Knowledge catalogue
NVIDIA’s SimReady Foundation provides a five‑step workflow to convert CAD models into simulation‑ready OpenUSD assets for robotics. The process imports an ABB YuMi robot from STEP files, applies visua
arXiv:2610.10021v1 Announce Type: cross Abstract: Mutual information (MI) provides an objective for suppressing or encouraging statistical dependence in implicit generative models. However, direct MI
arXiv:2610.09734v1 Announce Type: new Abstract: World Action Models (WAM) improve robot policies by augmenting sparse action supervision with dense future prediction. However, much of the predictable
arXiv:2610.10436v1 Announce Type: new Abstract: Adversarial perturbations can alter the predictions of frozen vision-language models (VLMs) while leaving their confidence and image--text similarity pa
arXiv:2610.08830v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable reasoning capabilities across vision and language tasks. However, their massive co
arXiv:2610.09569v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for emotional support, raising concern that sustained use may draw users away from their real-world r
arXiv:2610.09496v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong generalization in robotic manipulation by leveraging rich representations from pretrained vision
arXiv:2610.10274v1 Announce Type: new Abstract: Token-based world models enable fine-grained latent planning, but repeatedly processing large spatial token grids makes action search expensive. We intr
arXiv:2610.09498v1 Announce Type: new Abstract: Clinical vision models are often deployed as frozen black boxes with no access to internals, retraining, or ground truth at inference time. We introduce
arXiv:2610.10317v1 Announce Type: new Abstract: Tabular foundation models (TFMs) are pretrained across diverse tabular tasks and make predictions on a new table at inference time using its labeled exa
arXiv:2610.08164v1 Announce Type: cross Abstract: Low-rank quantization error compensation (LQEC) recovers the accuracy lost under aggressive weight quantization by attaching a closed-form rank-r adap
arXiv:2610.07791v1 Announce Type: new Abstract: Illusory pattern perception is a well-documented human cognitive tendency to infer meaningful relationships in data that is actually random. Such a tend
arXiv:2610.08564v1 Announce Type: new Abstract: Tabular foundation models (TFMs) can classify the nodes of a graph without training on it, by reading node and neighborhood features as table rows next
arXiv:2610.08150v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have become a central paradigm for robot policy learning, which predict actions in three forms: raw action chunks, d
arXiv:2610.06184v1 Announce Type: new Abstract: Generalization in multi-arm collaboration can be studied as composing familiar atomic skills in new ways across arms. However, existing evaluations offe
arXiv:2610.05409v1 Announce Type: new Abstract: Action-conditioned time-series forecasting requires accounting for how future actions and exogenous forcings influence multiple targets through partiall
arXiv:2610.04444v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate, complete, and transform information in settings where their outputs can shape conseque
arXiv:2610.06280v1 Announce Type: new Abstract: World action models (WAMs) have emerged as a promising paradigm for robotic manipulation. They act by first predicting how a task should be performed an
arXiv:2604.05114v2 Announce Type: replace-cross Abstract: We study a QA curation pipeline for improving long-context complex reasoning in large language models (LLMs). Our approach, pi^2, constructs h
arXiv:2610.04162v1 Announce Type: new Abstract: Selecting the best k items out of m candidates is a critical component of modern large language model systems, such as document selection in Retrieval-A
arXiv:2610.04913v1 Announce Type: new Abstract: World Action Models (WAMs) emerge as a promising foundation for policy refinement by predicting the consequences of sampled actions. However, visually p
arXiv:2610.04644v1 Announce Type: new Abstract: Neural approaches to Graph Edit Distance (GED) have achieved strong results under standard within-dataset evaluation, but much less is known about how w
arXiv:2610.05310v1 Announce Type: cross Abstract: Frozen speech foundation models (SFMs) make downstream adaptation efficient: the backbone can stay fixed while a task learns layer fusion and a lightw
arXiv:2610.04925v1 Announce Type: new Abstract: Time-series forecasting informs critical decisions in energy dispatch, industrial operations, and environmental monitoring; understanding the patterns m
arXiv:2610.06271v1 Announce Type: new Abstract: Adapting vision-language-action (VLA) models to deployment-time distribution shifts is important for reliable robotic operation, but conventional first-
arXiv:2610.03456v1 Announce Type: new Abstract: Drug-target affinity (DTA) prediction is widely used to prioritize candidate compounds before costly experimental screening. DTA models are often compar
arXiv:2610.02357v1 Announce Type: cross Abstract: Recent advances in deep learning for time series prediction have amplified the need for reliable uncertainty quantification. Conformal prediction has
arXiv:2610.03066v1 Announce Type: cross Abstract: GC--EI--MS is an important technique for analyzing volatile and semivolatile compounds in complex samples. However, conventional methods rely heavily
arXiv:2610.02347v1 Announce Type: new Abstract: Training-data attribution aims to identify which training examples shape model behavior, yet validating such claims is difficult because causal training
arXiv:2610.03641v1 Announce Type: new Abstract: Masked discrete diffusion models offer a promising alternative to autoregressive generation, but iterative sampling can be costly, and intractable seque
arXiv:2610.03326v1 Announce Type: new Abstract: Diffusion language model (dLLM) compression faces a known challenge because calibration is typically performed on clean, fully visible activations, wher
arXiv:2610.03664v1 Announce Type: new Abstract: Autoregressive (AR) video models excel at causal generation, but their reliance on next-chunk prediction confines them to a short-sighted, reactive para
Side note. The idea that current models are conscious is absurd and downright offensive. Do not confuse competent information processing and sentience, they are wholly unrelated. You can be entirely i
AI researchers Zainhas and Parthsareen from Ollama delivered a breakout presentation on October 2, 2026, focusing on evaluating open-source models under real‑world production traffic rather than just
arXiv:2610.02160v1 Announce Type: new Abstract: Precise control over camera and object motion is essential for professional video production. Existing methods control objects only coarsely, through im
arXiv:2610.01938v1 Announce Type: cross Abstract: Exam-style accuracy does not establish whether large language models (LLMs) reason well over clinical records. We define clinical reasoning as integra
arXiv:2610.00727v1 Announce Type: cross Abstract: Controlling an agent with vision requires being able to separate useful information from irrelevant background information. JEPA-style latent world mo
Each model got a photo and Blender, and had to write code to rebuild it as a 3D scene in 20 minutes with a 4 budget. A scoring script (not an AI) compared each render with the photo. GPT-6 Astra won a
Now you can jev without jev https://huggingface.co/ggml-org/OpenJev-GGUF https://huggingface.co/ggml-org/lev-GGUF https://huggingface.co/ggml-org/Julia-1-GGUF https://huggingface.co/ggml-org/Laya-GGUF
arXiv:2610.01093v1 Announce Type: cross Abstract: Spacecraft rendezvous and proximity operations (RPO) are currently planned through an expertise-intensive process in which engineers translate high-le
🚨Potentially huge deal: @SpeakerMenin has introduced NYC legislation that would OBLIGATE third party validation of AI models. Self-policing is not enough. Thrilled to see NYC stepping in where the fed
arXiv:2610.01302v1 Announce Type: new Abstract: Concept erasure suppresses a target concept while preserving behavior on unrelated inputs. Existing closed-form methods were designed for 2D image diffu
“We find that on medium-length, well-defined accounting tasks, frontier AI models are now faster and more accurate than junior accountants, even the best one in our study.” Eighteen months ago they sc
arXiv:2609.39902v1 Announce Type: cross Abstract: Large language models have achieved remarkable capabilities across diverse domains, yet their safety alignment remains vulnerable to jailbreak attacks
arXiv:2609.39134v1 Announce Type: new Abstract: Visual-token compression improves the efficiency of large vision-language models, but can expose failures that full-token evaluation misses. We study ad
We are introducing Clef and Clef-flash, open-source decision models hosted on Workers AI for high-speed classification and agentic workflows. Also launching: a new reinforcement learning platform that
arXiv:2410.02159v3 Announce Type: replace-cross Abstract: Language models (LMs) can 'memorize' information, i.e., encode training data in their weights in such a way that inference-time queries can le
arXiv:2609.38980v1 Announce Type: new Abstract: Radar hardware faults threaten automated perception, motivating accurate, compact diagnosis and understandable maintenance guidance. We introduce SCORE-
arXiv:2609.40177v1 Announce Type: new Abstract: Safe social navigation requires a robot to anticipate not only the future consequences of its actions, but also whether a nominal action can actually be
arXiv:2609.39661v1 Announce Type: new Abstract: Self-attention gives LLMs fine-grained, query-dependent access to context, but dense token interactions incur quadratic prefill cost and a key--value ca
arXiv:2609.38966v1 Announce Type: new Abstract: We present Video2SwimFish, an automated pipeline and benchmark for building controllable fish assets from real-fish videos for underwater embodied AI. G
Tasks where an MLLM doesn't write text but only makes a decision, which started with Jev, seem to be getting popular. (This is sometimes called 'typed decision'.) I thought it would be fun to use insi
arXiv:2609.37568v1 Announce Type: new Abstract: Audio-visual large language models (AVLLMs) have made remarkable progress in multimodal understanding and reasoning through interactions among visual, a
arXiv:2609.38057v1 Announce Type: new Abstract: Improving robot policies on new tasks without collecting additional expert demonstrations remains a central challenge in robot learning. World action mo
arXiv:2605.06206v2 Announce Type: replace Abstract: Mixture of experts has emerged as the primary mechanism for making Large Language Models (LLMs) computationally efficient. However, in distributed s
arXiv:2609.36458v1 Announce Type: cross Abstract: Semantic-preserving transformations can induce substantial motion in learned representations, while small changes may strongly affect model prediction
arXiv:2602.06155v2 Announce Type: replace Abstract: Diffusion models generate samples through a sequence of learned denoising steps, and recent work has studied how semantic structure appears along th
arXiv:2609.37042v1 Announce Type: new Abstract: Video Large Language Models (VideoLLMs) have achieved strong video understanding capabilities but incur substantial inference overhead due to the large
arXiv:2609.37690v1 Announce Type: new Abstract: Video world models require persistent scene memory to maintain consistency during long-horizon video generation. Existing spatial memory systems accumul
arXiv:2609.36577v1 Announce Type: cross Abstract: Audio large language models (ALLMs) can reason about the content of audio recordings to perform complex tasks. However, these capabilities usually col