Implementation Architecture Proposal for LLM's "Pre-Output Control" and "Time-Axis Independent Long-Term Memory" (Alaya-Core v2.0)
Published:Dec 27, 2025 23:06
•1 min read
•Zenn LLM
Analysis
This article analyzes a peculiar behavior observed in a long-term context durability test using Gemini 3 Flash, involving over 800,000 tokens of dialogue. The core focus is on the LLM's ability to autonomously correct its output before completion, a behavior described as "Pre-Output Control." This contrasts with post-output reflection. The article likely delves into the architecture of Alaya-Core v2.0, proposing a method for achieving this pre-emptive self-correction and potentially time-axis independent long-term memory within the LLM framework. The research suggests a significant advancement in LLM capabilities, moving beyond simple probabilistic token generation.
Key Takeaways
- •The article explores "Pre-Output Control" in LLMs, where the model corrects its output before completion.
- •This behavior was observed in a long-term context test with over 800,000 tokens.
- •The research likely proposes an architecture (Alaya-Core v2.0) to enable this and potentially time-axis independent long-term memory.
Reference
“"Ah, there was a risk of an accommodating bias in the current thought process. I will correct it before output."”