Explore to Evolve: Scaling Evolved Aggregation Logic via Proactive
  Online Exploration for Deep Research Agents
Deep research web agents not only retrieve information from diverse sources
such as web environments, files, and multimodal inputs, but more importantly,
they need to rigorously analyze and aggregate knowledge for insightful
research. However, existing open-source deep research agents predominantly
focus on enhancing information-seeking capabilities of web agents to locate
specific information, while overlooking the essential need for information
aggregation, which would limit their ability to support in-depth research. We
propose an Explore to Evolve paradigm to scalably construct verifiable training
data for web agents. Begins with proactive online exploration, an agent sources
grounded information by exploring the real web. Using the collected evidence,
the agent then self-evolves an aggregation program by selecting, composing, and
refining operations from 12 high-level logical types to synthesize a verifiable
QA pair. This evolution from high-level guidance to concrete operations allowed
us to scalably produce WebAggregatorQA, a dataset of 10K samples across 50K
websites and 11 domains. Based on an open-source agent framework, SmolAgents,
we collect supervised fine-tuning trajectories to develop a series of
foundation models, WebAggregator. WebAggregator-8B matches the performance of
GPT-4.1, while the 32B variant surpasses GPT-4.1 by more than 10% on GAIA-text
and closely approaches Claude-3.7-sonnet. Moreover, given the limited
availability of benchmarks that evaluate web agents' information aggregation
abilities, we construct a human-annotated evaluation split of WebAggregatorQA
as a challenging test set. On this benchmark, Claude-3.7-sonnet only achieves
28%, and GPT-4.1 scores 25.8%. Even when agents manage to retrieve all
references, they still struggle on WebAggregatorQA, highlighting the need to
strengthen the information aggregation capabilities of web agent foundations.
68f1acf66e0bef323a68fda5
Rui Wang
68f1acf66e0bef323a68fda6
Ce Zhang
68f1acf66e0bef323a68fda7
Jun-Yu Ma
68f1acf66e0bef323a68fda8
Jianshu Zhang
68f1acf66e0bef323a68fda9
Hongru Wang
68f1acf66e0bef323a68fdaa
Yi Chen
68f1acf66e0bef323a68fdab
Boyang Xue
68f1acf66e0bef323a68fdac
Tianqing Fang
claimed_verified
2025-10-17T05:56:46.664Z
641129818573c51c0458b793
/avatars/d4bc67c160a07146cf41c614678aa36b.svg
Tianqing Fang
user
tqfang229
68f1acf66e0bef323a68fdad
Zhisong Zhang
68f1acf66e0bef323a68fdae
Hongming Zhang
68f1acf66e0bef323a68fdaf
Haitao Mi
68f1acf66e0bef323a68fdb0
Dong Yu
68f1acf66e0bef323a68fdb1
Kam-Fai Wong
Explore to Evolve: Scaling Evolved Aggregation Logic via Proactive
  Online Exploration for Deep Research Agents
Deep research web agents not only retrieve information from diverse sources
such as web environments, files, and multimodal inputs, but more importantly,
they need to rigorously analyze and aggregate knowledge for insightful
research. However, existing open-source deep research agents predominantly
focus on enhancing information-seeking capabilities of web agents to locate
specific information, while overlooking the essential need for information
aggregation, which would limit their ability to support in-depth research. We
propose an Explore to Evolve paradigm to scalably construct verifiable training
data for web agents. Begins with proactive online exploration, an agent sources
grounded information by exploring the real web. Using the collected evidence,
the agent then self-evolves an aggregation program by selecting, composing, and
refining operations from 12 high-level logical types to synthesize a verifiable
QA pair. This evolution from high-level guidance to concrete operations allowed
us to scalably produce WebAggregatorQA, a dataset of 10K samples across 50K
websites and 11 domains. Based on an open-source agent framework, SmolAgents,
we collect supervised fine-tuning trajectories to develop a series of
foundation models, WebAggregator. WebAggregator-8B matches the performance of
GPT-4.1, while the 32B variant surpasses GPT-4.1 by more than 10% on GAIA-text
and closely approaches Claude-3.7-sonnet. Moreover, given the limited
availability of benchmarks that evaluate web agents' information aggregation
abilities, we construct a human-annotated evaluation split of WebAggregatorQA
as a challenging test set. On this benchmark, Claude-3.7-sonnet only achieves
28%, and GPT-4.1 scores 25.8%. Even when agents manage to retrieve all
references, they still struggle on WebAggregatorQA, highlighting the need to
strengthen the information aggregation capabilities of web agent foundations.
A new paradigm, Explore to Evolve, is proposed to enhance web agents' information aggregation by constructing a large dataset and developing foundation models that outperform existing models on a challenging benchmark.
Explore to Evolve proactive online exploration aggregation program high-level logical types verifiable QA pair WebAggregatorQA SmolAgents WebAggregator GPT-4.1 GAIA-text Claude-3.7-sonnet human-annotated evaluation split