arXiv:2605.27255v1 Announce Type: new Abstract: Long chain-of-thought reasoning has made autoregressive decoding the dominant inference cost of modern large language models. Existing methods target either the input side (latent compression) or the output side (speculative decoding and multi-token prediction, MTP), but the two lines of work have been pursued independently. Moreover, output-side methods must incur an expensive verifier pass to validate the unreliable draft tokens predicted by MTP. To address these issues, we propose \textbf{Pair-In, Pair-Out (PIPO)}, which unifies both sides by
The increasing scale of LLMs and the computational cost of their inference make efficiency a critical and immediate bottleneck, spurring research into new optimization techniques.
Improving LLM inference efficiency directly translates to lower operational costs, faster response times, and broader accessibility for advanced AI applications, impacting their commercial viability and deployment scale.
This research proposes a unified approach to LLM inference optimization, potentially overcoming limitations of previous methods by integrating input compression and reliable multi-token prediction without expensive verification passes.
- · AI developers
- · Cloud providers
- · LLM users
- · Inefficient LLM architectures
- · High-latency AI applications
More cost-effective and faster deployment of large language models for various applications.
Increased adoption of sophisticated LLMs in areas currently limited by compute or latency constraints.
Potentially democratizes access to advanced AI capabilities by reducing the barrier of entry for computation.
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Read at arXiv cs.CL