Knowledge catalogue
arXiv:2603.28439v2 Announce Type: replace Abstract: Robots are increasingly being deployed in agriculture to support sustainable practices and improve productivity. They offer strong potential to enab
arXiv:2606.02113v1 Announce Type: cross Abstract: Post-training has become a primary driver of recent progress in large reasoning models, and reasoning data are often the key variable determining whet
Bloomberg: A profile of Valve, which PrivCo estimates generated 5.2B in revenue and 1.5B in net income in 2025, as lawsuits allege its Steam store abuses market power — Lawsuits in the US and the UK a
arXiv:2606.00155v1 Announce Type: cross Abstract: Modern network intrusion detection systems (NIDS) are caught in a structural contradiction: the protocols carrying the highest threat intelligence are
arXiv:2606.00994v1 Announce Type: new Abstract: We describe a registry-bound large-language-model extraction pipeline producing evidence-grounded structured trait records at scale, on cultivated tropi
arXiv:2606.00129v1 Announce Type: cross Abstract: Large language models (LLMs) have emerged as powerful representation learners whose internal features increasingly align with human cognition. We stud
arXiv:2606.02370v1 Announce Type: new Abstract: Flapping-wing aerial vehicles (FWAVs) demonstrate remarkable agility but face substantial autonomy challenges due to their high sensitivity to aerodynam
arXiv:2606.01398v1 Announce Type: new Abstract: Underwater robots typically use both cameras and sonar for perception to leverage the rich semantic details of vision and the robust range measurements
arXiv:2606.01992v1 Announce Type: cross Abstract: Industrial anomaly detection has historically been a unimodal task. Recent multimodal vision-language models have produced systems that admit textual
arXiv:2505.08438v4 Announce Type: replace-cross Abstract: Event cameras are rapidly emerging as powerful vision sensors for 3D reconstruction, uniquely capable of asynchronously capturing per-pixel br
arXiv:2606.00630v1 Announce Type: new Abstract: Intraoperative ultrasound (ioUS) is a versatile, cost-effective modality in brain tumour surgery, but its interpretation is difficult: acquisition plane
arXiv:2602.10014v3 Announce Type: replace Abstract: Iterative self-improvement fine-tunes an autoregressive large language model (LLM) on reward-verified outputs generated by the LLM itself. In contra
arXiv:2606.01861v1 Announce Type: new Abstract: Self-play, a type of training algorithm that enables a model to self-improve, has recently shown promising empirical results in the context of formal th
arXiv:2604.05324v2 Announce Type: replace Abstract: Statistical evaluation aims to estimate the generalization performance of a model using held-out i.i.d. test data sampled from the ground-truth dist
arXiv:2511.19829v2 Announce Type: replace Abstract: Most prompt-optimization methods refine a single static template, making them ineffective in complex and dynamic user scenarios. Existing query-depe
arXiv:2506.10239v3 Announce Type: replace Abstract: Probabilistic Virtual Fixtures (VFs) enable the adaptive selection of the most suitable haptic feedback for each phase of a task, based on learned o
arXiv:2510.09288v2 Announce Type: replace-cross Abstract: The vulnerability of machine learning models to adversarial attacks remains a critical societal security challenge. Traditional defenses, such
arXiv:2606.02061v1 Announce Type: new Abstract: Dictionary learning with sparse autoencoders (SAEs) produces overcomplete bases from neural network activations that are often interpretable and reduces
arXiv:2507.08038v3 Announce Type: replace-cross Abstract: Language model agents are increasingly used to automate scientific research, yet evaluating their scientific contributions remains a challenge
arXiv:2606.01886v1 Announce Type: new Abstract: Financial AI agents often fail for a simple reason: they make users carry the complexity. A user must repeatedly restate goals, risk preferences, portfo
Many data engineers spend significant time managing compatibility and getting best performance across multiple analytics engines. To help solve this pain point, we are excited to announce gcs-analytic
arXiv:2606.01764v1 Announce Type: cross Abstract: We revisit the convergence guarantees of the Extragradient (EG) method for unconstrained biaffine min-max optimization. It is known that EG with a fix
arXiv:2606.01110v1 Announce Type: cross Abstract: Full waveform inversion (FWI) reconstructs heterogeneous material properties from receiver data but remains computationally demanding. Physics-informe
arXiv:2606.00920v1 Announce Type: cross Abstract: Run-level pass rate overstates retry-free coverage by up to 17.8 percentage points -- and the gap is largest precisely for mid-performing systems. We
arXiv:2606.00293v1 Announce Type: new Abstract: Tuning algorithms such as stochastic gradient descent (SGD) and stochastic gradient Langevin dynamics (SGLD) for approximate sampling and uncertainty qu
arXiv:2510.00615v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as agents in dynamic real-world environments, where success depends on maintaining precise re
arXiv:2604.01562v2 Announce Type: replace-cross Abstract: Voice cloning is often evaluated in terms of overall quality, but less is known about accent preservation and its perceptual consequences. We
arXiv:2606.00518v1 Announce Type: new Abstract: Agentic AI systems can plan over multiple steps, use tools, and execute tasks over time. When such systems cause harm, tort law struggles to allocate re
arXiv:2606.02459v1 Announce Type: new Abstract: Enabling Vision-Language Models (VLMs) to perform spatial reasoning remains challenging. Existing approaches treat VLMs as passive observers, which is d
arXiv:2603.09692v2 Announce Type: replace-cross Abstract: Reinforcement Learning from Human Feedback (RLHF) has become the standard for aligning Large Language Models (LLMs), yet its efficacy is bottl
arXiv:2606.01367v1 Announce Type: cross Abstract: Active scene reconstruction enables robots/UAVs to autonomously plan trajectories and reconstruct environments without costly manual data acquisition.
arXiv:2606.02569v1 Announce Type: cross Abstract: Video is temporally redundant: adjacent frames usually share most objects, background, and layout. Yet existing video multimodal large language models
arXiv:2606.01283v1 Announce Type: new Abstract: Modeling spatial dependencies is central to spatiotemporal data analysis using Graph Neural Networks (GNNs). Traditional methods rely on distance-based
arXiv:2606.01770v1 Announce Type: cross Abstract: Auto-harness systems such as A-Evolve, GEPA, and Meta-Harness improve LLM agents by optimizing prompts, skills, tools, memories, and supporting infras
arXiv:2606.00141v1 Announce Type: cross Abstract: Adaptive sensing strategies that selectively sample data are increasingly used in wearable health systems to improve prediction performance under limi
arXiv:2606.01104v1 Announce Type: new Abstract: VRR-QA evaluates whether video-language systems can infer spatial, temporal, viewpoint, depth, and visibility relations that are not always resolved by
arXiv:2602.05139v3 Announce Type: replace Abstract: We study bandits whose rewards depend on an unobserved Markov state that evolves independently of the learner's actions. The optimal arm can change
arXiv:2606.00295v1 Announce Type: new Abstract: Masked diffusion models have seen great success in capturing data distributions over discrete sequences in domains such as text and proteins. These mode
arXiv:2606.00459v1 Announce Type: new Abstract: Compliant force or torque control are approaches often investigated to achieve safe physical human-robot interaction (pHRI). However, these approaches h
arXiv:2606.01827v1 Announce Type: cross Abstract: Sharpness-Aware Minimization (SAM) has established itself as a powerful and widely adopted optimizer for training machine learning models. By explicit
arXiv:2602.18645v2 Announce Type: replace Abstract: Time series reasoning tasks often start with a natural language question and require targeted analysis of a time series. Evidence may span the full
arXiv:2508.17320v3 Announce Type: replace Abstract: Understanding the internal representations of large language models (LLMs) remains a central challenge for interpretability research. Sparse autoenc
arXiv:2511.00206v3 Announce Type: replace Abstract: Cognitive science faces ongoing challenges in research integration, formalization, conceptual clarity, and other areas, in part due to its multiface
arXiv:2512.00986v3 Announce Type: replace Abstract: A surge in academic publications calls for automated deep research (DR) systems, but accurately evaluating them is still an open problem. First, exi
arXiv:2606.01224v1 Announce Type: new Abstract: Early detection of at-risk students and timely academic intervention pose major challenges in advanced mathematics education, where complex conceptual h
arXiv:2606.00137v1 Announce Type: new Abstract: Texturing 3D meshes plays a vital role in determining the visual realism of digital objects and scenes. Although recent generative 3D approaches based o
arXiv:2504.19419v3 Announce Type: replace Abstract: Local clustering aims to identify specific substructures within a large graph without any additional structural information of the graph. These subs
OpenAI outlines initiatives and commitments to enhance youth safety while creating educational and economic opportunities for young people globally. The effort emphasizes OpenAI's role in responsible
arXiv:2606.01892v1 Announce Type: new Abstract: Robot localization systems are critical for autonomous navigation and safety. Adversarial perturbations can mislead these systems, resulting in mislocal
arXiv:2606.00914v1 Announce Type: new Abstract: LLM agents increasingly act after consuming ranked external information streams such as social feeds, search results, retrieval contexts, and email queu
arXiv:2606.00320v1 Announce Type: new Abstract: We present an online, distribution-free framework for controlling the Conditional Value-at-Risk (CVaR), extending conformal tail risk control to non-sta
arXiv:2606.00016v1 Announce Type: cross Abstract: Detecting AI-generated text is becoming increasingly challenging as modern language models approach human-level fluency and can evade detectors that r
arXiv:2606.02551v1 Announce Type: cross Abstract: Affordance understanding bridges visual perception and physical action, serving as an explainable interface for robot manipulation in open and unstruc
arXiv:2504.05871v3 Announce Type: replace Abstract: The increasing deployment of intelligent agents in digital ecosystems, such as social media platforms, has raised significant concerns about traceab
arXiv:2606.01508v1 Announce Type: cross Abstract: Traditional operating systems were designed around deterministic programs, explicit control flow, and human initiated workflows. Their core abstractio
arXiv:2511.14460v2 Announce Type: replace Abstract: Large language models (LLMs) have rapidly evolved from single-turn text generators into the foundation of increasingly capable agents. As these agen
arXiv:2606.01414v1 Announce Type: new Abstract: Reusable skills are a key mechanism for extending agent capabilities, allowing agents to accumulate experience and solve increasingly complex tasks. Yet
arXiv:2512.16310v3 Announce Type: replace-cross Abstract: LLM-based agents increasingly use multiple external tools to complete complex tasks. We study Tools Orchestration Privacy Risk (TOP-R): an age
arXiv:2606.02461v1 Announce Type: new Abstract: Language agents spend substantial inference time solving individual tasks, yet the experience acquired in one episode is often underutilized in future e
arXiv:2603.19005v2 Announce Type: replace-cross Abstract: Data science plays a critical role in transforming complex data into actionable insights across numerous domains. Recent developments in large