Quan Xu｜AI GEO/GEM @quangeai - Twitter Profile

the underlying attention deficit of LLM is precisely the core entry point of AI GTM, better credibility weight, and ultimately enter the pre-training library of LLM.

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Quan Xu｜AI GEO/GEM

@quangeAI

4 days ago

谷歌的论文向来很强，研究底层技术值得研究，但是就是最近谷歌的gemini模型有点销声匿迹了。

Akshay 🚀

@akshay_pachaar

4 days ago

Google just dropped a banger paper. If you write or curate skills for your agents, this one is for you. (bookmark it) Agent skills are just folders holding the instructions an agent follows for a task, and people increasingly let agents write those instructions themselves. The loop is simple. Run the agent on some tasks, read the runs that failed, rewrite the instructions, and keep the rewrite only if the score goes up on a held-out validation set. That last check matters, because an edit that quietly makes the agent worse would pile up over time. So the system always applies the edit, tests it, and reverts the file if the score drops. Here is where it breaks. Working out why the agent failed is the expensive part, since it means reading full traces and comparing failed runs against successful ones. But that diagnosis never gets written down. The only thing saved is the new instruction text it produced. So when that instruction reverts, the finding behind it goes too. The next round reads the same failures, reaches the same conclusion, and often proposes the fix that already lost, with no memory that it was ever tried. WikiSkill fixes this by splitting the one folder into three: 1. Raw traces, written once and never touched. 2. A wiki of accumulated knowledge, holding what keeps breaking, what has worked, and every edit tried so far with the reason it passed or failed. Nothing here is ever deleted. 3. The skills themselves, reverted whenever an edit makes things worse. So the instructions can roll back, but the understanding behind them never does. Their own example makes it click. An early skill gets rejected for being too vague, and instead of vanishing, that rejection is logged. The next round reads the log, sees the vague version failed, and writes a concrete rule in its place. That one gets accepted. Same failure, same analysis, but the second attempt starts from knowing what already did not work. The authors confirm this is where most of the gain comes from by turning the knowledge layer off. The average score drops by about a quarter. Two other results are worth the read on their own. Smaller models with evolved skills beat much larger models running without any, and skills evolved by one model transfer across families, sometimes working better than the ones a model wrote for itself. Paper → https://t.co/ungqaQVTma I wrote about a related idea (GEPA) earlier, which improves a model by having it reflect on its own mistakes in plain language and rewrite its prompt, with no weight updates involved. The detailed article is quoted below.