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Three Ohio State football players, Urban Meyer and Kirk Herbstreit (and his dog) headlined a Life Surge event in Columbus: 'I was stunned that it really wasn't a bigger story... These guys are literal

arXiv:2610.01459v1 Announce Type: new Abstract: We study logistic regression on linearly separable data under gradient descent with a large constant stepsize eta. Such dynamics may exhibit a character

arXiv:2610.00899v1 Announce Type: cross Abstract: Autoregressive Vision-Language-Action models often represent continuous robot actions as discrete token sequences, enabling action prediction with sta

Today we're unveiling Trillium Labs @trillium_labs, a new non-profit to foster the open science of frontier AI. We're building open post-training recipes and will expand into open infra to study RSI,

arXiv:2610.01826v1 Announce Type: cross Abstract: Collaborative embodied artificial intelligence (CEAI) enables multiple physical agents to perceive, reason, and act cooperatively in dynamic environme

arXiv:2610.01973v1 Announce Type: new Abstract: Reinforcement learning (RL) for video generation usually assigns one scalar reward to an entire sampled video. Yet a video is not uniformly flawed: some

arXiv:2610.00575v1 Announce Type: new Abstract: A common approach to world-model simulation for vision-language-action (VLA) systems is to predict future RGB observations and then re-encode them into

arXiv:2610.00309v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in privacy-critical domains (e.g., healthcare, finance, and government), but their propensity t

arXiv:2610.01005v1 Announce Type: cross Abstract: As artificial intelligence is increasingly deployed, algorithmic unfairness has raised growing concerns and intensified demands for transparent fairne

arXiv:2610.01763v1 Announce Type: new Abstract: Activation sparsity speeds up large language model (LLM) inference by setting unimportant activations to zero so that the corresponding computations can

arXiv:2610.01943v1 Announce Type: new Abstract: Robotic simulation and virtual reality increasingly require object assets that capture not only visual geometry but also the physical cues underlying ta

arXiv:2610.00718v1 Announce Type: new Abstract: We present a teleoperation system that enables a single operator to perform construction tasks on a Unitree G1 humanoid, combining extended reality (XR)

arXiv:2610.01647v1 Announce Type: cross Abstract: In this article, we present our vision and recent advancements toward creating a decentralized system capable of learning from real-time data within b

arXiv:2610.00438v1 Announce Type: new Abstract: Humanoid whole-body manipulation has advanced rapidly, enabling policies to coordinate locomotion, posture, bimanual interaction, and dexterous hand mov

arXiv:2610.01022v1 Announce Type: new Abstract: Memory-attention-based Video Instance Segmentation (VIS) methods have demonstrated strong zero-shot tracking capability, yet their substantial memory re

arXiv:2610.00895v1 Announce Type: cross Abstract: Foundation models remain vulnerable to spurious correlations and ``Clever Hans'' strategies. Explainable machine learning can find and remove such str

arXiv:2610.00590v1 Announce Type: cross Abstract: An autonomous cyber defender trained with reinforcement learning (RL) is typically tied to the network on which it was trained, limiting its ability t

arXiv:2610.01566v1 Announce Type: new Abstract: Practical Reinforcement Learning (RL) algorithms learn to solve Markov Decision Processes (MDPs) through iterative policy improvement in the presence of

arXiv:2610.01906v1 Announce Type: new Abstract: Scuba divers are taught to control their depth to avoid rapid ascents and descents, which could result in serious injuries such as gas embolisms and bar

arXiv:2610.01531v1 Announce Type: new Abstract: Vision-Language models (VLMs) are increasingly being explored in autonomous driving for tasks such as scene understanding, driving reasoning, decision-m

arXiv:2610.01052v1 Announce Type: new Abstract: We argue that as video generation extends to longer durations, subject consistency should be evaluated over extit{dynamic subject sets}. We therefore in

arXiv:2603.21152v4 Announce Type: replace-cross Abstract: Modern seismic networks resolve earthquake sequences in unprecedented detail, yet explaining how large earthquakes emerge from evolving fault

arXiv:2610.00822v1 Announce Type: new Abstract: Share the light, not the map. We study next-best-view selection for a team of robots, each of which builds its own 3D Gaussian Splatting map and keeps i

arXiv:2610.01887v1 Announce Type: cross Abstract: Dynamic resource assignment, the real-time allocation of task streams to heterogeneous processing nodes, is the backbone of modern computing infrastru

arXiv:2610.01323v1 Announce Type: new Abstract: Safety-aligned large language models (LLMs) often refuse a harmful request but comply once the same goal is spread over several turns. Preference object

arXiv:2610.00814v1 Announce Type: cross Abstract: Synthetic data are increasingly used to scale LLM training, yet more synthetic data do not necessarily produce better models. Useful synthetic data mu

arXiv:2610.01959v1 Announce Type: cross Abstract: Path finding and multi-robot motion planning require trajectories that are smooth, goal-directed, and collision-free in environments with complex geom

arXiv:2602.08538v2 Announce Type: replace-cross Abstract: Flow-based generative models have emerged as powerful priors for solving inverse problems. One option is to directly optimize the initial late

arXiv:2610.00420v1 Announce Type: cross Abstract: A weight space network (or metanetwork) takes the weights of another neural network as input and predicts properties of it. Most prior work trains suc

arXiv:2610.00835v1 Announce Type: new Abstract: Text-to-music generation models are trained on massive music collections, creating a growing need for data attribution methods that can quantify the con

arXiv:2610.02190v1 Announce Type: new Abstract: Step-size selection remains a central challenge in large-scale neural network optimization; conservative steps slow convergence, while aggressive steps

arXiv:2610.01282v1 Announce Type: new Abstract: The rapid evolution of Intelligent Transportation Systems and Logistics (ITS&L) has become a cornerstone of the modern social economy, relying heavily o

https://huggingface.co/Wa1k3r/Qwen3.8-27b-CODER-4q_xs-24GB-262k-Optimalcardfit Qwen3.8-27B CODER — IQ4_XS imatrix · 24 GB card fit · ~262k context · MTP draft Quantized, Abliterated, and fitted by Lex

I have been building a somewhat unusual local inference machine around two Huawei Atlas 300I Duo cards. They are relatively inexpensive, passive, dual-accelerator PCIe cards with 96 GB of device memor

arXiv:2610.00953v1 Announce Type: cross Abstract: An answer candidate in a masked diffusion MLLM can stabilize while its rationale is still unfolding. We distinguish retrospective stabilization of the

arXiv:2610.01590v1 Announce Type: cross Abstract: Continual learners that keep a task-specific adapter in every block of a pre-trained vision transformer accumulate storage linearly with the number of

arXiv:2610.00102v1 Announce Type: new Abstract: Many machine learning (ML) applications rely on expert labels, and qualified experts may provide different but plausible interpretations of the same obs

arXiv:2610.00188v1 Announce Type: cross Abstract: Voxel-based volumetric mapping is fundamental to 3D reconstruction, yet fixed-resolution grids remain inherently inefficient - wasting memory in unifo

arXiv:2610.01452v1 Announce Type: new Abstract: While state-of-the-art automated models for medical image segmentation achieve high mean performance, they frequently suffer from localized, catastrophi

arXiv:2610.00905v1 Announce Type: cross Abstract: With the advancement of LLM-based multi-agent systems (MAS), an increasing number of opensource projects are adopting multi-agent architectures as the

arXiv:2604.02751v3 Announce Type: replace Abstract: Diffusion models often degrade in latent spaces, yet the formal causes remain poorly understood. We quantify latent-space diffusability via the rate

arXiv:2610.00341v1 Announce Type: cross Abstract: As Large Multimodal Models (LMMs) transition toward natively unified architectures, evaluating their safety in synergistic harmful image-text generati

arXiv:2502.13141v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are vulnerable to attacks like prompt injection, backdoor attacks, and adversarial attacks, which manipulate prom

arXiv:2610.00878v1 Announce Type: cross Abstract: General-purpose embodied robots should support both navigation toward language-specified destinations and dynamic person tracking under arbitrary init

arXiv:2610.01984v1 Announce Type: cross Abstract: Byte-level byte-pair encoding (BBPE) tokenizers are attractive for multilingual large language models (LLMs) because they cover all Unicode text. In U

arXiv:2610.01981v1 Announce Type: new Abstract: Universal approximation is a necessary qualitative property of learning architectures to benefit from scaling laws. While it is generically verified on