2026-10-11 18:02 UTC

Independent replication will determine whether ActiveVision validly exposes a large, persistent human–frontier-model capability gap on tasks requiring repeated visual perception and interaction.

state: expiredheat: lowuncertainty: highknownscott: mediumactive-vision frontier-model-capabilities vision-reasoningOpenAI

What is this?

ActiveVision is presented in the case as a 17-task benchmark testing repeated visual perception and interaction, with a reported 10.6% score for GPT-5.5 versus 96.1% for humans. However, the supplied web results concern other active-vision research in reinforcement learning, robotics, and visual attention; they do not independently establish this benchmark, its creators, its scores, OpenAI’s involvement beyond the model attribution, or any replication. The central capability-gap claim therefore remains unverified by the provided snippets.

Why it matters to Scott

Scott already argues that capable agents require closed perception–action loops and should be evaluated on their paths and real-world feedback, not only final answers; see Agent Hands and Eyes, Perceptual Engineering, and Path Testing. A replicated 10.6%–96.1% gap would materially constrain his browser/vision-agent designs and capability allocation, but the supplied evidence does not yet verify the benchmark or result, so this currently adds no established position beyond those already held.
ip:concept.agent-hands-and-eyesip:concept.perceptual-engineeringip:concept.path-testingip:concept.capability-auditdev:project.remote-execradar:concept.agent-benchmarksradar:concept.ai-benchmarksradar:concept.benchmark-integrity
queries asked of Scott's wikis
  • interactive visual agent evaluation
  • multimodal agents and repeated perception-action loops
  • benchmark contamination and independent replication
  • human–model capability gaps in agentic tasks
  • GUI agents and active visual perception
  • static benchmarks versus interactive evaluations

Measured heat

no measured readings yet — the hourly heat pass fills this in

How the heat travelled

no chain yet — the hourly chain pass fills this in

Evidence (2) — ⭐ canonical anchor

sourceobjectauthorscorecomments
🟠 redditGPT-5.5 Scores 10.6% on ActiveVision, Humans Hit 96.1% [R]
MachineLearning
Justgototheeffinmoon28440
🟧 echo.paper ⭐Introduces 17 tasks across three categories designed to force repeated visual perception rather than a single static description, with the eActiveVision paper authors——

Interpretation history

Decision trace