Prépublication
· 2026 arXiv
EN
We study the cosmology of self-interacting ultralight dark matter. The scalar field evolves from an early Hubble-frozen phase to a quartic-dominated radiation-like phase and finally to a quadratic matter-like phase. The intermediate phase increases the pre-rec...
Seokcheon Lee, Jae-Weon Lee
#astro-ph.CO
Prépublication
· 2026 arXiv
EN
Reinforcement learning (RL) is an effective way to strengthen the long-context reasoning of large language models (LLMs), but it needs training data that are long, diverse, challenging, and verifiable. Human annotation of such data is expensive, naturally occu...
Zhennan Wan, Jianfei Chen
#cs.CL
Prépublication
· 2026 arXiv
EN
Existing sequential recommenders usually allocate a fixed computational budget to every item in a user's interaction history, regardless of the item's contribution to the final prediction. We challenge this de facto design with two diagnostic findings. (1) Non...
Zhanshuo Cao, Jiakai Tang et al.
#cs.IR
Prépublication
· 2026 arXiv
EN
Existing world models typically perceive a scene through current visual frame or a narrow history window. However, these observations may not reveal the physics properties that determine transition dynamics, such as an object's mass distribution, joint damping...
Yunhao Luo, Wentao He et al.
#cs.RO
Prépublication
· 2026 arXiv
EN
Through a tangle of real-world interactions, from synthetic training pipelines to data shared on the open web, frontier LLMs absorb what their predecessors and competitors create. How well does each model play its role in this ecosystem, as a creator and as a...
Shibo Jin, Zhiqiu Xu et al.
#cs.CL
Prépublication
· 2026 arXiv
EN
Consensus is a fundamental problem in distributed computing and at the core of state machine replication (SMR). Despite decades of active human research, discovering practical low-latency solutions to the problem is still open, as it requires combining protoco...
Clément Burgelin, Rachid Guerraoui et al.
#cs.DC
Prépublication
· 2026 arXiv
EN
Quantitative research is an iterative loop: a researcher proposes a market hypothesis, implements it as an executable signal, tests it on historical data, and decides what to keep. Existing benchmarks score formulas, code, or individual trading decisions, leav...
Houde Qian, Zheng Jiang et al.
#cs.CL
Prépublication
· 2026 arXiv
EN
Multimodal agents (MMAs) sustain interaction through reasoning, memory, tools, and feedback, most visibly as computer-use agents, while robotic systems (RSs) couple sensing and actuation under physical dynamics. We define multimodal embodied agents (MMEAs) as...
Yanzhe Chen, Ziyi Yang et al.
#cs.RO
Prépublication
· 2026 arXiv
EN
Sequential decision-making under partial observability is difficult because agents must explore to infer hidden, task-relevant information before acting effectively. Learning such information-gathering behaviors tabula rasa via online reinforcement learning is...
Sriyash Poddar, Yanda Bao et al.
#cs.RO
Prépublication
· 2026 arXiv
EN
Despite recent advances, current AI systems remain highly susceptible to domain shifts, particularly in biomedical imaging, where images are usually acquired under substantially different experimental conditions. Although recent domain adaptation methods have...
Sonja Topf, Ana Sanchez-Fernandez et al.
#cs.LG
Prépublication
· 2026 arXiv
EN
Many NP-complete (NPC) problems have a polynomial-time (P) twin that differs in a single structural detail, such as 3- versus 2-colouring a graph. A model that reasons rather than recalls should notice which side of this boundary a problem falls on, yet existi...
Nurul Labib Sayeedi, Md. Faiyaz Abdullah Sayeedi et al.
#cs.CL
Prépublication
· 2026 arXiv
EN
Reinforcement Learning with Human Feedback (RLHF) has become a central paradigm for aligning Large Language Models with human values. However, the reliance on sensitive preference data necessitates rigorous differential privacy (DP) guarantees to prevent user...
Meng Ding, Mingxi Lei et al.
#cs.LG