AI Research Product Brief Content Archive

63 posts · Page 1 of 1

  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
  35. LLMs lose to a simple forecast
  36. T1 makes the terminal a training loop
  37. Can your vector search prove itself?
  38. Fix the harness, not the failure.
  39. One model. Every sound.
  40. The router was in the model all along.
  41. 200K tokens just fit on a 24 GiB laptop.
  42. A 400M model beats 4 hours of AutoML.
  43. Ask twice. The rule gives way.
  44. One million tokens, 890 bytes each.
  45. Same tasks, 49% fewer tokens.
  46. 5,545 training tasks, no issues needed.
  47. A higher score, a worse harness.
  48. Taste, measured: 59.7%. Random is 25%.
  49. Group chat memory loses to BM25.
  50. Video models still lose the ball.
  51. Your agent can delete its own audit log.
  52. Dress rehearsal on 16,000 fake chats.
  53. One decoder, 23 layers, no retraining.
  54. 5,664 words taught a student to code.
  55. 92% finished. 44% were right.
  56. 8 silent tokens ignore the edit.
  57. 16 frames beat 62,094 tokens.
  58. Two links deep. 65,537 weights fix it.
  59. Learning when to compact: 30.4% → 39.6%
  60. A record for $1.68. $50 to match it.
  61. Sound and picture, locked to one clock.
  62. 16 tokens get you 96% of the gain.
  63. The state outweighs the weights at 70.

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