Knowledge catalogue
arXiv:2609.22062v1 Announce Type: new Abstract: Automatic dense packing is widely desired in warehouse operations but remains a fundamental challenge in robotic manipulation. Existing work on irregula
Grok 4.7 places @SpaceXAI as third, after Anthropic & OpenAI, for agentic coding. When factoring in that Grok is significantly faster & lower cost, it’s a great choice for your everyday workhorse. Gro
What if you don’t need a new model to get Jev-style decisions? I built hearim(헤아림. hea-a-rim. not Here, I am.. lol), an open-source gateway that turns ordinary local or hosted LLMs into a Jev-compatib
arXiv:2602.00993v2 Announce Type: replace-cross Abstract: End-to-end autonomous driving models increasingly benefit from large vision-language models for semantic understanding, yet safe and reliable
arXiv:2609.21454v1 Announce Type: cross Abstract: We revisit Breiman's observation that reducing inter-tree correlation without weakening individual trees can improve random forests. Building on this
arXiv:2609.21208v1 Announce Type: new Abstract: Self-play methods that co-train a single language model as both coder and test author promise to move code-generation RL beyond fixed test suites, but t
arXiv:2609.21379v1 Announce Type: new Abstract: Accurate traffic forecasting requires both understanding scene dynamics and synthesizing realistic future observations. Recent diffusion-based video gen
Jev and the System One Model: RLCD, intelligence/, reliable AI, & the end of chat-first AI https://www.latent.space/p/jev @typesafeai CEO @CompleteSkeptic explains why AI can solve extraordinarily har
Last week TypeSafe AI unveiled Jev, their first example of a new category of model that they are calling 'System One models' (I'm with Maggie Appleton, I think 'decision models' is a better name for t
arXiv:2609.21381v1 Announce Type: cross Abstract: We investigate whether coupling a time-series foundation model to hydraulic project knowledge improves surrogate forecasting of HEC-RAS water-surface
arXiv:2609.21962v1 Announce Type: new Abstract: Electrocardiograms (ECGs) carry subject-specific patterns enabling reliable individual discrimination, forming the basis of ECG biometrics. Beyond authe
arXiv:2609.21945v1 Announce Type: new Abstract: Urban transportation networks present complex optimization challenges spanning calibration of high-fidelity simulators and real-time operational control
arXiv:2609.21325v1 Announce Type: new Abstract: Agentic marketplaces are emerging where AI agents with varying capabilities autonomously complete specialized tasks for buyers. A major challenge of suc
arXiv:2609.21319v1 Announce Type: new Abstract: Physical control tasks in the natural world, such as driving or object manipulation, frequently exhibit dramatic variations in sensory and compute compl
arXiv:2609.21492v1 Announce Type: new Abstract: Chain-of-Thought (CoT) reasoning has been shown to improve the performance of large language models (LLMs), yet existing optimization methods largely re
arXiv:2609.21533v1 Announce Type: new Abstract: LLM-based multi-agent systems generate collaboration traces that record how agents plan tasks, verify intermediate results, and repair failures. Reusing
arXiv:2609.21018v1 Announce Type: new Abstract: Recent visual document retrieval (VDR) systems such as ColPali use multi-vector page embeddings, in which patch-level vectors enable fine-grained eviden
The surge in AI development has created unprecedented demand for compute capacity around the globe. This can have negative implications for data processing and pipelines with Apache Spark. Whether you
MiMo-V2.6-Pro debuts as the top open weights model on the Artificial Analysis Intelligence Index (46). At 0.13 per Intelligence Index task, it lands on the Intelligence vs. Cost per Task Pareto fronti
arXiv:2609.22083v1 Announce Type: new Abstract: We present MintAct, a family of vision-language models that unifies UI grounding, multi-step navigation across mobile, desktop, and web, and visual tool
arXiv:2609.21554v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have revolutionized artificial intelligence and how human interact with AIs. Despite impressive advancem
arXiv:2609.20997v1 Announce Type: new Abstract: Symbolic regression aims to recover closed-form equations from observations, providing interpretable models for scientific discovery. Existing approache
arXiv:2609.21664v1 Announce Type: new Abstract: Clustering is a fundamental task in data analysis, typically addressed through centroid-based methods such as K-means. In this work, we present a genera
arXiv:2609.21870v1 Announce Type: cross Abstract: Modern deep learning models achieve impressive generalization through over-parameterization, but this paradigm often struggles with overfitting and me
arXiv:2609.21424v1 Announce Type: new Abstract: Few-shot semantic segmentation (FSS) of strip steel surface defects (S^3D) has posed significant challenges distinct from natural scenes. Unlike natural
arXiv:2609.22053v1 Announce Type: new Abstract: Graph Convolutional Networks (GCNs) are highly sensitive to label noise, since corrupted supervision can propagate through the graph and degrade learned
arXiv:2609.22056v1 Announce Type: cross Abstract: Multi-hop retrieval failures are not uniformly distributed across queries: they cluster in structurally predictable subpopulations. We prove two resul
arXiv:2609.22040v1 Announce Type: new Abstract: Current Vision-Language-Action (VLA) models for autonomous driving operate primarily through feedforward inference across the perception--reasoning--pla
arXiv:2609.21369v1 Announce Type: cross Abstract: This paper presents a comprehensive framework for robot manipulation failure analysis that includes binary failure detection, failure categorization,
arXiv:2609.21462v1 Announce Type: new Abstract: Temporal action localization (TAL) in wearable sensor streams identifies action classes and temporal boundaries, enabling finer-grained activity underst
arXiv:2609.21541v1 Announce Type: new Abstract: Multi-label few-shot learning (MLFSL) remains a significant challenge in medical image analysis (MIA). Current metric-based meta-learning methods face t
arXiv:2609.21690v1 Announce Type: new Abstract: A robot can predict failure and still be unable to prevent it. By the time a safety mechanism reacts, the nominal plan may already have spent the contro
arXiv:2609.20857v1 Announce Type: cross Abstract: Quantifying mitral regurgitation severity remains limited by the assumptions of clinical flow convergence methods, while high-fidelity simulation and
arXiv:2603.23393v2 Announce Type: replace Abstract: Current trajectory prediction models are primarily trained in an open-loop manner, which often leads to covariate shift and compounding errors when
arXiv:2609.21522v1 Announce Type: new Abstract: Recent pre-trained foundation models provide rich multi-modal priors for downstream 3D vision tasks. However, the effectiveness of these representations
arXiv:2609.21108v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) has achieved strong performance across a wide range of continuous-control problems. These continuous-control policies,
arXiv:2609.21197v1 Announce Type: new Abstract: Sparse longitudinal CT follow-up limits lesion-size forecasting when only a few prior observations are available. We constructed a five-visit DLT-derive
arXiv:2609.21663v1 Announce Type: new Abstract: Word Error Rate (WER), the most commonly used metric for Automatic Speech Recognition (ASR), treats every lexical deviation from the reference as equall
arXiv:2609.20830v1 Announce Type: new Abstract: Revision-capable generation is appealing because it can insert or revise earlier content, but many non-autoregressive and edit-based approaches obtain t
arXiv:2609.21827v1 Announce Type: new Abstract: Speculative decoding accelerates LLM inference by drafting multiple tokens in parallel, with tree-based methods further improving efficiency through hie
arXiv:2609.21186v1 Announce Type: cross Abstract: Accurate initialization is essential for reliable visual-inertial odometry (VIO), but it is often ill-conditioned under degenerate motions. Existing m
arXiv:2609.21322v1 Announce Type: new Abstract: Heavy rainfall severely degrades outdoor videos by corrupting high-frequency details and introducing motion blur, critically undermining the reliability
arXiv:2609.21242v1 Announce Type: new Abstract: Reference-guided diffusion stylization aims to transfer visual style from a reference image while preserving the semantics specified by a text prompt. H
arXiv:2609.21666v1 Announce Type: cross Abstract: The success of Large Audio Language Models has driven the development of massive multimodal networks exceeding billions of parameters. However, the de
arXiv:2609.21740v1 Announce Type: new Abstract: Latent world models enable planning by predicting the effects of actions in a learned representation space, but their predictions can become unreliable
arXiv:2609.21032v1 Announce Type: cross Abstract: Science advances not in isolation but through collaboration, yet existing agentic systems capture little of this. Whether communicating agents help re
arXiv:2609.21709v1 Announce Type: new Abstract: Large language models (LLMs) provide limited support for sign language interaction. Unifying sign language translation (SLT) and generation (SLG) to ena
arXiv:2609.21402v1 Announce Type: new Abstract: Surgical instrument segmentation (SIS) plays a critical role in robotic assistance and surgical workflow analysis. However, most existing SIS methods fo
Bloomberg: Some startups, like Harvey, Abridge, Ramp, and Rogo, are embracing open-weight models or training their own models to reduce expensive reliance on frontier labs — The $15.6 billion legal st
arXiv:2609.21165v1 Announce Type: new Abstract: Off-target protein binding is a major source of adverse effects for small-molecule drugs, yet most structure-based molecular design methods focus on gen
arXiv:2609.21704v1 Announce Type: new Abstract: Running large language models (LLMs) locally continues to be limited by restrictions of compute and memory on consumer hardware. The popular acceleratio
StepFun’s new Step 5 Preview model is impressive! Had a chance to test it early. I've been testing it as a coding agent. It’s on the Pareto frontier for cost vs. capability. It is a very capable model
arXiv:2609.21650v1 Announce Type: cross Abstract: Fine-tuning Vision-Language-Action (VLA) models commonly relies on human teleoperation demonstrations, while reinforcement learning (RL) with sparse b
arXiv:2609.21084v1 Announce Type: cross Abstract: Modern automatic speech recognition (ASR) systems trained on extremely large datasets can produce transcripts with numbers written in Arabic numerals.
arXiv:2609.21133v1 Announce Type: cross Abstract: SQL has been augmented with AI operators, enabling modern data analytics platforms to derive insights from both structured and unstructured data. We o
arXiv:2609.21437v1 Announce Type: cross Abstract: We propose LoG-VGGT, a memory-efficient framework for long-sequence 3D reconstruction that balances local temporal modeling with global camera consist
arXiv:2609.21989v1 Announce Type: new Abstract: Latent flow models have proven to be a reliable and cost-effective method for time series generation. However, the latent compression induces unwanted a
arXiv:2609.21015v1 Announce Type: new Abstract: Improvements to Visual-Inertial Simultaneous Localization and Mapping (VI-SLAM) for low-cost autonomous underwater vehicles (AUVs) are critical for tran
arXiv:2609.20834v1 Announce Type: new Abstract: In the rapidly evolving landscape of cloud-native computing, Organizations are increasingly adopting infrastructure models that emphasize scalability, f
arXiv:2603.16086v2 Announce Type: replace-cross Abstract: While recent Vision-Language-Action (VLA) models have begun to incorporate audio, they typically treat sound as static pre-execution prompts o