Watching AI evolve.
Understanding what comes next.
Sorted according to the reading records on the site in the last 7 days; each article by the same reader will be counted once per day.
arXiv:2610.22408v1 Announce Type: new Abstract: Long-horizon GUI agents rely on visual history to maintain task states, but retaining historical screenshots increases visual-token usage and inference costs, while textual summaries can discard critical state information, undermining long-horizon reliability.
arXiv:2610.23070v1 Announce Type: cross Abstract: Universal function approximation underpins neural networks' capacity to represent nonlinear relationships, but realizing this capability in optical hardware requires engineering the computational features encoded in light.
arXiv:2610.22426v1 Announce Type: new Abstract: LLM agents are used to check records against an organization's requirements, for example whether a vulnerability report is ready for investigation. Requirements often allow several ways to be met, such as a severity rating with a curator-reviewed advisory or a published advisory link.
arXiv:2610.22397v1 Announce Type: new Abstract: Engineering design tools increasingly embed optimizers as ready-made components that users select by algorithm name, while operator settings and bound, integer and duplicate handling stay fixed inside each tool. This study tests whether the name predicts the result.
arXiv:2610.22420v1 Announce Type: new Abstract: Agent-based models are well-suited for modelling agro-ecological systems for decision-making, yet turning their simulations into actionable insight requires identifying which decisions actually drive the behaviours.
arXiv:2610.22400v1 Announce Type: new Abstract: Existing large language model (LLM)-based user encoders serialize heterogeneous behavioral logs and compress them into static representations through autoregressive (AR) prediction. However, their unidirectional constraint limits bidirectional information flow, hindering cross-modal interactions and the joint preservation of multi-source signals.
Anthropic said that it will commit 5% of its shares to the fund.
The filing also says that the “vast majority” of Character.AI employees who were offered jobs at Disney accepted them. However, this is a stalking horse bid, so another company could come forward with a higher offer.
TLDR: I explain what the technical AI alignment problem is and what its core difficulties are, in my view. This is a perspective that draws simultaneously on classical traditions in theoretical computer science / machine learning theory, ideas from analytic philosophy, and views that originated in the Machine Intelligence Research Institute / LessWrong.
Allowing everyone to freely create and experience an interactive world
Amid a wave of backlash, some industry-friendly groups are employing creative and time-tested strategies to woo the youth.
Adult performers are questioning why Meta allows its AI agent to sext while restricting their social media accounts.
