Knowledge catalogue
arXiv:2607.01630v2 Announce Type: replace Abstract: Dynamic expansion methods for class-incremental learning (CIL) protect task-specific knowledge by growing dedicated tokens or subnetworks, yet our a
arXiv:2609.12660v1 Announce Type: new Abstract: This paper presents a Model Predictive Control-based motion planning and control pipeline for autonomous car racing capable of adapting to different dri
arXiv:2510.10932v5 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models map multimodal perception and language instructions to executable robot actions, making them particularly
arXiv:2609.12557v1 Announce Type: new Abstract: We present DRS-VPT, a feed-forward transformer architecture for foundational image-to-scan registration. Given query images and a reference 3D point clo
arXiv:2609.13010v1 Announce Type: new Abstract: Optimal transport (OT) compares distributions and aligns datasets in machine learning, yet unregularized discrete OT requires a linear program with quad
arXiv:2607.10625v2 Announce Type: replace Abstract: Language-conditioned Imitation Learning (IL) is essential for enabling robots to perform complex tasks following natural language instructions. Howe
arXiv:2609.12872v1 Announce Type: new Abstract: We present DuplexDrama, the first synthesized spoken dialogue dataset that simultaneously covers four dimensions: (i) complete persona and scenario sett
arXiv:2609.12347v1 Announce Type: new Abstract: Humanoid robots must traverse cluttered obstacle fields using onboard proprioceptive and visual observations, yet existing methods usually process multi
arXiv:2511.15015v4 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) has become a practical architecture for scaling LLM capacity while keeping per-token compute modest, but deploying Mo
arXiv:2609.13053v1 Announce Type: cross Abstract: Visual goal and dynamics prediction can provide language-conditioned robot policies with both a target outcome and a representation of action-dependen
arXiv:2609.13042v1 Announce Type: new Abstract: Deep learning models for dynamic survival analysis (DSA) achieve strong predictive performance by incorporating longitudinal patient data, but their bla
arXiv:2609.12268v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) can improve knowledge-intensive question answering, but the first design choice is easy to overlook: how should the
arXiv:2603.17514v3 Announce Type: replace Abstract: Current methods for multimodal medical image classification (M3IC) face two major challenges. First, the prevailing 'fusion after unimodal image emb
arXiv:2609.12533v1 Announce Type: cross Abstract: Real-world Earth observation (EO) agents must translate high-level scientific questions into executable workflows to acquire observations, prepare dat
arXiv:2609.11954v1 Announce Type: new Abstract: As deep learning models are increasingly deployed on resource constrained devices, the demand for efficient model optimization techniques continues to g
arXiv:2609.12075v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models show high robotic manipulation capabilities via a two-stage design: a Vision-Language Model (VLM) stage followed b
arXiv:2609.12350v1 Announce Type: new Abstract: Close-range first-person field images are important for mobile maize phenotyping because many plant-level traits depend on in-canopy structures that are
arXiv:2609.12173v1 Announce Type: new Abstract: Transportation agencies need pedestrian volume estimates across entire road networks to prioritize safety investments, yet manual counts are expensive a
arXiv:2609.12310v1 Announce Type: new Abstract: We describe six submissions under the team name ESTS to the unconstrained WMT26 Model Compression Shared Task for English--Simplified Chinese and Englis
arXiv:2602.15329v2 Announce Type: replace Abstract: Online video understanding requires models to perform continuous perception and long-range reasoning within potentially infinite visual streams. Its
arXiv:2609.11962v1 Announce Type: cross Abstract: We study vector-valued binary affine refinement operators with finitely supported matrix masks and compactly supported continuous piecewise linear inp
arXiv:2609.13058v1 Announce Type: new Abstract: Reinforcement learning (RL) has become central to post-training of large language models. Recent advances in RL for Mixture-of-Experts (MoE) models have
arXiv:2609.12705v1 Announce Type: new Abstract: Handwritten text recognition resources are often small and distributed across collections that differ in language, script, document structure, and annot
arXiv:2609.11995v1 Announce Type: new Abstract: Mobile sensing enables longitudinal monitoring of behavioral and physiological patterns in everyday settings. However, accurate prediction remains chall
arXiv:2609.12639v1 Announce Type: new Abstract: Recent advances in explaining time series (TS) models have produced methods that identify which past values a prediction depends on. However, most exist
arXiv:2609.12179v1 Announce Type: new Abstract: Algorithmic recourse methods typically assume that a predictive model has access to all features of an individual. In practice, decisions are often made
arXiv:2609.12532v1 Announce Type: new Abstract: Enantiomers share atoms, bonds, and pairwise distances yet can behave differently in chiral environments, so molecular encoders must respect atom relabe
arXiv:2609.12107v1 Announce Type: new Abstract: Development and humanitarian organizations produce and support surveys, administrative registries, and other data resources to inform research, policy,
arXiv:2609.12953v1 Announce Type: new Abstract: Flow matching approaches to imaging inverse problems commonly incorporate measurements in two ways. Conditioning-based approaches supply measurement-der
arXiv:2609.12054v1 Announce Type: cross Abstract: Beam-induced background (BIB) from muon decay products will be an overwhelming and unavoidable background at a future Muon Collider. In order to devel
arXiv:2506.13061v4 Announce Type: replace Abstract: Diffusion probabilistic models generate samples by learning to reverse a noise-injection process that transforms data into noise. A key development
arXiv:2609.12078v1 Announce Type: new Abstract: Objects in post-fire environments often undergo irreversible physical transformations that change their geometry, material state, and visual appearance.
arXiv:2609.11937v1 Announce Type: new Abstract: The deployment of Federated Learning (FL) in multi-center clinical networks faces the challenge of 'knowledge dominance,' where high-volume hubs natural
arXiv:2609.12960v1 Announce Type: new Abstract: Sanskrit fuses case, number, person and tense into word endings and chains clauses into compounds, so it is information-dense per word. Whether that den
arXiv:2609.11993v1 Announce Type: new Abstract: Machine learning research in financial services is limited by the scarcity of representative open-source datasets. Existing resources are often narrowly
arXiv:2609.11998v1 Announce Type: new Abstract: Diffusion language models decode tokens in parallel, but their bidirectional denoiser rules out the naive key--value (KV) cache behind fast autoregressi
arXiv:2503.00785v2 Announce Type: replace Abstract: How to endow aerial robots with the ability to operate in close proximity remains an open problem. The core challenges lie in the propulsion system'
arXiv:2609.12433v1 Announce Type: new Abstract: Due to the highly deformable nature of garments, training a generalizable policy for robotic T-shirt folding and unfolding remains a significant challen
arXiv:2609.12298v1 Announce Type: new Abstract: Finger-level motor decoding is important for naturalistic brain-computer interface (BCI) control, yet individual-finger decoding from scalp electroencep
arXiv:2412.09378v4 Announce Type: replace-cross Abstract: Clinical trials bridge basic research and clinical application, serving as essential steps in drug development. This review examines clinical
arXiv:2609.12883v1 Announce Type: new Abstract: Magnetic levitation (MagLev) systems have great potential for application in high-mix, low-volume manufacturing due to their scalability and flexibility
arXiv:2609.11934v1 Announce Type: new Abstract: In networked dynamical systems, the parameter of primary mechanistic interest is signed interaction structure. Recovering this structure from perturbati
arXiv:2609.12191v1 Announce Type: new Abstract: Comparing and selecting task-oriented LLM agents increasingly relies on a low-cost offline evaluation gate: persona-driven LLM user-simulators converse
arXiv:2609.12965v1 Announce Type: new Abstract: Text-based person search (TBPS) aims to retrieve images of a target person from a large image gallery based on a given natural language description. Mos
arXiv:2609.12863v1 Announce Type: new Abstract: We introduce GenOR-Twin, a neuro-symbolic framework that bridges the translation gap between unstructured operational logs and rigorous mathematical opt
arXiv:2609.12627v1 Announce Type: cross Abstract: Deep learning on cortical surfaces faces a dilemma: capturing the complex topology of over 60 nomenclature-dependent sulci per hemisphere requires hig
arXiv:2512.18225v2 Announce Type: replace Abstract: This paper presents an applied AI pipeline for real-time geolocation from noisy microblog streams, unifying statistical hashtag segmentation, part-o
arXiv:2504.19023v2 Announce Type: replace-cross Abstract: Semantic reasoning aims to infer new knowledge from existing knowledge, with OWL ontologies serving as a standardized framework for organizing
arXiv:2609.13015v1 Announce Type: new Abstract: This work enhances global path planning via a pure-pursuit controller with multi-model kinematic switching that sustains plan fidelity across diverse te
arXiv:2609.12731v1 Announce Type: new Abstract: Text-to-image (T2I) diffusion models inevitably internalize sensitive or non-compliant concepts from large-scale pretraining data, necessitating post-ho
arXiv:2606.16000v3 Announce Type: replace Abstract: We introduce GRACE-DS, a Guarded Reward-guided Agent Correction Environment in Data Science for pre-deployment evaluation of LLM-powered AutoML agen
arXiv:2609.12424v1 Announce Type: new Abstract: Reinforcement learning is now the standard way to train large language model agents on long-horizon tasks, where dozens of interdependent actions preced
arXiv:2609.12448v1 Announce Type: new Abstract: Sensitive attributes such as age, income, and occupation can be inferred from user-generated content by aggregating indirect cues across many ordinary p
arXiv:2609.13035v1 Announce Type: new Abstract: Symmetries play a central role in reducing the complexity of reinforcement learning problems, yet most existing approaches rely on fixed group actions o
arXiv:2609.12408v1 Announce Type: new Abstract: Hazardous-freight operations at petrochemical logistics nodes are safety-critical for intelligent transportation systems, yet existing vision benchmarks
arXiv:2609.12216v1 Announce Type: new Abstract: Self-improving agent workflows create an audit problem when the same controller can change both its behavior and the conditions under which that behavio
arXiv:2609.12137v1 Announce Type: new Abstract: Measuring the preferences of human users remains a fundamental challenge of AI alignment. Existing elicitation approaches struggle to efficiently discov
arXiv:2309.06534v2 Announce Type: replace Abstract: Transfer learning is a critical technique that enables the application of knowledge gained from existing tasks or domains to improve performance on
arXiv:2609.11987v1 Announce Type: cross Abstract: An agentic coding system couples a language model to a harness: the tools, prompts and control flow that turn a chat model into an autonomous software
arXiv:2609.12835v1 Announce Type: new Abstract: Bone-marrow cytology is inherently structured: each cell belongs to a hematopoietic lineage, and many cell types lie on ordered maturation trajectories.