Knowledge catalogue
arXiv:2609.05405v1 Announce Type: new Abstract: Recent advances in wearable sensing enable continuous monitoring of physiological and behavioral signals, yet existing benchmarks rarely evaluate whethe
arXiv:2609.04827v1 Announce Type: new Abstract: Monocular depth estimation foundation models, such as the Depth Anything series, have achieved remarkable performance across diverse domains. However, t
arXiv:2609.04672v1 Announce Type: new Abstract: We introduce WEECFP, a parameter-free 1024-dimensional continuous molecular fingerprint that scatters each Morgan substructure across roughly thirty-two
arXiv:2609.04434v1 Announce Type: new Abstract: Hybrid language models combine attention with a fixed-size recurrent state, but the role of each channel remains unclear. We introduce two cache-level i
arXiv:2609.04518v1 Announce Type: new Abstract: Agent reinforcement learning (RL) increasingly runs through full execution harnesses, and a multi-harness recipe mixes two choices: exposing the policy
arXiv:2609.05198v1 Announce Type: new Abstract: On-Policy Distillation (OPD) has emerged as a widely adopted post-training paradigm for enhancing large language models in reasoning domains. However, t
arXiv:2609.05376v1 Announce Type: cross Abstract: Visuomotor imitation policies can achieve high performance under in-distribution visual conditions yet fail when visually similar objects or receptacl
arXiv:2609.04383v1 Announce Type: cross Abstract: Real-world dynamics are inherently compositional: multiple entities move simultaneously within a shared scene, each exhibiting distinct motion pattern
arXiv:2609.04582v1 Announce Type: cross Abstract: A 0.6B language model, asked to verify 1,200 logical conclusions (half valid, half corrupted by a single semantic edit), answers YES every time. Judge
arXiv:2609.04766v1 Announce Type: cross Abstract: Generative AI research has increasingly evaluated factuality, citation, coverage, and report structure. Yet passing such local checks does not by itse
arXiv:2609.04806v1 Announce Type: new Abstract: Financial large language models are increasingly deployed for summarization of reports and disclosures, where numerical hallucination poses significant
arXiv:2609.04861v1 Announce Type: new Abstract: Bidirectional discrete diffusion model appears naturally suited to genomic modeling because it can reconstruct missing sequence from both flanks. We dev
arXiv:2609.05370v1 Announce Type: cross Abstract: Decompilation recovers high-level source from compiled machine code and serves as a foundation for security tasks such as vulnerability detection and
arXiv:2609.04453v1 Announce Type: cross Abstract: Expert pruning reduces the memory and serving cost of Mixture-of-Experts (MoE) models by removing low-importance experts identified by the router, ass
arXiv:2609.04490v1 Announce Type: new Abstract: Quantization is widely used to reduce the computational and memory demands of neural-network inference. In recurrent networks, however, the quantized st
arXiv:2609.04281v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly deployed in high-stakes settings, where a response that is reasonable in general may still be unsafe fo
arXiv:2609.04381v1 Announce Type: cross Abstract: Recognizing specific objects onboarded without a labeled training set recurs across manufacturing and service robotics, yet the conventional renderabl
arXiv:2609.04741v1 Announce Type: new Abstract: Recent zero-shot 3D visual grounding methods leverage vision-language models (VLMs) to localize objects in 3D scenes from natural language queries. Howe
arXiv:2609.05346v1 Announce Type: new Abstract: The integration of GenAI tools into higher education assessment raises important questions about how students understand, interpret, and respond to AI-m
arXiv:2609.04801v1 Announce Type: new Abstract: Contextualized visual personalization can retrieve a true record yet apply it to the wrong visual subject. We formalize when a record may condition an a
arXiv:2609.04373v1 Announce Type: new Abstract: Large language models (LLMs) are being deployed at scale in consequential real-world systems, from financial markets to content moderation to hiring. We
arXiv:2609.04962v1 Announce Type: new Abstract: What is the relation between understanding and compression, and why does human understanding take such a heavily compressed form? Across information the
arXiv:2609.04707v1 Announce Type: cross Abstract: Wireless foundation models (WFMs) have emerged as a promising approach for learning reusable representations from large-scale wireless data and adapti
arXiv:2609.05416v1 Announce Type: new Abstract: We study the problem of generating a compositional 3D representation of a cluttered scene containing hundreds of objects. The goal is to represent the s
arXiv:2603.23037v2 Announce Type: replace-cross Abstract: The trustworthy object detection capabilities of a novel Kolmogorov-Arnold network framework are examined here. The approach addresses a key l
arXiv:2609.04384v1 Announce Type: cross Abstract: Chinese online comments often convey social meaning through indirect and playful language that is hard to interpret without context. Existing evaluati
arXiv:2609.03884v1 Announce Type: cross Abstract: Modern AI agent harnesses expose lifecycle hooks that bind shell commands to runtime events such as session start, tool calls, and file edits. These c
arXiv:2609.04170v1 Announce Type: new Abstract: Multi-agent AI science ecosystems rely on agents possessing tools that allow them to communicate, coordinate, and build on each other's work. Yet this s
arXiv:2412.06494v2 Announce Type: replace Abstract: Background. Federated learning (FL) has gained wide popularity as a collaborative learning paradigm enabling collaborative AI in sensitive healthcar
arXiv:2609.03687v1 Announce Type: new Abstract: Coreference resolution is an important task in contextual reasoning. In this paper, we investigate the mechanism for representing and retrieving singula
arXiv:2609.03129v1 Announce Type: cross Abstract: Several classical machine-learning methods, such as KRRs and SVRs, are both computationally and analytically tractable since their estimators either a
arXiv:2609.03794v1 Announce Type: new Abstract: Mobile robotic platforms offer a flexible alternative to fixed manipulators for non-destructive evaluation (NDE) of large aerospace structures, but thei
arXiv:2609.03621v1 Announce Type: new Abstract: Making science computable requires representations of both scientific knowledge and the physical world in which scientific claims are tested. A computab
arXiv:2609.04177v1 Announce Type: new Abstract: Explaining why a specific outcome occurred, and which inputs deserve the blame or credit, is central to philosophical, scientific, and policy analysis.
arXiv:2609.03891v1 Announce Type: new Abstract: Semantic mapping plays a crucial role in the ability of a robot to interact with objects, operate and navigate a complex environment. The most common pi
arXiv:2609.03337v1 Announce Type: new Abstract: Every engineered fill is specified by a maximum dry density and an optimum moisture content. Each determination needs a full Proctor test. Published cor
arXiv:2609.04018v1 Announce Type: new Abstract: Quantile treatment effects (QTEs) measure the effect of a treatment on the distribution of an outcome, and their estimation at extreme quantile levels i
arXiv:2609.04147v1 Announce Type: cross Abstract: This paper presents a low-cost, open experimental platform for research in end-to-end autonomous driving with miniature Ackermann vehicles. The platfo
arXiv:2609.03758v1 Announce Type: new Abstract: Soft eversion robots, also known as vine robots, have attracted growing interest for navigation and inspection tasks, including minimally invasive medic
arXiv:2511.02657v2 Announce Type: replace Abstract: We investigate robust federated learning, where a group of workers collaboratively train a shared model under the orchestration of a central server
arXiv:2609.04086v1 Announce Type: cross Abstract: For every coherent and sufficiently expressive finite syntactic system S, we prove the existence of at least one theorem that S cannot produce autonom
arXiv:2609.03809v1 Announce Type: new Abstract: Energy consumption is one of the largest operational expenditure items for mobile network operators, yet site-level energy inefficiencies such as faulty
arXiv:2609.03402v1 Announce Type: new Abstract: Artificial intelligence (AI) teaching assistants powered by large language models (LLMs) offer scalable educational support but often provide limited pe
arXiv:2511.02192v3 Announce Type: replace Abstract: This paper presents a quantitative comparison between centralised and distributed multi-agent reinforcement learning (MARL) architectures for contro
arXiv:2609.03788v1 Announce Type: cross Abstract: Isolated Sign Language Recognition (ISLR) is conventionally cast as closed-set classification over gloss labels, which cannot generalize to signs unse
arXiv:2609.02911v1 Announce Type: cross Abstract: The paper imports the Kontsevich Segal Witten criterion from quantum gravity into machine learning to evaluate complex linear maps Standard techniques
arXiv:2609.03457v1 Announce Type: new Abstract: Accurate cloud resource forecasting is essential for proactive resource provisioning, maintaining Quality of Service (QoS), and reducing operational cos
arXiv:2602.23996v2 Announce Type: replace Abstract: Masked Image Generation Models (MIGMs) have achieved great success, yet their efficiency is hampered by the multiple steps of bi-directional attenti
arXiv:2609.03366v1 Announce Type: new Abstract: Accountability means a decision can be examined, justified, and contested. LLMs make this hard: fluent output may be ungrounded, incomplete, or unfaithf
arXiv:2601.00038v2 Announce Type: replace-cross Abstract: This work develops an active learning framework to intelligently enrich data-driven reduced-order models (ROMs) of parametric dynamical system
arXiv:2609.03860v1 Announce Type: new Abstract: Retail supply chain operations rely on coupled decision modules that must adapt as requirements evolve. LLMs offer a natural-language interface for this
arXiv:2505.12037v2 Announce Type: replace Abstract: Learning the optimal policy for Markov decision process problems (MDPs) from samples is a fundamental problem in online and data-driven decision-mak
arXiv:2609.04096v1 Announce Type: cross Abstract: This paper proposes AdaRoboVLG, a task-adaptive Vision-Language-Grasp (VLG) framework that supports generalizable grasp synthesis across different rob
arXiv:2609.03046v1 Announce Type: cross Abstract: The last five-plus years have seen many protein engineering disciplines transformed by advances in machine learning (ML), but the same cannot be said
arXiv:2502.18407v2 Announce Type: replace-cross Abstract: Existing LLM-based agents have achieved strong performance on held-in tasks, but their generalizability to unseen tasks remains poor. Hence, s
arXiv:2609.03483v1 Announce Type: cross Abstract: Air-ground collaborative Vision-and-Language Navigation (VLN) pairs an unmanned aerial vehicle (UAV) with a global bird's-eye view and an unmanned gro
arXiv:2609.03992v1 Announce Type: new Abstract: We present Alignment-Free Text-Audiobox (Text-AB), a unified framework for high-quality voice dubbing and full-duplex dialogue synthesis. Building on a
arXiv:2609.03355v1 Announce Type: cross Abstract: Logit-based knowledge distillation for autoregressive language models usually aligns teacher and student next-token distributions over the entire voca
arXiv:2609.03505v1 Announce Type: new Abstract: Anomaly detection in Internet of Things (IoT) networks presents unique challenges due to the diversity of devices, lack of labeled data, and domain vari
arXiv:2609.03258v1 Announce Type: new Abstract: Parking spot classification is a fundamental task in intelligent transportation systems, yet most deep learning approaches rely on large amounts of anno