Deep research agents are increasingly capable of web search, tool use, multimodal evidence analysis, and information synthesis. However, existing benchmarks mainly evaluate medium-horizon exploration and rarely test whether agents can sustain long, dependency-heavy research processes. We introduce Mr. LHDR (Multimodal real-world Long-Horizon Deep Research), a benchmark for evaluating real-world deep research over long, irreducible chains of interdependent evidence across eight categories. Each question is constructed from a hidden Node-Relation graph and requires an average of 12.1 necessary intermediate conclusions with a mean dependency depth of 10.4 before reaching a short, unique, and verifiable answer. Questions incorporate multimodal evidence, including images, maps, PDFs, logos, charts, tables, and video frames, with at least one non-text element that changes the reasoning state. Mr. LHDR evaluates both final answers and the correctness of intermediate conclusions under annotated dependencies. We evaluate general models, deep research systems, and agent frameworks using Overall Accuracy (OA), Strict Accuracy (SA), Checklist Score (CS), and Dependency-Aware Checklist Score (DACS). Results show that even the strongest system achieves only 43.1% OA and 34.3% SA, indicating that final-answer accuracy substantially overestimates complete research success. Removing images reduces DACS by 12.6 points, demonstrating the importance of multimodal evidence, while SA consistently declines as reasoning chains become longer. These findings reveal sustained, dependency-consistent evidence integration, rather than isolated fact retrieval, as a key bottleneck for current deep research agents.
Ming-Hao Guo, Meng Cao, Sui-Feng Zhao et al.· 0 citations
Embodied navigation is a core task in embodied AI. It requires comprehensive scene understanding and precise spatial reasoning. Recent vision-language models (VLMs) with strong generalization capabilities and rich commonsense knowledge have shown remarkable performance when applied to embodied navigation tasks. However, these models still encounter insufficient understanding of 3D geometry and spatial semantics when applied to real-world 3D navigation. To address this, we propose CoNav, a collaborative cross-modal reasoning framework. First, we pretrain a 3D-language model with the curriculum learning schedule and prepare a pretrained vision-language navigation agent. Next, with lightweight fine-tuning on a small 2D-3D-text corpus, the vision-language navigation agent learns to combine visual evidence with knowledge from the 3D-language model. Finally, the pretrained 3D-language model communicates with the vision-language navigation agent, enabling collaborative cross-modal reasoning and resolving ambiguities during navigation. This yields more reliable and efficient image-3D fusion for embodied navigation. CoNav introduces a new collaborative framework between a 3D-language model and a vision-language navigation agent for embodied navigation. Notably, CoNav requires only a small 2D-3D-text corpus to align 3D and 2D data. CoNav achieves clear improvements on four standard embodied navigation benchmarks (R2R, CVDN, REVERIE, SOON) and two spatial reasoning benchmarks (ScanQA, SQA3D). Moreover, Under similar success rates, it also finds shorter paths than prior methods, as measured by SPL. The results demonstrate the value of collaborative 2D-3D reasoning for embodied navigation.
Haihong Hao, Mingfei Han, Changlin Li et al.· IEEE Transactions on Pattern...· 0 citations
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