Prépublication
· 2026 arXiv
EN
Significant efforts are currently being directed toward the implementation of large-scale quantum information processors, but realizing useful quantum advantage also requires the development of efficient quantum algorithms. While for many practical problems th...
Andi Gu, Alek H. Kemeny et al.
#quant-ph
Prépublication
· 2026 arXiv
EN
Predicting how the world responds to an action requires more than what can be seen: robots must account for environmental factors that are neither directly observable nor explicitly specified, from physical properties such as friction and mass to human-defined...
Chenyuan Zhou, Yining Hong et al.
#cs.RO
Prépublication
· 2026 arXiv
EN
Generative simulation can automate robot environment construction, but physically valid environments are not necessarily useful for training. The challenge is to identify environments that address a policy's failures and supervision that helps it overcome them...
Tianjiao Yu, Zhilin Ou et al.
#cs.RO
Prépublication
· 2026 arXiv
EN
Generative visual verifiers play a crucial role in advancing multimodal intelligence. To enhance the robustness of visual verifiers in complex multimodal scenarios involving compositional requirements, we move beyond conventional binary verifiers that produce...
Bowei Liu, Xinchen Zhang et al.
#cs.CL
Prépublication
· 2026 arXiv
EN
Mixture-of-experts (MoE) models have become an important approach for scaling model parameters. Modern MoE architectures increasingly combine two design choices: fine-grained expert segmentation and shared experts. The former increases expressiveness by enabli...
Bingcong Li, Hao Ma et al.
#cs.LG
Prépublication
· 2026 arXiv
EN
Existing agent benchmarks typically evaluate agents on well-specified tasks defined by issue reports, specifications, or task tickets. Real-world customer delivery is different: forward-deployed engineers (FDEs) must clarify ambiguous requirements, work in liv...
Dongxu Yang, Hongda Yan et al.
#cs.AI
Prépublication
· 2026 arXiv
EN
Large language models (LLMs) are expanding from assisting scientific research and engineering to autonomously developing and improving AI systems, advancing AI for AI (AI4AI) as a pathway toward recursive self-improvement (RSI). Workflow-driven machine learnin...
Shangheng Du, Jinxin Shi et al.
#cs.AI
Prépublication
· 2026 arXiv
EN
Recent 3D world models increasingly use features from geometry foundation models (GFMs) as predictive world states, benefiting from their rich geometric priors. However, the view dependence of these features entangles scene dynamics with camera-induced variati...
Hongchang Liu, Jian Liu et al.
#cs.CV
Prépublication
· 2026 arXiv
EN
We introduce 3DCodeVerse, a research ecosystem that enables coding agents to create editable and verifiable 3D content at scale. 3DCodeVerse comprises large-scale, high-quality datasets of executable 3D code, a coding harness for spatial reasoning and automate...
Yipeng Gao, Ziyao Zeng et al.
#cs.CV
Prépublication
· 2026 arXiv
EN
How can robots autonomously collect the contact-rich experience needed to learn dexterous manipulation? We present DSP: Dexterity from supervised play, a framework in which a robot learns a play policy from minutes of human play through behavior cloning, then...
Botao He, Yiming Dou et al.
#cs.RO
Prépublication
· 2026 arXiv
EN
Large language models (LLMs) can often tell when they are being tested. This ability, known as evaluation awareness, can lead models to act more cautiously during safety evaluations than in real use. Existing work has studied evaluation awareness almost entire...
Trizal Garg, Sibayan Mitra et al.
#cs.AI
Prépublication
· 2026 arXiv
EN
Deep search agents solve knowledge-intensive tasks through long-horizon, multi-turn interactions, where assigning credit to individual search decisions is fundamentally difficult. Outcome-based reinforcement learning provides only a single terminal reward shar...
Siqi Chen, Zhengxi Lu et al.
#cs.AI