Prépublication
· 2026 arXiv
EN
Tomographic weak gravitational lensing probes cosmic structure growth and geometry, making it a powerful probe of dynamical dark energy. We forecast information beyond the lensing power spectrum from non-Gaussian statistics of tomographic galaxy and CMB lensin...
Shu-Fan Chen, J. Colin Hill et al.
#astro-ph.CO
Prépublication
· 2026 arXiv
EN
Primordial tensor non-Gaussianity can probe massive particles present during inflation. We investigate this possibility in an effective field theory where the massless graviton mixes with the helicity-two mode of an additional massive spin-two field $σ_{ij}$ w...
Sukannya Bhattacharya, Matteo Fasiello et al.
#astro-ph.CO
Prépublication
· 2026 arXiv
EN
Axion-like particles are light pseudoscalars that appear in many extensions of the Standard Model and are candidates for dark matter. Through their mixing with photons, they imprint energy dependent modulations on the spectra of astrophysical sources shining t...
Oleksandr Khasai, Lidiia Zadorozhna et al.
#astro-ph.HE
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
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
Prépublication
· 2026 arXiv
EN
Perimeter triage faces a latency/semantics trade-off: signature engines are fast and format-safe but blind to unseen attacks, while generative models are semantic but slow and produce unparseable output. In late 2026 a class of commercial System One decision m...
Juan S. Santillana
#cs.CR