Knowledge catalogue
arXiv:2504.01875v3 Announce Type: replace Abstract: Stochastic Gradient Descent (SGD) and its variants, such as ADAM, are foundational to deep learning optimization, adjusting model parameters through
arXiv:2609.31546v1 Announce Type: new Abstract: Electrocardiogram (ECG) foundation models typically tokenize the signal into fixed-length patches that ignore cardiac structure, so a patch may split a
arXiv:2609.31306v1 Announce Type: new Abstract: Tabular in-context learners such as TabPFN, Mitra, or ConTextTab rely on alternating row and column attention over 2D sequences of latent embeddings. Th
arXiv:2609.30550v1 Announce Type: new Abstract: Benchy is a semantic language and execution engine for benchmarking AI programs. A benchmark is completely specified by a program, a scoring function, a
arXiv:2609.31301v1 Announce Type: cross Abstract: Large language model agents increasingly act on software systems, no longer merely generating text but also changing databases and online services. Ho
arXiv:2602.02043v2 Announce Type: replace-cross Abstract: Modern vision-language models struggle with basic compositional reasoning, failing to bind attributes to objects or relations to their referen
arXiv:2609.30747v1 Announce Type: cross Abstract: As artificial intelligence (AI) becomes embedded in everyday life, its environmental footprint, particularly water consumption remains largely invisib
arXiv:2609.31093v1 Announce Type: new Abstract: Scaling language models to long contexts is limited by the quadratic cost of self-attention. Block sparse attention offers an efficient alternative, but
arXiv:2609.30297v1 Announce Type: cross Abstract: Conversational recommendation agents are a new paradigm for content discovery, enabling users to express complex intents through natural language (e.g
arXiv:2609.30492v1 Announce Type: cross Abstract: Language models often produce homogeneous responses to open-ended tasks; such homogeneity can spawn groupthink-the convergence of ideas toward a singu
arXiv:2609.31363v1 Announce Type: new Abstract: Distributionally robust optimization (DRO) provides a principled framework for learning under distribution shift, but its practical use is hindered by t
arXiv:2609.30951v1 Announce Type: new Abstract: Differentiable simulation provides analytic gradients of robot dynamics, enabling fast and sample-efficient first-order policy optimization. However, ob
arXiv:2609.30884v1 Announce Type: new Abstract: Large language models rely on KV caching to reduce repeated prefill computation in long context and interactive applications. As lightweight adapters ev
arXiv:2609.31062v1 Announce Type: new Abstract: Patient-facing AI assistants promise valuable support to patients, but incoming queries can pose medical risks. To create guardrails, we work with oncol
arXiv:2505.16326v3 Announce Type: replace Abstract: Recent years have seen rapid progress in multimodal large language models (MLLMs) in the field of chemistry. However, chemical MLLMs that can handle
arXiv:2609.31339v1 Announce Type: cross Abstract: Reconstructing environments where parts of the scene change between captured image sets poses a challenge for 3D scene reconstruction. We present Chro
arXiv:2609.31014v1 Announce Type: new Abstract: Animals such as squirrels and even bears have adapted to rapidly climb up trees and other complex vertical terrain, but achieving comparable agility has
arXiv:2609.31418v1 Announce Type: new Abstract: A photorealistic 3D view tells a teleoperator where a robot is, but not what the scene contains, how well each object has been observed, or how to turn
arXiv:2604.04247v2 Announce Type: replace Abstract: Recent advances in prompt learning allow large language model agents to acquire task-relevant knowledge from inference-time context without paramete
arXiv:2609.30708v1 Announce Type: cross Abstract: A key challenge in medical image analysis is the scarcity of large annotated datasets for specific populations and diseases. As deep learning models r
arXiv:2609.31035v1 Announce Type: new Abstract: Cooperative payload transportation using multiple extit{Unmanned Aerial Vehicles} (UAVs) poses challenges in stability, coordination, and robustness, es
arXiv:2609.30546v1 Announce Type: cross Abstract: In this paper, we establish the first asymptotic convergence guarantees for the Muon algorithm through a more accurate proxy for the Newton-Schultz it
arXiv:2609.31211v1 Announce Type: new Abstract: Underwater robotic inspection depends on acquiring views that reveal task-relevant structure. For a structurally complex coral colony, recognising the t
arXiv:2609.30360v1 Announce Type: cross Abstract: Inspired by the problem of identifying the best model from a collection of large language models (LLMs) with heterogeneous querying costs, we formulat
arXiv:2609.30974v1 Announce Type: new Abstract: Lexical semantic change is usually summarized by a scalar distance between independently sampled period distributions. This measures how much a word cha
arXiv:2609.30714v1 Announce Type: new Abstract: Agentic AI systems are increasingly being explored in medical imaging to improve throughput and reduce clinician workload; however, safe deployment rema
arXiv:2609.30395v1 Announce Type: new Abstract: Real-time tiny object detection in aerial imagery is constrained by the weak spatial evidence of very small objects and the loss of high-resolution deta
arXiv:2609.31314v1 Announce Type: new Abstract: Cytopathology detection requires open-vocabulary recognition because cellular categories are fine-grained, long-tailed, and continuously evolving across
arXiv:2512.08996v2 Announce Type: replace-cross Abstract: Radiotherapy planning is a highly complex process that often varies significantly across institutions and individual planners. Most existing d
arXiv:2609.30863v1 Announce Type: cross Abstract: The emergence of AI agents is expected to reshape software engineering by moving beyond AI as assistants towards systems capable of planning, executin
arXiv:2609.30658v1 Announce Type: cross Abstract: Implicit neural representations (INRs) have gained momentum in scientific visualization due to their compactness and scalability to large datasets, ma
arXiv:2511.20561v3 Announce Type: replace-cross Abstract: Recent years have witnessed significant progress in Unified Multimodal Models, yet a fundamental question remains: Does understanding truly in
arXiv:2609.31112v1 Announce Type: new Abstract: Vision-language models (VLMs) enable open-vocabulary reasoning for robot manipulation, but their high inference latency limits responsiveness in dynamic
arXiv:2609.31551v1 Announce Type: cross Abstract: Disaggregating the two stages, Prefill and Decode, onto separate GPU pools is now a standard optimization for (text-only) LLM serving. However, multim
arXiv:2609.30882v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to assess long-form medical videos, but their effectiveness may depend on whether transcripts are pro
arXiv:2401.04282v2 Announce Type: replace Abstract: We introduce a model-agnostic framework for constrained post-hoc error correction in binary classifiers. Given a frozen base classifier, the method
arXiv:2609.30417v1 Announce Type: new Abstract: As electric vehicle (EV) adoption increases, ensuring efficient and well-distributed charging infrastructure has become a critical challenge. While many
arXiv:2609.30272v1 Announce Type: cross Abstract: We present extbf{ENAS}, a hardware-aware Neural Architecture Search (NAS) framework that combines a static feasibility check, a cell-based search spac
arXiv:2608.00008v2 Announce Type: replace Abstract: The local deployment of large language models (LLMs) is gaining traction due to privacy concerns and the desire for on-premise inference. However, t
arXiv:2609.30580v1 Announce Type: new Abstract: Virtual sensing repeatedly reconstructs physical fields from changing observations, often on a fixed geometry. We investigate how shared spatial computa
arXiv:2609.30849v1 Announce Type: new Abstract: Prior work has examined the self-consistency of LLM-generated explanations using surface-level perturbation methods. However, the strength of these pert
arXiv:2609.30929v1 Announce Type: new Abstract: Completed multi-horizon forecasts provide residual feedback for a fixed forecaster, but retaining full residual blocks increases auxiliary state. We pro
arXiv:2609.31040v1 Announce Type: new Abstract: Automating the analysis of whole-slide images (WSIs), a key step in cancer diagnosis, has high clinical value, as it can reduce pathologist's workload w
arXiv:2609.30982v1 Announce Type: cross Abstract: Modern AI image generators are increasingly deployed as opaque APIs, where customers can query the deployed service, but cannot inspect model weights
arXiv:2609.30968v1 Announce Type: new Abstract: Large language models are increasingly used as personalized writing assistants, but adapting a model across many authors can compromise individual writi
arXiv:2609.30535v1 Announce Type: new Abstract: Children in multilingual communities often code-switch, using multiple languages in a single utterance. Can we induce cross-lingual alignment in languag
arXiv:2609.30940v1 Announce Type: new Abstract: Individually protective decisions can produce avoidable collective failures. As large language model (LLM) agents take on greater roles in financial dec
arXiv:2609.30277v1 Announce Type: new Abstract: Implicit Graph Neural Networks (IGNNs) define node representations as fixed points of message-passing operators, enabling effectively infinite-depth pro
arXiv:2609.31128v1 Announce Type: new Abstract: Frequency-based adversarial attacks have recently grown popular by exploiting spectral sensitivities shared across neural architectures. Unlike spatial
arXiv:2609.30626v1 Announce Type: new Abstract: We present a frequency-modulated (FM) haptic display based on piezoelectric vibrating actuators. Existing haptic displays commonly encode haptic intensi
arXiv:2609.30629v1 Announce Type: cross Abstract: Mission-critical UAVs increasingly rely on split vision-language model (VLM) perception under tight onboard-resource and wireless-communication constr
arXiv:2609.30741v1 Announce Type: new Abstract: Binocular rendering requires two nearby views of the same scene and therefore repeats substantial visibility and shading work. We present a 2D Gaussian
arXiv:2609.30704v1 Announce Type: new Abstract: Human spatial attention is widely conceptualized as being guided by a priority map that integrates perceptual salience, current goals, and past experien
arXiv:2609.30391v1 Announce Type: new Abstract: Existing in-context reinforcement learning methods mainly pretrain Transformers with supervised behavior-prediction objectives. This enables task infere
arXiv:2512.18640v3 Announce Type: replace-cross Abstract: Event cameras offer a high temporal resolution over traditional frame-based cameras, which makes them suitable for motion and structure estima
arXiv:2609.31029v1 Announce Type: new Abstract: Reasoning systems usually treat premise use as a question of relevance: if a fact is available and useful, it may be selected for inference. Authorizati
arXiv:2609.30543v1 Announce Type: new Abstract: As robots transition from structured factory settings into homes, they are required to interact with an ever-increasing variety of objects. Many tasks r
arXiv:2609.31383v1 Announce Type: cross Abstract: End-to-end driving policies are commonly trained through open-loop behavior cloning, yet they must ultimately operate in closed-loop when deployed on
arXiv:2609.30797v1 Announce Type: new Abstract: Text-based memory and context compression support reuse of past interactions. Resizing continuous memory changes the input to a frozen LLM, coupling cap
arXiv:2609.30828v1 Announce Type: new Abstract: Precision manipulation with contact-critical interactions is often history-dependent: visually similar observations can correspond to different latent i