Large language models (LLMs) are increasingly used as web-facing agents for information-seeking tasks over live websites. Many such tasks require multi-step in-site navigation and cross-page evidence aggregation, a setting known as web traversal. Existing LLM-based web agents, including recent explore–critic architectures, typically store interaction history as an ever-growing unstructured text trace and mix high-level planning with low-level page actions. This makes long-horizon behavior brittle, obscures the source of evidence, and often leads to redundant browsing or premature stopping. To mitigate these issues, we propose WebTraveler, a hierarchical multi-agent framework that separates high-level information-seeking planning from local web exploration and maintains a structured trajectory memory of decision-relevant signals. This design provides compact information interfaces for evidence sufficiency checking and webpage navigation, improving long-horizon stability and cross-page evidence aggregation. To obtain scalable supervision, we further introduce path-guided data synthesis, which uses executable navigation paths to collect trajectories, removes explicit path hints, and filters invalid samples through answer-consistency checks. Experiments show that WebTraveler consistently outperforms previous methods across LLM scales, especially on tasks requiring deeper navigation, with further gains from path-guided supervision. Moreover, WebTraveler also shows generalization to broader web navigation tasks. Code is available at https://github.com/RongchangLi/WebTraveler.

In active retrieval (AR), large language models (LLMs) need first assess whether they possess knowledge to answer a given query, to decide whether to invoke a retrieval module. Existing methods primarily rely on training classification models or using the confidence of the model’s answer to determine knowledge boundaries. However, training-based methods may have limited generalization, and our analysis reveals that LLMs struggle to reliably assess whether they possess the required information based on their answers, often biased by prior cognitive tendencies (e.g., tokens’ semantic preferences). To address this, we propose Debiased Historical In-Context Learning (DH-ICL) to identify knowledge boundaries in AR. DH-ICL aims to reframe this self-awareness metacognitive task as a structured pattern-learning problem by retrieving similar historical queries as high-confidence in-context examples to guide LLMs to identify knowledge boundaries. Furthermore, we introduce a historical bias calibration strategy that leverages deviations in the model’s past response logits to mitigate cognitive biases in its current knowledge boundary assessment. Experiments on four QA benchmarks show that DH-ICL achieves performance comparable to full retrieval on LLaMA with only half the number of retrievals, without any additional training.