ArXiv

ExpertVerse: A General-Purpose Benchmark for Expert-Level Reasoning in Knowledge-Intensive Visual Synthesis

2026-07-21 · 17:59 UTC ·Yuan Wang, Yongchao Du, Mengting Chen... ·1 min read
Authors
Yuan Wang, Yongchao Du, Mengting Chen...
Categories
cs.CV
arXiv
https://arxiv.org/abs/2607.19341v1
PDF
https://arxiv.org/pdf/2607.19341v1

Brief

ExpertVerse (Wang et al., 2026) is a capability-centric benchmark for knowledge-intensive visual synthesis that organizes tasks into 9 cognitive capabilities × 8 expert disciplines (58 sub-disciplines). The authors curate 1,611 expert-annotated instances and an automated pipeline to expand to ExpertVerse-100K with reasoning traces. They train KnowThinker, a VLM reasoning engine, via RL and propose BPPO (with BRR and CPAF) to address multi-reward conflicts; evaluations show critical reasoning gaps in existing models.

Why it matters

ExpertVerse stratifies knowledge-intensive visual reasoning across 9 cognitive capabilities and 8 expert disciplines (58 sub-disciplines) and provides 1,611 expert-annotated instances spanning single-image editing, multi-image composition, and text-to-image generation.

Key details

  • The authors build an automated pipeline to produce ExpertVerse-100K with reasoning traces and knowledge-anchored rationale annotations, train KnowThinker (a VLM reasoning engine) with RL fine-tuning, and introduce Bootstrapped Pareto Policy Optimization (BPPO) which fuses Bootstrapping Reward Rectification (BRR) and Conflict-Aware Pareto Advantage Fusion (CPAF).
  • Extensive evaluations on both open-source and proprietary models reveal significant reasoning deficits, motivating the need for knowledge-intensive benchmarks; paper by Yuan Wang et al., posted to arXiv 2026-07-21 (arXiv:2607.19341v1).
Source evidence

Abstract

Recent advances in multimodal generative models have enabled instruction-based image generation to move beyond semantic manipulation to knowledge-driven visual reasoning. However, these methods focus on explicit commonsense reasoning, shallow causal understanding, and direct knowledge recall, failing at knowledge-intensive generation. We develop \textbf{ExpertVerse}, a capability-centric benchmark to evaluate generative models via knowledge-intensive lens. ExpertVerse stratifies reasoning generation across an orthogonal taxonomy of \textit{9 cognitive capabilities} and \textit{8 expert disciplines}, yielding \textit{58 sub-disciplines}. We curate 1,611 expert-annotated instances covering single-image editing, multi-image composition, and text-to-image generation. We further develop an automated workflow to produce \textbf{ExpertVerse-100K}, a large-scale dataset with reasoning traces and knowledge-anchored rationale annotations. Based on this, we train \textbf{KnowThinker} with RL fine-tuning, a VLM reasoning engine with world knowledge that jointly generates thinking processes and refined instructions. Towards the cross-modal credit misalignment and multi-objective gradient conflicts in multi-reward optimization, we propose a tailored Bootstrapped Pareto Policy Optimization (BPPO), which synergizes Bootstrapping Reward Rectification (BRR) and Conflict-Aware Pareto Advantage Fusion (CPAF). Extensive results of both open-source and proprietary models exposes critical reasoning deficits, highlighting imperative for knowledge-intensive benchmarks towards next-generation visual generation.