Hongbo Fang

Jiessie Tie, Bingsheng Yao, Tianshi Li et al.

Software engineers are increasingly incorporating AI assistants into their workflows to enhance productivity and alleviate cognitive load. However, experiences with large language models (LLMs) such as ChatGPT vary widely. While some engineers find them useful, others deem them counterproductive due to inaccuracies in their responses. Researchers have also observed that ChatGPT often provides incorrect information. Given these limitations, it is crucial to determine how to effectively integrate LLMs into software engineering (SE) workflow. Analyzing data from 26 participants in a complex web development task, we identified nine failure types categorized into incorrect or incomplete responses, cognitive overload, and context loss. Users attempted to mitigate these issues through scaffolding, prompt clarification, and debugging. However, 17 participants ultimately chose to abandon ChatGPT due to persistent failures. Our quantitative analysis revealed that unhelpful responses increased the likelihood of abandonment by a factor of 11, while each additional prompt reduced abandonment probability by 17%. This study advances the understanding of human-AI interaction in SE tasks and outlines directions for future research and tooling support.

Bin Ren, Hang Guo, Yan Shu et al.

This paper reviews the NTIRE 2026 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects, such as runtime, parameters, and FLOPs, while maintaining PSNR of around 26.90 dB on the DIV2K_LSDIR_valid dataset, and 26.99 dB on the DIV2K_LSDIR_test dataset. The challenge had 95 registered participants, and 15 teams made valid submissions. They gauge the state-of-the-art results for efficient single-image super-resolution.

Hongbo Fang, James Evans

Scientific progress has long been understood as recombinant, with breakthroughs arising when existing ideas are joined in new ways. Empirical work in this tradition has focused on the inputs to discovery, asking whether a paper draws together atypical or distant prior knowledge. Far less is known about how knowledge is supplied for downstream recombination, or how individual contributions are forged to play distinct and distant roles in the broader system of science. Using citation networks from tens of millions of publications in OpenAlex and the Web of Science, here we show that scientific contributions stably decompose into three functional types, foundations, extensions, and generalizations, distinguishable by the local structure of their forward citations. This decomposition of the 'functional role' of scientific work presents an unseen pattern of scientific production: foundational and extensional work, which respectively build and elaborate within disciplines, dominated the post-war decades but has declined steadily since the early 1990s, while generalizations, meaning compressed and modular contributions reused in distant fields, have risen sharply. Stacked difference-in-differences analyses that exploit venues' transitions to online access and authors' adoption of large language models provide causal evidence that digital knowledge infrastructure is driving this shift. The locus of innovation has thus migrated from within what Simon might characterize as nearly decomposable disciplinary modules to the interfaces between them, recasting the much-discussed decline of disruption as a structural reorganization of science rather than a slowdown, and revealing a growing misalignment between how science now advances and how it is recognized and rewarded.