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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
Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 09:10

CTkvr: KV Cache Retrieval for Long-Context LLMs via Centroid then Token Indexing

Published:Dec 17, 2025 15:56
1 min read
ArXiv

Analysis

This article introduces CTkvr, a novel approach for efficiently retrieving KV caches in long-context LLMs. The method utilizes a two-stage process: first, identifying relevant centroids, and then indexing tokens within those centroids. This could potentially improve the performance and scalability of LLMs dealing with extensive input sequences. The paper's focus on KV cache retrieval suggests an effort to optimize the memory access patterns, which is a critical bottleneck in long-context models. Further evaluation is needed to assess the practical impact and efficiency gains compared to existing methods.
Reference

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PermalinkArXiv