Archivo de contenido de AI Research Product Brief

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  1. Gemma 3 brings multimodal AI closer to the product edge
  2. MMSkills turns screenshots into reusable agent memory
  3. JoyNexus turns VLA post-training into a shared service
  4. Nemotron 3 Embed brings retrieval models to agent memory
  5. Schema gates turn agent chat into auditable runs
  6. Ramen picks its neighbors for mixed-domain VLMs
  7. The page is an API, too: designing agent-ready websites
  8. RIMRULE teaches tool-using agents from failures
  9. Visual tokens can stay. Their operators do not all have to run.
  10. Tail-Aware Adaptive-k makes RAG choose its context size
  11. Nanbeige4.2-3B makes the local agent smaller, not simpler
  12. Open-SWE-Traces turns 20,000 PRs into agent training data
  13. Shadow evaluations expose the research gap in AI agents
  14. BM25 Wins at Scale, Not Everywhere
  15. TokTier makes prompt caching pay off sooner
  16. RAG needs two gates, not one score
  17. The model is fast. The catch is concurrency.
  18. Search less. Read the right section.
  19. A high score can hide the research failure
  20. LLMs can retrieve. Embeddings still win the default path.
  21. Agents need one current workspace
  22. Tool calling needs a training stage
  23. Tools can fail silently. Give agents a way back.
  24. Keep the prompt. Drop the middle.
  25. Put reasoning outside the weights
  26. Compress the skill graph, keep every route
  27. Tune small. Predict large.
  28. Stop before the score.
  29. The KV cache does not need a score.
  30. Compile once. Run locally.
  31. Passing tests is not enough.
  32. One call. Three actions.
  33. Teach agents when to leave the GUI
  34. Teach the agent to test first

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