Just 10 more hours to qwen 3.8 27b…
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Ok, maybe it was more like 7 hours let's see. Zhipu doesn't have anything fancy, neither scale nor architecture. Pure brutal training competence. How …
Ok, maybe it was more like 7 hourslet's see. Zhipu doesn't have anything fancy, neither scale nor architecture. Pure brutal training competence. How far further could…
CW-BASS v2: Saturation-Aware Pseudo-Label Selection for Semi-Supervised Segmentation under Foundation-Model Teachers
Semi-supervised semantic segmentation has long turned on one question, which pseudo-labels to trust, and a generation of selection rules, dynamic thresholds, per-class curricula, soft confidence weights,…
new X update makes me feel like DeepSeek, maybe that's why I empathize so much
new X update makes me feel like DeepSeek, maybe that's why I empathize so muchJMB 🧙♂️: pretty good for a blind guy tho. i had to…
H2R-Bench: Benchmarking Human-to-Robot Manipulation Video Generation in World Models
Large-scale manipulation data is essential for robot learning, yet collecting robot demonstrations remains expensive and difficult to scale. Meanwhile, abundant egocentric human manipulation videos provide rich…
Try Grok 4.6 image & video understanding, it’s a major upgrade!
Try Grok 4.6 image & video understanding, it’s a major upgrade!Yun-Ta Tsai: Grok 4.6 multimodal is a step change from Grok 4.5. It’s one of the…
Guys, look here please. RedNote's AI lab says they have a new RL regimen (TEMPO; beta version) that rewards world exploration allowing a 16B active Mo…
Guys, look here please.RedNote's AI lab says they have a new RL regimen (TEMPO; beta version) that rewards world exploration allowing a 16B active MoE to…
Lmao Makes no sense though, it doesn’t use fewer steps or tokens than Flash. How is this data generated? (Most likely still Pareto-mogging)
LmaoMakes no sense though, it doesn’t use fewer steps or tokens than Flash. How is this data generated?(Most likely still Pareto-mogging)Zain: whats insane is that DeepSeek-v4-pro…
RT @zephyr_z9: The magic of AFD (Attention FFN Disagg) It can handle the big boi Sol too (over 3T parameters) This is the first commerciall…
RT @zephyr_z9: The magic of AFD (Attention FFN Disagg)It can handle the big boi Sol too (over 3T parameters)This is the first commerciall…
LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers
No single large language model (LLM) is optimal across all queries and budget constraints, making model routing essential for cost-effective deployment. Existing routers adopt diverse formulations…
DreamX-Phi 1.0: Action-Conditioned Video World Model for Robotic Manipulation
We present DreamX-Phi 1.0, an action-conditioned video world model for robotic manipulation that, given an observed frame, a language instruction, and a prescribed action sequence comprising…
Spatial Memory Agent: Experience-Grounded Procedure Memory for Spatial Intelligence
Spatial intelligence is becoming a foundation for embodied agents, robotic planning, and multimodal assistants. To improve the spatial reasoning ability of VLM agents, existing work has…
Closed models used to be the default. Not anymore. NYC devs: come chat about it in person. Together AI × OpenRouter are hosting a fireside chat on th…
Closed models used to be the default. Not anymore.NYC devs: come chat about it in person.Together AI × OpenRouter are hosting a fireside chat on the…
Interpretable Causal Discovery via Causal-Effect Constraints
arXiv:2608.12640v1 Announce Type: cross Abstract: Causal discovery aims to uncover the underlying causal relationships given data generated from a system. The goal, however, is not merely…
When Explanations Betray Backdoors: Black-Box Auditing for Language Model Classifiers
arXiv:2608.12623v1 Announce Type: cross Abstract: Language model classifiers with explanations are used for moderation, routing, topic triage, and low-resource annotation. We study black-box auditing when the…
Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks
arXiv:2605.07060v3 Announce Type: replace-cross Abstract: Physics-informed neural networks (PINNs) provide a mesh-free framework for solving PDE-constrained inverse problems, but their extension to Bayesian inversion still faces…
Supervised Mixed-Frequency Learning for Macro-Financial Forecasting When Factors are Weak
arXiv:2608.12589v1 Announce Type: cross Abstract: Factor-MIDAS regressions forecast a low-frequency target by extracting common factors from a large panel of high-frequency predictors via principal component analysis…
Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust
arXiv:2608.12470v1 Announce Type: cross Abstract: Large astrophysical simulation campaigns often generate training data by sampling parameters across a Uniform prior box. Due to the proposal's sharp…
Multiview Representation Learning via Distributed Joint Latent Space Structuring
arXiv:2504.18455v2 Announce Type: replace Abstract: We study distributed multiview representation learning, a problem in which $K$ clients each observe a distinct but possibly statistically correlated view.…
Online Inference for Quantile Temporal Difference Learning in Distributional Reinforcement Learning
arXiv:2608.12973v1 Announce Type: new Abstract: In this paper, we study how to perform statistical inference for quantile temporal difference learning (QTD) in distributional reinforcement learning. Assuming…
High-dimensional networks and mean squared error for possibly misspecified models
arXiv:2608.13171v1 Announce Type: new Abstract: To avoid missing important variables and their connections in networks, more and more variables are included in network analysis. Here we…
Wasserstein Filtering: A Sample Selection Method for Robust Distribution Learning
arXiv:2608.13418v1 Announce Type: new Abstract: Given a dataset where a portion of the samples are contaminated, our goal is to recover the underlying clean population distribution.…
Sinkhorn Linearization and the Spectral Proxy: Unifying the Statistical and Algorithmic Theory of Feature-Parameterized Inverse Optimal Transport via a Single Spectral Sandwich
arXiv:2608.13201v1 Announce Type: new Abstract: We develop the statistical and algorithmic theory of inverse optimal transport (IOT) under the feature-parameterized cost C_theta(i,j) = -theta^T phi(i,j). The…
Identifiability and Stability of Generative Drifting in the Companion-Elliptic Kernel Family
arXiv:2604.24196v4 Announce Type: replace Abstract: A drifting model is a one-step generator trained by moving each sample along a field of kernel-weighted attraction toward data samples…