Author

Sebastian Schreiber

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#artificial intelligence May 2026

CoHyDE: Iterative Co-Training of LLM Rewriter & Dense Encoder for Tool Retrieval

CoHyDE is introduced, an iterative procedure that trains the dense encoder and the LLM rewriter as a single co-evolving system that improves over the strongest single-component baseline by +2.5 pp NDCG@5 on standard queries and +6.3 pp on held-out vague queries.

Vaishali Senthil, Ashutosh Hathidara, Sebastian Schreiber · arXiv.org · 1 citation

#artificial intelligence Jun 2026

ToolSense: A Diagnostic Framework for Auditing Parametric Tool Knowledge in LLMs

This work introduces ToolSense, an open-source LLM-powered diagnostic framework that takes any tool catalog as input and automatically generates three benchmarks: a Realistic Retrieval Benchmark (RRB) with queries at three ambiguity tiers, an MCQ probing benchmark, and a QA probing benchmark.

Ashutosh Hathidara, Sai Shruthi Sistla, Sebastian Schreiber et al. · arXiv.org · 2 citations