Knowledge catalogue
arXiv:2608.21466v1 Announce Type: cross Abstract: We develop spectral algorithms for selecting state-space partitions that define averaging kernels for finite, ergodic and reversible Markov chains. Fo
arXiv:2608.22023v1 Announce Type: new Abstract: Context-adaptive Kalman filters calibrate their noise covariance matrices Q and R from innovation residuals via online regression. When the underlying s
arXiv:2608.23382v1 Announce Type: new Abstract: Instance encoding is a popular empirical technique for privacy enhancement when sharing data to an untrusted server. It transforms sensitive data throug
arXiv:2608.22223v1 Announce Type: cross Abstract: Anti-amyloid therapies and blood-based biomarkers are changing Alzheimer disease workups into a two-stage measurement workflow: screen broadly with ch
I’ve been experimenting with a custom inference/runtime research project called CRANE V2, mostly because I wanted to answer a stupid question: How far can you push absurdly large MoE models on an ordi
arXiv:2608.23317v1 Announce Type: cross Abstract: Analog circuit topology synthesis remains challenging because useful designs occupy a tiny fraction of a combinatorial search space, and small structu
arXiv:2608.22729v1 Announce Type: new Abstract: Reinforcement learning (RL) algorithms have made strides over the past decade applying them to a wide range of problems and control tasks. However, the
arXiv:2608.22072v1 Announce Type: new Abstract: Autonomous underwater vehicles (AUVs) are increasingly important tools in industries ranging from research, to energy, to defense. AUVs are power-constr
arXiv:2608.22077v1 Announce Type: new Abstract: Computer use agents (CUAs) are increasingly deployed as multi-agent systems that decompose a task into multiple subtasks executed across parallel virtua
arXiv:2608.23163v1 Announce Type: new Abstract: Conventional multirotor aerial vehicles actively suppress yaw rotation during hover, expending power to maintain a fixed heading despite the fact that y
arXiv:2608.23018v1 Announce Type: cross Abstract: Federated fine-tuning of on-device large language models (LLMs) faces a significant computing burden. To overcome this limitation, split learning (SL)
arXiv:2608.22772v1 Announce Type: new Abstract: Text-to-SQL aims to translate natural language questions into executable SQL queries over relational databases, requiring multi-stage structured reasoni
arXiv:2608.23290v1 Announce Type: new Abstract: Accurate visual localization on robotic and wearable platforms remains challenging in dense urban environments. Existing methodologies typically rely on
arXiv:2608.21393v1 Announce Type: new Abstract: Running large language models inside enterprise environments has always bumped up against a practical wall: the data lives in one place, the AI horsepow
arXiv:2501.13200v2 Announce Type: replace-cross Abstract: Coordination in decentralized multi-agent reinforcement learning (MARL) necessitates that agents share information about their behavior and in
arXiv:2608.23493v1 Announce Type: new Abstract: Self-reflection is a powerful mechanism for credit assignment in human learning, converting sparse outcome feedback into actionable guidance. However, i
arXiv:2608.21952v1 Announce Type: new Abstract: Recent advances in sequence modeling have highlighted Mamba as a state space architecture offering efficient long-range dependency modeling and providin
arXiv:2608.22132v1 Announce Type: cross Abstract: Biomedical multi-hop question answering (QA) requires models to connect evidence across intermediate entities such as diseases, drugs, proteins, and p
arXiv:2602.14681v4 Announce Type: replace-cross Abstract: LLM-powered Multi-Agent Systems (MAS) have emerged as an effective approach towards collaborative intelligence, and have attracted wide resear
arXiv:2608.23034v1 Announce Type: cross Abstract: Controlling restricted knowledge in large language models is essential for model alignment and safe deployment. Test-time unlearning avoids costly ret
arXiv:2312.10376v2 Announce Type: replace Abstract: Typical methods for visual prompt tuning follow the sequential modeling paradigm originating from NLP, learning a sequence of unordered parameterize
arXiv:2608.22538v1 Announce Type: new Abstract: Policy-governed agents must interpret case evidence while following an authorized procedure. We present extsc{Stage}, an executable-graph framework that
arXiv:2608.22008v1 Announce Type: new Abstract: Disorientation is a common and distressing experience for people living with dementia. It often manifests as confusion about time, place, or personal co
arXiv:2608.23120v1 Announce Type: cross Abstract: Pnar, an Austroasiatic language spoken by approximately 0.4 million people in the Jaintia Hills of Meghalaya, lacks the digital corpora and natural la
arXiv:2603.17825v2 Announce Type: replace Abstract: In this work, we study the role of Massive Activations (MAs), which are rare, high-magnitude spikes confined to a few fixed hidden dimensions in vid
arXiv:2506.20756v4 Announce Type: replace Abstract: Recent video depth estimation methods achieve great performance by following the paradigm of image depth estimation, i.e., typically fine-tuning pre
arXiv:2608.21710v1 Announce Type: new Abstract: With the advance of deep neural networks, the quality of disparity maps obtained through stereo matching has steadily improved. However, existing stereo
arXiv:2608.22309v1 Announce Type: new Abstract: We benchmark transport-based generative models as well as distillation-based few-step methods for the probabilistic forecasting of stochastic fluid flow
arXiv:2608.22953v1 Announce Type: new Abstract: We analyze a variant of stochastic gradient descent with initial regularization (SGDIR) and derive dimension-free upper bounds on its expected excess ri
arXiv:2608.22874v1 Announce Type: cross Abstract: Neurobiological studies and representation learning have observed that representations of objects belonging to the same category in high-dimensional n
arXiv:2608.23411v1 Announce Type: new Abstract: LLM value studies often merge questionnaire ratings, pairwise choices, and values inferred from generated text into one profile. That merge assumes that
arXiv:2608.23475v1 Announce Type: new Abstract: As large language models are increasingly used in data-scarce and evolving task scenarios, few-shot in-context learning (ICL) has become a key paradigm
arXiv:2608.22527v1 Announce Type: new Abstract: Recently, machine unlearning, the removal of specific training data influence from a model, has gained increasing attention. In large language models (L
arXiv:2608.22044v1 Announce Type: cross Abstract: Modern machine learning (ML) methods are highly effective for prediction tasks, but many commonly used representations reduce complex data to fixed di
arXiv:2608.21607v1 Announce Type: new Abstract: We investigate when a sparse nonnegative matrix can be recovered from a real-valued matrix of much lower rank by zeroing out its negative elements. The
arXiv:2608.22896v1 Announce Type: new Abstract: Robotic navigation in human environments requires a spatio-temporal semantic representation that can rec- oncile open-vocabulary perception with long-te
arXiv:2608.23499v1 Announce Type: new Abstract: We present a method for analyzing the internal representations of Vision Transformers (ViTs) exploiting the geometry of their learned parameters. Each a
arXiv:2608.23564v1 Announce Type: cross Abstract: Modern software systems accumulate technical debt over decades of development, which makes migration expensive and largely manual. As coding agents be
arXiv:2608.22086v1 Announce Type: cross Abstract: We present SweepLSD, a line segment detector that reads the image exactly once and emits each segment within a few rows of its last pixel passing the
arXiv:2608.23068v1 Announce Type: new Abstract: Source seeking arises in applications such as gas leak localization, radiation monitoring, and environmental surveillance, where the origin of an unknow
arXiv:2608.21409v1 Announce Type: cross Abstract: In medicine, claims remain valid when supported by empirical evidence grounded in stable biological reality. In law, by contrast, truth is contingent,
arXiv:2608.22112v1 Announce Type: new Abstract: We present a machine learning framework for identifying sparse, interpretable models of dynamical systems directly from time-series data. Our approach p
arXiv:2608.22532v1 Announce Type: new Abstract: Domain shift across imaging modalities and acquisition sites remains a significant barrier to the clinical deployment of segmentation models. Source-fre
arXiv:2608.21673v1 Announce Type: cross Abstract: Longitudinal electronic health records (EHRs) document patients' sequences of clinical visits over time, preserving the temporal evolution of disease
arXiv:2510.01028v2 Announce Type: replace Abstract: Large language models have made revolutionary progress in generating human-like text, yet their outputs often tend to be generic, exhibiting insuffi
I am not a huge fan of AI, but my friends like to joke and say I am torturing computers because I use all sorts of virtual machines, commands, and once split in half a 1000 defective laptop (accident)
arXiv:2608.22594v1 Announce Type: new Abstract: Tabular foundation models (TFMs) have recently emerged as a promising paradigm for machine learning on tabular data, offering the ability to generalize
arXiv:2608.22788v1 Announce Type: new Abstract: Large-scale rollouts have become a core component of modern LLM systems, spanning reinforcement learning (RL) post-training, on-policy distillation (OPD
arXiv:2608.22117v1 Announce Type: cross Abstract: A standard Transformer block separates cross-token interaction in self-attention from a nonlinear feed-forward network applied independently at each p
arXiv:2608.22299v1 Announce Type: new Abstract: The assessment of image denoising results depends on the respective application area, i.e. image compression, still-image acquisition, and medical image
arXiv:2608.22128v1 Announce Type: new Abstract: Printability assessment in additive manufacturing is typically conducted at the geometry level before printing to determine whether a computer-aided des
arXiv:2608.21487v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) exhibit strong zero-shot capabilities, making them an attractive solution for continual learning across diverse tasks. H
arXiv:2608.22622v1 Announce Type: cross Abstract: Clinical decision-making relies on identifying relevant patient information to guide diagnosis and treatment, a challenge that is especially difficult
arXiv:2608.22716v1 Announce Type: cross Abstract: Despite their remarkable success, machine learning models, particularly in vision applications, are alarmingly vulnerable to a range of security threa
Marissa Newman / Bloomberg: Tel Aviv and NYC-based Alice, which works to protect AI models from misuse and rogue behavior, raised 140M led by Apax Digital at a valuation “close to 1B” — An Israeli sta
arXiv:2507.18061v4 Announce Type: replace-cross Abstract: Spoken Language Models (SLMs) are expected to support natural spoken interaction beyond task completion. However, existing SLM benchmarks prim
Tempo Software Inc. today introduced Workforce Intelligence, an Atlassian Marketplace app that connects artificial intelligence usage and its cost to the individual Jira issues the work was done on. T
WeMM-Embedding-9B is a universal multimodal embedding model built on Qwen3.5. It accepts text, images, videos, visual documents, and interleaved multimodal inputs, and returns a 4,096-dimensional L2-n
arXiv:2608.21555v1 Announce Type: cross Abstract: Modern machine learning compilers select tensor memory layouts to minimize execution cost under hardware constraints. Layout selection is global: an o
arXiv:2608.21942v1 Announce Type: new Abstract: Software systems have traditionally been organized around applications where human users act as principal decision-makers. Recent developments in agenti