Knowledge catalogue
arXiv:2607.10183v2 Announce Type: replace-cross Abstract: Running large language models on consumer devices such as laptops and desktops is challenging because model weights often exceed GPU memory ca
arXiv:2607.12220v1 Announce Type: new Abstract: Synthesizing deployable robot behavior trees (BTs) from natural language (NL) requires grounding to ensure every generated BT references only skills a r
arXiv:2607.12750v1 Announce Type: new Abstract: Colorectal cancer (CRC) is the third most common cancer worldwide and the second leading cause of cancer-related deaths globally, with approximately 1,9
arXiv:2511.09483v3 Announce Type: replace Abstract: While multimodal large language models can describe visual content, their ability to generate executable procedures remains underexplored. CrochetBe
arXiv:2607.12172v1 Announce Type: cross Abstract: Decentralized gradient descent (DGD) is widely used for solving distributed optimization problems over networks of agents. While its convergence prope
arXiv:2509.21842v2 Announce Type: replace Abstract: Travel planning (TP) agent has recently worked as an emerging building block to interact with external tools/resources for travel itinerary generati
arXiv:2607.13010v1 Announce Type: new Abstract: Dermatological practice routinely involves measuring and tracking lesion size, morphology and texture, as critical components of wound or skin cancer sc
arXiv:2607.12056v1 Announce Type: new Abstract: Online shopping is increasingly shifting toward a model in which AI agents independently search for products, compare options, evaluate constraints, and
arXiv:2011.02565v2 Announce Type: replace-cross Abstract: Temporal abstraction allows reinforcement learning agents to represent knowledge and develop strategies over different temporal scales. The op
arXiv:2607.12787v1 Announce Type: new Abstract: Recent advances in multimodal large language models (MLLMs) have significantly improved the performance of multimodal emotion recognition (MER) and enab
arXiv:2607.12095v1 Announce Type: cross Abstract: Sensor-rich data-driven applications increasingly use Bayesian approaches to infer latent states of dynamic systems from noisy sensor measurements and
arXiv:2607.13007v1 Announce Type: new Abstract: Simulation-based algorithms are especially suited for high-uncertainty environments such as adversarial board games with significant elements of randomn
arXiv:2509.15120v2 Announce Type: replace Abstract: In high-stakes scenarios, such as medical imaging applications, it is critical to equip the predictions of a regression model with reliable confiden
arXiv:2512.01113v2 Announce Type: replace-cross Abstract: Algorithmic reasoning -- the ability to perform step-by-step logical inference -- is a synthetic benchmark for evaluating multi-step reasoning
arXiv:2607.12888v1 Announce Type: new Abstract: Tensegrity form-finding and physical property prediction are fundamental inverse problems in structural mechanics, which aim to determine equilibrium co
arXiv:2602.02244v3 Announce Type: replace-cross Abstract: The standard post-training recipe for large reasoning models, supervised fine-tuning followed by reinforcement learning (SFT-then-RL), may lim
arXiv:2509.24372v3 Announce Type: replace-cross Abstract: Fine-tuning large language models (LLMs) for downstream tasks is an essential stage of modern AI deployment. Reinforcement learning (RL) has e
arXiv:2603.05842v2 Announce Type: replace Abstract: Reinforcement learning has demonstrated significant potential in the field of autonomous driving. However, it suffers from defects such as training
arXiv:2607.12584v1 Announce Type: cross Abstract: The rapid advancement of synthetic speech generation methods has made audio deepfake detection a critical challenge in multimedia forensics. While rec
arXiv:2607.12352v1 Announce Type: new Abstract: Filtering noise is a fundamental part of data preparation that enhances image quality for applications such as object segmentation, detection, and recog
arXiv:2607.12121v1 Announce Type: cross Abstract: Diffusion models have become the central backbone for modern image, video, and audio generation, but their efficient service remains a challenge. Unli
arXiv:2607.12474v1 Announce Type: new Abstract: Recent advances in foundation models have transformed AI for Science, enabling remarkably accurate predictive performance across domains ranging from pr
arXiv:2512.09925v2 Announce Type: replace Abstract: Recent advances in Gaussian Splatting-based inverse rendering extend Gaussian primitives with shading parameters and physically grounded light trans
arXiv:2603.13421v2 Announce Type: replace-cross Abstract: Generative models based on the Flow Matching objective, particularly Rectified Flow, have emerged as a dominant paradigm for efficient, high-f
arXiv:2607.11947v1 Announce Type: cross Abstract: Typical semi-supervised learning (SSL) methods rely on distributional assumptions, and their performance degrades when these are violated. While PNU l
arXiv:2607.12489v1 Announce Type: new Abstract: Distributed swarms typically rely on either active wireless communication or passive vision, and they are frequently hindered by bandwidth constraints o
arXiv:2606.19387v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have achieved remarkable success in software development. However, they are susceptible to hallucinations, meanin
arXiv:2607.12659v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have achieved impressive performance on diverse embodied tasks. However, deploying VLA models on low-power onboard
arXiv:2604.04241v2 Announce Type: replace Abstract: Risk scoring systems are widely used in high-stakes domains to assist decision-making. However, existing approaches often focus on optimizing predic
arXiv:2607.12775v1 Announce Type: cross Abstract: Scenario-based model predictive control (SBMPC) is a variant of model predictive control (MPC) that explicitly accounts for uncertainty by optimizing
arXiv:2607.12026v1 Announce Type: cross Abstract: Finite groups are rigid algebraic objects, whose Cayley graphs expose a rich network geometry through which group-theoretic structure can be measured,
arXiv:2607.12684v1 Announce Type: new Abstract: The segmentation of lesions in Moderate to Severe Traumatic Brain Injury (msTBI) from T1-weighted MRI presents a significant clinical challenge due to t
arXiv:2510.01841v2 Announce Type: replace Abstract: Person search aims to jointly perform person detection and re-identification by localizing and identifying a query person within a gallery of uncrop
arXiv:2607.12885v1 Announce Type: new Abstract: LLM judges are increasingly being used to evaluate open-ended model responses, often in no-reference settings where a ground-truth answer is unavailable
arXiv:2607.12711v1 Announce Type: new Abstract: Vision--Language Models (VLMs) have recently demonstrated promising performance on structured visual reasoning tasks, including grid-based puzzles. Howe
arXiv:2607.12409v1 Announce Type: new Abstract: Parachutes are widely utilized in aviation, aerospace and lifesaving missions. As the initial stage of parachute deployment, suspension line extraction
arXiv:2607.12893v1 Announce Type: new Abstract: Long-term memory has become a foundational capability for LLM-based agents that accompany users across extended, multi-session interactions. Existing be
arXiv:2607.12000v1 Announce Type: new Abstract: Current visual generation models are capable of producing high-quality content, yet they lack a coherent perception of the spatial structure. Existing g
arXiv:2607.11937v1 Announce Type: new Abstract: Mirror Theory proposes that an intelligent system should be studied not only by what it represents, but by what coherent continuations it can sustain un
arXiv:2607.11994v1 Announce Type: cross Abstract: Classifying cybersecurity vulnerabilities using the Common Weakness Enumeration (CWE) taxonomy is challenging due to extreme class imbalance and stron
arXiv:2605.26095v2 Announce Type: replace Abstract: Automated pavement distress assessment requires more than image-level classification or coarse bounding box detection, demanding precise localizatio
arXiv:2607.12417v1 Announce Type: new Abstract: Although vast amounts of data, such as audio signal spectra, are naturally represented using complex numbers, conventional machine learning methods ofte
arXiv:2607.10560v2 Announce Type: replace-cross Abstract: Mesh deformation, the process of altering the vertex positions of a 3D mesh while preserving its topological structure, is a cornerstone of co
arXiv:1809.01898v2 Announce Type: replace-cross Abstract: The growing complexity of software calls for innovative solutions that support the deployment of reliable and secure software. Machine Learnin
arXiv:2607.12246v1 Announce Type: new Abstract: Personalization in two-sided marketplaces relies heavily on user-level features, yet for platforms with infrequent, high-consideration purchases, a larg
arXiv:2603.27270v2 Announce Type: replace Abstract: Credal sets, i.e., closed convex sets of probability measures, provide a natural framework to represent aleatoric and epistemic uncertainty in machi
arXiv:2607.12909v1 Announce Type: cross Abstract: Falling detection is vital for elderly care and intelligent surveillance; however, prevailing vision-based approaches predominantly frame it as static
arXiv:2508.19186v2 Announce Type: replace-cross Abstract: Reactive obstacle avoidance methods often cause agents to become trapped in local minima, because they can often only reason one step ahead (i
arXiv:2607.09759v2 Announce Type: replace-cross Abstract: Building assistants that can continually watch the world, remember what they see, and reason over their accumulated experience is a long-stand
arXiv:2607.12052v1 Announce Type: cross Abstract: Synthetic image attribution aims at identifying the generator responsible for a given AI-generated image. Training-free reference-based attribution me
arXiv:2607.12097v1 Announce Type: new Abstract: Video games are a dynamic medium experienced over time. While there are many Procedural Content Generation (PCG) approaches for generating video game le
arXiv:2607.12840v1 Announce Type: cross Abstract: Unconventional computing systems must demonstrate robust performance under real-world environmental conditions to enable practical deployments. We hav
arXiv:2607.12753v1 Announce Type: new Abstract: Image super-resolution (ISR) has witnessed remarkable progress with diffusion models and flow matching. The dominant text-to-image (T2I) based approache
arXiv:2503.06796v2 Announce Type: replace Abstract: Robot design is a complex and time-consuming process that requires specialized expertise. Gaining a deeper understanding of robot design data can en
arXiv:2507.11498v3 Announce Type: replace Abstract: Humanoid robots have seen remarkable advances in dexterity, balance, and locomotion, yet their role in expressive domains such as music performance
arXiv:2607.12337v1 Announce Type: cross Abstract: With the growing demand for satellite sensing and communication, the limited wireless resources are difficult to support multiple satellite systems. T
arXiv:2607.12245v1 Announce Type: new Abstract: Few-shot industrial defect detection remains difficult for standard supervised detectors, which achieve poor performance on boundary-dominated industria
arXiv:2506.12339v2 Announce Type: replace-cross Abstract: We present SheetMind, a modular multi-agent framework powered by large language models (LLMs) for spreadsheet automation via natural language
arXiv:2607.11975v1 Announce Type: cross Abstract: Current machine unlearning methods predominantly rely on global, coarse-grained intervention strategies. They lack precise pilot signals to guide the
arXiv:2607.11624v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) algorithms classically suffer from poor sample efficiency. In robotics, a recent line of work has emerged addressi