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Can LLMs Improve Their Own Harness? A Controlled Study on ARC-AGI-3

Jens Lehmann, Sahar Vahdati · 2026 · arXiv

DOI: 10.48550/arXiv.2610.30499 · open-access PDF

German

Ein im Allgemeinen intelligentes System erwirbt neue Fähigkeiten effizient, ohne dass Menschen ihre Software-Stickbar für jede neue Aufgabe neu gestalten müssen. Dennoch handeln Sprachmodelle selten alleine. Ein Harness ist ein symbolisches Programm, das Anrufe an das Modell orchestert, Wahrnehmung, Speicher, Werkzeugnutzung, Wiederherstellung und Schlussallokation im Namen des Modells abwickelt. ARC-AGI-3 ist ein interaktives Benchmark, in dem Agenten unbekannte Umgebungen erkunden, ihre Ziele auf dem Fliegen herausfinden und weiter lernen müssen.

Machine translation (Meta M2M100); the English original follows.

Abstract

A generally intelligent system acquires new skills efficiently without requiring humans to redesign its software scaffold for every new task. Yet language models rarely act alone. A harness is a symbolic program that orchestrates calls to the model, handling perception, memory, tool use, recovery, and inference allocation on the model's behalf. ARC-AGI-3 is an interactive benchmark in which agents must explore unfamiliar environments, figure out their goals on the fly, and keep learning as they go. On it, human-steered systems already report ceiling scores across the public games, so we know e

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