Knowledge catalogue
arXiv:2604.20357v1 Announce Type: cross Abstract: Sign-language datasets are difficult to preprocess consistently because they vary in annotation schema, clip timing, signer framing, and privacy const
arXiv:2604.20208v1 Announce Type: new Abstract: Safety of stochastic dynamic systems in environments with dynamic obstacles is studied in this paper through the lens of stochastic barrier functions. W
arXiv:2604.20688v1 Announce Type: cross Abstract: Storm surge forecasting remains a critical challenge in mitigating the impacts of tropical cyclones on coastal regions, particularly given recent tren
arXiv:2604.20472v1 Announce Type: cross Abstract: Recent advances in vision-language-action (VLA) models for robotics have highlighted the importance of reliable uncertainty quantification in sequenti
arXiv:2604.19810v1 Announce Type: new Abstract: Can scientific discovery be made arbitrarily easy by choosing the right representation, collecting enough data, and deploying sufficiently powerful algo
arXiv:2601.09373v2 Announce Type: replace Abstract: Do Large Language Models (LLMs) genuinely grasp the compositional semantics of events, or do they rely on surface-level probabilistic heuristics? We
arXiv:2604.19749v1 Announce Type: new Abstract: Equipping LLMs with external tools effectively addresses internal reasoning limitations. However, it introduces a critical yet under-explored phenomenon
arXiv:2502.11478v3 Announce Type: replace-cross Abstract: In high-noise environments such as factories, subways, and busy streets, capturing clear speech is challenging. Throat microphones can offer a
arXiv:2604.20231v1 Announce Type: new Abstract: Connected autonomous vehicles (CAVs), which represent a significant advancement in autonomous driving technology, have the potential to greatly increase
arXiv:2512.12463v3 Announce Type: replace-cross Abstract: Classical statistical learning theory predicts a U-shaped relationship between test loss and model capacity, driven by the bias-variance trade
Bloomberg: US child safety group NCMEC received 1.5M reports of suspected CSAM with ties to AI in 2025, a significant surge compared to 67,000 in 2024 and 4,700 in 2023 — William Michael Haslach was a
arXiv:2308.00513v2 Announce Type: replace Abstract: This paper introduces UVIO, a multi-sensor framework that leverages Ultra Wide Band (UWB) technology and Visual-Inertial Odometry (VIO) to provide r
arXiv:2604.20755v1 Announce Type: new Abstract: We introduce V-tableR1, a process-supervised reinforcement learning framework that elicits rigorous, verifiable reasoning from multimodal large language
arXiv:2408.00929v2 Announce Type: replace Abstract: As privacy concerns escalate in the realm of machine learning, data owners now have the option to utilize machine unlearning to remove their data fr
arXiv:2604.20712v1 Announce Type: new Abstract: Peg-in-hole (PiH) assembly is a fundamental yet challenging robotic manipulation task. While reinforcement learning (RL) has shown promise in tackling s
arXiv:2604.19998v1 Announce Type: new Abstract: Evaluating AI-generated reviews by verdict agreement is widely recognized as insufficient, yet current alternatives rarely audit which concerns a system
arXiv:2604.20749v1 Announce Type: new Abstract: Situated conversational recommendation (SCR), which utilizes visual scenes grounded in specific environments and natural language dialogue to deliver co
arXiv:2601.14249v4 Announce Type: replace Abstract: Long chain-of-thought (CoT) trajectories provide rich supervision signals for distilling reasoning from teacher to student LLMs. However, both prior
arXiv:2604.20131v1 Announce Type: new Abstract: Increasingly, studies are exploring using Large Language Models (LLMs) for accelerated or scaled qualitative analysis of text data. While we can compare
arXiv:2604.20789v1 Announce Type: cross Abstract: We investigate the integration of human-like working memory constraints into the Transformer architecture and implement several cognitively inspired a
Sam Tobin / Reuters: A UK tribunal rules Microsoft must face a lawsuit alleging it overcharged UK businesses to run Windows Server on cloud services from Amazon, Google, and Alibaba — Microsoft (MSFT.
arXiv:2602.04713v2 Announce Type: replace-cross Abstract: Aligning text-to-image generation with user intent remains challenging, as users frequently provide ambiguous inputs and struggle with model i
arXiv:2604.19059v1 Announce Type: new Abstract: Language-guided unmanned aerial vehicles (UAVs) often fail not from bad reasoning or perception, but from execution mismatch: the gap between a planned
'I know a bubble when I see one.' That's what Sen. Elizabeth Warren (D-MA), who led the push to create a new consumer financial regulator in the wake of the 2008 recession, told a crowd at a Vanderbil
arXiv:2406.18344v2 Announce Type: replace Abstract: We study the intriguing connection between visual data, deep networks, and the brain. Our method creates a universal channel alignment by using brai
arXiv:2604.19238v1 Announce Type: new Abstract: Real-world image super-resolution (Real-SR) has been revolutionized by leveraging the powerful generative priors of large-scale diffusion and flow-based
Anthropic’s own internal security blows. Anthropic said Mythos was too dangerous to release. Then four random guys in a Discord gained access on day one by guessing the URL... This is pretty insane: →
arXiv:2604.18789v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) is central to aligning Large Language Models (LLMs), yet it introduces a critical vulnerability: an im
arXiv:2604.03037v2 Announce Type: replace-cross Abstract: Long-horizon robotic manipulation remains challenging for reinforcement learning (RL) because sparse rewards provide limited guidance for cred
arXiv:2604.19509v1 Announce Type: new Abstract: Vision-language models (VLMs) have demonstrated remarkable capabilities in understanding human-object interactions, but their application to robotic sys
arXiv:2506.22174v2 Announce Type: replace-cross Abstract: The transport industry has recently shown significant interest in unmanned surface vehicles (USVs), specifically for port and inland waterway
arXiv:2604.19217v1 Announce Type: cross Abstract: Crop yield prediction is one of the most important challenge, which is crucial to world food security and policy-making decisions. The conventional fo
arXiv:2604.19204v1 Announce Type: cross Abstract: LLMs are increasingly being considered for prediction tasks in high-stakes social service settings, but their algorithmic fairness properties in this
Renju Jose / Reuters: Australia's eSafety Commissioner issues transparency notices to Roblox, Microsoft's Minecraft, and other online gaming platforms to detail child safety measures — Australia's int
arXiv:2604.18993v1 Announce Type: cross Abstract: Perception robustness under adverse weather remains a critical challenge for autonomous driving, with the core bottleneck being the scarcity of real-w
arXiv:2601.11037v2 Announce Type: replace Abstract: RL-based agentic search enables LLMs to solve complex questions via dynamic planning and external search. While this approach significantly enhances
arXiv:2506.06414v2 Announce Type: replace-cross Abstract: Existing language model safety evaluations focus on overt attacks and low-stakes tasks. In reality, an attacker can easily subvert existing sa
arXiv:2604.18972v1 Announce Type: cross Abstract: We study finite-horizon continuous-time policy evaluation from discrete closed-loop trajectories under time-inhomogeneous dynamics. The target value s
arXiv:2509.26238v4 Announce Type: replace Abstract: Monitoring large language models' (LLMs) activations is an effective way to detect harmful requests before they lead to unsafe outputs. However, tra
arXiv:2601.15755v3 Announce Type: replace Abstract: Large language models are increasingly used to represent human opinions, values, or beliefs, and their steerability towards these ideals is an activ
arXiv:2604.19281v1 Announce Type: cross Abstract: The use of Large Language Models (LLMs) to support patients in addressing medical questions is becoming increasingly prevalent. However, most of the m
arXiv:2604.17457v2 Announce Type: replace-cross Abstract: Dynamic programming is one of the most fundamental methodologies for solving Markov decision problems. Among its many variants, Q-value iterat
arXiv:2511.21893v2 Announce Type: replace Abstract: Multi-modal foundation models align images, text, and other modalities in a shared embedding space but remain vulnerable to adversarial illusions [3
arXiv:2512.19302v2 Announce Type: replace Abstract: Large Vision--Language Models (LVLMs) hold great promise for advancing optical remote sensing (RS) analysis, yet existing reasoning segmentation fra
arXiv:2604.18781v1 Announce Type: new Abstract: Large-scale automated morphometric analysis of brain MRI is limited by the thick-slice, anisotropic acquisitions prevalent in routine clinical practice.
arXiv:2512.05747v3 Announce Type: replace Abstract: Evaluating and optimising authorial style in long-form story generation remains challenging because style is often assessed with ad hoc prompting an
arXiv:2604.18589v1 Announce Type: cross Abstract: Thematic analysis is difficult to scale: manual workflows are labor-intensive, while fully automated pipelines often lack controllability and transpar
arXiv:2511.06168v3 Announce Type: replace Abstract: This paper primarily demonstrates a method to quantitatively assess the alignment between multi-step, structured reasoning in large language models
Gary Marcus critiques ChatGPT's lack of embodied understanding and spatial reasoning, arguing that the language model struggles with physical concepts that humans intuitively grasp through bodily expe
arXiv:2510.26782v3 Announce Type: replace-cross Abstract: A world model is an internal model that simulates how the world evolves. Given past observations and actions, it predicts the future physical
arXiv:2604.19431v1 Announce Type: cross Abstract: Generative AI systems are known to amplify biases present in their training data. While several inference-time mitigation strategies have been propose
arXiv:2604.19632v1 Announce Type: new Abstract: Graphic design images consist of multiple editable layers, such as text, background, and decorative elements, while most generative models produce raste
arXiv:2604.18816v1 Announce Type: cross Abstract: Principal Component Analysis (PCA) is a fundamental tool for representation learning, but its global linear formulation fails to capture the structure
arXiv:2604.19296v1 Announce Type: new Abstract: Neural operators are widely used to approximate solution maps of complex physical systems. In many applications, however, the goal is not to recover the
arXiv:2511.22099v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have driven major advances across domains, yet their massive size hinders deployment in resource-constrained sett
arXiv:2604.19141v1 Announce Type: new Abstract: Diffusion- and flow-based models usually allocate compute uniformly across space, updating all patches with the same timestep and number of function eva
arXiv:2505.20562v2 Announce Type: replace Abstract: Robotic surgery represents a major breakthrough in medical interventions, which has revolutionized surgical procedures. However, the high cost and l
arXiv:2604.19566v1 Announce Type: cross Abstract: Reliable biomedical and clinical retrieval requires more than strong ranking performance: it requires a practical way to find systematic model failure
arXiv:2602.05993v2 Announce Type: replace-cross Abstract: Flow and diffusion models produce high-quality samples, but adapting them to user preferences or constraints post-training remains costly and
arXiv:2604.19135v1 Announce Type: new Abstract: This paper presents the first exploration of text-to-image diffusion models for zero-shot sketch-based 3D shape retrieval (ZS-SBSR). Existing sketch-bas