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The paper introduces Question's Gambit, a first-move retrieval module that improves deep research agent performance by decomposing questions and providing a ranked context before iterative search, showing significant accuracy gains on BrowseComp-Plus.
WorldCupArena is a dynamic benchmark for evaluating language models and deep-research agents on football match prediction, using the 2026 FIFA World Cup as its first evaluation. It compares model predictions against actual results, providing fine-grained scoring beyond result accuracy.
New research from Cornell University shows that a single snippet of user-generated text as short as 13 words from sites like Reddit or Wikipedia can be used to manipulate AI search tools like ChatGPT and Google AI Search, highlighting a growing vulnerability in AI-powered information retrieval.
This paper introduces a claim-centric auditing framework for identifying error spans in deep-research agent trajectories, along with a new benchmark TELBench, improving process-level reliability assessment.
DR³-Eval is a benchmark for evaluating deep research agents on multimodal, multi-file report generation with a realistic web environment simulation and comprehensive evaluation framework measuring information recall, factual accuracy, citation coverage, instruction following, and depth quality.