u/Financial-Freedom462

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最近、AIにかなりの部分を任せて、LinuxベースのOSを作る実験をしています。

名前は 「RinOS（燐OS）」。

「AIにOS開発を任せたら、実際どこまで作れるんだろう？」というところから始めました。

現在は、

- 独自Waylandコンポジタ「Hata」

- 独自のデスクトップUI

- A/Bアップデート＋自動ロールバック

- Recovery Mode / Safe Mode

- アプリごとの権限管理

- サンドボックス

- ハードウェアに合わせた自動最適化

- 自動テスト・回帰テスト

などを作っています。

AIにはコードを書かせるだけではなく、設計、テスト、バグ修正、検証などもかなり任せています。

もちろん、まだ実験段階です。

RinOS自体はまだ一般公開していません。

今は「本当にこれでちゃんとしたOSになるのか？」を試しながら開発しています。

開発状況を見たり、意見を言ったりできるDiscordも作っています。

興味があればどうぞ👇

Discord：https://discord.gg/f9RQ9ABVu

「そこAIに任せるのは無理じゃない？」みたいな意見も含めて、いろいろ聞いてみたいです。

I've been thinking about a conceptual framework that may apply to both human cognition and large language models (LLMs). I'm curious whether similar ideas already exist in cognitive science or computational neuroscience.

The basic idea is that many reasoning errors may not primarily result from poor evaluation of available options, but from the failure to retrieve the correct option into the active search space before evaluation even begins.

I imagine the process as three functional layers:

Layer 1: Long-term representation

Humans: semantic and episodic memory

LLMs: distributed knowledge stored in model parameters

Layer 2: Candidate activation (retrieval / working memory)

Context activates only a limited subset of available representations.

Only these activated candidates become available for further reasoning.

Layer 3: Evaluation and selection

Humans: executive control, attention, affective state, time pressure.

LLMs: decoding strategy (temperature, top-k, top-p, etc.).

The key hypothesis is that there are two qualitatively different kinds of reasoning failures.

1. Retrieval failure The correct representation exists in long-term memory (or model parameters), but it is never activated into the current candidate space.

2. Evaluation failure The correct candidate is activated, but another candidate is assigned a higher value or probability.

Formally, if the correct solution D is not contained in the activated candidate set C,

[ D \notin C, ]

then no evaluation process can recover it; the system can only choose among the candidates that are actually available.

This perspective seems relevant to several existing areas of research:

Working memory and retrieval limitations in cognitive psychology

Availability-based retrieval and dual-process theories

Global Workspace Theory

ACT-R and other cognitive architectures separating retrieval from selection

Retrieval-Augmented Generation (RAG) in LLMs

Research on hallucination in autoregressive language models

I'm not claiming this is a new cognitive theory, but rather wondering whether framing reasoning errors primarily as candidate activation failures versus evaluation failures could provide a useful way of connecting findings from cognitive science and AI.

Some questions I'd be interested in discussing:

Are there existing cognitive architectures that explicitly formalize this distinction?

Are there experimental paradigms that estimate the probability that the correct representation never enters working memory before a decision?

Does this distinction help explain why techniques such as Retrieval-Augmented Generation improve factual accuracy without changing model parameters?

Relevant references that motivated this idea include:

Ji et al. (2023), Survey of Hallucination in Natural Language Generation.

Kahneman (2011), Thinking, Fast and Slow.

Volz et al. (2006), Decision-making under uncertainty.

Bengio et al. (2015), Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks.

I'd appreciate any feedback, related literature, or criticism from people working in cognitive science or computational modeling.