SportD: How do VLMs physically strategize?
arXiv:2607.14616v3 Announce Type: replace Abstract: Vision-language models (VLMs) can describe a scene, but can they act well within one? We study whether VLMs can make sound strategic decisions, usin
Knowledge catalogue
arXiv:2607.14616v3 Announce Type: replace Abstract: Vision-language models (VLMs) can describe a scene, but can they act well within one? We study whether VLMs can make sound strategic decisions, usin
arXiv:2608.11699v1 Announce Type: new Abstract: Constructing a unified 3D scene understanding model has long been hindered by the topological discrepancies across sensor modalities. While applying the
arXiv:2608.11433v1 Announce Type: new Abstract: Online communities increasingly provide spaces where survivors of sexual violence can share their experiences and seek support. Although prior research
arXiv:2608.11500v1 Announce Type: cross Abstract: Full justified representation (FJR) is among the strongest known satisfiable proportionality axioms for approval-based committee elections. Recent wor
arXiv:2608.12113v1 Announce Type: new Abstract: The same event can be reported from different perspectives depending on the experiences, background, and beliefs of the writer or speaker. A variety of
arXiv:2608.11255v1 Announce Type: new Abstract: Accurate prediction of vapor--liquid equilibrium (VLE) for hydrocarbon-nitrogen mixtures remains challenging for cubic equations of state, particularly
arXiv:2608.12090v1 Announce Type: new Abstract: Protein language models (PLMs) have transferred the latest advances from natural language processing to computational biology. These models, trained on
arXiv:2603.02943v2 Announce Type: replace Abstract: Despite achieving state-of-the-art generation quality, diffusion models are hindered by the substantial computational burden of their iterative samp
arXiv:2608.11820v1 Announce Type: new Abstract: Visual data is typically a prerequisite for training existing video anomaly detection (VAD) methods. However, obtaining sufficient annotated anomaly dat
arXiv:2608.11788v1 Announce Type: cross Abstract: Continual pre-training (CPT) has been widely adopted as a method for domain adaptation in large language models. However, CPT has consistently been ac
arXiv:2608.11318v1 Announce Type: cross Abstract: Many sequential construction tasks exhibit exact symmetry at completion while their execution remains directed and history-dependent. We develop a dec
arXiv:2608.11970v1 Announce Type: new Abstract: The parity problem--deciding whether the number of ones in a binary vector is odd or even--remains challenging for standard neural networks due to linea
Test-time training was popularized during the ARC Prize 2024 competition, after being explored in particular by @MindsAI_Jack and team. To date, I believe ARC 1-2 are the only datasets where TTT stron
arXiv:2608.12175v1 Announce Type: new Abstract: Realistic 3D human generation plays a crucial role in many graphics applications. However, current methods still struggle to generate high-quality human
Thanks. The fact that it works so well for ARC is also a clue to the extent of generalization in these models. It was not at all obvious that it would work originally because the model is never traine
arXiv:2608.12111v1 Announce Type: cross Abstract: A novel advective Fisher-Rao metric is introduced for optimization tasks on paths of probability measures governed by the continuity equation. This me
arXiv:2608.11230v1 Announce Type: new Abstract: This paper introduces the edge-based contiguous p-median (ECpM) problem to partition the roads in a network into a given number of compact and contiguou
The way most people leverage test-time compute today is via a form of test-time NL reasoning that is computationally equivalent to test-time search (sometimes with a verifier / grader in the loop). Th
arXiv:2608.11427v1 Announce Type: new Abstract: Full attention exposes every token pair, whereas kernel attention compresses a sequence into a fixed-dimensional sketch. We show that this distinction b
arXiv:2608.11760v1 Announce Type: cross Abstract: We revisit the Sinkhorn-Knopp (SK) algorithm for the matrix scaling problem. Despite extensive literature on the global convergence of SK and its vari
arXiv:2604.07037v2 Announce Type: replace-cross Abstract: Accelerator-based neutrino physics is entering an energy-frontier regime in which interactions reach the TeV scale and produce exceptionally d
arXiv:2608.11802v1 Announce Type: cross Abstract: Methods to increase the resilience of systems to cyber-attacks become increasingly important. Control-flow monitoring provides a principled basis to e
arXiv:2509.24235v2 Announce Type: replace Abstract: This paper proposes an optimization-based task and motion planning framework, named 'Logic Network Flow,' that integrates temporal logic specificati
arXiv:2608.12057v1 Announce Type: new Abstract: Feature selection is one of the most important and fundamental tasks in data mining, tackled by a family of methods with an established set of evaluatio
arXiv:2608.11785v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong potential for financial analysis and trading, but direct trading remains challenging because the predicti
arXiv:2608.11860v1 Announce Type: cross Abstract: Data-driven inverse design enables efficient generation of nanophotonic structures with prescribed optical responses, but spectrum-to-geometry mapping
arXiv:2312.00114v3 Announce Type: replace Abstract: Event cameras are a novel type of biologically inspired vision sensor known for their high temporal resolution, high dynamic range, and low power co
arXiv:2604.03924v2 Announce Type: replace-cross Abstract: Goal-oriented conversational systems require making sequential decisions under uncertainty about the user's intent, where the algorithm must b
arXiv:2608.11280v1 Announce Type: cross Abstract: Skin cancer diagnosis from dermoscopic images remains challenging due to high intra-class variability, inter-class similarity, class imbalance, and th
arXiv:2608.12027v1 Announce Type: cross Abstract: Pairwise constrained clustering typically relies on hard must-link/cannot-link labels, whereas realistic pairwise supervision may be real-valued and e
arXiv:2608.11840v1 Announce Type: cross Abstract: Growing demand for artificial intelligence (AI) inference services requires scalable infrastructure, yet centralized serving costs rise with demand. W
arXiv:2603.17450v2 Announce Type: replace-cross Abstract: Sequential Recommendation (SR) in multimodal settings typically relies on small frozen pretrained encoders, which limits semantic capacity and
arXiv:2608.11425v1 Announce Type: new Abstract: Underwater image restoration consists of recovering an image which looks like there is no water present. To date, evaluation has not been systematic. Th
arXiv:2608.11843v1 Announce Type: new Abstract: The Seungjeongwon Ilgi, a UNESCO Memory of the World record, is only 37.4% translated, and the most conspicuous failure mode in automatic translation is
As concerns around data privacy in machine learning grow, the ability to unlearn, or remove, specific data points from trained models becomes increasingly important. While state of the art unlearning
arXiv:2608.12150v1 Announce Type: new Abstract: Standard evaluation of large language models assumes stable model rankings across inference conditions. We challenge this assumption by varying the toke
arXiv:2512.15211v4 Announce Type: replace Abstract: Infrared and visible image fusion is a pivotal technology in low-altitude Unmanned Aerial Vehicle (UAV) reconnaissance missions, enabling robust tar
arXiv:2608.12276v1 Announce Type: new Abstract: High-fidelity image generation faces a trade-off between speed and quality. Diffusion models produce strong visuals but require costly iterative samplin
arXiv:2608.11663v1 Announce Type: new Abstract: Open-vocabulary change detection (OVCD) enables the identification of user-specified land-cover changes in bitemporal remote sensing images, but existin
arXiv:2608.11050v1 Announce Type: cross Abstract: Deep learning systems perform mainly within the 2D for a single image domain and take the face as a single-dimension representation, losing sight of t
arXiv:2602.06811v3 Announce Type: replace Abstract: Butterfly-inspired flapping-wing robots use broad compliant wings and low-frequency actuation, but pronounced wingbeat-synchronous body dynamics and
arXiv:2606.14498v2 Announce Type: replace-cross Abstract: Predicting the Kohn-Sham Hamiltonian with machine learning can accelerate density functional theory while retaining access to molecular orbita
arXiv:2608.10245v1 Announce Type: cross Abstract: Inventory and distribution planning in Physical Internet networks requires coordinating factory-hub assignments, factory supply, lateral transshipment
arXiv:2608.11143v1 Announce Type: new Abstract: This paper develops a method for sensor-subset selection for tracking. Prior work showed that low-cost acoustic Received Signal Strength Indicator (RSSI
arXiv:2511.00366v3 Announce Type: replace-cross Abstract: Digital twins are developed to model the behavior of a specific physical asset (or twin), and they can consist of high-fidelity physics-based
arXiv:2608.10351v1 Announce Type: new Abstract: In this work we present a method to accelerate the optimization of learning high dimensional functions using deep neural network (DNN). This optimizatio
arXiv:2608.10817v1 Announce Type: new Abstract: Zero-shot object-goal navigation (ZSON) in open-vocabulary scenarios is challenging, as it requires a robot to locate an arbitrarily specified object in
arXiv:2608.11123v1 Announce Type: new Abstract: Augmentation can corrupt a training example when an image and its annotations receive different random changes. A crop must use the same coordinates for
arXiv:2608.09989v1 Announce Type: cross Abstract: There has been a tremendous amount of image processing and machine learning research to measure and classify disease progression from live optical coh
arXiv:2607.11796v2 Announce Type: replace Abstract: Selective state-space models such as Mamba route information through a bank of first-order modes whose input coupling is set by a learned selection
arXiv:2608.10606v1 Announce Type: new Abstract: ASR-roundtrip evaluation is widely used as a scalable proxy for text-to-speech (TTS) intelligibility, but it can produce false negatives for reading err
arXiv:2608.10678v1 Announce Type: cross Abstract: Chinese web pollution has surfaced in LLMs, motivating audits of upstream Chinese corpora. However, auditing such corpora faces three challenges: (1)
arXiv:2608.10401v1 Announce Type: cross Abstract: Intracytoplasmic sperm injection (ICSI) operators frequently adjust the field-of-view (FOV) during procedures, which interrupts workflow and increases
arXiv:2608.11061v1 Announce Type: new Abstract: Large-scale neural recommender systems are typically trained with a softmax cross-entropy objective over the full item vocabulary. For a typical large n
arXiv:2608.11197v1 Announce Type: cross Abstract: Shani et al. (2026) show that LLM representations broadly recover human category boundaries, while failing to reflect fine-grained typicality structur
arXiv:2608.10237v1 Announce Type: new Abstract: Contrastive learning and Siamese embedding models have become the foundation of modern verification systems, where decisions are governed not by discret
arXiv:2608.10349v1 Announce Type: cross Abstract: Machine-learning intrusion-detection studies commonly emphasize predictive accuracy while treating explanation generation as a computationally free po
arXiv:2608.10744v1 Announce Type: new Abstract: 4D generation synthesizes dynamic 3D scenes from conditions such as text or images. Existing methods either reconstruct generated RGB videos with a sepa
arXiv:2608.10271v1 Announce Type: cross Abstract: Breast density classification is a critical component of breast cancer risk assessment, yet AI models often struggle to generalize across clinical sit
arXiv:2608.10479v1 Announce Type: new Abstract: Latent diffusion models have recently advanced video frame interpolation by synthesizing intermediate frames between input images. However, handling lar