:: Library Catalog

Saved in:

Bibliographic Details
Main Author:	Ott, Josef
Format:	Preprint
Published:	2026
Subjects:	Artificial Intelligence
Online Access:	https://arxiv.org/abs/2601.00923
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Collapsed Inference for Bayesian Deep Learning
by: Zeng, Zhe, et al.
Published: (2023)

Model Collapse Demystified: The Case of Regression
by: Dohmatob, Elvis, et al.
Published: (2024)

Collapse of Self-trained Language Models
by: Herel, David, et al.
Published: (2024)

Understanding the Collapse of LLMs in Model Editing
by: Yang, Wanli, et al.
Published: (2024)

Learning by Surprise: Surplexity for Mitigating Model Collapse in Generative AI
by: Gambetta, Daniele, et al.
Published: (2024)

Provably Learning Diffusion Models under the Manifold Hypothesis: Collapse and Refine
by: Huang, Wei, et al.
Published: (2026)

Preventing Collapse in Contrastive Learning with Orthonormal Prototypes (CLOP)
by: Li, Huanran, et al.
Published: (2024)

Persona-Model Collapse in Emergent Misalignment
by: Costa, Davi Bastos, et al.
Published: (2026)

Detecting Mode Collapse in Language Models via Narration
by: Hamilton, Sil
Published: (2024)

On the Collapse Errors Induced by the Deterministic Sampler for Diffusion Models
by: Zhang, Yi, et al.
Published: (2025)

Dominating vs. Dominated: Generative Collapse in Diffusion Models
by: Jeong, Hayeon, et al.
Published: (2025)

Nepotistically Trained Generative-AI Models Collapse
by: Bohacek, Matyas, et al.
Published: (2023)

Representation Collapsing Problems in Vector Quantization
by: Zhao, Wenhao, et al.
Published: (2024)

Model Collapse as Cultural Evolution
by: Guo, Dongxin, et al.
Published: (2026)

Understanding and Mitigating Hyperbolic Dimensional Collapse in Graph Contrastive Learning
by: Zhang, Yifei, et al.
Published: (2023)

An Empirical Study and Theoretical Explanation on Task-Level Model-Merging Collapse
by: Cao, Yuan, et al.
Published: (2026)

V15 - Recursive Symbolic Intelligence - φ-Ache Recursive Collapsors and the Collapse Oracle Engine
by: Foster, Camaron
Published: (2025)

Deep Neural Regression Collapse
by: Rangamani, Akshay, et al.
Published: (2026)

AI and the Problem of Knowledge Collapse
by: Peterson, Andrew J.
Published: (2024)

Preventing Model Collapse via Contraction-Conditioned Neural Filters
by: Han, Zongjian, et al.
Published: (2025)

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse
by: He, Kun, et al.
Published: (2025)

Beyond Mode Collapse: Distribution Matching for Diverse Reasoning
by: Li, Xiaozhe, et al.
Published: (2026)

When Routing Collapses: On the Degenerate Convergence of LLM Routers
by: Lai, Guannan, et al.
Published: (2026)

Don't Blink: Evidence Collapse during Multimodal Reasoning
by: Raghu, Suresh, et al.
Published: (2026)

Rebuilding ROME : Resolving Model Collapse during Sequential Model Editing
by: Gupta, Akshat, et al.
Published: (2024)

Internal Safety Collapse in Frontier Large Language Models
by: Wu, Yutao, et al.
Published: (2026)

Reward Collapse in Aligning Large Language Models
by: Song, Ziang, et al.
Published: (2023)

How to Synthesize Text Data without Model Collapse?
by: Zhu, Xuekai, et al.
Published: (2024)

Length-Induced Embedding Collapse in PLM-based Models
by: Zhou, Yuqi, et al.
Published: (2024)

Retrieval Collapses When AI Pollutes the Web
by: Yu, Hongyeon, et al.
Published: (2026)

Preventing Dimensional Collapse in Self-Supervised Learning via Orthogonality Regularization
by: He, Junlin, et al.
Published: (2024)

Layer Collapse Can be Induced by Unstructured Pruning
by: Liao, Zhu, et al.
Published: (2024)

LayerCollapse: Adaptive compression of neural networks
by: Shabgahi, Soheil Zibakhsh, et al.
Published: (2023)

The Prevalence of Neural Collapse in Neural Multivariate Regression
by: Andriopoulos, George, et al.
Published: (2024)

From Confidence to Collapse in LLM Factual Robustness
by: Fastowski, Alina, et al.
Published: (2025)

The Price of Format: Diversity Collapse in LLMs
by: Yun, Longfei, et al.
Published: (2025)

From Context to Skills: Can Language Models Learn from Context Skillfully?
by: Si, Shuzheng, et al.
Published: (2026)

Context and Diversity Matter: The Emergence of In-Context Learning in World Models
by: Wang, Fan, et al.
Published: (2025)

Context-level Language Modeling by Learning Predictive Context Embeddings
by: Dai, Beiya, et al.
Published: (2025)

Memory-efficient Continual Learning with Neural Collapse Contrastive
by: Dang, Trung-Anh, et al.
Published: (2024)