KnowAct-GUIClaw Hits 64.1% SR on MobileWorld, Beats GPT-5.5

A new agent-framework paper from the Lychee Team at Harbin Institute of Technology, Shenzhen puts a concrete number on how much the memory-plus-skill-library story is actually worth on mobile GUI tasks. Running on the open-source Kimi-K2.6 model, their KnowAct-GUIClaw system reportedly hits 64.1% on the MobileWorld GUI-only benchmark, ahead of Seed-2.0-Pro (63.2%), GPT-5.5 (62.4%), Claude-Opus-4.7 (56.4%) and a raw Kimi-K2.6 baseline (55.6%). The authors report it beats every closed-source agentical model in their comparison table.

The mechanism is a four-stage loop the authors call Know-Route-Act-Reflect. A host agent decomposes a long-horizon request, a lightweight GUI subagent called GUIClaw does the actual tapping and swiping over a hybrid action space of raw GUI primitives, distilled skills, validated Android deeplinks and intents, and intervention calls back to the user; a reflection stage after each run turns useful traces into an experience-memory entry or a state-validated skill. Two persistent stores, a memory-and-history store and a skills-and-shortcuts store, feed the next task. Extending OpenClaw is the framing: the paper argues OpenClaw-style local assistants lack both cross-platform GUI support and any built-in self-evolution mechanism.

The transfer result is the one worth watching. Distilling memory and skills from Kimi-K2.6 trajectories and dropping them into a Qwen3.5-35B-A3B executor lifts that model's score from 24.8% to 41.0% on the same benchmark, a 16.2-point gain, with no weight update. Cross-platform runs report 76.2% on 63 HarmonyOS tasks and 70.0% on 30 Windows desktop tasks spanning browser, file, office, terminal, and system-control workflows.

One caveat: these are the authors' own runs, not third-party replications, and the strongest numbers use a specific configuration where Kimi-K2.6 serves as both the host and the GUI executor. The writeup also skips the compute cost of building the transferable skill library in the first place, whether persistent user-profile memory holds up in privacy-sensitive deployments, and how the state-validated skills degrade when target apps push UI updates. What it does establish, at least in the Kimi-to-Qwen direction, is that reusable skills and distilled experience genuinely travel across model families, which is the part that shifts where the next round of GUI-agent work will concentrate.