Multi-hop question answering (MHQA) requires models to retrieve and reason over evidence distributed across multiple documents. However, training dense retrievers for MHQA remains challenging because labeled query-document pairs are difficult to obtain for the evolving intermediate queries in multi-hop reasoning. Recent approaches use LLM likelihoods for step-wise supervision over queries conditioned on accumulated evidence, which anchors retrieval to known facts and biases supervision toward answer-bearing documents, leaving bridging evidence undiscovered. To address this, we propose PROBE, a framework that optimizes dense retrievers for MHQA without requiring manually annotated query-document pairs. It decouples the reasoning state into a factual summary and a prospective query that anticipates missing information. For training, we transfer cross-encoder signals only at the final hop, providing trajectory-level supervision without penalizing early exploration. Experiments on three MHQA benchmarks show that PROBE consistently improves both retrieval and QA performance over strong iterative baselines, achieving an average gain of 5.7 points while using only about 5% of the training data of the prior method.

With the rapid advancement of Large Language Models (LLMs), there is an increasing need for challenging benchmarks to evaluate their capabilities in handling complex tabular data. However, existing benchmarks are either based on outdated data setups or focus solely on simple, flat table structures. In this paper, we introduce RealHiTBench, a comprehensive benchmark designed to evaluate the performance of both LLMs and Multimodal LLMs (MLLMs) across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG. RealHiTBench also includes a diverse collection of tables with intricate structures, spanning a wide range of task types. Our experimental results, using 25 state-of-the-art LLMs, demonstrate that RealHiTBench is indeed a challenging benchmark. Moreover, we also develop TreeThinker, a tree-based agent that organizes hierarchical headers into a tree structure for enhanced tabular reasoning, validating the importance of improving LLMs’ perception of table hierarchies. We hope that our work will inspire further research on tabular data reasoning and the development of more robust models. The code and data are available at https://github.com/cspzyy/RealHiTBench.