HiEviDR-Bench - Wiki (Deep Research): leaderboard

HiEviDR-Bench (UCAS, Tsinghua and others, 2026) tests multimodal deep-research reports on 1,000 Wikipedia questions under the iterative deep-research pipeline. A Qwen3-VL-235B-A22B judge scores each report against a hierarchical evidence graph on five 0-20 dimensions (report quality, evidence traceability, and gated citation, claim and answer scores); the score is their sum from 0 to 100, higher is better.

Metric: Overall score (0-100): sum of five 0-20 dimension scores (multimodal report quality, evidence traceability, and progressively gated citation, claim and answer scores) judged by Qwen3-VL-235B-A22B-Instruct against the hierarchical evidence graph, on the 1,000-question Wikipedia subset; MLLM with Deep Research: up to three rounds of question refinement with top-20 retrieval, re-ranked to 25 text chunks and 5 images; higher is better. Source: arxiv.org. Saturation forecast: Around 2033. 16 models tracked.

Top models

#ModelScoreOverall rank
1Grok 4.546.41#82
2GPT-5.6 Sol45.13#30
3Gemma 4 31B (IT)43.71#342
4GPT-543.31#167
5Qwen 3.5 27B42.44#359
6GPT-5 Mini42.25#348
7Qwen 3.5 35B A3B41.94#482
8Qwen 3.5 4B40.35#1175
9gemma-4-E4B-it40.25#1469
10Qwen 3.5 9B39.88#803
11Qwen 3 VL 8B Instruct39.12#981

No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.

Interactive version: theaggregate.ai/benchmark?slug=hievidr-bench-wiki-deep-research · How It Works · Data refreshed daily, snapshot 2026-11-13.