BriLLM: Brain-inspired Large Language Model

• URL: http://arxiv.org/abs/2503.11299v6
• Date: Tue, 05 Aug 2025 11:19:51 GMT
• Title: BriLLM: Brain-inspired Large Language Model
• Authors: Hai Zhao, Hongqiu Wu, Dongjie Yang, Anni Zou, Jiale Hong,
• Abstract summary: BriLLM is a brain-inspired large language model that redefines the foundations of generative language modeling.<n>We release initial Chinese and English BriLLM versions with sizes 2B and 1B parameters, respectively, achieving performance comparable to GPT-1.
• Score: 51.849486186292914
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce BriLLM, a brain-inspired large language model that redefines the foundations of generative language modeling. Departing from Transformer architectures, GPT frameworks, and traditional input-output constrained paradigms, BriLLM is built on the Signal Fully-connected flowing (SiFu) mechanism - a directed graph-based neural network design that enables full interpretability across all nodes, in contrast to conventional models limited to input-output interpretability. In this framework, tokens are represented as graph nodes, with signal flows - either randomly initialized or user-defined - propagating along paths following a "least resistance" principle. The next token to be generated emerges as the target of this signal flow. Theoretically, BriLLM supports infinitely long n-gram modeling, with model size decoupled from input and prediction length. Its signal propagation dynamics mimic human-like cognitive patterns, enabling recall activation and inherent multi-modal compatibility. We release initial Chinese and English BriLLM versions (4000 tokens, 32-dimensional nodes, 32-token sequence prediction capacity) with sizes ~2B and ~1B parameters, respectively, achieving performance comparable to GPT-1.

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