Recent agentic search frameworks enable deep research via iterative planning and retrieval, reducing hallucinations and enhancing factual grounding. However, they remain text-centric, overlooking the multimodal evidence that characterizes real-world expert reports. We introduce a pressing task: multimodal long-form generation. Accordingly, we propose Deep-Reporter, a unified agentic framework for grounded multimodal long-form generation. It orchestrates: (i) Agentic Multimodal Search and Filtering to retrieve and filter textual passages and information-dense visuals; (ii) Checklist-Guided Incremental Synthesis to ensure coherent image-text integration and optimal citation placement; and (iii) Recurrent Context Management to balance long-range coherence with local fluency. We develop a rigorous curation pipeline producing 8K high-quality agentic traces for model optimization. We further introduce M2LongBench, a comprehensive testbed comprising 247 research tasks across 9 domains and a stable multimodal sandbox. Extensive experiments demonstrate that long-form multimodal generation is a challenging task, especially in multimodal selection and integration, and effective post-training can bridge the gap.
Figures & tables
Figure 1: Comparison of paradigms in long-form report generation. While traditional systems (a) lack visual engagement and pure T2I approaches (b) suffer from factual hallucinations and narrative fragmentation, our method (c) achieves high coherence and factuality by retrieving and integrating real-world visual evidence.
Figure 2: Deep-Reporter Architecture. (a) The multi-agent framework orchestrates planning, multimodal information seeking, and incremental writing to generate professional reports. (b) The data synthesis pipeline constructs high-quality expert trajectories to equip open-weight models with deep research capabilities.
Table 1: M 2 LongBench versus existing benchmarks. Evi. denotes candidate sets for text/image retrieval. Modality describe the expected input/output format (MM=Multimodal).
Method
Backbone
Search
Filter
Selection
Text
Image
Overall
Text
Image
Overall
Text
Image
Overall
Naïve RAG
Qwen3-8B
34.9 (54.6)
32.7 (27.3)
33.8 (81.9)
-
-
-
46.3 (18.7)
7.8 (0.9)
27.0 (19.6)
Naïve RAG
Qwen3-32B
35.0 (54.7)
32.8 (27.4)
33.9 (82.0)
-
-
-
45.7 (21.0)
10.4 (1.0)
28.1 (22.0)
Storm-MM
Qwen3-32B
29.5 (105.2)
38.4 (53.8)
34.0 (159.0)
-
-
-
48.3 (26.3)
12.7 (1.4)
30.5 (27.7)
Deep-Reporter
Qwen3-8B
29.3 (103.8)
37.9 (52.4)
33.6 (156.3)
45.5 (50.4)
50.2 (30.5)
47.9 (81.0)
49.6 (24.5)
8.3 (0.7)
28.9 (25.2)
w/o Filter
Qwen3-8B
29.5 (104.0)
37.2 (52.5)
33.4 (156.5)
-
-
-
43.6 (31.9)
3.8 (0.3)
23.7 (32.2)
Table 2: Evidence quality on M 2 LongBench benchmark. Each cell shows precision (%) and quantity of evidence that are either retrieved/retained/cited. The best score is in purple boldface and second best is underlined . ♠ and ♣ denote SFT and DPO respectively. w/o Filter removes the filter module; w/o Recur. removes recurrent context management; w/o Both removes both.
Method
Backbone
Section Anchor
Section Content
Full Report
Overall
Report
Desc
Check
Avg
Rich
Coh
Place
Clar
Avg
Coh
Flu
Rep
Term
Avg
Length
Naïve RAG
Qwen3-8B
0.0
2.0
1.0
6.1
7.9
7.1
8.5
7.4
0.0
0.0
6.9
23.1
7.5
5.3
1.9k
Naïve RAG
Qwen3-32B
0.0
1.2
0.6
6.9
5.7
6.3
6.7
6.4
0.0
0.0
10.1
24.3
8.6
5.2
2.0k
Storm-MM
Qwen3-32B
22.3
25.3
23.8
7.7
5.7
7.3
6.5
6.8
10.1
20.7
19.4
14.6
16.2
15.6
3.2k
Deep-Reporter
Qwen3-8B
14.2
19.4
16.8
8.1
3.6
6.9
3.6
5.6
10.5
23.5
21.9
18.6
18.6
13.7
3.9k
w/o Filter
Qwen3-8B
13.0
14.6
13.8
8.1
2.0
6.1
3.2
4.9
8.1
21.9
17.8
20.7
17.1
11.9
3.8k
Table 3: Main results of generation quality. Section Anchor : Desc=Description, Check=Checklist. Section Content : Rich=Richness, Coh=Image-text coherence, Place=Placement, Clar=Clarity. Full Report : Coh=Coherence, Flu=Fluency, Rep=Repetition, Term=Termination. ♠ and ♣ denote SFT and DPO respectively. w/o Recur. removes recurrent context management; w/o Both removes both filtering and recurrent context.
Figure 3: Overall performance with output tokens.
Appendix figures & tables8 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 4: Demonstration of the sandbox construction.
Domain
Source Report Metadata
Tasks
Sandbox Scale (Original / Expanded)
Silver Annot.
Count
Image
Token
Sec.
Check.
Web Pages
PDF Docs
Images
Chunks
Images
Chunks
AI
43
12.2
7.3k
5.7
3.6
28.5 / 59.8
16.8/ 27.9
39.7 / 289.9
103.0k / 179.3k
98.7
170.2
Finance
37
10.8
7.5k
5.9
3.4
16.4 / 79.5
1.7/ 13.3
20.8 / 285.9
59.3k / 238.2k
110.5
176.0
Geography
12
8.8
4.1k
5.6
3.0
8.3 / 80.1
2.0 / 9.5
45.6 / 548.3
29.9k / 240.0k
99.7
165.3
Health
28
7.0
4.2k
5.8
3.3
19.2 / 77.0
5.5 / 6.7
45.3 / 287.5
70.0k / 230.6k
108.5
172.5
History
22
7.4
3.3k
5.1
3.1
3.3 / 59.0
1.5 / 11.1
8.3 / 592.6
23.0k / 324.1k
99.1
154.0
Appendix
Table 4: Statistical overview of M 2 LongBench . Source Report Metadata reflects the complexity of the ground-truth blueprints. Sandbox Scale highlights the difficulty of the retrieval task by contrasting original citations with our significantly expanded search space (Expanded), demonstrating the “needle-in-a-haystack” nature of the benchmark.
Model
Revision (Git commit SHA)
Qwen3-8B
b968826d9c46dd6066d109eabc6255188de91218
Qwen3-32B
9216db5781bf21249d130ec9da846c4624c16137
Llama-3.3-70B-Instruct
6f6073b423013f6a7d4d9f39144961bfbfbc386b
InternVL3-5-38B
de99855be3642cd44fe97c9b72d70e5ce2c07f69
Jina-Embeddings-v4
5f4b9cbb80cc95ba44fe6667dfd75710f7db2947
Appendix
Table 5: Model versions and Hugging Face Hub revisions used in our experiments.
Figure 5: Model performance evaluation. (A-C) Overall comparisons: comprehensive metrics, retrieval pipeline stages, and detailed quality indicators. (D-I) Subject-specific retrieval performance for image and text modalities across different pipeline stages.
Figure 6: Subject-specific generation quality score. (J-O) Article and section quality across six dimensions: coherence, fluency, repetition control, termination, description completeness, and checklist adherence. (P-T) Citation quality across five dimensions: overall performance, richness, coherence, placement, and clarity. Performance patterns vary significantly across academic domains.
Multimodal generation creates coordinated text, images, audio, video, 3D, and other outputs from mixed inputs while preserving meaning and consistency.
Large Language Models (LLMs) have advanced autonomous agents from deep search, which retrieves concise factual answers, to deep research, which synthesizes scattered evidence into long-form reports. However, verifiable multimodal deep research remains challenging due to open-ended synthesis without deterministic ground truth and the need to interleave textual arguments with visual evidence. We propose Ptah, a multi-agent harness for interleaved report generation. Ptah orchestrates the lifecycle from user query to rendered web report through planning, research, and writing stages, where specialized agents construct visual-aware plans, collect claim-grounded evidence, maintain source-aligned images in a Visual Working Memory, and compose reports through declarative multimodal tool use. A verifier agent serves as the harness's acceptance function, enforcing factual grounding, citation fidelity, and cross-modal consistency throughout the workflow. We further introduce PtahEval, an evaluation protocol that augments existing benchmarks with image-level and presentation-level assessments. Experiments on deep research benchmarks show that Ptah produces more reliable, visually informative, and usable human-facing multimodal reports than strong baselines. Our code is released at https://github.com/SnowNation101/Ptah
Chenghao Zhang, Guanting Dong, Yufan Liu +3
Gaoling School of Artificial Intelligence, Renmin University of China
Deep research agents are increasingly capable of searching the open web and synthesizing evidence, yet they remain poorly suited to controllable report generation in multimodal deep research. This task requires agents to handle interleaved textual and visual inputs, gather multimodal evidence through multi-step web search, and deliver comprehensive illustrated reports under user-specified requirements. Existing systems face two limitations. During reasoning, they often underuse images discovered along the search trajectory; during generation, they tend to produce fixed-form answers with limited control over report granularity, multimodal composition, and evidence grounding. We introduce MIRA, a Multimodal Illustrated Deep Research Agent for controllable reports. MIRA integrates textual and visual evidence throughout research and writing. At its core, MIRA-Harness translates user requirements into generation contracts and connects four stages: evidence collection, report submission, feedback-driven revision, and final delivery. Building on this harness, MIRA-Data constructs high-quality multi-hop multimodal research trajectories, and we post-train foundation models to operate within the workflow. Using Qwen3.6-35B-A3B, MIRA achieves state-of-the-art performance among open agents on 7 representative, established multimodal-search and deep-research-agent benchmarks, while producing user-aligned illustrated reports with traceable textual and visual evidence.
Hongrui Wu, Kaiwen Tuo, Congcong Wang +7
Stanford University · The Hong Kong University of Science and Technology · Fudan University +2
We aim to develop a multimodal research agent capable of explicit reasoning and planning, multi-tool invocation, and cross-modal information synthesis, enabling it to conduct deep research tasks. However, we observe three main challenges in developing such agents: (1) scarcity of search-intensive multimodal QA data, (2) lack of effective search trajectories, and (3) prohibitive cost of training with online search APIs. To tackle them, we first propose Hyper-Search, a hypergraph-based QA generation method that models and connects visual and textual nodes within and across modalities, enabling to generate search-intensive multimodal QA pairs that require invoking various search tools to solve. Second, we introduce DR-TTS, which first decomposes search-involved tasks into several categories according to search tool types, and respectively optimize specialized search tool experts for each tool. It then recomposes tool experts to jointly explore search trajectories via tree search, producing trajectories that successfully solve complex tasks using various search tools. Third, we build an offline search engine supporting multiple search tools, enabling agentic reinforcement learning without using costly online search APIs. With the three designs, we develop MM-DeepResearch, a powerful multimodal deep research agent, and extensive results shows its superiority across benchmarks. Code is available at https://github.com/HJYao00/MM-DeepResearch
Huanjin Yao, Qixiang Yin, Min Yang +5
ByteDance · Zhongguancun Academy · Nanyang Technological University +2