# multimodal agent reasoning

The authors introduce VISTA, a visual harness that gives a general-purpose multimodal model long-horizon vision by letting it perceive the environment through visual observations, keep a lossless visual memory of past observations in their original form, and actively retrieve and reorganize its visual input while reasoning; on ARC-AGI-3 it raises Claude Opus 5.0's Relative Human Action Efficiency from 40.68 to a perfect 100.00, completing all 25 public games with 57.4% fewer actions than first-time human participants, and it substantially outperforms baselines using the same underlying model with minimal harnesses across three additional benchmarks covering diverse visual games and puzzles.

The authors introduce VISTA, a visual harness that gives a general-purpose multimodal model long-horizon vision by letting it perceive the environment through visual observations, keep a lossless visual memory of past observations in their original form, and actively retrieve and reorganize its visual input while reasoning; on ARC-AGI-3 it raises Claude Opus 5.0's Relative Human Action Efficiency from 40.68 to a perfect 100.00, completing all 25 public games with 57.4% fewer actions than first-time human participants, and it substantially outperforms baselines using the same underlying model with minimal harnesses across three additional benchmarks covering diverse visual games and puzzles.