Knowledge catalogue
arXiv:2608.21592v1 Announce Type: new Abstract: Contact-rich robotic manipulation requires an accurate model of the kinematic relationship between a robot's joints and the task features it senses. Thi
arXiv:2510.17021v2 Announce Type: replace-cross Abstract: Large language model (LLM) unlearning is a key approach for removing undesired data, knowledge, or behaviors from pretrained models while reta
arXiv:2608.21544v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as tool-augmented agents, where responses can depend on tool calls and external observations ra
arXiv:2608.23353v1 Announce Type: new Abstract: Mixed-integer programming (MIP) lies at the core of operations research and industrial optimization. While large language models (LLMs) have recently sh
arXiv:2601.23182v2 Announce Type: replace Abstract: Despite the non-autoregressive potential of diffusion language models (dLLMs), existing decoding strategies demonstrate positional bias, failing to
arXiv:2608.22883v1 Announce Type: new Abstract: Multimodal speculative decoding accelerates vision-language models by allowing a lightweight draft model to propose candidate tokens for parallel verifi
arXiv:2608.21854v1 Announce Type: new Abstract: Weakly supervised video anomaly detectors are trained with video-level labels but are commonly evaluated as temporal localizers using Micro-AUROC or AP
arXiv:2608.23000v1 Announce Type: new Abstract: Fully online embodied learning requires synaptic adaptation to acquire new behaviors while preserving previously learned dynamics during ongoing interac
arXiv:2608.22224v1 Announce Type: cross Abstract: Temporal Domain Generalization (TDG) aims to learn from historical domains and generalize to unseen future distributions under concept drift. Neverthe
arXiv:2608.21702v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) grounds LLM generation on retrieved documents, but the standard terminal retrieval stage--dense-vector similarity,
arXiv:2608.22522v1 Announce Type: new Abstract: Data valuation is a cornerstone of data-centric learning, where prior efforts primarily focus on designing algorithms to classify training samples as ei
arXiv:2608.22566v1 Announce Type: new Abstract: Multi-agent large language model (LLM) systems are designed to improve reasoning by decomposing tasks across multiple agents with specialized functions,
arXiv:2608.22452v1 Announce Type: new Abstract: Surprisal, the negative log-probability a language model assigns to a word given its preceding context, reliably predicts adult reading times. Does it c
arXiv:2608.23070v1 Announce Type: new Abstract: With the rapid progress of diffusion models and large-scale video generation, generative world models are increasingly expected to replace traditional s
arXiv:2608.23045v1 Announce Type: new Abstract: Web search agents powered by Large Language Models (LLMs) show strong promise, but deep research tasks expose a recurring failure mode: once an agent ha
arXiv:2508.05078v2 Announce Type: replace-cross Abstract: Parameter-Efficient Fine-Tuning (PEFT) is essential for adapting Large Language Models (LLMs) to multi-task scenarios. A prevailing trend in t
arXiv:2608.21668v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to simulate students at different mastery levels. These simulations can generate synthetic training d
arXiv:2608.23254v1 Announce Type: new Abstract: In surgery, patient safety is threatened not only by technical issues but also by poor teamwork. However, existing surgical AI-based solutions focus mai
arXiv:2506.18919v5 Announce Type: replace-cross Abstract: As a multimodal communication medium that integrates images and text, memes often convey implicit harmful content through metaphors, satire, a
arXiv:2608.21897v1 Announce Type: new Abstract: Reliable planning requires converting natural-language instructions into executable symbolic specifications, yet large language models remain brittle wi
arXiv:2608.22063v1 Announce Type: new Abstract: Agents built on Large Language Models (LLMs) increasingly reach enterprise data through the Model Context Protocol (MCP), and many MCP database servers
arXiv:2608.21476v1 Announce Type: cross Abstract: Website redundancy does not have a single fixed meaning. The same repeated element may distract during one task and provide backup during another. We
arXiv:2608.22513v1 Announce Type: new Abstract: Neural networks can learn algebraic operations from finite examples, but it remains unclear whether this ability transfers across mathematically equival
arXiv:2608.21621v1 Announce Type: new Abstract: In on-device NLP tasks, limited resources of embedded hardware, such as the Raspberry Pi 5, require efficient inference strategies. This paper introduce
arXiv:2608.22462v1 Announce Type: cross Abstract: Artificial neural networks can acquire new capabilities but often damage existing ones when they continue to learn. This stability-plasticity problem
arXiv:2608.23248v1 Announce Type: cross Abstract: Traditional clinical prediction models rely on task-specific pipelines and curated, structured data, which scale poorly and underutilize unstructured
arXiv:2608.21833v1 Announce Type: new Abstract: Recent large language models (LLMs) can operate as coding agents that build complete games from natural language requests. Game development is especiall
arXiv:2608.22272v1 Announce Type: cross Abstract: Generative adversarial networks (GANs) can provide efficient image generation, while diffusion models offer high-quality image restoration but require
arXiv:2608.21371v1 Announce Type: cross Abstract: Perimeter-gated single-photon avalanche diodes (pg-SPADs) are known for their dynamic dark noise modulation capabilities. They are reported to trade n
arXiv:2608.21993v1 Announce Type: new Abstract: We introduce Gated Decoupled Compositional Bandits (GDCB), a family of contextual bandit algorithms with three structural innovations that jointly fall
arXiv:2608.21467v1 Announce Type: cross Abstract: Gaussian distributions are used to model uncertainty in signals and states, and Gaussian mixtures are often used when the underlying distribution is m
arXiv:2608.22324v1 Announce Type: new Abstract: Parameter estimation is a central task in data-driven learning of dynamical systems. It aims to recover the underlying physical parameters from observed
arXiv:2608.21849v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has achieved remarkable success in novel view synthesis; however, reconstructions under sparse views often exhibit noticeab
arXiv:2608.22593v1 Announce Type: new Abstract: Autoencoder (AE)-based federated learning (FL) is attractive for anomaly detection when clients have limited local data. However, conventional FL exchan
arXiv:2608.22326v1 Announce Type: new Abstract: Graph-of-Convex-Sets (GCS)-based trajectory optimization represents collision-free regions in configuration space as a finite collection of convex sets
arXiv:2608.22055v1 Announce Type: new Abstract: Suppose one embodied agent knows what must be built, while its teammate alone knows which transformation its workcell can perform. Neither local view de
arXiv:2608.21605v1 Announce Type: new Abstract: Logic Tensor Network-Enhanced Generative Adversarial Networks (LTN-GANs) inject background knowledge by grounding each logical axiom as a predicate and
arXiv:2602.18422v2 Announce Type: replace Abstract: Extended reality (XR) demands generative models that respond to users' tracked real-world motion, yet current video world models accept only coarse
arXiv:2608.21401v1 Announce Type: cross Abstract: Most contract litigation turns on contracts that imperfectly record parties' bargains. When the parties' dispute can't be solved by interpreting the t
arXiv:2608.22746v1 Announce Type: cross Abstract: This paper studies the Sinkhorn distributionally robust hypothesis testing (SDRHT) problem, seeking a robust detector against least-favorable distribu
arXiv:2608.21440v1 Announce Type: cross Abstract: Vision-language-action (VLA) models have advanced end-to-end autonomous driving by leveraging foundation models for semantic reasoning and long-tail g
arXiv:2608.22453v1 Announce Type: new Abstract: We propose a holonomy-based framework for discretizing curvature on graphs equipped with local symmetric positive-definite metrics. Each vertex carries
arXiv:2608.23479v1 Announce Type: new Abstract: Side-Scan Sonar (SSS) is a primary modality for large-scale underwater mapping, yet automated perception and cross-modal alignment are severely bottlene
arXiv:2607.02486v2 Announce Type: replace Abstract: Descriptor-free visual localization eliminates high-dimensional descriptor storage, preserves scene privacy, and simplifies map maintenance, yet its
arXiv:2608.21652v1 Announce Type: new Abstract: Neural-network surrogate models are increasingly used to accelerate scientific simulations, but their deployment in extrapolative and autoregressive set
arXiv:2608.22634v1 Announce Type: cross Abstract: Current work on improving reliability in large language model (LLM)- generated answers has primarily leveraged Retrieval-Augmented Generation (RAG), k
arXiv:2608.23486v1 Announce Type: new Abstract: World action models (WAMs) have recently gained increasing attention as a framework for jointly modeling scene evolution and ego actions in autonomous d
arXiv:2608.22619v1 Announce Type: cross Abstract: Generative segmentation provides an alternative to direct pixel-wise prediction by operating on learned latent representations, but effective image-to
arXiv:2608.21388v1 Announce Type: new Abstract: Reliable and continuous human tracking is essential for natural human-robot interaction, particularly for companion robots. However, many existing appro
arXiv:2608.21550v1 Announce Type: new Abstract: Disassembling end-of-life electric vehicle (EV) battery packs is dull and dangerous work, performed almost entirely by humans. We present GOLEM (General
Mike Scarcella / Reuters: Google launches Gemini Enterprise for Legal, expanding its platform with specialized AI agents and integrations with Thomson Reuters, LexisNexis, and Harvey — Alphabet's Goog
Build a governed weekly reporting workflow with Amazon Quick Desktop and Amazon FSx for NetApp ONTAP. An Amazon S3 access point exposes an approved folder to a Quick knowledge base, and a custom skill
arXiv:2606.16151v2 Announce Type: replace Abstract: Many reasoning tasks require models to reason over input context, from document-grounded question answering to rule-based deduction. Chain-of-Though
arXiv:2603.26756v2 Announce Type: replace Abstract: Deep ConvNets suffer from gradient signal degradation as network depth increases, limiting effective feature learning in complex architectures. ResN
arXiv:2608.22381v1 Announce Type: cross Abstract: Scientific papers may relate by problem, method, result, or contribution, but document-level retrievers collapse these into a single similarity score
IBM has released Granite 4.2, an open‑source agentic AI model that adds native reasoning, tool use, coding instruction following and speech capabilities to enterprise agents. The suite of pre‑built mo
arXiv:2608.23054v1 Announce Type: new Abstract: SIMON and SIMECK belong to a family of Lightweight Cryptographic Algorithms (LCAs) based on the Feistel block cipher, designed for Internet of Things (I
arXiv:2510.03536v3 Announce Type: replace-cross Abstract: Multi-turn medical question answering (QA) aims to model realistic clinical diagnosis, where a doctor gathers patient information across multi
arXiv:2607.10357v2 Announce Type: replace-cross Abstract: The automatic detection and classification of cardiovascular disease (CVD) from computed tomography (CT) images plays an important role in cli
arXiv:2608.22229v1 Announce Type: new Abstract: Normative rules like institutional charters and workplace policies must be both human-readable and operationally verifiable against actual environment r