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
Prépublication
· 2026 arXiv
EN
Vision-language-action (VLA) models have shown promise in robotic manipulation, but imitation learning (IL) alone often struggles to master complex long-horizon skills. Reinforcement learning (RL) offers an effective avenue for further improving VLA policies....
Likui Zhang, Zhe Zhu et al.
#cs.RO
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
Prépublication
· 2026 arXiv
EN
Parallel AI agents can produce useful local edits yet degrade a shared solution when their edits are combined. We model this coordination problem as discrete block optimization with an exactly evaluable objective and a known dependency graph. Independent sets...
Hanmin Li, Yury Demidovich et al.
#cs.MA
Prépublication
· 2026 arXiv
EN
Data agents built on ReAct-style loops now execute complex analytical workflows over structured data, yet their intermediate reasoning still runs almost entirely through text. Human analysts instead move between numbers, tables, and charts, reading visual evid...
Xinzhe Wang, Xudong Weng et al.
#cs.AI