3 citations · 3 across the 10 of their papers we have counts for
7 papers · 1 filter
Can Agents Design Libraries for Agents?
Gabriel Orlanski, Alex L. Zhang, Avi Trost +4
Agents increasingly build on code written by other agents, and they reimplement rather than reuse, growing the codebases later agents must work in. To measure how well agents desig…
X-Planner: Event-Structured Task Planning for Embodied Intelligence
Howard Lu, Shalfun Li, Porter Pan +30
Task planning bridges high-level instructions and executable behavior in long-horizon manipulation, yet modern Vision-Language-Action (VLA) systems often leave this intermediate st…
WALL-SS: Scaling Long-horizon World Models via Next-Scale Autoregression
Maeve Zhang, Rain Sun, Xiang Wang +22
Generative world models provide robots with predictive models of how the world evolves under interaction, with growing potential for simulation, planning, policy evaluation, and ro…
OSWorld 2.0: Benchmarking Computer Use Agents on Long-Horizon Real-World Tasks
Mengqi Yuan, Zilong Zhou, Xinzhuang Xiong +33
Existing computer-use benchmarks fail to capture the realism, complexity, and long-horizon demands of real-world computer use, limiting their ability to reveal the limitations of f…
Agents' Last Exam
Yiyou Sun, Xinyang Han, Weichen Zhang +378
Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional d…
Continual Learning Bench: Evaluating Frontier AI Systems in Real-World Stateful Environments
Parth Asawa, Christopher M. Glaze, Gabriel Orlanski +7
Continual learning, the ability of AI systems to improve through sequential experience, has attracted substantial interest, but no high-quality benchmark exists to evaluate it. We…
Wall-OSS-0.5 Technical Report
Ryan Yu, Pushi Zhang, Starrick Liu +24
Large-scale Vision-Language-Action (VLA) pretraining is increasingly adopted as the foundation for robot policies, yet the evidence for pretrained VLAs is almost invariably reporte…