ARXIV:2606.07500 · CONTINUAL LEARNING AI · SUBMITTED 08 JUN · 17:14 UTC · FRESHNESS FRESH

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Sparse Subspace-to-Expert Sharing for Task-Agnostic Continual Learning

Fatema Siddika · Md Anwar Hossen · Tanwi Mallick · Ali Jannesari · arXiv

Framework for task-agnostic continual learning to prevent forgetting in AI models.

Ship in 2-4 weeks›Score6.0Evidence unverified

Opportunity summary

Pain Framework for task-agnostic continual learning to prevent forgetting in AI models.

Evidence 0 refs | 3 sources | 50% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Framework for task-agnostic continual learning to prevent forgetting in AI models. Existing methods typically treat parameters uniformly, failing to distinguish between specific task knowledge and shared capabilities.

METHOD

Full abstract

Continual learning in Large Language Models (LLMs) is hindered by the plasticity-stability dilemma, where acquiring new capabilities often leads to catastrophic forgetting of previous knowledge. Existing methods typically treat parameters uniformly, failing to distinguish between specific task knowledge and shared capabilities. We introduce Mixture of Sparse Experts for Task Agnostic Continual Learning (SETA), a framework that resolves the plasticity-stability conflict through adaptive sparse subspace decomposition into task-specific expert modules. Unlike standard updates, where tasks compete for the same parameters, SETA separates knowledge into unique experts, designed to isolate task-specific patterns, and shared experts, responsible for capturing common features. This structure is maintained through adaptive elastic anchoring and a routing-aware regularization that jointly protect shared knowledge at both the weight and routing levels and enable a unified gating network to automatically retrieve the correct expert combination during inference. Extensive experiments across diverse domain-specific benchmarks demonstrate that SETA achieves competitive or superior overall performance relative to state-of-the-art continual learning baselines, with particularly strong retention of early-task knowledge and improved backward transfer on LLaMA-2 7B and Qwen3-4B.

RESULT

ScienceToStartup currently rates this 6.0/10 on the public viability pass. This structure is maintained through adaptive elastic anchoring and a routing-aware regularization that jointly protect shared knowledge at both the weight and routing levels…

WHY NOW

Continual Learning AI moved forward this cycle; last verified June 2026. Public score 6.0/10. Production flags indicate code availability.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score6.0

PainFramework for task-agnostic continual learning to prevent forgetting in AI models.

Evidence0 refs | 3 sources | 50% coverage

Blockerno shell-level blocker reported

Analysis summary

Framework for task-agnostic continual learning to prevent forgetting in AI models.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

References(39)

Low-Rank Adapters Meet Neural Architecture Search for LLM Compression

2025J. P. Munoz, Jinjie Yuan et al.

SMT: Fine-Tuning Large Language Models with Sparse Matrices

2025Haoze He, Juncheng B. Li et al.

SLIM: Let LLM Learn More and Forget Less with Soft LoRA and Identity Mixture

2024Jiayi Han, Liang Du et al.

AdaZeta: Adaptive Zeroth-Order Tensor-Train Adaption for Memory-Efficient Large Language Models Fine-Tuning

2024Yifan Yang, Kai Zhen et al.

AdaMoLE: Fine-Tuning Large Language Models with Adaptive Mixture of Low-Rank Adaptation Experts

2024Zefang Liu, Jiahua Luo

Analyzing and Reducing Catastrophic Forgetting in Parameter Efficient Tuning

2024Weijieying Ren, Xinlong Li et al.

Adaptive Rank Selections for Low-Rank Approximation of Language Models

2024Shangqian Gao, Ting Hua et al.

RankAdaptor: Hierarchical Dynamic Low-Rank Adaptation for Structural Pruned LLMs

2024Changhai Zhou, Shijie Han et al.

An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-Tuning

2023Yun Luo, Zhen Yang et al.

ReLoRA: High-Rank Training Through Low-Rank Updates

2023V. Lialin, Namrata Shivagunde et al.

MeetingBank: A Benchmark Dataset for Meeting Summarization

2023Yebowen Hu, Timothy Jeewun Ganter et al.

Trillion Dollar Words: A New Financial Dataset, Task & Market Analysis

2023Agam Shah, Suvan Paturi et al.

Progressive Prompts: Continual Learning for Language Models

2023Anastasia Razdaibiedina, Yuning Mao et al.

20 Minuten: A Multi-task News Summarisation Dataset for German

2023Tannon Kew, M. Kostrzewa et al.

C-STANCE: A Large Dataset for Chinese Zero-Shot Stance Detection

2023Chenye Zhao, Yingjie Li et al.

Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering

2022Pan Lu, Swaroop Mishra et al.

NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks

2022Swaroop Mishra, Arindam Mitra et al.

Mixture-of-Experts with Expert Choice Routing

2022Yanqi Zhou, Tao Lei et al.

BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models

2021Elad Ben-Zaken, Shauli Ravfogel et al.

Hash Layers For Large Sparse Models

2021Stephen Roller, Sainbayar Sukhbaatar et al.

Showing 20 of 39 references

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References(39)

Low-Rank Adapters Meet Neural Architecture Search for LLM Compression

2025J. P. Munoz, Jinjie Yuan et al.

SMT: Fine-Tuning Large Language Models with Sparse Matrices

2025Haoze He, Juncheng B. Li et al.

SLIM: Let LLM Learn More and Forget Less with Soft LoRA and Identity Mixture

2024Jiayi Han, Liang Du et al.

AdaZeta: Adaptive Zeroth-Order Tensor-Train Adaption for Memory-Efficient Large Language Models Fine-Tuning

2024Yifan Yang, Kai Zhen et al.

AdaMoLE: Fine-Tuning Large Language Models with Adaptive Mixture of Low-Rank Adaptation Experts

2024Zefang Liu, Jiahua Luo

Analyzing and Reducing Catastrophic Forgetting in Parameter Efficient Tuning

2024Weijieying Ren, Xinlong Li et al.

Adaptive Rank Selections for Low-Rank Approximation of Language Models

2024Shangqian Gao, Ting Hua et al.

RankAdaptor: Hierarchical Dynamic Low-Rank Adaptation for Structural Pruned LLMs

2024Changhai Zhou, Shijie Han et al.

An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-Tuning

2023Yun Luo, Zhen Yang et al.

ReLoRA: High-Rank Training Through Low-Rank Updates

2023V. Lialin, Namrata Shivagunde et al.

MeetingBank: A Benchmark Dataset for Meeting Summarization

2023Yebowen Hu, Timothy Jeewun Ganter et al.

Trillion Dollar Words: A New Financial Dataset, Task & Market Analysis

2023Agam Shah, Suvan Paturi et al.

Progressive Prompts: Continual Learning for Language Models

2023Anastasia Razdaibiedina, Yuning Mao et al.

20 Minuten: A Multi-task News Summarisation Dataset for German

2023Tannon Kew, M. Kostrzewa et al.

C-STANCE: A Large Dataset for Chinese Zero-Shot Stance Detection

2023Chenye Zhao, Yingjie Li et al.

Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering

2022Pan Lu, Swaroop Mishra et al.

NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks

2022Swaroop Mishra, Arindam Mitra et al.

Mixture-of-Experts with Expert Choice Routing

2022Yanqi Zhou, Tao Lei et al.

BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models

2021Elad Ben-Zaken, Shauli Ravfogel et al.

Hash Layers For Large Sparse Models

2021Stephen Roller, Sainbayar Sukhbaatar et al.

Showing 20 of 39 references

Sparse Subspace-to-Expert Sharing for Task-Agnostic Continual Learning

Sparse Subspace-to-Expert Sharing for Task-Agnostic Continual Learning

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