arXiv:2510.13217v2 Announce Type: replace-cross Abstract: Search systems are increasingly used for reasoning-intensive queries, where what makes a document relevant requires understanding or reasoning over the query-document relation rather than relying on surface vocabulary or topical similarity. The standard recipe - a cheap embedding-based retriever followed by an LLM verifier - works only when the embedding model places the right documents in its top-k, an assumption that recent reasoning-intensive IR benchmarks show often fails to hold even for SOTA embedding models. Recent query-side fix
The increasing reliance on LLMs for complex information retrieval tasks highlights their current limitations in reasoning-intensive scenarios, necessitating advanced search methodologies.
This research directly addresses a critical bottleneck in the performance of AI systems for complex, reasoning-intensive tasks, impacting the efficacy of AI agents and advanced analytical tools.
The development of LLM-guided hierarchical search offers a pathway to more accurate and reliable information retrieval for queries requiring deep understanding beyond surface-level similarity.
- · AI research labs
- · Developers of search systems
- · Industries relying on complex data analysis
- · LLM providers
- · Legacy embedding-based retrievers
- · Users relying solely on basic semantic search for complex queries
Improved performance of AI systems in tasks requiring advanced reasoning and information synthesis.
Acceleration in the development and deployment of more sophisticated AI agents capable of handling nuanced real-world problems.
Enhanced automation of knowledge work, leading to new economic efficiencies and shifts in labor requirements.
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Read at arXiv cs.LG