productivity software developers

Abstract: Web-based information work spans independently developed pages, with goals and coordination needs often emerging through browsing. Manual coordination requires repeated low-level actions, yet explicitly specifying operations for automation can interrupt engagement with information that shapes users' next steps. Our approach constructs reusable, schema-based page representations and identifies coordination actions through correspondences across schemas. We instantiate this approach in Cling, which surfaces coordination opportunities in situ for users to select, configure, and execute incrementally while directing the broader task. A technical evaluation demonstrates accurate identification of useful opportunities across diverse domains. A 12-participant study found that Cling reduced task durations by more than half compared with both manual interaction and an end-to-end AI agent, while requiring 61% fewer clicks and 80% fewer tab switches than manual interaction. Its small-step automation supported control and verification as actions unfolded, while accommodating intent as it emerged and evolved through information encountered on the page.

Abstract: Deep research (DR) agents address open-ended information needs through multi-step reasoning, long-horizon web search, and evidence synthesis. However, existing benchmarks primarily focus on academic research or synthetic tasks, leaving DR agents' capabilities in real-world life and professional productivity settings insufficiently evaluated. To bridge this gap, we introduce LifeSearch, a challenging benchmark comprising 203 Chinese and English tasks derived from real-user query seeds across 7 concrete life and productivity domains. With more than 4,500 hours of human annotation, we construct over 4,000 expert-written, fine-grained rubrics, covering both report-content criteria for coverage and accuracy and concrete criteria specifying values, units, dates, and URLs. We conduct extensive experiments on 12 advanced DR agents under a unified evaluation harness, systematically analyzing their performance patterns and failure modes. We find that even the best-performing frontier agent, GPT-5.6 Sol, achieves a score of only 63.4%, leaving substantial room for improvement on realistic life and professional DR tasks. We select the grading configuration by comparing candidate LLM judges against human rubric labels, and a human ranking study shows strong alignment between aggregate benchmark scores and human judgments (Spearman's $ρ= 0.89$, Kendall's $τ= 0.81$). LifeSearch provides a rigorous evaluation framework and foundation for advancing DR agents toward real-world life and productivity.