Knowledge catalogue
arXiv:2605.18504v1 Announce Type: new Abstract: Machine Translation (MT) for Ancient Greek (AG) to Modern Greek (MG) is a low-resource task, constrained by the lack of large-scale, high-quality parall
arXiv:2605.16309v1 Announce Type: new Abstract: LLM-based agents can recover from individual execution errors, yet they repeatedly fail on the same fault when the underlying process knowledge--operato
arXiv:2605.16295v1 Announce Type: cross Abstract: We present ANVIL, a multimodal generative system that automates the production of analogy-based instructional animations for computer science topics.
arXiv:2605.17212v1 Announce Type: new Abstract: A unified framework for learning under covariate shift is presented, in which a constrained density-ratio network approximates the Radon-Nikodym derivat
arXiv:2603.04727v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have demonstrated impressive general competence in video understanding, yet their reliability for rea
arXiv:2605.16294v1 Announce Type: cross Abstract: A Nature survey from 2023 involving 1,600 researchers shows that scientists are ``concerned, as well as excited, by the increasing use of artificial-i
arXiv:2605.18229v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) are a core interpretability tool for large language models, and progress on SAE architectures depends on benchmarks that re
arXiv:2603.11395v2 Announce Type: replace-cross Abstract: Continual reinforcement learning challenges agents to acquire new skills while retaining previously learned ones with the goal of improving pe
arXiv:2605.18645v1 Announce Type: new Abstract: Retrieving the 3D kinematics of articulated objects from monocular video is a fundamental challenge in computer vision. Existing methods rely on complex
arXiv:2605.16902v1 Announce Type: new Abstract: Scientific artifacts such as models and datasets are foundations for research. With the rapid growth of platforms like HuggingFace, researchers now have
arXiv:2605.16844v1 Announce Type: new Abstract: Between the narrow systems we deploy and the general intelligence we speculate about lies an entire regime of machine behavior that has never received i
arXiv:2605.17461v1 Announce Type: cross Abstract: Whether an interviewee's honest and deceptive responses can be detected by facial expression signals in videos has been debated and requires further r
arXiv:2605.17228v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in high-stakes domains such as clinical decision support and medical documentation. However, the
arXiv:2605.16582v1 Announce Type: new Abstract: We present ArtMesh, a mesh-native method for reconstructing articulated objects explicitly as connected triangle meshes with per-part rigid motion from
arXiv:2603.23566v2 Announce Type: replace-cross Abstract: Optimizing AscendC (Ascend C) operators for Ascend NPUs is difficult for two reasons. First, unlike CUDA, the ecosystem offers few public kern
arXiv:2605.16462v1 Announce Type: cross Abstract: Detecting unauthorized knowledge distillation from a deployed LLM API is hard because the defender controls neither the attacker's training pipeline n
arXiv:2605.17324v1 Announce Type: cross Abstract: Clarification-seeking behavior is widely regarded as a desirable property of LLM agents, enabling them to resolve ambiguity before acting on underspec
arXiv:2605.18295v1 Announce Type: new Abstract: Robust pedestrian safety is crucial to the next-generation of intelligent transportation systems. Such systems rely on active pedestrian localization an
arXiv:2512.23978v2 Announce Type: replace Abstract: Generative artificial intelligence (GenAI) is shifting from conversational assistants toward agentic systems -- autonomous decision-making systems t
arXiv:2605.17933v1 Announce Type: new Abstract: Vision-language model (VLM) agents increasingly rely on memory-augmented reinforcement learning to reuse experience across long-horizon tasks, yet most
arXiv:2605.16649v1 Announce Type: new Abstract: Recent diffusion-based video generators have achieved remarkable visual fidelity and prompt controllability, yet scaling them to ultra-high-resolution (
arXiv:2605.16823v1 Announce Type: new Abstract: Molecular representation learning has become a central approach in AI-driven drug discovery, yet existing molecular tokenizations such as SMILES remain
arXiv:2605.17123v1 Announce Type: cross Abstract: At a time when drones are increasingly associated with hostile operations, we re-purpose them for humanitarian and life-saving applications. However,
arXiv:2605.18231v1 Announce Type: new Abstract: Binary function classifiers play a crucial role in maintaining the security and integrity of software systems by detecting malicious code and unauthoriz
arXiv:2605.16579v1 Announce Type: new Abstract: Autoregressive (AR) video diffusion is a powerful paradigm for streaming and interactive video generation. However, its reliance on softmax self-attenti
arXiv:2605.16550v1 Announce Type: new Abstract: Video periocular recognition is the task of recognizing an individual's identity based on the region around an individual's eyes. The periocular area is
arXiv:2605.17685v1 Announce Type: cross Abstract: Electrocardiogram (ECG)-based biometric recognition has emerged as a promising solution for secure authentication and liveness detection. However, mos
arXiv:2605.17310v1 Announce Type: cross Abstract: Existing adversarial attacks on vision-language models (VLMs) can steer model outputs toward attacker-specified target responses, but their effectiven
arXiv:2605.17887v1 Announce Type: cross Abstract: We propose OASIS, an outlier- and sink-aware technique built on inter-layer null signaling. As AttnResidual architectures introduce an additional dept
arXiv:2605.16386v1 Announce Type: new Abstract: Multimodal large language models (LLMs) are increasingly explored as automated evaluators in clinical settings, yet their scoring behavior on ordinal cl
arXiv:2605.16354v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as automated evaluators of AI systems, including in high-stakes applications. In this role, LLMs ar
arXiv:2605.17765v1 Announce Type: new Abstract: Recent healthcare foundation models have achieved strong predictive performance through large scale self supervised learning, yet their latent represent
arXiv:2605.18748v1 Announce Type: new Abstract: Recent video editing models have converged on a unified conditioning design: a single diffusion transformer jointly consumes text, source video, and ref
arXiv:2603.23069v2 Announce Type: replace-cross Abstract: The task of authorship style transfer involves rewriting text in the style of a target author while preserving the meaning of the original tex
arXiv:2510.20584v3 Announce Type: replace-cross Abstract: Assessing communication and collaboration at scale depends on a labor-intensive task of coding communication data into categories according to
arXiv:2502.18632v4 Announce Type: replace Abstract: Knowledge components (KCs) mapped to problems help model student learning, tracking their mastery levels on fine-grained skills thereby facilitating
arXiv:2605.17561v1 Announce Type: cross Abstract: Issues faced when using software are reported in the form of bug reports. However, many bug reports are invalid, meaning they do not require code chan
arXiv:2603.12145v2 Announce Type: replace-cross Abstract: Translating complex reinforcement learning (RL) environments into high-performance implementations has traditionally required months of specia
arXiv:2605.16567v1 Announce Type: cross Abstract: Unsupervised outlier detection is attractive because it eliminates the need for labeled data. Moreover, forming multi-model ensembles can improve dete
arXiv:2605.17602v1 Announce Type: new Abstract: Aligning Text-to-Image (T2I) generation models with human preferences increasingly relies on image reward models that score or rank generated images acc
arXiv:2605.17978v1 Announce Type: new Abstract: Vectorization via Single Instruction, Multiple Data (SIMD) architectures is a cornerstone of high-performance computing. To fully exploit hardware poten
arXiv:2605.16446v1 Announce Type: cross Abstract: Semi-supervised learning (SSL) enables prediction with limited labels, but high-stakes tabular applications (medical, credit, recidivism) require stat
arXiv:2605.16768v1 Announce Type: new Abstract: Semantic segmentation of multi-source remote sensing images is a fundamental task for Earth observation applications. Existing methods often struggle wi
arXiv:2605.16725v1 Announce Type: new Abstract: Executable world models can be read, edited, executed, and reused for planning, but only if the program captures the environment's transition law rather
arXiv:2605.17971v1 Announce Type: cross Abstract: Despite rigorous safety alignment, Large Language Models (LLMs) remain vulnerable to jailbreak attacks. Existing black-box methods often rely on heuri
arXiv:2605.17937v1 Announce Type: cross Abstract: Quantitative backtesting is essential for evaluating trading strategies but remains hampered by high technical barriers and limited scalability. While
arXiv:2605.17839v1 Announce Type: cross Abstract: Knowledge distillation transfers knowledge from a high capacity teacher to a compact student using a mixture of hard and soft losses. On imbalanced da
arXiv:2510.00304v3 Announce Type: replace-cross Abstract: Deep learning models excel in stationary data but struggle in non-stationary environments due to a phenomenon known as loss of plasticity (LoP
arXiv:2605.17608v1 Announce Type: cross Abstract: Construction projects frequently experience schedule delays and forecasting uncertainty due to variability in labor productivity, material availabilit
arXiv:2605.16673v1 Announce Type: new Abstract: A 'path-based sensor' produces a single observation along a continuous path. For example, a boolean path-based sensor returns a single '1' if an event o
arXiv:2510.16727v2 Announce Type: replace-cross Abstract: Large language models internalize a structural trade-off between truthfulness and obsequious flattery, emerging from reward optimization that
arXiv:2605.17435v1 Announce Type: new Abstract: Biomedical question answering often requires decisions from retrieved literature whose relevance, quality, and support for candidate answers are uneven.
arXiv:2605.18059v1 Announce Type: new Abstract: Robustness is a critical requirement for deploying autonomous driving systems in the real world. Existing robustness benchmarks for autonomous driving h
arXiv:2605.17416v1 Announce Type: cross Abstract: Anthropic's April 2026 Mythos materials combine benchmark claims with concrete bug-finding stories across OpenBSD, FreeBSD, Linux, FFmpeg, and browser
arXiv:2603.21787v2 Announce Type: replace Abstract: Event cameras are attractive for industrial robotics because they provide high temporal resolution, high dynamic range, and reduced motion blur. How
arXiv:2605.18491v1 Announce Type: new Abstract: Methods: Nine SSL methods spanning four pretext-task families were pretrained from scratch using the same 10{,}412 3D CT scans (1.89~M 2D axial slices)
arXiv:2605.17508v1 Announce Type: cross Abstract: Split Federated Learning (SFL) enables privacy-preserving collaborative training by partitioning models between clients and a server. However, under n
arXiv:2603.18972v3 Announce Type: replace Abstract: Multi-dueling bandits, where a learner selects m geq 2 arms per round and observes only the winner, arise naturally in many applications including r
arXiv:2605.18193v1 Announce Type: new Abstract: Finding correspondences is a fundamental and extensively researched problem in computer vision and graphics. In this work, we examine the underexplored
arXiv:2605.18667v1 Announce Type: new Abstract: Earth embedding models transform Earth observation data into embeddings uniquely tied to locations on the Earth's surface. These models are typically ev