Knowledge catalogue
🚨 JUST NOW: Elon Musk and Jensen Huang give an INSIDE LOOK at the big meeting between top AI firms and President Trump today, saying they'll now be 'grading each other's homework' 😆 @elonmusk: 'We sig
🚨 JUST NOW: Elon Musk, shoulder to shoulder with President Trump, explains why AI is going to lift up ALL of humanity -- not force them into poverty @elonmusk: 'I think by FAR the most likely outcome
arXiv:2609.34082v1 Announce Type: cross Abstract: Interpreting architecture, engineering, and construction (AEC) drawings is hard for general Multimodal Large Language Models (MLLMs) and vision-langua
arXiv:2609.34045v1 Announce Type: cross Abstract: Between them, the members of a research group or a circle of friends own several consumer computers, none large enough to run a capable large language
arXiv:2609.32203v1 Announce Type: new Abstract: Large vision-language models (VLMs) can judge visual similarity, but their judgments are not directly available as compact image embeddings for efficien
arXiv:2609.33074v1 Announce Type: new Abstract: High-performance GPU kernels are essential to modern machine learning systems, yet automatically generating kernels that are both correct and efficient
arXiv:2609.32182v1 Announce Type: new Abstract: Vision-language models are increasingly used to understand long videos and continuous streams. However, dense visual tokens accumulate with video durati
arXiv:2609.34507v1 Announce Type: cross Abstract: Financial candlestick forecasting is fundamental to quantitative investment, yet it remains exceptionally challenging due to extremely low signal-to-n
arXiv:2609.31888v1 Announce Type: cross Abstract: X-ray diffraction (XRD) is a experimental technique for determining the phase composition and structure of crystalline materials. However, interpretin
arXiv:2407.20737v3 Announce Type: cross Abstract: This study explores the application of Kolmogorov-Arnold networks (KANs) in predicting nuclear binding energies, leveraging their ability to decompose
Big data management company Komprise Inc. is trying to make life easier for artificial intelligence agents and large language models that leverage the open-source Model Context Protocol to access thir
arXiv:2609.33350v1 Announce Type: new Abstract: Learning population dynamics from temporally sparse, unpaired distribution snapshots is a fundamental challenge in developmental biology. Recent approac
arXiv:2609.32610v1 Announce Type: cross Abstract: Large language models represent context with a key-value (KV) cache. Caches are model-specific: for the same text, models with different architectures
arXiv:2609.35750v1 Announce Type: new Abstract: Scaling the horizon of agentic LLMs is bottlenecked by the need to fit ever longer context traces in GPU memory. Context compaction has been the most po
arXiv:2609.34060v1 Announce Type: new Abstract: Prompt-specialized multi-agent systems enable multiple agents to share a model while performing complementary roles to solve complex tasks. However, age
LA Tech Week is coming up in two weeks, and we’re doing our monthly LA Agentic AI Meetup during it. Tuesday, October 13 at Gulp in Playa Vista. 5–7pm. A few live demos, drinks, and a bunch of real peo
arXiv:2609.35710v1 Announce Type: new Abstract: We introduce LHFM, a geometric framework for learning image dynamics. Drawing on structures central to classical mechanics, symplectic geometry, and geo
arXiv:2609.31988v1 Announce Type: new Abstract: We investigate the applicability of Lagrangian and Hamiltonian Neural Network models to a dissipative system that has explicit time dependence in its La
arXiv:2609.35288v1 Announce Type: cross Abstract: Joint-embedding self-supervised learning typically combines an invariance objective across augmented views with additional mechanisms to prevent repre
arXiv:2609.32556v1 Announce Type: cross Abstract: Based on an analysis of the role of language in thought and communication, this article proposes a new concept for designing corporate knowledge bases
arXiv:2609.31665v1 Announce Type: new Abstract: Action anticipation predicts future human actions from partial video under incomplete context and temporal uncertainty. Recent systems introduce large l
The article discusses the newly released Jev AI model, which functions as a general‑purpose text classifier that can perform classification tasks more quickly and cheaply than large language models li
arXiv:2609.33630v1 Announce Type: cross Abstract: Differentiable optimization brings the structural guarantees of mathematical optimization to network pipelines, allowing them to be trained end-to-end
arXiv:2609.35335v1 Announce Type: new Abstract: Machine learning task type identification is essential for constructing valid ML pipelines, yet in practice it is typically specified manually. We inves
arXiv:2609.33423v1 Announce Type: new Abstract: Large Language Models (LLMs) can contribute useful engineering knowledge to robot design, but directly generated designs may rely on implicit assumption
arXiv:2609.32933v1 Announce Type: new Abstract: In safety-critical applications, deployment uses a single policy, whose performance and constraint satisfaction should hold directly rather than only fo
arXiv:2609.35260v1 Announce Type: new Abstract: Spiking neural networks are attractive for low-power speech command recognition, yet their latency has received far less attention than their energy eff
arXiv:2609.32132v1 Announce Type: new Abstract: In cross-silo split learning, Parallel Split Learning with Global Sampling forms representative pooled batches when class distributions differ across cl
arXiv:2609.32794v1 Announce Type: new Abstract: Latent generative models make 3D medical image synthesis computationally practical by generating in a compressed space. However, we show that the common
arXiv:2609.31720v1 Announce Type: new Abstract: Transferring deformation between characters with different geometry and topology is challenging because conventional rigs encode behaviour through chara
arXiv:2609.33843v1 Announce Type: cross Abstract: The shipped Laya Typed-Decisions checkpoint, a 421M-parameter ModernBERT-large assessor that answers typed choice/noul/score questions over workflow s
arXiv:2609.33136v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student to match a teacher's next-token distributions on student-generated trajectories. However, privileged infor
arXiv:2609.34478v1 Announce Type: new Abstract: Task-specific fine-tuning can rewrite a language model's answers beyond the training task, complicating updates that must preserve existing behavior. We
arXiv:2609.35665v1 Announce Type: cross Abstract: Interior-point methods (IPMs) are among the most widely used algorithms for constrained optimization, yet their Newton-based search directions require
arXiv:2605.06050v2 Announce Type: replace Abstract: Graph-based learning from functional magnetic resonance imaging (fMRI) has shown strong potential for brain network analysis. However, existing meth
arXiv:2609.35598v1 Announce Type: cross Abstract: We introduce the Functional Spectral-Newton Method (FSNM) for learning the leading singular structure of a conditional expectation operator without fi
arXiv:2509.01135v3 Announce Type: replace Abstract: Electroencephalography (EEG)-based emotion recognition plays a critical role in affective Brain-Computer Interfaces (aBCIs), yet its practical deplo
arXiv:2609.33620v1 Announce Type: new Abstract: Modern language models are likely to be updated throughout their lifetime rather than trained once and frozen. Each update therefore participates in a r
arXiv:2601.22988v2 Announce Type: replace Abstract: Real-world robotic manipulation requires visuomotor policies capable of robust 3D scene reasoning under varying camera viewpoints. While recent 3D-a
arXiv:2609.32958v1 Announce Type: new Abstract: In many teleoperation applications, collecting a large number of demonstrations as required for traditional probabilistic learning from demonstration (L
arXiv:2609.34851v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) algorithms for movement control are typically evaluated and benchmarked on sequential decision tasks where imprecise a
arXiv:2609.33659v1 Announce Type: new Abstract: Multimodal large language models can expose task-relevant evidence through generation, but producing useful evidence does not by itself determine how it
arXiv:2609.35767v1 Announce Type: new Abstract: Unified multimodal models can both look at and render images, so in principle they can repair their own generations: diagnose what an image gets wrong,
arXiv:2606.15685v2 Announce Type: replace-cross Abstract: Embodied Continual Learning (ECL) aims to enable robots to continually acquire new manipulation tasks while retaining previously learned behav
arXiv:2609.34064v1 Announce Type: new Abstract: Reinforcement learning (RL) has become an effective post-training paradigm for long-horizon large language model (LLM) agents. However, we find that the
arXiv:2609.34711v1 Announce Type: new Abstract: Learning local geometry enables graph neural networks (GNNs) to adapt how they compare and integrate neighborhood information. However, estimating geome
arXiv:2609.34614v1 Announce Type: new Abstract: Managing water resources in mountainous regions depends heavily on reliable Snow Water Equivalent (SWE) and Snow Height (HS) data, yet these variables r
arXiv:2609.32820v1 Announce Type: cross Abstract: This paper studies learning of shuffle ideals with membership queries as well as with contrastive queries---a form of membership query that reveals no
arXiv:2609.31662v1 Announce Type: new Abstract: Face Image Quality Assessment determines the suitability of captured face images for automated face recognition (FR), a critical capability for reliable
arXiv:2609.32613v1 Announce Type: new Abstract: Graph neural networks lose much of their advantage on heterophilic graphs, where connected nodes often carry different labels. Graph rewiring is a popul
arXiv:2609.35541v1 Announce Type: new Abstract: We propose a distributionally robust learning framework where parameters defining the robustness mechanism are learned from held-out data instead of ext
arXiv:2609.32272v1 Announce Type: cross Abstract: Multimodal tabular-image learning is gaining growing attention, yet it faces challenges due to tabular data unavailable at test time. A practical solu
arXiv:2609.35003v1 Announce Type: new Abstract: Vision-language-action (VLA) models have demonstrated strong capabilities in robotic manipulation, but they are typically developed and evaluated with a
arXiv:2510.02284v4 Announce Type: replace Abstract: Recent video generation models have achieved remarkable progress and are now deployed in film, social media production, and advertising. Beyond thei
arXiv:2609.34205v1 Announce Type: new Abstract: Optimization modeling is central to many decision-making scenarios, but traditionally requires extensive domain expertise. While Large Language Models (
arXiv:2609.35166v1 Announce Type: new Abstract: Discrete diffusion models offer the ability to re-draft, revisiting and correcting earlier tokens throughout generation. This capability depends on the
arXiv:2609.35539v1 Announce Type: new Abstract: Video segmentation models maintain object identities by carrying instance information across frames. Under prolonged occlusion, reappearance, or interac
arXiv:2609.34344v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a key paradigm for enhancing the reasoning of large language models, yet the high dimensionality of parameter upd
arXiv:2603.11950v2 Announce Type: replace-cross Abstract: Multimodal language models have demonstrated strong semantic understanding and reasoning over physiological and behavioral signals captured fr
arXiv:2609.32632v1 Announce Type: cross Abstract: Multiobjective Bayesian optimization (MOBO) with Gaussian process (GP) surrogates is a sample efficient approach to solving multiobjective optimizatio