aiqbench analysis
This paper presents Mobius-v0, a novel architecture separating knowledge storage from reasoning processes to improve efficiency and compression in large language models. The approach demonstrates significant gains, including near fourfold inference speedups when fine-tuning existing Qwen3.5 weights while maintaining comparable downstream performance with substantially less training data. For researchers focused on model optimization and architectural innovation, this decoupled design offers a promising path toward more scalable foundation models without sacrificing capability.
Intern-S2-Mobius: Foundation Model with Decoupled Knowledge and Reasoning
arXiv:2608.14290v1 Announce Type: new Abstract: We introduce Mobius-v0, an architecture that comprises a globally shared Memory (FFN) that stores knowledge vectors and multiple Reasoners (Self-Attn) that iteratively achieve compositional reasoning. Using hidden states as cache and carrier, reasoners repeatedly query memory for required knowledge-vectors, while the knowledge is transmitted back to reasoning operators. Through this knowledge-reasoning-separation architecture, Mobius achieves better knowledge compression and reasoning efficiency. Built upon Mobius-v0 architecture: 1) Our 7B model trained-from-scratch achieves similar downstream score as a 7B Transformer baseline with 62.6% of baseline's training data. 2) Our Intern-S2-Mobius, continually-pretrained from Qwen3.5-35B, achieves similar downstream score while delivering nearly 4x end-to-end inference speedup.
Abstract:We introduce Mobius-v0, an architecture that comprises a globally shared Memory (FFN) that stores knowledge vectors and multiple Reasoners (Self-Attn) that iteratively achieve compositional reasoning. Using hidden states as cache and carrier, reasoners repeatedly query memory for required knowledge-vectors, while the knowledge is transmitted back to reasoning operators. Through this knowledge-reasoning-separation architecture, Mobius achieves better knowledge compression and reasoning efficiency. Built upon Mobius-v0 architecture: 1) Our 7B model trained-from-scratch achieves similar downstream score as a 7B Transformer baseline with 62.6% of baseline's training data. 2) Our Intern-S2-Mobius, continually-pretrained from Qwen3.5-35B, achieves similar downstream score while delivering nearly 4x end-to-end inference speedup.
Artificial Intelligence (cs.AI)
arXiv:2608.14290 [cs.AI]
(or arXiv:2608.14290v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.14290 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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