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 demonstrated unprecedented capability in code generation. However, LLM-generated code is still plagued with a wide range of functional errors, especially for complex programming tasks that LLMs have not seen before. Recent studies have shown that developers often struggle with inspecting and fixing incorrect code generated by LLMs, diminishing their productivity and trust in LLM-based code generation. Inspired by the mutual grounding theory in communication, we propose an interactive approach that leverages code comments as a medium for developers and LLMs to establish a shared understanding. Our approach facilitates iterative grounding by interleaving code generation, inline comment generation, and contextualized user feedback through editable comments to align generated code with developer intent. We evaluated our approach on two popular benchmarks and demonstrated that our approach significantly improved multiple state-of-the-art LLMs, e.g., 17.1% pass@1 improvement for code-davinci-002 on HumanEval. Furthermore, we conducted a user study with 12 participants in comparison to two baselines: (1) interacting with GitHub Copilot, and (2) interacting with a multi-step code generation paradigm called Multi-Turn Program Synthesis. Participants completed the given programming tasks 16.7% faster and with 10.5% improvement in task success rate when using our approach. Both results show that interactively refining code comments enables the collaborative establishment of mutual grounding, leading to more accurate code generation and higher developer confidence.

Recognizing software entities such as library names from free-form text is essential to enable many software engineering (SE) technologies, such as traceability link recovery, automated documentation, and API recommendation. While many approaches have been proposed to address this problem, they suffer from small entity vocabularies or noisy training data, hindering their ability to recognize software entities mentioned in sophisticated narratives. To address this challenge, we leverage the Wikipedia taxonomy to develop a comprehensive entity lexicon with 79K unique software entities in 12 fine-grained types, as well as a large labeled dataset of over 1.7M sentences. Then, we propose self-regularization, a noise-robust learning approach, to the training of our software entity recognition (SER) model by accounting for many dropouts. Results show that models trained with self-regularization outperform both their vanilla counterparts and state-of-the-art approaches on our Wikipedia benchmark and two Stack Overflow benchmarks. We release our models11https://huggingface.co/taidng/wikiser-bert-base; https.//huggingface.co/taidng/wikiser-bert-large., data, and code for future research.22https://github.com/taidnguyen/software_entity_recognition

Stack Overflow (SO) is becoming an indispensable part of modern software development workflow. However, given the limited time, attention, and memory capacity of programmers, navigating SO posts and comparing different solutions is time-consuming and cumbersome. Recent research has proposed to summarize SO posts to concise text to help programmers quickly assess the relevance and quality of SO posts. Yet there is no large dataset of high-quality SO post summaries, hindering the development and evaluation of post summarization techniques. We present SOSum, a dataset of 2,278 popular SO answer posts with manually labeled summative sentences. Questions in SOSum cover 669 tags with a median view count of 253K and a median post score of 17. This dataset will foster research on sentence-level summarization of SO posts and has the potential to facilitate text summarization research on other types of textual software artifacts such as programming tutorials.