Anthropic CEO Dario Amodei said he plans to challenge the Department of Defense’s designation of the AI firm as a supply chain risk. He claims most Anthropic customers are unaffected by the label.
arXiv:2602.12534v1 Announce Type: new Abstract: In truncated linear regression, samples $(x,y)$ are shown only when the outcome $y$ falls inside a certain survival set $S^star$ and the goal is to estimate the unknown $d$-dimensional regressor $w^star$. This problem has a long history of study in Statistics and Machine Learning going back to the works of (Galton, 1897; Tobin, 1958) and more recently in, e.g., (Daskalakis et al., 2019; 2021; Lee et al., 2023; 2024). Despite this long history, […]
arXiv:2505.01078v3 Announce Type: replace-cross Abstract: Backward stochastic differential equation (BSDE)-based deep learning methods provide an alternative to Physics-Informed Neural Networks (PINNs) for solving high-dimensional partial differential equations (PDEs), offering potential algorithmic advantages in settings such as stochastic optimal control, where the PDEs of interest are tied to an underlying dynamical system. However, standard BSDE-based solvers have empirically been shown to underperform relative to PINNs in the literature. In this paper, we identify the root cause of this performance […]
arXiv:2601.19029v1 Announce Type: new Abstract: Automated piano performance evaluation traditionally relies on symbolic (MIDI) representations, which capture note-level information but miss the acoustic nuances that characterize expressive playing. I propose using pre-trained audio foundation models, specifically MuQ and MERT, to predict 19 perceptual dimensions of piano performance quality. Using synthesized audio from PercePiano MIDI files (rendered via Pianoteq), I compare audio and symbolic approaches under controlled conditions where both derive from identical source data. The best model, MuQ […]
arXiv:2601.06478v1 Announce Type: cross Abstract: Gradient descent on log-sum-exp (LSE) objectives performs implicit expectation–maximization (EM): the gradient with respect to each component output equals its responsibility. The same theory predicts collapse without volume control analogous to the log-determinant in Gaussian mixture models. We instantiate the theory in a single-layer encoder with an LSE objective and InfoMax regularization for volume control. Experiments confirm the theory’s predictions. The gradient–responsibility identity holds exactly; LSE alone collapses; variance prevents dead components; decorrelation […]
Codex Security is an AI application security agent that analyzes project context to detect, validate, and patch complex vulnerabilities with higher confidence and less noise.
arXiv:2602.21389v1 Announce Type: new Abstract: Autonomous robots can transform how we observe marine ecosystems, but close-range operation in reefs and other cluttered habitats remains difficult. Vehicles must maneuver safely near animals and fragile structures while coping with currents, variable illumination and limited sensing. Previous approaches simplify these problems by leveraging soft materials and bioinspired swimming designs, but such platforms remain limited in terms of deployable autonomy. Here we present a sea turtle-inspired autonomous underwater robot that closed the […]
Multi–task learning seeks to improve the generalization error by leveraging the common information shared by multiple related tasks. One challenge in multi–task learning is identifying formulations capable of uncovering the common information shared between different but related tasks. This paper provides a precise asymptotic analysis of a popular multi–task formulation associated with misspecified perceptron learning models. The main contribution of this paper is to precisely determine the reasons behind the benefits gained from combining multiple related tasks. Specifically, […]
Allen Institute for AI (AI2) Researchers introduce SERA, Soft Verified Efficient Repository Agents, as a coding agent family that aims to match much larger closed systems using only supervised training and synthetic trajectories. What is SERA? SERA is the first release in AI2’s Open Coding Agents series. The flagship model, SERA-32B, is built on the Qwen 3 32B architecture and is trained as a repository level coding agent. On SWE bench Verified at 32K context, SERA-32B reaches 49.5 […]
I’m a CS student currently learning Reinforcement Learning and working with Gymnasium for building environments and training agents. The aim is to move past simple 2D examples (such as CartPole) and create a bespoke 3D simulation environment, such as an F1-themed autonomous vehicle project where an agent learns to control a 3D environment with obstacles, physics, and realistic controls. What roadmap would you use if you were starting again today? Share links, tips, war stories, or hard truths […]