generalizing vision-language models to novel domains: A comprehensive survey Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 175

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Observation 56e2f820-1ebf-457c-ac67-2d2552239c7d · inbound

Little by Little: Continual Learning via Incremental Mixture of Rank-1 Associative Memory Experts cites this paper.

Little by Little: Continual Learning via Incremental Mixture of Rank-1 Associative Memory Experts Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:06:34.632662Z

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Observation 37bfc647-31c5-4390-a389-0a0dc140b1ba · inbound

Time Series Foundation Models for Multivariate Financial Time Series Forecasting cites this paper.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T18:49:27.233404Z

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Observation ee869c9b-da67-4ebc-82a6-09b7c939ad15 · inbound

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints cites this paper.

ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 2

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Observation d8a05faa-bf20-4393-b495-be402daa5a27 · inbound

Differentially Private Federated Low Rank Adaptation Beyond Fixed-Matrix cites this paper.

Differentially Private Federated Low Rank Adaptation Beyond Fixed-Matrix Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 2

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Implementing Adaptations for Vision AutoRegressive Model cites this paper.

Implementing Adaptations for Vision AutoRegressive Model Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 4

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Observation 002c2b2a-b1d8-49b9-9e84-d8cc888a64fc · inbound

Sensitivity-LoRA: Low-Load Sensitivity-Based Fine-Tuning for Large Language Models cites this paper.

Sensitivity-LoRA: Low-Load Sensitivity-Based Fine-Tuning for Large Language Models Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-04T19:43:36.387826Z

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Observation 1bc12882-944b-4ae0-a888-95f331b2fb26 · inbound

HyperAdapt: Simple High-Rank Adaptation cites this paper.

HyperAdapt: Simple High-Rank Adaptation Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 2

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Observation 45b4c998-8dc2-47fa-afcd-10502b6782c3 · inbound

CR-Net: Scaling Parameter-Efficient Training with Cross-Layer Low-Rank Structure cites this paper.

CR-Net: Scaling Parameter-Efficient Training with Cross-Layer Low-Rank Structure Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-18T14:52:41.232875Z

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Observation 8a167dd9-1f8e-435e-bfa9-174b65e0dede · inbound

Towards Understanding the Shape of Representations in Protein Language Models cites this paper.

Towards Understanding the Shape of Representations in Protein Language Models Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 3

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Observation f016db4c-ba5d-4b3e-964d-5a53db5126e9 · inbound

The 3D Mirage: Probing and Taming 3D Hallucinations cites this paper.

The 3D Mirage: Probing and Taming 3D Hallucinations Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T15:53:51.075843Z

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Observation 665dab8a-29d2-481e-bcad-7deecdb057a0 · inbound

Training Transformers in Cosine Coefficient Space cites this paper.

Training Transformers in Cosine Coefficient Space Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:10:50.614560Z

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Observation ff19c483-7f68-4852-8a70-36294a079198 · inbound

ARIA: Adaptive Retrieval Intelligence Assistant -- A Multimodal RAG Framework for Domain-Specific Engineering Education cites this paper.

ARIA: Adaptive Retrieval Intelligence Assistant -- A Multimodal RAG Framework for Domain-Specific Engineering Education Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-16T07:52:32.993245Z

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Observation d75d2492-c916-4b1a-8a03-ba0c2360f08b · inbound

TLoRA: Task-aware Low Rank Adaptation of Large Language Models cites this paper.

TLoRA: Task-aware Low Rank Adaptation of Large Language Models Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 53

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T09:48:48.158531Z

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Observation 3b4877fd-5944-4dfb-9b32-8ef277f7df81 · inbound

DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs cites this paper.

DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 39

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T02:53:29.787076Z

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Observation b65a58da-bc9b-41ee-bc96-a9a425084f6f · inbound

UniVidX: A Unified Multimodal Framework for Versatile Video Generation via Diffusion Priors cites this paper.

UniVidX: A Unified Multimodal Framework for Versatile Video Generation via Diffusion Priors Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 138

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T15:26:07.973751Z

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Observation aab1b2b6-82cd-428d-b2f8-df0a0e6e27cf · inbound

DataDignity: Training Data Attribution for Large Language Models cites this paper.

DataDignity: Training Data Attribution for Large Language Models Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T19:26:10.346139Z

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Observation fd531250-db85-4c7b-b79b-c52b6954e381 · inbound

Emergent Symbolic Structure in Health Foundation Models: Extraction, Alignment, and Cross-Modal Transfer cites this paper.

Emergent Symbolic Structure in Health Foundation Models: Extraction, Alignment, and Cross-Modal Transfer Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 62

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T03:45:55.999225Z

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Observation f8d21098-b5aa-40f6-963f-0df9d9285249 · inbound

Combining pre-trained models via localized model averaging cites this paper.

Combining pre-trained models via localized model averaging Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 162

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T17:57:32.958945Z

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Observation ade2d6e1-3f8c-4ceb-96ee-3673f1bf50ba · inbound

LoCO: Low-rank Compositional Rotation Fine-tuning cites this paper.

LoCO: Low-rank Compositional Rotation Fine-tuning Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:53:42.612237Z

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Observation c618919c-8d8f-4d0e-bc9b-bf01b0096178 · inbound

Interpretable Discriminative Text Representations via Agreement and Label Disentanglement cites this paper.

Interpretable Discriminative Text Representations via Agreement and Label Disentanglement Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-21T05:49:41.172032Z

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Observation 51b84195-2dbd-4263-b5e7-0efc268326f9 · inbound

The Fine-Tuning Trap: Evaluating Negative Transfer and the Role of PEFT in Sub-1B Mathematical Reasoning cites this paper.

The Fine-Tuning Trap: Evaluating Negative Transfer and the Role of PEFT in Sub-1B Mathematical Reasoning Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:07:09.177227Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b4226690-c68b-4daf-a2f8-b7792fc8f400 · inbound

Reversible Foundations: Training a 120B Sparse MoE through State-Preserving Scaling cites this paper.

Reversible Foundations: Training a 120B Sparse MoE through State-Preserving Scaling Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:07:09.428884Z

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Observation a486ae7c-c52b-4449-a8bc-d74d99a942f0 · inbound

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems cites this paper.

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 103

Resolution: verified exact; arxiv_id, observed 2026-07-04T11:09:46.578767Z

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Observation 4bef54d0-6bfa-4883-91f2-d635b64cc5bc · inbound

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems cites this paper.

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 93

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Observation d7ca4583-d419-4370-ac31-038f60b9c54c · inbound

FRAME: Learning the Adaptation Domain with a Mixture of Fractional-Fourier Experts cites this paper.

FRAME: Learning the Adaptation Domain with a Mixture of Fractional-Fourier Experts Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T19:57:19.212262Z

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Observation d436607c-5239-4eea-ab53-6e46bd3c16ec · inbound

Co-Adaptive Multi-Task LoRA: Transfer-Aware, Label-Free Control of Domain Participation cites this paper.

Co-Adaptive Multi-Task LoRA: Transfer-Aware, Label-Free Control of Domain Participation Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 70

Resolution: unresolved; no resolver link, observed 2026-07-12T01:54:44.256835Z

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Observation a3dd770c-182c-44ab-b863-463604ebb909 · inbound

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling cites this paper.

SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 262

Resolution: unresolved; no resolver link, observed 2026-08-02T09:51:03.879892Z

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Observation b31f9d26-ec54-4dfe-8363-e3cea7a3fc57 · inbound

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning cites this paper.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:03.104886Z

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Observation 5dba6b06-94b8-4175-a97c-3c6a7dfd912c · inbound

Complexity and Stability of Neural Activity Across Aging and Neurodegenerative Disease cites this paper.

Complexity and Stability of Neural Activity Across Aging and Neurodegenerative Disease Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T21:49:05.458457Z

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