H-Net + Transformer: A New Path Toward True Superintelligence
Modern LLMs, built on Transformer architectures, have reached a remarkable level of fluency—but also a fundamental ceiling.
They operate in a deductive mode: reasoning only within the limits of what was already given in training data.
They can generate, but not learn from what they generate.
Recently, a different idea has emerged—H-Net (Hierarchical Dynamic Chunking / H‑Net++).
Unlike Transformers, H-Net doesn’t rely on tokenization.
It uses a sliding-block mechanism to directly discover semantic structures and patterns from raw signals, forming conceptual hierarchies through induction.
If we see Transformers as logicians, skilled at deduction,
then H-Net behaves like a philosopher, discovering new abstractions from noise.
By combining the two—H-Net for induction, Transformer for deduction—we can form a closed cognitive loop that mimics the human thought process:
Induction → Deduction → Re-induction
In this loop, the Transformer’s generated output feeds back into H-Net, which re-abstracts it into new concepts and integrates it into its semantic network.
This way, the system can learn from its own reasoning, instead of discarding every generation like today’s LLMs do.
Such a system would:
-
Continuously refine its conceptual network.
-
Verify outputs through abstraction consistency.
-
Learn autonomously without human fine-tuning.
It’s a model of “two feet lifting each other”—a possible path toward self-reinforcing, brain-like intelligence.
I’d love to hear what the Hugging Face research community thinks:
Could such an inductive–deductive loop be implemented in practice?
What would be the best way to make H-Net and Transformers communicate efficiently?
1 Like
ransformers can theoretically capture the overall meaning of long sentences or paragraphs, but the cost is huge: deep layers, massive attention computations, and extreme sensitivity to typos or punctuation.
H-Net (Hierarchical Induction Network) handles this more efficiently by abstracting directly at the block level, producing stable semantic chunks with far less computation. This makes it ideal as an inductive module in a feedback loop with Transformers.
By combining the two models, H-Net first inductively abstracts concepts from words and phrases, which Transformers then use to perform reasoning and generate new knowledge. This new knowledge is subsequently re-abstracted by H-Net into higher-level concepts—sentences, paragraphs, and even entire articles—allowing Transformers to perform further reasoning on these higher-order abstractions. Through this iterative induction–deduction loop, the system continuously refines and expands its understanding, ultimately producing progressively more comprehensive and robust knowledge, while drastically reducing computation and improving semantic stability.
The key innovation of this architecture is that H-Net is not limited to inducing concepts from raw text alone. Instead, it can abstract higher-level concepts from knowledge generated by Transformers, forming multi-level conceptual hierarchies—sentence, paragraph, article, and even cross-article themes. By iteratively feeding these abstractions back into Transformers for further reasoning and generating new knowledge, the system can progressively build a full knowledge conceptual map, potentially encompassing all information the AI has access to. This iterative induction–deduction loop is what enables the architecture to scale beyond the limitations of either model alone.