TriRoute: Unified Learned Routing for Joint Adaptive Attention, Experts, and KV-Cache Allocation
arXiv:2607.06601v1 Announce Type: new Abstract: Conditional computation can decouple language model quality from per-token inference cost, yet leading techniques act on a single axis in isolation: Mixture-of-Experts (MoE) sparsifies the FFN, Mixture-of-Depths (MoD) skips whole transformer blocks, and KV-cache quantization compresses attention memory. We argue these three decisions (attention resolution, expert selection, and cache bit-width) are strongly coupled and should be made jointly: a token rare enough to warrant full attention may also need high-precision caching regardless of which ex
The continuous drive for more efficient and powerful large language models necessitates novel approaches to conditional computation that transcend isolated optimizations.
This research outlines a unified approach to LLM optimization, potentially leading to significant reductions in inference cost and improvements in model quality.
Current isolated optimization techniques for attention, experts, and KV-cache may be superseded by joint, learned routing mechanisms, fundamentally altering LLM architecture design.
- · AI compute infrastructure providers
- · LLM developers
- · Cloud service providers
- · AI-driven application sectors
- · Companies reliant on inefficient, monolithic LLM architectures
- · Legacy AI hardware not adaptable to dynamic computation
More efficient and scalable large language models become broadly accessible, reducing operational costs.
The economic viability of deploying larger, more complex AI models increases, accelerating AI integration across industries.
This could contribute to the development of more complex and agentic AI systems that operate with greater autonomy and lower resource demands.
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Read at arXiv cs.LG