---
title: "TriRoute: Unified Learned Routing for Joint Adaptive Attention, Experts, and KV-Cache Allocation — Stuff That Spins"
description: "arXiv:2607.06601v1 Announce Type: new Abstract: Conditional computation can decouple language model quality from per-token inference cost, yet leading techniqu…"
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date: "2026-07-09T04:00:00+00:00"
modified: "2026-07-09T06:04:48.342778+00:00"
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# TriRoute: Unified Learned Routing for Joint Adaptive Attention, Experts, and KV-Cache Allocation

**Source:** Unknown  
**Published:** July 9, 2026  
**Original:** https://arxiv.org/abs/2607.06601  

## On this page

- [Overview](#overview)

<a id="overview"></a>

## Overview

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 tok

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