arXiv:2608. 20319v1 Announce Type: cross Abstract: Naturalistic computer-use traces, passively recorded screenshots and mouse or keyboard actions, are a valuable resource for deriving symbolic, auditable, and reusable models of how everyday work is done.
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Work2Skill is a framework that captures human GUI experience to induce reusable agent skills. It introduces WorkRecorder, an infrastructure that aligns screen recordings, mouse and keyboard events, and accessibility-tree data, and uses it to build WorkDemo-350, the first synchronized multimodal dataset of 350 human desktop GUI demonstrations. The proposed skill‑induction method extracts workflow rules, step dependencies, and completion checks, achieving higher scores and success rates than baseline methods on MyPCBench and OSWorld benchmarks.
arXiv:2606. 14654v1 Announce Type: new Abstract: Sequential or time-stamped interaction logs provide objective records of digital application usage, yet their granularity and noise often obscure meaningful insights into people's work.
arXiv:2606. 18191v1 Announce Type: new Abstract: Deep research (DR) systems are increasingly used for complex information-seeking tasks, but existing works mainly focus on generating reports and summaries.
arXiv:2606. 16988v1 Announce Type: cross Abstract: Benchmark scores tell you what an agent got right; they do not tell you how it got there.
arXiv:2610.14966v1 Announce Type: new Abstract: Long-horizon agents must complete workflows in which later activities depend on earlier outputs and changes to shared state. A key challenge for evalua...
arXiv:2606. 06893v1 Announce Type: new Abstract: Large language model agents increasingly rely on Skills to encode procedural knowledge, yet high-quality Skills remain costly to hand-write.