Knowledge catalogue
arXiv:2507.22345v2 Announce Type: replace Abstract: Wheel-legged robots integrate leg agility on rough terrain with wheel efficiency on flat ground. However, most existing designs do not fully capital
arXiv:2604.24532v1 Announce Type: new Abstract: Many sequential decision-making tasks involve optimizing multiple conflicting objectives, requiring policies that adapt to different user preferences. I
arXiv:2604.23166v1 Announce Type: cross Abstract: Poverty statistics guide social policy, but in many low- and middle-income countries, censuses and household surveys that collect these data are costl
arXiv:2504.06176v3 Announce Type: replace-cross Abstract: Foundation Models, which leverage large neural networks pre-trained on unlabelled data before fine-tuning for specific tasks, are increasingly
arXiv:2604.22877v1 Announce Type: cross Abstract: GBM is a highly aggressive primary malignancy in adults, necessitating personalized therapeutic strategies due to its inherent molecular heterogeneity
This post discusses the importance of understanding character identification and lore as part of employee onboarding at Runway, likely referencing key figures or mascots associated with the company's
arXiv:2604.24468v1 Announce Type: cross Abstract: Fine-tuning unlocks large language models (LLMs) for specialized applications, but its high computational cost often puts it out of reach for resource
arXiv:2604.23622v1 Announce Type: new Abstract: In the hyperspectral image (HSI) classification task, each pixel is categorized into a specific land-cover category or material. Convolutional neural ne
arXiv:2604.23027v1 Announce Type: new Abstract: Large language models (LLMs) have become central to modern AI workflows, powering applications from open-ended text generation to complex agent-based re
arXiv:2604.24589v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly proposed as general-purpose tools for scientific data interpretation, yet their reliability on real astro
arXiv:2604.23114v1 Announce Type: new Abstract: In limited-data settings, a single endpoint mean of an evaluation metric such as the Continuous Ranked Probability Score (CRPS) is itself a random varia
arXiv:2604.23386v1 Announce Type: cross Abstract: Federated Learning (FL) typically assumes unconditional collaboration, a premise that overlooks the complexities of real-world, multi-stakeholder envi
arXiv:2512.03915v3 Announce Type: replace-cross Abstract: In large-scale AI training, Sparse Mixture-of-Experts (s-MoE) layers enable scaling by activating only a small subset of experts per token. An
arXiv:2604.23167v1 Announce Type: new Abstract: A levelset free but a hybrid image segmentation approach based on a modified version of the piece wise constant shape gradient of an Mumford Shah shape
arXiv:2604.23184v1 Announce Type: cross Abstract: We propose a unified fractional regularization framework for sparse signal recovery based on the ell_1/ell_p^q model. Our main theoretical contributio
Bob Van Voris / Bloomberg: A US judge denied Sam Bankman-Fried's request for a new trial based on what SBF called new evidence; SBF tried to withdraw his request, but the judge refused — A federal jud
arXiv:2604.24043v1 Announce Type: new Abstract: Designing heuristics for combinatorial optimization problems (COPs) is a fundamental yet challenging task that traditionally requires extensive domain e
arXiv:2510.23215v2 Announce Type: replace-cross Abstract: Eigenvalue problems are among the most important topics in many scientific disciplines. With the recent surge and development of machine learn
arXiv:2604.22901v1 Announce Type: new Abstract: Diffusion models achieve remarkable success in time series generation. However, slow inference limits their practical deployment. We propose E^2-CRF (Er
arXiv:2604.22850v1 Announce Type: new Abstract: Industrial visual inspection systems often suffer from a severe scarcity of labeled defect data, particularly during the early stages of New Product Int
arXiv:2604.23821v1 Announce Type: cross Abstract: Autonomous neutron spectroscopy must solve three distinct tasks: detection (where is the signal?), inference (which Hamiltonian governs it?), and refi
arXiv:2604.22794v1 Announce Type: cross Abstract: Reinforcement learning (RL) offers a promising approach for adaptive wind farm flow control, yet its practical deployment is hindered by slow training
The ACL Rolling Review (ARR) operates on a two-monthly review cycle , and the March 2026 cycle refers to one of these periodic submission and peer review rounds for computational linguistics research.
arXiv:2604.23278v1 Announce Type: new Abstract: The proliferation of agentic artificial intelligence has outpaced the conceptual tools needed to characterize agency in computational systems. Prevailin
Actually Smart Summon now reacts a lot faster I. Love. Smart Summons. I am going to use it as much as I can now. It truly is usable. Here are some examples. Go test it out. I think you will be very im
arXiv:2604.24407v1 Announce Type: new Abstract: The recent surge in content consumption through streaming services has driven a growing demand for personalized content. Personalized advertisements (ad
arXiv:2409.01579v2 Announce Type: replace-cross Abstract: Retrieved documents containing noise will hinder RAG from detecting answer clues and make the inference process slow and expensive. Therefore,
arXiv:2604.23239v1 Announce Type: new Abstract: Accurate long-term time series forecasting (LTSF) requires the capture of complex long-range dependencies and dynamic periodic patterns. Recent advances
arXiv:2604.24175v1 Announce Type: cross Abstract: Large language models have demonstrated strong reasoning capabilities in general knowledge question answering. However, their ability to handle tempor
arXiv:2604.23999v1 Announce Type: cross Abstract: Randomized neural networks (RaNNs) are attractive for partial differential equations (PDEs) because they replace expensive end-to-end training with a
arXiv:2508.10599v4 Announce Type: replace Abstract: Activation steering offers a promising approach to controlling the behavior of Large Language Models by directly manipulating their internal activat
arXiv:2604.24219v1 Announce Type: new Abstract: Multi-intent natural language understanding requires retrieval systems that simultaneously achieve high accuracy and computational efficiency, yet exist
This entry describes NVIDIA's NV-Raw2Insights-US AI model, which uses physics-informed machine learning to enhance ultrasound imaging by adaptively processing raw ultrasound data. The system applies d
arXiv:2603.21362v2 Announce Type: replace Abstract: LLM-as-Judge evaluation fails agent tasks because a fixed rubric cannot capture what matters for this task: code debugging demands Correctness and E
Added native metric logging + @TrackioApp integration to ml intern so that you can follow every training run it kicks off in real time. Try it by asking 'train a tiny model on a tiny dataset, find som
arXiv:2602.14914v2 Announce Type: replace Abstract: Off-policy evaluation (OPE) is essential for assessing ranking and recommendation systems without costly online interventions. Self-Normalised Inver
arXiv:2604.24474v1 Announce Type: new Abstract: Molecular similarity plays a central role in ligand-based drug discovery, such as virtual screening, analog searching, and goal-directed molecular gener
arXiv:2407.05595v3 Announce Type: replace Abstract: Medical palpation is more than force transmission. It is a bidirectional cognitive and emotional exchange between doctor and patient. We model two c
arXiv:2510.15479v2 Announce Type: replace Abstract: We study counterfactual prediction under assignment bias and propose a mathematically grounded, information-theoretic approach that removes treatmen
arXiv:2604.22840v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong potential in agentic tasks, particularly in slide generation. However, slide generation poses a
arXiv:2508.06206v4 Announce Type: replace-cross Abstract: Affordance grounding focuses on predicting the specific regions of objects that are associated with the actions to be performed by robots. It
arXiv:2604.15184v2 Announce Type: replace Abstract: In the past year, researchers have created agentic systems that can design real-world CAD-style objects in a training-free setting, a new variety of
arXiv:2604.24661v1 Announce Type: new Abstract: Visual reinforcement learning aims to empower an agent to learn policies from visual observations, yet it remains vulnerable to dynamic visual perturbat
arXiv:2604.23581v1 Announce Type: cross Abstract: Agentic systems that chain reasoning, tool use, and synthesis into multi-step workflows are entering production, yet prevailing evaluation practices l
arXiv:2603.21357v3 Announce Type: replace Abstract: LLM agents fail on the majority of real-world tasks -- GPT-4o succeeds on fewer than 15% of WebArena navigation tasks and below 55% pass@1 on ToolBe
arXiv:2604.23483v1 Announce Type: new Abstract: Multi-component natural language processing (NLP) pipelines are increasingly deployed for high-stakes decisions, yet no existing adversarial method can
arXiv:2604.23924v1 Announce Type: new Abstract: We instruct an AI agent to construct two separate agentic AI platforms: one for autonomous training of predictive ML models for human-human and virus-hu
arXiv:2604.24473v1 Announce Type: new Abstract: Multiple myeloma is managed through sequential lines of therapy over years to decades, with each decision depending on cumulative disease history distri
This Databricks blog post discusses agentic data engineering capabilities enabled by Genie Code and Lakeflow, which are tools designed to automate and streamline data engineering workflows through AI-
arXiv:2604.23758v1 Announce Type: new Abstract: The discovery of novel materials is critical for global energy and quantum technology transitions. While deep learning has fundamentally reshaped this l
arXiv:2603.00130v2 Announce Type: replace-cross Abstract: Current multi-agent AI systems operate with a fixed number of agents whose roles are specified at design time. No formal theory governs when a
arXiv:2604.24203v1 Announce Type: cross Abstract: Auditing the semantic properties of proprietary data creates a fundamental tension: verification requires transparent access, while proprietary rights
arXiv:2604.24039v1 Announce Type: cross Abstract: Embodied AI agents increasingly rely on large language models (LLMs) for planning, yet per-step LLM calls impose severe latency and cost. In this pape
arXiv:2604.24038v1 Announce Type: new Abstract: Static benchmarks measure what AI agents can do at a fixed point in time but not how they are adopted, maintained, or experienced in deployment. We intr
arXiv:2604.22836v1 Announce Type: new Abstract: This report describes a Ref-VOS pipeline centered on Sa2VA and organized with explicit agent roles. The key idea is that Sa2VA should provide the first
arXiv:2604.24657v1 Announce Type: cross Abstract: Autonomous AI agents extend large language models into full runtime systems that load skills, ingest external content, maintain memory, plan multi-ste
arXiv:2604.23701v1 Announce Type: cross Abstract: Crop disease diagnosis from field photographs faces two recurring problems: models that score well on benchmarks frequently hallucinate species names,
arXiv:2604.23251v1 Announce Type: cross Abstract: Code review is central to software engineering education but hard to scale in capstone projects due to tight deadlines, uneven peer feedback, and limi
arXiv:2604.23280v1 Announce Type: new Abstract: AI agents are now running real transactions, workflows, and sub-agent chains across organizational boundaries without continuous human supervision. This
talkie: AI researchers launch talkie, a 13B vintage language model trained on historical text with a 1930 cutoff, to see if it can replicate scientific breakthroughs — Why vintage language models? — H