Knowledge catalogue
arXiv:2608.27906v2 Announce Type: replace Abstract: Interactive web application generation requires models to produce usable HTML, CSS, and JavaScript applications from natural language requests. Unli
arXiv:2608.29381v1 Announce Type: cross Abstract: AI agents are moving toward persistent, stateful execution across various applications, accumulating execution state and external effects that are cos
arXiv:2608.30889v1 Announce Type: cross Abstract: Deploying a new control policy for voltage control in active distribution grids requires evidence that physical limits will be satisfied before the po
arXiv:2606.20122v2 Announce Type: replace Abstract: Open-ended deep research (OEDR) requires systems to acquire knowledge through multi-round retrieval and generate coherent long-form reports. The out
arXiv:2608.31075v1 Announce Type: new Abstract: Recent advances in large reasoning models (LRMs) have shown that reinforcement learning with verifiable rewards (RLVR) can substantially improve reasoni
arXiv:2608.30165v1 Announce Type: cross Abstract: Scientific progress depends not only on finding solutions, but on learning the rules that explain why they work and using that understanding to design
arXiv:2606.15872v2 Announce Type: replace Abstract: Frontier scientific reasoning remains a major challenge for large language models (LLMs), where even the strongest commercial systems fall short of
arXiv:2608.29953v1 Announce Type: new Abstract: Flat retrieval-augmented generation treats a corpus as a bag of chunks, discarding document hierarchy and cross document structure. We introduce SearchW
arXiv:2608.30541v1 Announce Type: new Abstract: Pixel-based language models (LMs) replace traditional tokenizers by processing rendered images of text, making cross-lingual transfer heavily dependent
arXiv:2608.29475v1 Announce Type: new Abstract: Surface material recognition from incomplete visual observations remains a challenging problem in robotic perception and environmental understanding. Th
arXiv:2608.31052v1 Announce Type: new Abstract: Semantic segmentation decomposes an image into distinct mask regions corresponding to different object categories, such as people, cars, signs or buildi
arXiv:2608.30714v1 Announce Type: new Abstract: Verifying image authenticity is increasingly difficult, posing serious risks across journalism, law enforcement, and political domains. Most existing fo
arXiv:2608.30070v1 Announce Type: new Abstract: Sparse representations are often expected to make models smaller and also reduce inference cost. For Fourier Neural Operators (FNOs), these objectives a
arXiv:2608.28647v1 Announce Type: new Abstract: Target-only post-training can improve performance in a specialized domain while degrading behaviors that a general-purpose base model acquired before ad
arXiv:2608.29575v1 Announce Type: new Abstract: Large language models are increasingly used to read documents and produce downstream text, creating a provenance problem when the document owner cannot
arXiv:2608.29888v1 Announce Type: new Abstract: Neural operators provide fast surrogates for partial differential equation (PDE) solvers, but their reliability can degrade for high-dimensional spatial
arXiv:2512.18128v4 Announce Type: replace Abstract: High-resolution mapping of canopy height is essential for forest management and biodiversity monitoring. Although recent studies have led to the adv
arXiv:2608.29153v1 Announce Type: new Abstract: Automated radiological report generation can alleviate clinical workloads and eliminate observer variability. However, standard free-text generation mod
arXiv:2608.28600v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong performance on mathematical reasoning benchmarks, yet the mathematically meaningful skills underlying their
arXiv:2605.05726v2 Announce Type: replace Abstract: As LLM agents are increasingly deployed with large libraries of reusable skills, selecting the right skill for a user request has become a critical
arXiv:2608.30005v1 Announce Type: new Abstract: Rubric-based reinforcement learning extends RL beyond tasks with exact answers or rule-based verifiers by scoring responses against instance-specific cr
arXiv:2608.31023v1 Announce Type: new Abstract: We study dynamic Gaussian Splatting from monocular videos. While recent advancements in dynamic Gaussian splatting offer a promising foundation for mode
arXiv:2608.29768v1 Announce Type: new Abstract: Deploying robot policies in the physical world requires satisfying two fundamental desiderata: reliability and smooth real-time execution. However, depl
arXiv:2608.30716v1 Announce Type: new Abstract: Recent advances in Large Multimodal Models (LMMs) have greatly improved video understanding, yet their ability to reason about human-centered social sit
arXiv:2508.03448v4 Announce Type: replace-cross Abstract: Music recordings often suffer from audio quality issues such as excessive reverberation, distortion, clipping, tonal imbalances, and a narrowe
arXiv:2608.29974v1 Announce Type: cross Abstract: Detecting hallucinations in Large Vision-Language Models (LVLMs) requires both accurate span localization and well-calibrated confidence scores. Fine-
arXiv:2608.30214v1 Announce Type: new Abstract: Scientific reasoning remains challenging for open-source models, largely due to the lack of high-quality scientific reasoning data. Existing datasets ar
arXiv:2608.30978v1 Announce Type: new Abstract: Modularity in deep neural networks has been proposed as a means of improving both interpretability and training by promoting disentangled representation
arXiv:2608.30368v1 Announce Type: new Abstract: Tactile sensing is essential for physical interaction in robotics and human--machine systems. However, combining rich tactile information with compact h
arXiv:2608.29326v1 Announce Type: cross Abstract: Positive psychology dialogue aims to support emotional distress and positive resource building, requiring models to produce not only empathetic replie
arXiv:2509.24711v4 Announce Type: replace Abstract: Current answering paradigms for Large Reasoning Models (LRMs) often fail to account for the fact that some questions may lie beyond the model's oper
arXiv:2608.28907v1 Announce Type: cross Abstract: Task handover requires communicating enough current state for a successor to resume work, yet the relevant information is often divided between system
arXiv:2608.30113v1 Announce Type: new Abstract: A supraglacial lake on the Greenland Ice Sheet ends its melt season in one of four ways: it drains rapidly through a hydrofracture, drains slowly across
arXiv:2603.22341v2 Announce Type: replace-cross Abstract: While prior red-teaming efforts have focused on eliciting harmful text outputs from large language models (LLMs), such approaches fail to capt
arXiv:2608.29564v1 Announce Type: new Abstract: Gradient-based jailbreak suffix optimization methods typically update the suffix by retaining the candidate with the lowest current loss. We show that t
arXiv:2608.29496v1 Announce Type: new Abstract: Accurate estimation of body-composition outcomes, including body fat percentage (BFP), bone mineral density (BMD), and appendicular lean mass (ALM), is
arXiv:2608.29099v1 Announce Type: cross Abstract: Temperature scaling is a core component of knowledge distillation, yet its role and effect are still not fully understood. Transformed Teacher Matchin
arXiv:2608.30505v1 Announce Type: cross Abstract: Large language models (LLMs) are built from structured high-dimensional objects such as token representations, weights, adaptation updates, caches, an
arXiv:2608.29322v1 Announce Type: new Abstract: Recent video diffusion models have achieved remarkable generation quality, but high-fidelity results still largely depend on closed-source systems or co
arXiv:2608.31084v1 Announce Type: new Abstract: The Jacobian Lens (J-lens) is a recent tool for interpreting LLMs. It reads a hidden state as a ranked list of vocabulary tokens, leaving multi-token co
arXiv:2608.28980v1 Announce Type: cross Abstract: Can the specialized architectures that machine learning has traditionally built for structured data be replaced by language-based models? This questio
arXiv:2608.30585v1 Announce Type: new Abstract: Large language models are trained to follow instructions while refusing harmful requests. Jailbreaks exploit this balance to elicit content a model woul
arXiv:2608.28595v1 Announce Type: new Abstract: Biomedical NLP pipelines routinely presuppose clean input text, yet large-scale corpora assembled through automated PDF parsing harbour pervasive OCR-li
arXiv:2608.29093v1 Announce Type: new Abstract: We propose Titans-QFWP, a hybrid reinforcement learning architecture integrating a Quantum Fast Weight Programmer with Titans-style memory (Persistence,
arXiv:2608.29170v1 Announce Type: new Abstract: This paper proposes the Sinitic Romanization Ecosystem, a cross-lingual Sinitic romanization design framework with supporting digital infrastructure and
arXiv:2603.02465v2 Announce Type: replace-cross Abstract: Machine learning-based wildfire detection has advanced significantly using deep learning models trained on large wildfire image and video data
arXiv:2608.29596v1 Announce Type: new Abstract: Autonomous large language model (LLM) agents increasingly face reliability, context consumption, and execution stability bottlenecks when deployed on co
arXiv:2608.29016v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated considerable promise in program generation for small-scale and conventional application development; howe
arXiv:2608.30081v1 Announce Type: cross Abstract: Understanding which training samples influence a generated image is an important problem in generative modeling. In flow matching, training samples in
arXiv:2608.31117v1 Announce Type: cross Abstract: Generative models have become central across science and industry, from image and text synthesis to the design of molecules and materials. Quantum gen
arXiv:2608.29160v1 Announce Type: cross Abstract: We aim to improve frozen flow-matching image generators by adding inference computation inside the denoiser, without changing model weights or the out
arXiv:2506.17251v3 Announce Type: replace-cross Abstract: Although large language models (LLMs) have achieved remarkable performance, the inherent stochasticity of their reasoning processes and varyin
arXiv:2602.09888v2 Announce Type: replace Abstract: Mobile manipulators broaden the operational envelope for robot manipulation. However, the whole-body teleoperation of such robots remains a problem:
arXiv:2608.30924v1 Announce Type: new Abstract: Travel itinerary generation requires balancing strict spatio-temporal constraints with human preferences. Existing LLM-based planners mainly rely on str
arXiv:2508.20766v2 Announce Type: replace-cross Abstract: Safety alignment in Large Language Models (LLMs) often involves mediating internal representations to refuse harmful requests. Recent research
arXiv:2511.11743v4 Announce Type: replace-cross Abstract: Deploying deep neural networks on resource-constrained devices faces two critical challenges: maintaining accuracy under aggressive quantizati
arXiv:2608.30390v1 Announce Type: new Abstract: Accurate uncertainty estimation is essential for machine learning systems de- ployed in high-stakes domains such as medicine. Traditional approaches pri
arXiv:2602.01605v2 Announce Type: replace Abstract: Time Series Foundation Models (TSFMs) leverage extensive pretraining to accurately predict unseen time series during inference, without the need for
arXiv:2601.18552v2 Announce Type: replace Abstract: LLMs expand accessibility and provide wide-reaching access to information. Yet these interactions also create opportunities to embed subtle, goal-or
arXiv:2409.11018v3 Announce Type: replace Abstract: The LiDAR 3D object detector that balances accuracy and speed is crucial for achieving real-time perception in autonomous driving. However, many exi