AutoIndex learns executable programs that shape how search corpora are represented
mrdrozdov · x · 2026-07-22
The post points to AutoIndex, a new research project on Learning Representation Programs for Retrieval.
- AutoIndex learns executable programs that decide how a corpus should be represented for a search engine.
- The authors frame it as part of a broader class of AI systems that optimize the executable programs around them.
- The post includes a site, arXiv paper, and code release.
More from Research
- Multi-resolution image stacks beat pyramidal magnitude in a new audio test — johnowhitaker · 2026-07-22
- AlphaFold3 MSA retraining probes whether it learns inverse covariance structure — anshulkundaje · 2026-07-22
- New NBER paper on how organizations use AI completes a three-paper series — daveholtz · 2026-07-22
- OAT uses 100 successful trajectories to debug failing AI agents without failure labels — TheTuringPost · 2026-07-22
- MoE, the mixture-of-experts architecture behind many top LLMs — vista8 · 2026-07-22
- NexForge synthesizes agent training data from requirements and lifts Qwen3.5 by 30 points — nex-agi · 2026-07-22