Knowledge catalogue
arXiv:2501.01669v4 Announce Type: replace Abstract: Inverse reinforcement learning (IRL) has progressed significantly toward accurately learning the underlying rewards in both discrete and continuous
arXiv:2605.27546v1 Announce Type: new Abstract: Crisis Responders (CRs) rapidly assess thousands of youth SMS conversations each year to identify mental health concerns and guide support. Yet youth di
arXiv:2605.27734v1 Announce Type: new Abstract: Generative models, from diffusion models to large language models, achieve remarkable performance but at a cost in training data orders of magnitude lar
arXiv:2605.28462v1 Announce Type: new Abstract: Fast catching of free-flying objects is difficult because of short reaction time, impact uncertainty, and kinodynamic constraints. We use reinforcement
arXiv:2510.15839v2 Announce Type: replace Abstract: Random Utility Models (RUMs) are a classical framework for modeling user preferences and play a key role in reward modeling for Reinforcement Learni
arXiv:2605.28328v1 Announce Type: cross Abstract: When generating outputs for domains with specific validity constraints (e.g., a program should compile), LLMs often fail in a small number of focused
arXiv:2605.28309v1 Announce Type: new Abstract: In large-scale benchmarking of stochastic optimization algorithms, the key challenge is no longer whether repeated runs are needed for reliability, but
arXiv:2602.03855v2 Announce Type: replace-cross Abstract: Inverse problems are often ill-posed and require optimization schemes with strong stability and convergence guarantees. While learning-based a
arXiv:2605.28075v1 Announce Type: new Abstract: Many learning problems require predicting how populations evolve under an unknown transformation. A natural representation for such populations is a pro
arXiv:2605.28693v1 Announce Type: cross Abstract: Backpropagation is the core learning mechanism underlying deep learning. However, whether and how this algorithm is implemented in the brain remains h
arXiv:2602.23754v2 Announce Type: replace-cross Abstract: We present Neural Image Space Tessellation effect (NIST), a lightweight screen-space post-processing approach for reducing the faceted silhoue
IBM has introduced new classroom accounts that provide educators with expanded access to quantum computing resources for teaching and research purposes. These accounts are designed to make quantum tec
arXiv:2605.28634v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models offer a promising paradigm for generalist robotic policies, yet their adaptation is hindered by data inefficiency an
arXiv:2605.28625v1 Announce Type: new Abstract: Generative modeling provides a powerful framework for learning data distributions. These models initially relied on probabilistic methods such as Gaussi
arXiv:2605.27824v1 Announce Type: new Abstract: Recent studies have shown that Large Language Models (LLMs) can achieve strong reasoning performance by incorporating functional symbolic representation
arXiv:2605.28048v1 Announce Type: new Abstract: Sequence-based visual place recognition (VPR) for SLAM and robot relocalization must decide whether the retrieved top-1 candidate is safe to accept. Con
arXiv:2605.28394v1 Announce Type: new Abstract: Animation of 2D hand-drawn sketches provides an effective medium for visual communication. However, these sketches pose challenges, particularly in hand
In this post, we demonstrate how to build a secure Flask-based MLflow proxy service that provides HTTPS access to Amazon SageMaker MLflow without requiring the MLflow SDK. This solution is for organiz
“The mechanism is always the same in every story I've been covering. The demo works in a controlled environment with clean inputs. The deployment fails because real kitchens, real intersections, and r
arXiv:2510.21890v2 Announce Type: replace-cross Abstract: This book presents the core principles that have guided the development of diffusion models, tracing their origins and showing how diverse for
arXiv:2605.28600v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting substantially improves the sample efficiency of transformers, reducing the complexity of tasks like parity learning fro
arXiv:2605.28132v1 Announce Type: new Abstract: Spatial intelligence requires visual representations that capture both semantic objects and geometric structure in the physical world. To support this,
arXiv:2604.02028v2 Announce Type: replace Abstract: Diffusion models have become a standard approach for generative modeling in continuous domains, yet their application to discrete data remains chall
arXiv:2605.26163v1 Announce Type: cross Abstract: Competitive resource allocation problems over frequency and space can be formulated as minimax interaction between transmit power and worst-case inter
arXiv:2605.27174v1 Announce Type: cross Abstract: Artificial intelligence is increasingly being integrated into professional audio production workflows, yet a gap persists between the tools developers
arXiv:2605.26366v1 Announce Type: new Abstract: Recent studies on hallucination detection have shown that hallucination-related signals are more strongly encoded in intermediate layers than in the fin
arXiv:2605.26376v1 Announce Type: cross Abstract: Hepatocellular carcinoma (HCC) is biologically heterogeneous, shaped by the interplay between hepatic functional reserve and tumor-related oncologic f
🆕Biohub’s Protein World Model: ESMC-6B, ESMFold2, 6.8B proteins, 1.1B structures, antibody design, SAEs, & the bitter lesson for biology https://www.latent.space/p/esmfold2 @biohub Head of Science @al
arXiv:2605.26820v1 Announce Type: new Abstract: Vision-language-action (VLA) models provide a promising foundation for general-purpose robotics. However, their successful deployment in real-world scen
arXiv:2605.27281v1 Announce Type: new Abstract: Predicting the effect of interventions with many possible variations, e.g., therapeutic content that affects mental health outcomes or an earnings call
arXiv:2605.27146v1 Announce Type: new Abstract: Self-Supervised Learning (SSL) has emerged as a powerful paradigm to mitigate the reliance on large, annotated datasets, a common bottleneck in medical
arXiv:2601.22519v2 Announce Type: replace-cross Abstract: Discrete flow models (DFMs) have been proposed to learn the data distribution on finite state space, offering a flexible framework as an alter
arXiv:2405.16417v2 Announce Type: replace Abstract: Recent vision-language pre-trained models (VL-PTMs) have shown remarkable success in open-vocabulary tasks. However, downstream use cases often invo
arXiv:2603.16870v2 Announce Type: replace-cross Abstract: Recent advances in video generation have revealed an unexpected phenomenon: diffusion-based video models exhibit non-trivial reasoning capabil
arXiv:2601.20796v2 Announce Type: replace Abstract: Transformer-based multimodal large language models often exhibit in-context learning (ICL) abilities. Motivated by this phenomenon, we ask: how do t
arXiv:2605.26680v1 Announce Type: cross Abstract: Recent video multimodal large language models (MLLMs) increasingly couple step-by-step reasoning with on-demand visual evidence retrieval, allowing mo
AI adoption has become a culture shift for enterprises, moving beyond technical implementation to fundamentally reordering how organizations work, compete and grow. Few partnerships are better positio
arXiv:2605.26823v1 Announce Type: new Abstract: Synthetic data offers a promising solution to two persistent barriers in supply chain analytics: data scarcity and data privacy. However, for synthetic
arXiv:2603.09551v2 Announce Type: replace Abstract: While Vision-Language Models (VLMs) have significantly advanced remote sensing interpretation, enabling them to perform complex, step-by-step reason
arXiv:2504.08593v5 Announce Type: replace-cross Abstract: This work tackles the challenge of continuous sign language segmentation, a key task with huge implications for sign language translation and
arXiv:2605.27310v1 Announce Type: new Abstract: Cross-view spatial reasoning remains a weak spot for vision-language models (VLMs): they often reason in language and lose the fine-grained geometry nee
In this post, we share how we built NarrateAI using Amazon Bedrock AgentCore to deliver business intelligence at scale for the AWS SMGS (Sales, Marketing and Global Services) organization. You will le
arXiv:2512.17090v2 Announce Type: replace-cross Abstract: Squared tensor networks (TNs) and their extension as computational graphs--squared circuits--have been used as expressive distribution estimat
The N.E.A.T (NeuroEvolution of Augmenting Topologies) algorithm is an evolutionary machine learning approach that evolves neural networks to solve control problems. This post describes applying N.E.A.
arXiv:2605.26914v1 Announce Type: new Abstract: We present an image-conditioned point cloud completion approach that treats images as the primary geometric source rather than a secondary guide. To thi
Identifiability is what it means to learn the right latent space, the Gaussian is why LeJEPA works, and it's what makes planning transfer. I think identifiability is the right definition of what it me
arXiv:2605.26356v1 Announce Type: new Abstract: In-context learning has recently been linked to implicit gradient descent in linear self-attention models, suggesting that context can induce a forward-
Introducing DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretation http://pub.sakana.ai/diffusionblocks What if we didn’t have to hold an entire neural network in memory to t
It is refreshing to have some new arguments pointing at specific distributions (Gaussian) our embeddings must follow! Especially now that we know how to do scalable distribution matching in high dimen
Boston Tech Week featured a session hosted by Mason Dry and Sydney Runkle with the Blitzy AI team focused on building deep agents. The event was promoted via Luma Events and appears to be an education
arXiv:2605.26725v1 Announce Type: new Abstract: Accurate interpretation of street-level imagery is essential for large-scale urban mapping and the creation of Spatial Digital Twin (SDT) environments.
arXiv:2605.26850v1 Announce Type: new Abstract: Learning an energy-based model from data samples is a central problem in machine learning. Many recent and popular methods, such as denoising score matc
arXiv:2601.03079v4 Announce Type: replace Abstract: Moral sensitivity is the most fundamental capability underlying human moral competence. Although many approaches aim to align large language models
arXiv:2605.26933v1 Announce Type: new Abstract: Unsupervised visual object tracking is a challenging task that requires following arbitrary targets in videos without training on ground-truth annotatio
arXiv:2506.11253v2 Announce Type: replace Abstract: Machine unlearning removes certain training data points and their influence from AI models (e.g., when a data owner revokes their consent to allow m
arXiv:2605.26741v1 Announce Type: cross Abstract: Inverse design of materials has significantly advanced target-driven formulation optimization, yet existing materials machine learning benchmarks rema
arXiv:2605.26454v1 Announce Type: new Abstract: Large language models (LLMs) learn undesirable properties during pretraining, including dangerous knowledge and toxic text generation. Just as post-trai
arXiv:2605.27306v1 Announce Type: new Abstract: We consider training classifiers for 3D medical images using only one binary label for the entire volume rather than a label for each 2D slice. In such
arXiv:2605.26649v1 Announce Type: new Abstract: RGB-based imitation learning requires many demonstrations to generalize to unseen objects or scenes, motivating research into intermediate representatio
arXiv:2605.26840v1 Announce Type: new Abstract: Reinforcement learning with evaluation metrics as rewards is widely used to enhance specific capabilities of language models. However, for tasks such as