Knowledge catalogue
arXiv:2609.21425v1 Announce Type: new Abstract: Zero-shot time-series forecasting (TSF) is often described as forecasting without target-specific parameter updates, but that training-status condition
arXiv:2609.20905v1 Announce Type: cross Abstract: In this work, we present an uncertainty-driven training framework for three-dimensional computed tomography (CT) lung nodule classification, where val
arXiv:2609.21225v1 Announce Type: new Abstract: Parametric CAD reconstruction requires recovering both precise geometry and editable modeling operations from visual observations, making it challenging
arXiv:2505.10202v2 Announce Type: replace Abstract: Large Language Models (LLMs) have achieved remarkable success but face significant computational and memory challenges, particularly due to their ex
arXiv:2609.21942v1 Announce Type: cross Abstract: A robot that fails at a task faces the first decision in corrective dialogue: act on its own diagnosis, consult another onboard sensor, or interrupt a
Not a pic sharing thread, bunch of those already - mainly just a discussion about first impressions Very underwhelming to me personally. I know its a matter of settings but normally comfy default temp
we're a small lab (switzerland + south africa) and just open-sourced our first model: a 27b fine-tune of qwen3.8-27b specialized for creative writing and short-form personal text (stories, dialogue, r
I wanted to share a quick update and performance video running the full Moonshot AI Kimi K3 (moonshotai/Kimi-K3) model across my 16x GB10 cluster. Getting a 2.8T parameter model running smoothly r
I have been anti-low quant. I can feel the difference, I swear. I am also anti-quantized KV cache. I have been burned there - I think the tradeoffs compound, especially at longer contexts. Due to this
We're excited to announce our AI Engineer New York speaker lineup! Presented by @arizeai CEO @ExaAILabs CTO @tryramp CEO @ValsAI CEO @RogoAI CTO @modal CEO @trybasis CEO @SonarSource CEO @runwayml CTO
A new release of our Agent Skills and Docs eval library, Cultivar is available! Now, use @typesafeai's Jev model to evaluate how well your agents use docs and Skill against specified tasks in Modal sa
Banger paper from Google Cloud AI Research. If you follow autonomous research agents, this one is worth your time. ScientistTwo takes a problem from a human expert and runs the full discovery cycle wi
Banger paper from MIT and Sakana AI. They show that self-improving coding agents work. The best part is that their approach, Self-Improvement via Fast Tree-search (SIFT), runs at a tenth of the CPU ho
Hello, I know some of us may be tempted to rent out our expensive GPUs to recoup some of the cost of self-hosting, and it should be obvious that this can be a risky decision. I decided to try hosting
TL;DR: I run a mismatched Tesla V100-PCIE pair—one 16 GB card and one 32 GB card, 48 GB total—in a Proxmox/LXC-based local-inference lab. The practical winner so far is a recent CUDA build of llama.cp
One of the craziest use cases I’ve found for Jev: verifiers. I am so excited about this that I at least wanted to share the high-level idea. I used Jev to build a custom verifier for the /goal feature
Took me a while since I'm on a family trip and have limited hardware, but here it is! Von: Open-source 'System One' drop-in replacement for TypeSafe's JEV. https://github.com/wfzyx/von https://hugging
arXiv:2609.20162v1 Announce Type: new Abstract: Injecting stochastic noise into a consolidation rule can improve a network's retention of earlier tasks up to an optimal level, then degrade it -- an in
arXiv:2609.20301v1 Announce Type: new Abstract: AI agents increasingly orchestrate long-running activities with users, tools, and system resources for days and weeks. To improve agent quality, safety,
arXiv:2609.20325v1 Announce Type: new Abstract: Agricultural image understanding requires fine-grained recognition of plant diseases, pests, crop structures, and botanical species under complex real-w
arXiv:2609.19437v1 Announce Type: new Abstract: Bayesian Optimization (BO) is widely used for optimizing expensive black-box functions, yet many real-world optimization problems contain substantially
arXiv:2609.20223v1 Announce Type: new Abstract: We study weakly supervised incremental telecom fraud detection from raw telephone audio, where training provides only conversation-level labels and pred
arXiv:2602.16870v2 Announce Type: replace Abstract: The Boreas Road Trip (Boreas-RT) dataset extends the multi-season Boreas dataset to new and diverse locations that pose challenges for modern autono
arXiv:2609.20819v1 Announce Type: new Abstract: Perceiving and remembering the visual world is fundamental to navigating and interacting with our environment. Current 4D foundation models, such as cam
arXiv:2609.19354v1 Announce Type: cross Abstract: Automated action quality assessment (AQA) in Olympic sports remains a challenging task due to the complexity of human motion and the subjectivity inhe
arXiv:2609.19911v1 Announce Type: new Abstract: 3D grounding aims to localize target entities in complex scenes from natural language and plays a fundamental role in embodied perception and spatial re
arXiv:2609.19892v1 Announce Type: cross Abstract: As agent systems become more widely used, multiple agent sessions increasingly run alongside pre-existing user tasks in the same environment, sharing
arXiv:2609.19585v1 Announce Type: cross Abstract: In mental health care, reasoning over patient journeys is a key task for clinicians. Yet these journeys, encompassing a longitudinal progression of bi
arXiv:2609.20386v1 Announce Type: new Abstract: Iris presentation attack detection (PAD) is security-critical when a subsystem that appears reliable during development encounters presentation attack i
arXiv:2609.20352v1 Announce Type: new Abstract: Large-scale routing often requires visiting clusters of nodes in a prescribed order, giving rise to the Ordered Clustered Traveling Salesman Problem (OC
arXiv:2509.09121v2 Announce Type: replace Abstract: Large language models (LLMs) excel in general-domain applications, yet their performance often degrades in specialized tasks requiring domain-specif
arXiv:2609.19465v1 Announce Type: new Abstract: Compositional reasoning is critical for real-world problem solving: since training data is necessarily limited, models must generalize by composing lear
arXiv:2609.19789v1 Announce Type: new Abstract: Multi-agent trading systems built on large language models (LLMs) are beginning to appear in quantitative finance, yet their robustness to adversarial i
arXiv:2609.19670v1 Announce Type: new Abstract: Direct forecasting has become a standard paradigm for multivariate time-series forecasting because it predicts the full future horizon in a single pass.
arXiv:2609.20461v1 Announce Type: cross Abstract: Studying the dynamical behavior of a system often depends on characterizing how it transitions between long-lived states. Because such transitions are
arXiv:2609.19531v1 Announce Type: cross Abstract: We propose PaRID (PaRallel Instruction Duplication), a software-directed soft error detection framework that requires only compile-time effort for mul
arXiv:2609.20304v1 Announce Type: new Abstract: A deployed control policy can conceal consequential dynamics changes: an actuator may lose effectiveness without affecting the current task when the pol
arXiv:2609.19805v1 Announce Type: new Abstract: Grapheme-to-phoneme (G2P) conversion turns raw text into its phonemic form and is an essential part of both text-to-speech (TTS) and automatic speech re
arXiv:2601.22488v3 Announce Type: replace Abstract: Modern sequence models are typically trained at a fixed computational capacity, while real-world applications require deployment across platforms wi
arXiv:2609.19659v1 Announce Type: cross Abstract: Training embodied foundation models typically requires massive-scale datasets and extensive computational resources, yet often suffers from three crit
arXiv:2609.19742v1 Announce Type: new Abstract: Autonomous Underwater Vehicles (AUVs) navigating without GPS typically fuse inertial measurements with acoustic Doppler Velocity Log (DVL) velocities an
arXiv:2609.20175v1 Announce Type: cross Abstract: The filter bubble is a notorious issue in Recommender Systems (RSs), which describes the phenomenon whereby users are exposed to a limited and narrow
arXiv:2609.19475v1 Announce Type: new Abstract: Diffusion-based Vision-Language-Action (VLA) models achieve strong performance in embodied tasks, but their iterative sampling imposes heavy computation
arXiv:2609.20067v1 Announce Type: new Abstract: Deep learning models for multi-modal breast cancer diagnosis achieve high predictive accuracy but remain clinically unacceptable without actionable, cou
arXiv:2609.20026v1 Announce Type: new Abstract: Urban traffic forecasting often relies on information distributed across stakeholders who may be unable to share raw data due to privacy or commercial c
arXiv:2609.20340v1 Announce Type: new Abstract: Ultrasound image analysis plays a crucial role in cancer screening and prenatal diagnosis, yet comprehensive assessment requires jointly addressing task
arXiv:2609.19553v1 Announce Type: new Abstract: Model fusion integrates the capabilities from source models into a single target model. As of June 2026, Hugging Face hosts more than 2M models. This gr
arXiv:2609.20016v1 Announce Type: cross Abstract: The EU AI Act (Regulation 2024/1689) imposes technical obligations on high-risk AI providers, yet Articles 8-15 were drafted for predictive AI and lea
arXiv:2609.19956v1 Announce Type: new Abstract: Tree-based Monte-Carlo Tree Search (MCTS) duplicates the same state when it is reached through different trajectories, which can waste simulations in st
As enterprises invest in generative AI, tech leaders keep seeing the same pattern: Developers test AI tools for a week, hit setup problems, and then drift back to the backlog. Nothing ships. The real
arXiv:2609.19755v1 Announce Type: cross Abstract: Accurate classification of brain disorders from neuroimaging data remains challenging because of substantial inter-subject heterogeneity and the compl
arXiv:2609.20309v1 Announce Type: new Abstract: Spatially heterogeneous partial differential equations (PDEs) exhibit location-dependent dynamics arising from variations in geometry and physical coeff
arXiv:2609.20300v1 Announce Type: new Abstract: Current offline reinforcement learning (ORL) algorithms tend to overfit the training dataset and exhibit poor in-distribution generalization and robustn
arXiv:2609.19414v1 Announce Type: new Abstract: Goal-conditioned reinforcement learning aims to learn policies that reach specified goals, but remains challenging in offline settings with sparse rewar
arXiv:2607.15394v2 Announce Type: replace Abstract: Black-box models limit the adoption of artificial intelligence in medicine because their predictions are difficult to interpret and reproduce. We pr
arXiv:2609.20752v1 Announce Type: cross Abstract: Falsification searches for counterexamples to formal specifications in cyber-physical systems (CPS). With specifications written in Signal Temporal Lo
arXiv:2609.19778v1 Announce Type: new Abstract: Hateful memes spread abusive content through implicit interactions between images and text, posing serious threats to the safety of online communities.
arXiv:2609.19786v1 Announce Type: new Abstract: The inverse problem of reconstructing optical properties, specifically absorption and scattering coefficients, in layered biological media from time-dom
arXiv:2609.19515v1 Announce Type: new Abstract: Verifier-based selection improves LLM performance by generating multiple candidate solutions and using a verifier to select the most promising one. Howe
arXiv:2609.20068v1 Announce Type: new Abstract: This paper builds a theoretical bridge between the economic notion of marginal utility and two machine-learning constructs, matrix factorization and the