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Towards A Generative Protein Evolution Machine with DPLM-Evo

arXiv:2605.00182v3 Announce Type: replace Abstract: Proteins are shaped by gradual evolution under biophysical and functional constraints. Protein language models learn rich evolutionary constraints from large-scale sequences, and discrete diffusion-based protein language models~(eg, DPLMs) are promising for both understanding and generation.…

RUBAS: Rubric-Based Reinforcement Learning for Agent Safety

arXiv:2606.04051v1 Announce Type: new Abstract: The evolution of LLMs into tool-enabled agents creates a new class of safety challenges associated with real-world execution rather than simple text generation. Existing alignment methods often rely on coarse refusal signals or static supervision,…

AutoNumerics-Zero: Automated Discovery of State-of-the-Art Mathematical Functions

arXiv:2312.08472v2 Announce Type: replace-cross Abstract: Transcendental functions, such as the exponential, are central to scientific computing, yet they cannot be natively calculated by digital hardware. Instead, computers must approximate these functions by combining basic operations, such as ${+, -, times,…

A Goal-Set Characterization of Task Composition in the Boolean Task Algebra

arXiv:2606.04053v1 Announce Type: new Abstract: The Boolean Task Algebra (BTA) provides a principled framework for zero-shot task composition in reinforcement learning by equipping goal-reaching tasks with Boolean operations. We revisit its structural assumptions and formalize a collapse in the space…

Path-conditioned training: a principled way to rescale ReLU neural networks

arXiv:2602.19799v2 Announce Type: replace-cross Abstract: Despite recent algorithmic advances, we still lack principled ways to leverage the well-documented rescaling symmetries in ReLU neural network parameters. While two properly rescaled weights implement the same function, the training dynamics can be dramatically…

Spectral Scaling Laws of Muon

arXiv:2606.04058v1 Announce Type: new Abstract: Orthonormalized update rules have rapidly become a leading choice of optimizer for training large language models, with recent open-source state-of-the-art models adopting Muon. To keep these updates tractable, Muon performs the orthonormalization with the Newton–Schulz…

Bayesian learning for the stochastic shortest path problem

arXiv:2606.04845v1 Announce Type: cross Abstract: Sequential decision-making problems are often modelled as a Markov decision process (MDP). We focus on the stochastic shortest path (SSP) problem, which is an infinite-horizon undiscounted MDP with absorbing terminal states. We develop a Bayesian…