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Research Paper#Language Modeling, Transformers, Continual Learning, Test-Time Training🔬 ResearchAnalyzed: Jan 3, 2026 16:01

End-to-End Test-Time Training for Long Context Language Modeling

Published:Dec 29, 2025 18:30
2 min read
ArXiv

Analysis

This paper proposes a novel approach to long-context language modeling by framing it as a continual learning problem. The core idea is to use a standard Transformer architecture with sliding-window attention and enable the model to learn at test time through next-token prediction. This End-to-End Test-Time Training (TTT-E2E) approach, combined with meta-learning for improved initialization, demonstrates impressive scaling properties, matching full attention performance while maintaining constant inference latency. This is a significant advancement as it addresses the limitations of existing long-context models, such as Mamba and Gated DeltaNet, which struggle to scale effectively. The constant inference latency is a key advantage, making it faster than full attention for long contexts.
Reference

“TTT-E2E scales with context length in the same way as Transformer with full attention, while others, such as Mamba 2 and Gated DeltaNet, do not. However, similar to RNNs, TTT-E2E has constant inference latency regardless of context length, making it 2.7 times faster than full attention for 128K context.”

PermalinkArXiv
Research#llm🔬 ResearchAnalyzed: Dec 25, 2025 00:34

Large Language Models for EDA Cloud Job Resource and Lifetime Prediction

Published:Dec 24, 2025 05:00
1 min read
ArXiv ML

Analysis

This paper presents a compelling application of Large Language Models (LLMs) to a practical problem in the Electronic Design Automation (EDA) industry: resource and job lifetime prediction in cloud environments. The authors address the limitations of traditional machine learning methods by leveraging the power of LLMs for text-to-text regression. The introduction of scientific notation and prefix filling to constrain the LLM's output is a clever approach to improve reliability. The finding that full-attention finetuning enhances prediction accuracy is also significant. The use of real-world cloud datasets to validate the framework strengthens the paper's credibility and establishes a new performance baseline for the EDA domain. The research is well-motivated and the results are promising.
Reference

“We propose a novel framework that fine-tunes Large Language Models (LLMs) to address this challenge through text-to-text regression.”

PermalinkArXiv ML
Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 08:47

SWiT-4D: Sliding-Window Transformer for Lossless and Parameter-Free Temporal 4D Generation

Published:Dec 11, 2025 17:54
1 min read
ArXiv

Analysis

This article introduces SWiT-4D, a novel approach using a sliding-window Transformer for 4D generation. The key claims are lossless generation and parameter-free operation, suggesting efficiency and potentially high-fidelity results. The use of a sliding-window mechanism is likely intended to improve computational efficiency and handle temporal dependencies effectively. The source being ArXiv indicates this is a research paper, likely detailing the methodology, experiments, and results of the proposed SWiT-4D model.
Reference

“The article likely details the methodology, experiments, and results of the proposed SWiT-4D model.”

PermalinkArXiv