Knowledge catalogue
arXiv:2609.05575v1 Announce Type: cross Abstract: Understanding how concepts are encoded in the internal representations of machine learning models is a central problem in mechanistic interpretability
arXiv:2609.08419v1 Announce Type: new Abstract: Multiple Instance Learning (MIL) is widely used for weakly supervised learning, particularly in digital pathology, where fine-grained annotations are co
arXiv:2609.06931v1 Announce Type: new Abstract: Invasive coronary angiography (CAG) is the gold standard for diagnosing coronary artery disease, but interpretation varies substantially among observers
arXiv:2603.00039v2 Announce Type: replace Abstract: LLM-as-a-judge ensembles are the standard paradigm for scalable evaluation, but their aggregation mechanisms suffer from a fundamental flaw: they im
arXiv:2609.06772v1 Announce Type: cross Abstract: Ensemble fuzzing runs multiple fuzzers on a target while a scheduler allocates CPU time among them. Existing schedulers base these decisions on compac
arXiv:2609.07094v1 Announce Type: new Abstract: Reasoning is a promising route to the generalization that autonomous driving requires in the long tail, as it can infer how the elements of a scene depe
arXiv:2510.00527v2 Announce Type: replace Abstract: Deterministic models for 3D hand pose reconstruction, whether single-staged or cascaded, struggle with pose ambiguities caused by self-occlusions an
arXiv:2609.08638v1 Announce Type: new Abstract: An action chunk can span several stages of a manipulation task, yet a label for its first step describes only the current stage. We introduce Chunk-Alig
arXiv:2609.08853v1 Announce Type: new Abstract: Model-based reinforcement learning (MBRL) is a family of RL methods that learn a model of the environment and use it for action selection, making it wel
arXiv:2609.06098v1 Announce Type: cross Abstract: Count-valued variables arise in many scientific and applied settings, yet explicit structural models that allow full identification of causal DAGs fro
arXiv:2404.06349v3 Announce Type: replace Abstract: The ability to understand causality significantly impacts the competence of large language models (LLMs) in output explanation and counterfactual re
arXiv:2609.08686v1 Announce Type: new Abstract: Long-form instructional videos require automatic chaptering to support browsing, navigation, and knowledge access. Recent long-context language models c
arXiv:2609.06623v1 Announce Type: cross Abstract: Can exploratory UAV waypoint sequences be generated from multimodal onboard observations and a fixed-dimensional recurrent internal state without main
arXiv:2510.25323v4 Announce Type: replace Abstract: Normalizing flows are deep generative models that enable efficient likelihood estimation and sampling through invertible transformations. A key chal
Certain companies train their AIs to plagiarise work by unnamed scholars, extending an AI history permeated by plagiarism and misattributions. It started a long time ago. In the 1900s, the old method
arXiv:2609.08561v1 Announce Type: new Abstract: Class disentanglement (the separation of a representation's class-conditional point clouds along depth and over training) is usually read off descriptiv
arXiv:2609.06586v1 Announce Type: cross Abstract: A shared reward gives agents a common objective, but leaves open when, how and even whether they must cooperate to succeed. We address these questions
arXiv:2609.07004v1 Announce Type: new Abstract: Accurate segmentation of pulmonary lesions is essential for effective clinical diagnosis and treatment strategies. Existing segmentation approaches ofte
arXiv:2606.14518v2 Announce Type: replace Abstract: The removal of learned data from Machine Learning models through Machine Unlearning (MU) has been widely studied; however, there is no agreed-upon s
arXiv:2609.05702v1 Announce Type: cross Abstract: Quantum Machine Learning (QML) has shown rapid advances by utilizing quantum computing for machine learning tasks. Meanwhile, the privacy risks accomp
arXiv:2609.08657v1 Announce Type: new Abstract: Charts are structured visual compositions whose elements have distinct functional roles, semantic correspondences, and visibility relations. This struct
arXiv:2503.13938v3 Announce Type: replace Abstract: Comprehensive traffic scene understanding is a foundational capability for Intelligent Transportation Systems (ITS) underpinning applications such a
arXiv:2609.07188v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection is critical for safe deployment of medical AI systems. Recently, test-time adaptation (TTA) has emerged as a new par
arXiv:2609.06353v1 Announce Type: new Abstract: Understanding collaborative behavior in children is important for analyzing social participation, peer interaction, shared attention, and engagement dur
arXiv:2609.08709v1 Announce Type: new Abstract: Designing foundation models for graphs is challenging due to the irregular structure of graphs and the different sizes and characteristics of embeddings
arXiv:2609.07183v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is sensitive to which problems a model trains on, yet existing selection criteria--difficulty fi
arXiv:2609.07981v1 Announce Type: cross Abstract: Audio provenance attribution - which system produced a synthetic utterance - is reported at near-ceiling accuracy on clean benchmarks, yet audio reach
arXiv:2504.15928v3 Announce Type: replace Abstract: Artificial intelligence (AI) shows remarkable potential in medical imaging diagnostics, yet most current models require retraining when applied acro
arXiv:2609.09011v1 Announce Type: cross Abstract: The local dark matter density determines the strength of the signal expected in direct-detection experiments, yet published estimates from stellar mot
arXiv:2609.05783v1 Announce Type: cross Abstract: In autonomous driving, Bird's-Eye View (BEV) representations provide a structured, top-down abstraction of the vehicle's surroundings and have become
Big-data company Cloudera Inc. is pushing to become the go-to partner for enterprise-grade sovereign artificial intelligence workloads after announcing a massive, nine-figure strategic partnership wit
arXiv:2609.07606v1 Announce Type: new Abstract: Time series data is very common in many real-world applications and in numerous domains, with increasing interest for automated information extraction u
arXiv:2505.15215v4 Announce Type: replace-cross Abstract: Data fusion, the process of combining observational and experimental data, can enable the identification of causal effects that would otherwis
arXiv:2609.07058v1 Announce Type: new Abstract: Cranial nerves (CNs) play essential roles in sensory, motor, and autonomic functions. Accurate CN parcellation from multimodal magnetic resonance imagin
Code mode gives an agent a sandbox instead of a longer tool list. Here is why it fixes the too-many-tools problem, what it costs you in sandboxing and debuggability, and when plain tool calling is sti
arXiv:2609.07779v1 Announce Type: cross Abstract: As AI-code assistant tools become widespread, automatic assessment of the correctness of generated code becomes a significant challenge. Code LLMs are
arXiv:2510.09712v4 Announce Type: replace Abstract: The proliferation of Large Language Models (LLMs) has enabled a new class of psychologically grounded malicious comments, shifting fake news attacks
In july I posted here about aletha codex and I got a lot of support, and a decent amount of people started using it. I also got a lot of criticism too, which really helped me funnel this version 1.5 t
arXiv:2603.21913v2 Announce Type: replace Abstract: In structured multi-agent transportation systems, agents often must follow predefined routes, making spatial rerouting undesirable or impossible. Th
arXiv:2609.06433v1 Announce Type: new Abstract: The generation of safety-critical traffic scenarios is essential for training and evaluating autonomous vehicles. Prior approaches typically perturb the
arXiv:2609.06721v1 Announce Type: new Abstract: AI companions are envisioned as always-on assistants that support users in daily life. With this regard, we introduce BuddyVQA, a benchmark for companio
arXiv:2609.06807v1 Announce Type: new Abstract: In this work, we comprehensively evaluate three popular feature-extraction paradigms in AI-based neuroimaging modeling: (1) computation of anatomical su
arXiv:2508.10228v3 Announce Type: replace Abstract: A local-valley (LV) centered approach to assessing the quality of sampling from Restricted Boltzmann Machines (RBMs) was applied to the latest gener
arXiv:2609.08766v1 Announce Type: new Abstract: Cultural heritage collections often contain contemporary and historical visual records of the same physical object. Linking these records is difficult b
arXiv:2508.11854v3 Announce Type: replace Abstract: As 3D Gaussian Splatting (3DGS) gains rapid adoption in safety-critical tasks for efficient novel-view synthesis from static images, how might an ad
arXiv:2609.06133v1 Announce Type: new Abstract: Sequential inference on small devices requires a model to retain useful history without repeatedly processing a long input record. A Recurrent Tsetlin M
arXiv:2609.07838v1 Announce Type: new Abstract: Connected robotics is an emerging 6G application where mobile robots follow natural-language instructions to manipulate physical objects. The Vision-Lan
arXiv:2609.08999v1 Announce Type: new Abstract: Partially Relevant Video Retrieval (PRVR) retrieves untrimmed videos when queries describe only short moments. Although recent methods improve local rep
arXiv:2609.05454v1 Announce Type: cross Abstract: Oligonucleotide melting temperature is a fundamental determinant of nucleic acid hybridization and underpins the design of molecular diagnostics, poly
arXiv:2609.07905v1 Announce Type: new Abstract: Timed Partial Orders (TPOs), originally proposed for workflows, provide an interpretable framework for robot task specification with planning algorithms
arXiv:2609.07086v1 Announce Type: cross Abstract: Transformers are a dominant architecture in modern machine learning, powering applications across vision, language, and beyond. At the core of their s
arXiv:2609.05821v1 Announce Type: cross Abstract: Vision-language models (VLMs) often answer new questions about recurring visual content, where reusing the key-value (KV) cache can avoid re-encoding
Congratulations to Tristan Buckmaster and Levent Alpöge for developing the AI assisted techniques for solving an important math problem. Congratulations to OpenAI for not letting any good customer tra
arXiv:2609.05688v1 Announce Type: new Abstract: We study variance-preserving diffusion of the response in mixed linear regression (MLR) with unknown mixing weights. Our analysis separates the statisti
arXiv:2609.06093v1 Announce Type: new Abstract: Connectomes, graph-level maps of neurons and their synaptic connections, provide a structural basis for understanding how brain circuits support functio
arXiv:2602.13960v2 Announce Type: replace Abstract: Constant-stepsize stochastic approximation (SA) is widely used in learning for computational efficiency, yet the distribution of the iterates is typ
arXiv:2609.07294v1 Announce Type: new Abstract: Data-driven constitutive models for path-dependent plasticity are commonly formulated using nonlinear recurrent neural networks, whose sequential state
arXiv:2609.06830v1 Announce Type: new Abstract: The deployment of Hierarchical Federated Learning (HFL) in resource-constrained Internet of Things (IoT) environments requires careful configuration to
arXiv:2609.06921v1 Announce Type: new Abstract: We study constrained online convex optimization with adversarial constraints when constraint values and gradients are observed through unbiased noise. G
arXiv:2606.13877v2 Announce Type: replace Abstract: Contact-rich manipulation requires world models to capture complex interaction dynamics from heterogeneous visual and tactile observations, yet the