AI Research
MUX: Continuous Reasoning via Multiplexed Tokens
Compressing reasoning chains into latent space to overcome computational bottlenecks in LLMs.
Language models solve complex problems by articulating intermediate reasoning steps in natural language. While effective, this process is computationally bottlenecked: each reasoning step conveys only a single subword, and many are spent expressing a thought instead of carrying o...
AC
ArXiv CS.AI
Source
2 min
ArXiv CS.AI
AI Analysis
Generated insight by OnAirToday AI
MUX addresses the computational inefficiency of traditional language model reasoning by compressing multiple reasoning steps into single, multiplexed latent tokens. This approach increases bandwidth and allows for parallel exploration, significantly enhancing model performance and speed in complex problem-solving tasks.
Powered by AI • Not a substitute for human analysisMore Insights →
Share
Filed under:AI Research
Comments
Keep comments relevant and respectful. No links, advertising, self-promotion, or spam.