Knowledge catalogue
arXiv:2607.20973v1 Announce Type: new Abstract: This paper addresses defensive blocking in autonomous racing, where a vehicle must prevent a faster opponent from overtaking while operating near its dy
arXiv:2607.20972v1 Announce Type: new Abstract: Coding agents ship with one kind of memory: documents. Instruction files, plan artifacts, and auto-written memory directories are deliberately authored
arXiv:2607.21131v1 Announce Type: cross Abstract: Estimating heat-related mortality risk is a core task in environmental epidemiology, typically addressed with Distributed Lag Non-linear Models (DLNMs
arXiv:2607.20630v1 Announce Type: cross Abstract: Traditional query processing engines require continuous development and extensions to support new techniques and user requirements, and in some cases,
arXiv:2607.21458v1 Announce Type: new Abstract: The rise of human-AI collaborative writing has created a growing need for fine-grained detection methods that support localizing likely LLM-generated co
arXiv:2607.20590v1 Announce Type: cross Abstract: Neural network misclassifications exhibit characteristic spectral instability in internal activations that is invisible at the output layer. This phen
arXiv:2607.21243v1 Announce Type: new Abstract: AI-enabled visual perception systems are increasingly deployed in intelligent transportation infrastructure and autonomous vehicle related applications.
arXiv:2607.20491v1 Announce Type: new Abstract: Standard evaluation benchmarks measure what a tool-using agent decides, not whether it arrives at that decision through the same process each time. We i
arXiv:2602.13904v2 Announce Type: replace Abstract: Chain-of-thought (CoT) reasoning is fundamental to modern LLM architectures and represents a critical intervention point for AI safety. However, CoT
arXiv:2607.21185v1 Announce Type: new Abstract: Neurosymbolic (NeSy) systems integrate neural networks with logical reasoning to achieve both generalization and interpretability, but recent work has s
arXiv:2607.20900v1 Announce Type: new Abstract: With the increasing diversity of spoofing attacks, there is a growing demand for unified Face Anti-Spoofing (FAS) models capable of detecting both physi
arXiv:2607.21371v1 Announce Type: cross Abstract: Open-vocabulary semantic segmentation (OVSS) leverages textual semantics to segment objects beyond predefined categories. While the self-supervised mo
arXiv:2607.20487v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to answer questions about political information, including in election-adjacent information settings
arXiv:2607.20446v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly used by students to generate natural language responses and program code, there is growing interest in
arXiv:2607.20992v1 Announce Type: new Abstract: Trajectory optimization for multi-robot systems remains a critical challenge, particularly when navigating highly non-convex, non-linear, and non-differ
arXiv:2607.20984v1 Announce Type: new Abstract: This paper proposes the Distribution-Alignment Bridge (DAB), a framework that reconceptualizes text-to-video retrieval as a distribution alignment task
arXiv:2607.20522v1 Announce Type: new Abstract: This paper tests whether holonomy concentrates on active sparse-autoencoder (SAE) feature planes in Gemma 2 2B, a concrete operationalization of the bro
arXiv:2607.21065v1 Announce Type: new Abstract: Pathology vision-language models (VLMs) have recently progressed rapidly and are commonly evaluated by answer accuracy on pathology VQA benchmarks. Howe
arXiv:2607.20448v1 Announce Type: new Abstract: We introduce Domyn-Small, a 10-billion-parameter open-weight reasoning language model released under the MIT license. Domyn-Small is the product of an i
arXiv:2607.21540v1 Announce Type: new Abstract: We present DONDO, a family of open, permissively licensed automatic speech recognition (ASR) base models for African languages, built on the w2v-BERT 2.
arXiv:2607.20555v1 Announce Type: new Abstract: The lottery ticket hypothesis proposes that large random neural networks contain sparse subnetworks that can match the performance of dense models after
arXiv:2508.16947v2 Announce Type: replace-cross Abstract: Despite significant progress, imitation learning-based autonomous driving planners remain largely restricted to reproducing high-frequency bia
arXiv:2607.20457v1 Announce Type: cross Abstract: Inference with large language models (LLMs) on long sequences is computationally expensive due to the quadratic complexity of self-attention. Distribu
arXiv:2607.20692v1 Announce Type: cross Abstract: We present a privacy-preserving framework for synthetic lung CT slice generation developed for the Image-CLEFmed GANs 2026 challenge. The approach com
arXiv:2607.21138v1 Announce Type: new Abstract: Accurate forest inventory and large-scale mapping are essential for ecosystem monitoring and sustainable forest management. Multiple low-cost edge platf
arXiv:2508.21797v2 Announce Type: replace-cross Abstract: Industry 4.0's highly networked Machine Tool Controllers (MTCs) are prime targets for replay attacks that use outdated sensor data to manipula
arXiv:2607.20531v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed over Model Context Protocol (MCP) servers, yet the benchmarks used to evaluate them score th
arXiv:2607.20820v1 Announce Type: new Abstract: Body-based emotion recognition is important for real-time affective systems, but graph-based skeleton models can be computationally expensive. This pape
arXiv:2607.21529v1 Announce Type: cross Abstract: Test-Time Tuning (TTT) on pretrained diffusion models has emerged as a powerful paradigm for video editing. However, there exists a foundational misma
arXiv:2607.20771v1 Announce Type: cross Abstract: We consider the problem of learning compositional robot policies end-to-end from expert demonstrations, without any pre-specified notion of task decom
arXiv:2607.21356v1 Announce Type: new Abstract: Fine-tuning an aligned language model on a narrow stream of bad advice can make it broadly misaligned on questions unrelated to the training data, a phe
arXiv:2607.21013v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved impressive performance in multimodal emotion recognition (MER) tasks and lifted MER to a new leve
arXiv:2602.11658v2 Announce Type: replace Abstract: Immersive affective content generation aims to create visually compelling VR imagery with controllable emotional nuance, yet existing methods typica
arXiv:2607.20480v1 Announce Type: new Abstract: Accurate distribution system topology is essential for outage localization, voltage analytics, and operation of distribution grids, yet maintaining reli
arXiv:2602.10230v2 Announce Type: replace Abstract: Audio language models process input audio into rich frame-level representations, but the standard approach to temporal localization generates timest
arXiv:2607.21191v1 Announce Type: cross Abstract: Event-B is a formal method rooted in predicate logic and set theory. We encoded over 600 proof rules in Prolog, enabling a systematic, comprehensible
arXiv:2607.20674v1 Announce Type: new Abstract: We consider the problem of learning high-dimensional semi-global feedback controllers under hard safety constraints enforced by control barrier function
arXiv:2607.20889v1 Announce Type: new Abstract: Editable 3D scene creation requires object instances and lights that can be inspected, moved, and imported into standard engines, yet existing single-im
arXiv:2607.20814v1 Announce Type: new Abstract: The electrocardiogram (ECG) is a cornerstone of cardiac as- sessment, yet clinical deployment of deep learning models remains con- strained by limited i
arXiv:2512.04966v2 Announce Type: replace-cross Abstract: Accurate channel state information (CSI) underpins reliable and efficient wireless communication. However, acquiring CSI via pilot estimation
arXiv:2509.21913v2 Announce Type: replace-cross Abstract: Background: Cone-beam computed tomography CBCT is a commonly used modality for image guided radiotherapy. It offers real time anatomical visua
arXiv:2607.21475v1 Announce Type: cross Abstract: Deterministic KV-cache eviction keeps the top-k tokens under an importance score and deletes the rest. We prove that this design cannot know what it d
arXiv:2607.21412v1 Announce Type: new Abstract: Large Language Models (LLMs) excel at natural language understanding and generation but remain unreliable for multi-step logical reasoning, especially i
arXiv:2607.20527v1 Announce Type: new Abstract: Agentic LLM systems such as OpenScholar and PaperQA2 read the scientific literature and return cited answers, and both they and their benchmarks already
arXiv:2604.25077v2 Announce Type: replace Abstract: Weak-to-strong alignment offers a promising route to scalable supervision, but it can fail when a strong model becomes confidently wrong on examples
arXiv:2607.20589v1 Announce Type: new Abstract: Persona simulation involves utilizing large language models (LLMs) to anticipate human choices or interactions based on specific characteristic informat
arXiv:2410.00945v2 Announce Type: replace-cross Abstract: Gene-expression profiling is widely used in research and central to many areas of precision oncology, but remains costly and not universally a
arXiv:2603.18385v4 Announce Type: replace-cross Abstract: We present a new solution concept called evolutionarily stable Stackelberg equilibrium (SESS). We study the Stackelberg evolutionary game sett
arXiv:2607.20489v1 Announce Type: new Abstract: Text-to-SQL has advanced rapidly with large language models, but complex database queries still require reasoning beyond one-shot generation, including
arXiv:2607.20586v1 Announce Type: new Abstract: We study vector-valued affine refinement operators of the form [ (Wgamma)(t)=sum_{jinZ} A_jgamma(Mt-j)+B(t), ] with finitely supported matrix mask and c
arXiv:2607.20499v1 Announce Type: new Abstract: Large Language Models generate plausible backend code, but a single-pass paradigm provides no guarantee of correctness or runtime reliability. We presen
arXiv:2607.21585v1 Announce Type: new Abstract: Flow-based generative models have enabled remarkable progress in fast and controllable generation across continuous and discrete state spaces, yet exist
arXiv:2607.20485v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable performance on standard benchmarks, yet it remains largely unexplored whether they truly meet
arXiv:2607.21302v1 Announce Type: new Abstract: Behavior prior reinforcement learning (BPRL) has emerged as a promising paradigm to improve sample efficiency in online reinforcement learning (RL) by l
arXiv:2607.21209v1 Announce Type: cross Abstract: In the field of Artificial Intelligence, an agent is a system which is able to autonomously make decisions in order to reach a desired goal. As these
arXiv:2607.21210v1 Announce Type: cross Abstract: Existing approaches to multi-agent belief combination have established mature foundations for combining uncertain beliefs under common assumptions: co
arXiv:2607.21007v1 Announce Type: new Abstract: Deepfake detection is moving beyond binary classification decisions toward systems that can also explain the visual evidence supporting those decisions.
arXiv:2607.20819v1 Announce Type: new Abstract: Stress is a dynamic process characterized by significant individual variability in facial expression. Traditional architectures, such as Recurrent Neura
arXiv:2607.21184v1 Announce Type: new Abstract: Inductive Logic Programming (ILP) originated within the Logic Programming community in the Nineties as a framework for combining symbolic learning with
arXiv:2607.20675v1 Announce Type: new Abstract: Machine learning (ML) models are increasingly integrated into optical network automation frameworks to support tasks such as failure management, perform