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· Past edition · 12 stories
Retrospective edition reconstructed from later captures. Paper versions may include later updates; coverage labels describe the evidence used.
Research & academia
A two-call refinement baseline beat a five-role pipeline in one local-model study.
Why it matters: The comparison shows why extra agents do not guarantee better results; measure the chosen model and task rather than assuming a larger pipeline wins.
Research & academia
Two research case studies found agents handled engineering but failed the research objective.
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Why it matters: The two case studies highlight research judgment and backtracking as practical questions for evaluating autonomous research.
Research & academia
Metis compresses prior information into a persistent memory state and updates that state through forward computation while model weights remain frozen. The captured v2 abstract is a later revision from 2026-08-04.
Why it matters: Persistent native memory offers a different way to carry experience across interactions.
Research & academia
TurboVLA directly fuses visual observations and language for action prediction. Its September 27 v3 abstract reports 97.6% average LIBERO success and 31.2 ms inference latency on an RTX 4090. The captured v3 abstract is a later revision from 2026-09-27.
Why it matters: Lower-cost vision-language-action inference could matter for real-time robots.
Research & academia
The authors compare constitutional midtraining with a control and report durability after benign fine-tuning in some evaluations, but weaker persistence under in-context pressure. The captured abstract is the August 18 v3 revision. The captured v3 abstract is a later revision from 2026-08-18.
Why it matters: Durability of alignment after later training and pressure is an empirical question.