MARCH: Evaluating the Intersection of Ambiguity Interpretation and Multi-hop Inference

2025-09-26 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

The MARCH benchmark, introduced in April 2026, evaluates the intersection of ambiguity interpretation and multi-hop inference in real-world multi-hop Question Answering (QA) systems. This benchmark comprises 2,209 multi-hop ambiguous questions, meticulously curated using multi-LLM verification and validated by human annotation with high agreement. Existing benchmarks primarily focus on single-hop ambiguity, leaving the complex interaction between multi-step reasoning and layered uncertainty underexplored. Experiments with MARCH reveal that even advanced models struggle significantly with this combined challenge. To address these limitations, the authors propose CLARION, a two-stage agentic framework that explicitly separates ambiguity planning from evidence-driven reasoning, demonstrating superior performance over current approaches.

Key takeaway

For research scientists developing advanced QA systems, you should consider the MARCH benchmark to rigorously test your models' ability to handle complex, ambiguous multi-hop queries. Integrating a two-stage framework like CLARION, which separates ambiguity resolution from evidence retrieval, can significantly improve performance on real-world, uncertain reasoning tasks, paving the way for more robust AI applications.

Key insights

Multi-hop QA requires models to navigate layered ambiguity across complex reasoning paths.

Principles

• Ambiguity can occur at any stage of multi-hop reasoning.
• Decoupling ambiguity planning improves reasoning systems.

Method

CLARION is a two-stage agentic framework that explicitly separates ambiguity planning from evidence-driven reasoning to enhance multi-hop QA performance.

In practice

• Use MARCH to evaluate multi-hop ambiguous QA.
• Implement two-stage agentic frameworks for complex queries.

Topics

• Multi-hop Question Answering
• Ambiguity Resolution
• MARCH Benchmark
• CLARION Framework
• Large Language Models

Best for: Research Scientist, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.