Can AI Agents Learn From Experience Now? Wikis, Red Teams, and Failure Mining — the Papers Read Inside China

Why can’t ChatGPT remember what you taught it last week? For years that was the wage-earner’s consolation about AI. A late-August arXiv batch plus a Google Research project aim to retire the consolation: agents that keep notes. Our Chinese-language column’s roundup ran under “AI has learned to do post-mortems — how many years is experience still worth?” This digest translates the reading; paper claims follow their abstracts.

The claims

• WikiSkill (2608.27454): a two-tier architecture — raw execution experience stays raw; distilled knowledge goes into a persistent wiki that co-evolves with a skill library, accumulating across iterations. This round’s pitfalls are next round’s detours.
• RedEvoAgent (2608.27439): the same compounding, weaponized — a red-team agent that distills attack experience into skills for jailbreaking product-grade agent environments, where triggering an agent is no longer “says a rude thing” but “executes a dangerous action with real tools.”
• CritICL (2608.27455): small models within one family exhibit structured failure modes that transfer across scale; those failure catalogs can steer large models at inference time — improving the strong with the weak’s mistakes, without repeated generation or external verifiers.
• Google Research’s ReasoningBank (blog): agents storing reasoning experience as retrievable memory — big-lab and paper-lab traffic converging on the same intersection.

How the Chinese column reads it

The anchoring image is corporate, not sci-fi: the project post-mortem document — except the humans’ version sits unread while the agent’s version is genuinely consulted (“it took companies a decade to build the review habit; AI installed it with one paper”). On the red-team paper, the chill the column isolates: experience doesn’t check motives — the defense’s AI writes its wiki, the attack’s AI writes its wiki, two thickening notebooks shelling each other with our data in between. CritICL gets the inversion prize: failure used to be training’s garbage; now it is mined as ore. And the column’s three-layer depreciation schedule for human moats: information gone, skill-and-process depreciating quarterly, judgment-and-accountability intact — for reasons of responsibility, not capability.

Cold water

Lab environments, all of them. Compounding cuts both ways — an agent accumulating confidently in the wrong direction is a bigger liability than a goldfish. And the column names the clock: the gap between these papers and your workstation is measured in quarters, which is the only window the advice-buying public gets.

What to watch

Memory becoming a product surface (whose wiki? which sandbox? portable across vendors?); red-team compounding forcing a re-price of agent permissions in production; and whether “experience” claims start appearing in model marketing — the moment that word reaches a landing page, this batch becomes a spec sheet.

Sources

• WikiSkill (arXiv:2608.27454)
• RedEvoAgent (arXiv:2608.27439)
• CritICL (arXiv:2608.27455)
• Google Research blog (ReasoningBank)

Provenance & disclosure. Originally published in Chinese on our WeChat channel on 2026-08-29 (“AI学会复盘了，经验还值几年钱？”); drafted with AI assistance under human editorial direction. Translated to English on 2026-08-29 (AI-assisted, human-reviewed). Paper claims follow the arXiv abstracts as listed by the column; ReasoningBank is cited from Google Research’s blog, not a paper, and is labeled as such. This is translated commentary — not a SigPulse measurement. Our first-party measurements live in the dispatches and the /data/ ledger.