# Continuum-Interaction-Driven Intelligence: Human-Aligned Neural Architecture via Crystallized Reasoning and Fluid Generation

arXiv: https://arxiv.org/abs/2504.09301 · HTML: https://mlsanity.com/paper/2504.09301/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 12 of its 42 references that are in the map.
- First posted on arXiv 12 Apr 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference](https://mlsanity.com/paper/1902.01007/) (2019)
- [The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence](https://mlsanity.com/paper/2002.06177/) (2020)
- [The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning](https://mlsanity.com/paper/2205.03401/) (2022)
- [Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems](https://mlsanity.com/paper/2504.01990/) (2025)
- [Thinking Fast and Slow in AI](https://mlsanity.com/paper/2010.06002/) (2020)
- [Cognitive Dissonance: Why Do Language Model Outputs Disagree with Internal Representations of Truthfulness?](https://mlsanity.com/paper/2312.03729/) (2023)
- [Models as Agents: Optimizing Multi-Step Predictions of Interactive Local Models in Model-Based Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2303.17984/) (2023)
- [Implicit Probabilistic Reasoning Does Not Reflect Explicit Answers in Large Language Models](https://mlsanity.com/paper/2406.14986/) (2024)

## Built on by (most cited first, 1 of 1)

- [Why do AI agents communicate in human language?](https://mlsanity.com/paper/2506.02739/) (2025)

## Abstract (arXiv)

Current AI systems based on probabilistic neural networks, such as large language models (LLMs), have demonstrated remarkable generative capabilities yet face critical challenges including hallucination, unpredictability, and misalignment with human decision-making. These issues fundamentally stem from the over-reliance on randomized (probabilistic) neural networks-oversimplified models of biological neural networks-while neglecting the role of procedural reasoning (chain-of-thought) in trustworthy decision-making. Inspired by the human cognitive duality of fluid intelligence (flexible generation) and crystallized intelligence (structured knowledge), this study proposes a dual-channel intelligent architecture that integrates probabilistic generation (LLMs) with white-box procedural reasoning (chain-of-thought) to construct interpretable, continuously learnable, and human-aligned AI systems. Concretely, this work: (1) redefines chain-of-thought as a programmable crystallized intelligence carrier, enabling dynamic knowledge evolution and decision verification through multi-turn interaction frameworks; (2) introduces a task-driven modular network design that explicitly demarcates the functional boundaries between randomized generation and procedural control to address trustworthiness in vertical-domain applications; (3) demonstrates that multi-turn interaction is a necessary condition for intelligence emergence, with dialogue depth positively correlating with the system's human-alignment degree. This research not only establishes a new paradigm for trustworthy AI deployment but also provides theoretical foundations for next-generation human-AI collaborative systems.