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# LLM research agents

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  1. arXiv

    Handing forecast verdicts to AI agents: in no-signal worlds Opus, Sol and Terra still claim predictability 34%, 16% and 44% of the time

    In simulated worlds with a planted, known signal, Claude Opus 5.5, GPT-6.1 Sol, GPT-6.1 Terra and Qwen3-32B were asked whether a forecasting model beats a static benchmark. After Gaussian shocks removed a resampling artifact, the three commercial models claimed predictability in 34%, 16% and 44% of 110 no-signal worlds, all above the nominal 5%; once false-positive rates are matched, an in-sample test with a multiple-testing adjustment detects weak signals about as well as the best agent.

  2. arXiv

    AI agents vote down their own run: a diamond-spin lab rejects a passing experiment, then earns the evidence

    A laboratory of large-language-model agents, each holding a separate research role, asked how many calibration measurements are needed to predict a diamond nitrogen-vacancy spin ensemble's response to arbitrary phase-controlled microwave pulse sequences. The principal-investigator agent tightened a proposed bound of 91 calibrations to an exact minimum of 10; the first acquisition met all ten precommitted accuracy criteria but failed phase and reference controls and was ruled inconclusive, and after the agents revised the protocol without relaxing the criteria, the final measurement supported the prediction while two interpolation methods predicted zero.

  3. arXiv

    Agents that never play the game built a Hearthstone AI that climbed the live ladder to a 1.60 average placement

    AutoTavern coordinates specialised LLM research agents through a shared typed research memory and five verification gates, and in 20 days they built TavernGym, a full-content reproduction of Hearthstone Battlegrounds, plus 23 versions of TavernAgent. The agent plays ranked games on the live ladder by itself, with its rating rising from 6,000 to about 15,000; on the public leaderboard's measure its last 20 records average a placement of 1.60 with 80% first places, against 2.34 and 59% for the server's ten highest-rated players together. Asked to make the same decisions directly, frontier LLMs answer at most 63% of play tasks, while TavernAgent answers 91%.

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