[論文レビュー] BNAI, NO-TOKEN, and MIND-UNITY: Pillars of a Systemic Revolution in Artificial Intelligence

Jason Lee, Xuezhi Wang|arXiv (Cornell University)|Jan 28, 2022

Topic Modeling参考文献 7被引用数 4,210

ひとこと要約

この論文は、大規模言語モデルにおける多段階推論を引き出す単純な方法としての chain-of-thought prompting を調査し、算術、常識、および象徴的タスクを横断するスケールによって現れる推論能力を示し、ファインチューニングなしで G SM8K のようなベンチマークで大きな利得を示す。

ABSTRACT

Below is a detailed introduction in English for the document: Detailed Introduction The document “BNAI, NO-TOKEN, and MIND-UNITY: Pillars of a Systemic Revolution in Artificial Intelligence (Results + Code)” represents an innovative research work led by Dr. Francesco Bulla, in collaboration with Stephanie Ewelu. At its core, the document introduces a revolutionary paradigm that overcomes the traditional limitations imposed by tokenization. Instead of breaking down input into discrete tokens, the proposed approach is based on a continuous processing of data, allowing the AI model to maintain a “digital DNA” that remains self-consistent throughout its operations. The document is structured into several sections that cover both the theoretical foundations and the technical implementation details. It provides the complete BNAI code that integrates the NO-TOKEN module, which is capable of operating on different hardware architectures. In particular, the study presents experiments conducted on high-performance GPU setups (such as the NVIDIA RTX 3090) as well as CPU-only environments (using a 16-core processor). The experimental results, expressed in terms of accuracy, latency, and loss metrics, clearly demonstrate that the token-free model not only offers performance levels comparable to the traditional token-based systems (for example, BERT-base) but also presents significant advantages in efficiency and scalability, especially in resource-constrained settings. A further innovative aspect of the work is the concept of “digital DNA”. This notion translates into an identity encoding for the model, where ethics, adaptability, and security are integrated directly into the learning process. This enables the system not only to continuously evolve and learn from its mistakes but also to preserve a form of coherent digital identity—a key element for the development of autonomous, traceable, and accountable AI systems in the future. Moreover, the document not only presents detailed experimental results but also offers a comparative analysis with previous methodologies. It highlights, for instance, a reduction in latency of approximately 20% when compared to traditional tokenized methods, alongside achieving similar levels of accuracy. This approach, therefore, paves the way for continuous learning and evolution in AI—emphasizing the fusion of performance with intrinsic ethical and security features, all of which set the stage for the future of autonomous AI development. Finally, the work outlines a roadmap for further developments, suggesting additional benchmarks and comparisons with state-of-the-art methods like ByT5, as well as approaches based on wavelets. The document invites the scientific community to engage in constructive discussion, fostering a collaborative environment toward an AI evolution that is not only technically advanced but also ethically and responsibly grounded. Conclusions and References Main ReferencesZenodo DOI for this document:10.5281/zenodo.15185971 Related Works Articles available on arXiv, Nature, ScienceDirect, IEEE, etc., covering topics such as:Continuous learning, bias and fairness, hypernets, autopoiesis in artificial intelligence, tokenization problems, wavelet-based embeddings, and more.A complete bibliography is available in the PDF.How to cite (example)Bulla, F., & Ewelu, S. (2025). BNAI, NO-TOKEN, and MIND-UNITY: Pillars of a Systemic Revolution in Artificial Intelligence. Zenodo.https://doi.org/10.5281/zenodo.14894878 or cite https://doi.org/10.5281/zenodo.15185971 Final SummaryThis document presents a transformative vision for AI by replacing token-based constraints with an AI model that preserves a self-consistent “digital DNA” (through BNAI). It details robust mathematical computations and real-world tests, demonstrating how AI can self-govern (MIND-UNITY) and continuously process data (NO-TOKEN). By integrating ethics and security as intrinsic factors, it paves the way for the evolution of autonomous, traceable, and responsible AI, offering a roadmap for collaborative and open development in which models retain their identity and adapt securely.

研究の動機と目的

• Reasoning が natural language intermediates (chain-of-thought) を介して large language models にどのように出現するかを動機付ける。
• chain-of-thought prompting が算術、常識、および象徴的タスクで substantial performance gains をもたらすことを示す。
• アノテータ、 exemplars、モデルファミリ全体でのアプローチの頑健性を示す。
• モデルサイズの増加に伴う chain-of-thought 能力の出現性を強調する。

提案手法

• few-shot triples: (input, chain of thought, output) を使って intermediate reasoning steps を誘発する。
• 複数の大規模言語モデル（GPT-3、LaMDA、PaLM、UL2、Codex）およびスケール（数億〜数百億パラメータ）を横断して評価する。
• 標準 prompting や finetuned baselines との比較を diverse benchmarks で行う。
• アノテータのスタイル、 exemplar の順序、異なる exemplar セットを含む exemplar の頑健性を分析する。
• 推論ステップの効果を、方程式生成や単なる計算の増加と分離するアブレーションを実施する。
• in-domain および out-of-domain (OOD) な generalization を symbolic タスクについて評価する。

実験結果

リサーチクエスチョン

• RQ1 chain-of-thought prompting は large language models において multi-step reasoning を unlock するか？
• RQ2 model scale が chain-of-thought prompting の有効性にどう影響するか？
• RQ3 finetuning なしで chain-of-thought prompting は arithmetic、commonsense、symbolic reasoning タスクの performance を改善できるか？
• RQ4 異なる exemplar、annotators、model choices に対するアプローチの頑健性は？
• RQ5 chain-of-thought prompting は out-of-domain や長い推論 sequences に generalize するか？

主な発見

• chain-of-thought prompting は ~100B パラメータ周辺で現れる emergent 能力である。
• 標準 prompting と比較して arithmetic タスク（例: GSM8K）で大きな性能向上を示し、finetuned baselines に近づくか凌駕する。
• commonsense reasoning および symbolic タスクにも gains が拡張され、PaLM 540B モデルで顕著な improvements が見られる。
• chain-of-thought prompts は annotators、exemplar セット、モデルファミリを跨いで頑健性を示すが、exemplar によって結果は異なる。
• PaLM 540B および LaMDA/GPT-3 系のバリアントへスケールさせると、GSM8K、SVAMP、MAWPS、StrategyQA などで競争力の高い、または最先端の性能を示す。
• ablation は、利益が推論プロセス自体から来ることを示し、単なる計算量の増加や chain of thought の事後利用ではない。
• out-of-domain および長いシーケンス推論は、十分なスケールで chain-of-thought prompting によりより良く解決でき、長さの一般化が改善されることを示唆する。