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SPD Learn: A Geometric Deep Learning Python Library for Neural Decoding Through Trivialization

arXiv:2602.22895v2 Announce Type: replace-cross Abstract: Implementations of symmetric positive definite (SPD) matrix-based neural networks for neural decoding remain fragmented across research codeba

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Thinking Hard, Not Smart: Reasoning Models Fail to Ration Test-Time Compute Across Questions

DGX agent

arXiv:2608.07968v1 Announce Type: cross Abstract: Reasoning language models increasingly use test-time compute to improve performance, but existing evaluations typically study this compute one questio

tutorialsarxiv-cs-ai

11 Aug 2026

TLDChoiceNet: Quantitatively Choosing a Transfer Learning Dataset

arXiv:2608.09091v1 Announce Type: cross Abstract: Transfer learning is particularly useful in settings with limited training data, and within image classification it is common to transfer learn upon m

Transformer Circuits Can Realize Clustering Algorithms

arXiv:2506.19125v2 Announce Type: replace-cross Abstract: Although transformers are most commonly optimized as statistical sequence models, it is unclear to what extent they can implement and learn ex

Transformer Explainer: Learning LLM Transformers with Interactive Visual Explanation and Experimentation

arXiv:2408.04619v2 Announce Type: replace-cross Abstract: The Transformer architecture underpins modern large language models powering state-of-the-art text generation and AI applications. However, it

TS-Mob: Social and Geographical-Aware Time Series Foundation-Model Framework for Human Mobility Prediction

arXiv:2507.00945v2 Announce Type: replace Abstract: Short-term forecasting of aggregated human mobility flows supports urban planning, intelligent transportation systems, and emergency response, yet e

UniDFKD: A Unified Semantic Prior Framework for Architecture-Agnostic Data-Free Knowledge Distillation

arXiv:2608.09287v1 Announce Type: cross Abstract: Data-Free Knowledge Distillation (DFKD) transfers knowledge from a pretrained teacher model to a compact student model by synthesizing semantically in

UniScale: Arbitrary-Scale Industrial Anomaly Generation

arXiv:2608.07864v1 Announce Type: new Abstract: Industrial anomaly inspection faces a major challenge due to the lack of real-world anomaly samples. While generative models are used to create anomaly

When Latents Forget Pixels: Restoring Fidelity in Diffusion Transformer Super-Resolution

arXiv:2608.09133v1 Announce Type: cross Abstract: Image super-resolution (SR) with large generative models has recently achieved remarkable perceptual quality, yet maintaining fidelity to the LR obser

Writing formats I can no longer read because AI has beaten them to death

I don’t even care anymore whether these posts are *actually* AI-generated. The problem is that there’s now a very specific style of internet writing that instantly makes my brain refuse to continue re

tutorialsr-chatgpt

C2Dex: Contact-Consistent Reconstruction and Retargeting for Dexterous Manipulation from Monocular Video

arXiv:2608.07045v1 Announce Type: cross Abstract: High-quality demonstrations for dexterous robot manipulation are costly and difficult to collect, whereas monocular human videos provide a scalable so

Decoupling Intention from Trajectory: A Representational Deduction Framework for World Action Models

arXiv:2608.06994v1 Announce Type: cross Abstract: World Action Models (WAMs) aim to construct a unified architecture capable of understanding world state evolution and guiding to generative motion pla

Defining Energy Indicators for Impact Identification on Aerospace Composites: A Structured Feature Selection Approach Guided by Domain Knowledge

arXiv:2511.01592v2 Announce Type: replace Abstract: Energy estimation is critical to impact identification on aerospace composites, where low-velocity impacts can induce internal damage that is undete

Discovering Conceptual Metaphors Across Topics and Media Types

arXiv:2608.06652v1 Announce Type: new Abstract: Conceptual metaphors guide our thinking and actions by allowing us to reason about more abstract experiences (e.g., paying taxes) in terms of more concr

Don't `Well, Actually' Me Unless You Know What You're Talking About: Weak Presupposition Verification Degrades General QA Performance

arXiv:2608.06539v1 Announce Type: new Abstract: False-presupposition QA (FPQA) tests LLMs on their ability to identify false presuppositions in questions and abstain or correct them rather than reinfo

Embedded Variational Neural Stochastic Differential Equations for Learning Heterogeneous Dynamics

arXiv:2604.00669v2 Announce Type: replace Abstract: This study examines the challenges of modeling complex and noisy data related to socioeconomic factors over time, with a focus on data from various

H2AL: Hyperbolic Hierarchy-aware Aggregative Learning for Registration-based Few-shot Medical Image Segmentation

arXiv:2608.07340v1 Announce Type: cross Abstract: Registration-based Few-shot medical image segmentation (RFMIS) aims to generate pseudo-labels for unlabeled images by warping a labeled image through

Improving Attributed Long-form Question Answering with Intent Awareness

arXiv:2603.27435v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly being used to generate comprehensive, knowledge-intensive reports. However, while these models a

InstanceSplat: Instance-Aware Feed-Forward 3D Gaussian Splatting for Scene Understanding

arXiv:2608.07144v1 Announce Type: new Abstract: Feed-forward 3D Gaussian Splatting (3DGS) enables efficient and generalizable 3D reconstruction, but current feed-forward 3DGS methods for scene underst

Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin

arXiv:2608.06411v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) achieve strong performance across diverse vision-language tasks, but their efficiency is limited by the cost of

Limit Points of Reflow with Minibatch Optimal Transport

arXiv:2608.07042v1 Announce Type: cross Abstract: Rectified flows, also called flow matching or stochastic interpolants, are generative models that learn a time-dependent vector field steering a proba

LoRAScan: Detecting Backdoor Prompts in Low-Rank Adapters for Large Language Models via Down-Projection Activation Spikes

arXiv:2608.06795v1 Announce Type: cross Abstract: Low-rank adaptation (LoRA) enables efficient specialization and distribution of large language models through compact adapters. However, untrusted ada

MuST-VAD: Mutual Structured Learning for Video Anomaly Detection

arXiv:2608.06913v1 Announce Type: new Abstract: In this paper, we propose MuST-VAD, a mutual structured learning framework for weakly supervised video anomaly detection (VAD) in which an anomaly detec

Omni-modal decomposition autoencoders learn full-stack wearable disentangled representations

arXiv:2608.07385v1 Announce Type: cross Abstract: Learning disentangled representations is a key requirement for developing versatile, general-purpose, and sustainable models in multi-modal wearable c

Optimization as a Dynamical System: Generative Schedules from Latent ODEs

arXiv:2509.23052v2 Announce Type: replace Abstract: We present a new meta-learning method to determine the optimal learning rate schedule for gradient descent. It leverages training runs from a hyperp

Run interactive IDEs on Amazon EKS with SageMaker AI to power up your AI workflows

The Amazon SageMaker AI Spaces add-on for Amazon EKS runs managed JupyterLab and Code Editor environments on the cluster your ML team already operates. This post shows how to install and configure the

tutorialsaws-ml-blog

Solver-Guided Reasoning for Mixed-Equilibrium Strategies

arXiv:2608.06741v1 Announce Type: new Abstract: Reasoning in large language models (LLMs) is often grounded in human text, human demonstrations, and human-generated rationales. For equilibrium reasoni

Stochastic Autoregressive Learning

arXiv:2608.07224v1 Announce Type: new Abstract: Motivated by LLMs, which generate outputs by iteratively sampling from next-token distributions, we introduce a PAC-learning model for binary stochastic

Target-Weighted Neyman Allocation: Experimental Design for Heterogeneous Treatment Effects under Population Shift

arXiv:2608.06512v1 Announce Type: new Abstract: Randomized experiments are often run in one population to guide decisions in another. Allocating by experimental proportions wastes budget on groups tha

TaskSense: Focusing on What Matters in World Models

arXiv:2608.06544v1 Announce Type: new Abstract: World models for visual control typically learn compact latent states by reconstructing observations, implicitly encouraging representations to preserve

Uncovering expert objectives in production planning via inverse optimization: An industrial case study

arXiv:2608.07398v1 Announce Type: cross Abstract: Production planning in the manufacturing industry often relies on the use of optimization models, but defining an appropriate objective function can b

UniJEPA: A Unified Joint-Embedding Predictive Architecture for Task-Agnostic Visual World Modeling

arXiv:2608.07409v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPAs) have emerged as a principled framework for self-supervised learning of world models in compact latent s

When Do LLMs Admit Their Mistakes? Understanding The Role Of Model Belief In Retraction

arXiv:2505.16170v4 Announce Type: replace Abstract: We study the internal mechanisms that govern when LLMs choose to retract wrong answers, i.e., spontaneously and immediately acknowledge errors in th

Why Knowing Both Hops Is Not Enough: Understanding Two-Hop Generalization in Language Models

arXiv:2608.07261v1 Announce Type: new Abstract: Large language models (LLMs) can solve complex multi-hop problems yet exhibit puzzling failures on simple two-hop queries: although a model may correctl

A lot of the questions we get from developers are about the concepts behind the API: TTFT, context windows, sampling, fine-tuning, quantizat…

A lot of the questions we get from developers are about the concepts behind the API: TTFT, context windows, sampling, fine-tuning, quantization, deployment tradeoffs. We added Learn to the Together do

tutorialstogether-ai--x

ALTER: Modeling Longitudinal Changes via Regional Differencing for 3D CT Report Generation

arXiv:2608.05615v1 Announce Type: new Abstract: Computed tomography (CT) is widely used for clinical diagnosis and longitudinal follow-up, yet automatically generating accurate and complete radiology

Analogy as Nonparametric Bayesian Inference over Relational Systems

arXiv:2006.04156v2 Announce Type: replace Abstract: Our inferences in the real world are rarely naive - we acquire experiences through our lifetime that can help us more quickly understand the structu

Bar-JEPA: Extracting Values from Bar Chart with Joint-Embedding Predictive Architecture

arXiv:2608.06062v1 Announce Type: new Abstract: Bar charts are commonly used in data visualization, and while they are easily understood by humans, it is non-trivial to extract the underlying data com

BioKD: Selective Physiology-to-Video Knowledge Distillation via Reliability Gate for Emotion Recognition

arXiv:2608.06023v1 Announce Type: new Abstract: To address the limitations of video-based emotion recognition under ambiguous or socially masked behavioral cues, as well as the poor deployability of p

BioM-JEPA: joint-embedding prediction of graph-connected gene blocks in single cells

arXiv:2608.05928v1 Announce Type: new Abstract: Single-cell transcriptomes are sparse observations of coordinated biological programmes, yet most self-supervised models learn by reconstructing individ

Cautious Context Steering for Language Model Personalization

arXiv:2608.05813v1 Announce Type: new Abstract: Personalizing language models (LMs) to individual user preferences is essential for aligning responses with diverse goals and backgrounds. Existing meth

Continuous-Time Piecewise-Linear Recurrent Neural Networks

arXiv:2602.15649v2 Announce Type: replace Abstract: In dynamical systems reconstruction (DSR) we aim to recover the dynamical system (DS) underlying observed time series. Specifically, we aim to learn

Dynamics of Learning under User Choice: Overspecialization and Peer-Model Probing

arXiv:2602.23565v3 Announce Type: replace Abstract: In many economically relevant contexts where machine learning is deployed, multiple platforms obtain data from the same pool of users, each of whom

EffectLearner: World-Aware Object-Effect Reasoning for Real-World Video Object Removal

arXiv:2608.05565v1 Announce Type: new Abstract: Video object removal must eliminate not only the target object but also its induced effects while maintaining high-fidelity and spatiotemporally coheren

ErgoSurf: Ergodic Control for the Coverage of Unknown Surfaces

arXiv:2608.06208v1 Announce Type: new Abstract: Contact-centric tasks on surfaces, ranging from inspection and cleaning to sanding and polishing, require robots to systematically cover the surface whi

tutorialsarxiv-cs-ro

How to Recognize New Words: A Comparison Between Context Biasing Methods and Speech LLMs

arXiv:2608.05759v1 Announce Type: new Abstract: Recognizing new and rare words - named entities, acronyms, domain specific special words, and other items scarce in training data - remains a key challe

Hybrid-Adaptive Thread Tuning to Mitigate Simulation Execution Bottlenecks in High-Performance Reinforcement Learning Inference

arXiv:2608.06025v1 Announce Type: new Abstract: In simulation-in-the-loop decision-making systems, reinforcement learning (RL) inference is often constrained by simulator-side execution overhead, wher

IFlowNets: Extending Generative Samplers to Learn Strategies in Incomplete Information Games

arXiv:2608.05422v1 Announce Type: new Abstract: While many algorithms blend reinforcement learning (RL) with counterfactual regret (CFR) methods to leverage tradeoffs in computational speed and perfor