Saw a poster at ICLR showing contrastive learning (InfoNCE included!) ≈ a closed-form spectral decomposition in RKHS. Got curious whether a map could adapt embedding spaces across model families, while preserving NDCG retrieval perf. Did some experiments on my flights back to sf. Video shows diff maps on simple swiss roll data.
Excited to share that our paper "Analytical Search" has been accepted at #SIGIR2026! 🎉
Traditional search paradigms only emphasize naive information finding and struggle with complex analytical information need (like trend analysis and causal impact assessment). Analytical Search reframes search as an evidence-governed, process-oriented analytical workflow rather than just finding an isolated puzzle piece. Our unified framework integrates:
1. Query Understanding: Extracting implicit intent and decomposing tasks.
2. Evidence Retrieval: A recall-oriented approach across heterogeneous sources.
3. Reasoning-Aware Fusion: Multi-step inference and quantitative/logical aggregation.
4. Adaptive Verification: Generating verifiable, traceable conclusions.
It’s time to shift from simple document ranking to end-to-end problem solving!
📄 Read the full pre-print here:
https://t.co/uVkSxuV9IQ
#SIGIR2026 #InformationRetrieval #SearchEngines #LLMs #DataAnalytics #AI