MoE sparsifies FFN, MoD lets the entire Transformer layer skip, KV cache quantization compresses attention memory — these three "compute-saving" paths of LLM inference have all fought on their own in the past. TriRoute, proposed by Balashov and Ponomarova in arXiv:2607.06601, packs them into a single lightweight controller for the first time: every token at every layer, this router has to decide three things at once — skip / local / full which attention, which FFN experts to take, how many bits of precision to keep for the KV cache; the null-expert design also lets MoD be uniformly expressed as a special case of MoE. On the training side, Gumbel-Softmax plus straight-through handles the discrete decision, paired with load-balanced top-k for expert routing, plus a Lagrangian budget constraint to make "average compute / memory" an adjustable knob. The real difficulty lies in "joint training" — the paper very clearly explains the cross-axis routing-collapse cascade that naive schemes hit: one axis collapsing will drag down the other two. The authors use per-axis normalization + coupling-aware balance loss to solve this pit. On 160M to 1.3B decoder-only models at compute-optimal token counts, TriRoute Pareto beats the assembly of three independent schemes under the same FLOPs and memory, and is more stable on tail-robustness of rare entities, code, and arithmetic — exactly the parts that perplexity-only optimization tends to lose. Post-hoc analysis can even read out the router's "semantics": sentence starts, rare subwords, named entities always get full attention + high-precision cache, function words get cheaply dismissed. The remaining questions are very real: can the joint routing's GPU kernel scheduling overhead be amortized on small batches? Can it scale to 7B+ real production models? The interface with vLLM, SGLang, TriRoute and other expert-scheduling frameworks is also open. If the answer is yes, the "small model + smart routing" play in the second half of 2026 will be more interesting than simply piling on parameters.