Existing web agents typically initiate exploration from the root URL, which is inefficient for complex websites with deep hierarchical structures.Without a global view of the website's structure, agents frequently fall into navigation traps, explore irrelevant branches, or fail to reach target information within a limited budget.We propose MANGO, a multiagent web navigation method that leverages the website structure to dynamically determine optimal starting points.We formulate URL selection as a multi-armed bandit problem and employ Thompson Sampling to adaptively allocate the navigation budget across candidate URLs.Furthermore, we introduce an episodic memory component to store navigation history, enabling the agent to learn from previous attempts.Experiments on WebVoyager demonstrate that MANGO achieves a success rate of 63.6% when using GPT-5-mini, outperforming the best baseline by 7.3%.Furthermore, on WebWalkerQA, MANGO attains a 52.5% success rate, surpassing the best baseline by 26.8%.We also demonstrate the generalizability of MANGO using both open-source and closed-source models as backbones.

Large Language Models (LLMs) have shown promising performance in code generation.However, how to reliably evaluate code generated by LLMs remains an unresolved problem.This paper presents CODEJUDGE , a code evaluation framework that leverages LLMs to evaluate the semantic correctness of generated code without the need for test cases.We investigate different ways to guide the LLM in performing "slow thinking" to arrive at an in-depth and reliable evaluation.We experimented with four LLMs as evaluators on four code generation datasets and five programming languages.The results show that CODEJUDGE significantly outperformed existing methods in most settings.Furthermore, compared with a SOTA GPT-3.5-basedcode evaluation method, CODE-JUDGE achieved better results even when using a much smaller model, Llama-3-8B-Instruct.Our code and datasets are available on GitHub https://github.com/VichyTong/CodeJudge.