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Research#llm🏛️ OfficialAnalyzed: Jan 3, 2026 06:32

AI Model Learns While Reading

Published:Jan 2, 2026 22:31
1 min read
r/OpenAI

Analysis

The article highlights a new AI model, TTT-E2E, developed by researchers from Stanford, NVIDIA, and UC Berkeley. This model addresses the challenge of long-context modeling by employing continual learning, compressing information into its weights rather than storing every token. The key advantage is full-attention performance at 128K tokens with constant inference cost. The article also provides links to the research paper and code.
Reference

“TTT-E2E keeps training while it reads, compressing context into its weights. The result: full-attention performance at 128K tokens, with constant inference cost.”

Permalinkr/OpenAI
Paper#llm🔬 ResearchAnalyzed: Jan 3, 2026 15:57

Efficient Long-Context Attention

Published:Dec 30, 2025 03:39
1 min read
ArXiv

Analysis

This paper introduces LongCat ZigZag Attention (LoZA), a sparse attention mechanism designed to improve the efficiency of long-context models. The key contribution is the ability to transform existing full-attention models into sparse versions, leading to speed-ups in both prefill and decode phases, particularly relevant for retrieval-augmented generation and tool-integrated reasoning. The claim of processing up to 1 million tokens is significant.
Reference

“LoZA can achieve significant speed-ups both for prefill-intensive (e.g., retrieval-augmented generation) and decode-intensive (e.g., tool-integrated reasoning) cases.”

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