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Agents That Learn From Their Own Mistakes: The Rise of Retroactive AI

Mistakes are useful only when they are converted into something operational. That is the small, inconvenient detail often missing from agent hype. An LLM agent can fail at a web-shopping task, wander through a simulated room, push the wrong Sokoban box, or uncover the wrong MineSweeper cell. Fine. Failure happens. The useful question is not whether the agent failed. The useful question is whether the system can extract a reusable signal from that failure before the next attempt. ...

March 12, 2026 · 16 min · Zelina
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Mirror, Mirror on the Agent: Teaching LLMs to Judge Their Own Actions

The agent did exactly what it was taught. That was the problem. A familiar business agent failure does not look dramatic. It looks boring. The agent searches the database, clicks the wrong record, receives an error, retries the same action, receives the same error, retries again, and then politely informs the user that it has encountered “temporary difficulty.” Very professional. Completely useless. ...

March 12, 2026 · 16 min · Zelina
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Mirror, Signal, Trade: How Self‑Reflective Agent Teams Outperform in Backtests

TL;DR for operators TradingGroup is best read as an operating design for financial agents, not as a permission slip to hand the treasury account to a chatbot with a brokerage API. The paper proposes a five-agent trading system that combines news sentiment, financial-report retrieval, technical forecasting, trading-style selection, and final trade decisions. Around that agent team, it adds two mechanisms that matter more than the agent labels themselves: self-reflection from logged outcomes, and dynamic risk management through stop-loss, take-profit, and position-sizing rules.1 ...

August 26, 2025 · 14 min · Zelina