Knowledge catalogue
arXiv:2505.13741v2 Announce Type: replace Abstract: Direct training of Spiking Neural Networks (SNNs) on neuromorphic hardware can greatly reduce energy costs compared to GPU-based training. However,
arXiv:2605.07607v1 Announce Type: new Abstract: Image-to-point cloud registration is often challenged by viewpoint changes, cross-modal discrepancies, and repetitive textures, which induce scale ambig
arXiv:2605.07060v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) provide a mesh-free framework for solving PDE-constrained inverse problems, but their extension to Bayesian i
arXiv:2605.07698v1 Announce Type: new Abstract: Grammar-constrained generation is often combined with local vocabulary masking and speculative decoding, but the resulting sampling law is not the gramm
arXiv:2605.07133v1 Announce Type: cross Abstract: Graph Anomaly Detection (GAD) is a critical task in graph machine learning with vital applications in financial fraud detection and social platform go
arXiv:2605.07442v1 Announce Type: new Abstract: LLM-based game generation promises to turn natural-language specifications into executable games, but progress is limited by the lack of reliable automa
arXiv:2602.13506v2 Announce Type: replace-cross Abstract: Optimizing non-convex functions is a fundamental challenge across machine learning and combinatorial optimization. We introduce and study gamm
arXiv:2605.07692v1 Announce Type: new Abstract: Large-scale social simulators are essential for studying complex social patterns. Prior work explores hybrid methods to scale up simulations, combining
arXiv:2605.06734v1 Announce Type: cross Abstract: Fast Weight Programmers (FWPs) encode temporal dependencies through dynamically updated parameters rather than recurrent hidden states. Quantum FWPs (
arXiv:2510.07625v2 Announce Type: replace Abstract: While Model Predictive Control (MPC) delivers strong performance across robotics applications, solving the underlying (batches of) nonlinear traject
arXiv:2605.07817v1 Announce Type: cross Abstract: Human visual reasoning is governed by active vision, a process where metacognitive control drives top-down goal-directed attention, dynamically routin
arXiv:2605.07329v1 Announce Type: new Abstract: We introduce GC-ART (Global Curve Adaptive Rational Tone-mapping), a lightweight differentiable pre-processing module for robust image classification. G
arXiv:2605.07326v1 Announce Type: new Abstract: World models, which simulate environmental dynamics and generate sensor observations, are gaining increasing attention in autonomous driving. However, p
arXiv:2512.20974v3 Announce Type: replace-cross Abstract: Bayesian Reinforcement Learning (BRL), a subclass of Meta-Reinforcement Learning (Meta-RL), provides a principled framework for generalisation
arXiv:2605.06918v1 Announce Type: cross Abstract: Previous methods that predict system-wide travel time, predominantly grounded in graph neural networks, remain limited to typical and recurring demand
arXiv:2502.17500v3 Announce Type: replace-cross Abstract: This paper investigates in depth the fundamental properties of the two-parameter generalized Euler logarithm and its inverse, the associated d
arXiv:2605.07432v1 Announce Type: new Abstract: Chatbots are robots that can communicate with humans using text or voice signals. Legal chatbots improve access to justice, since legal representation a
arXiv:2605.07319v1 Announce Type: cross Abstract: We introduce Flux Matching, a new paradigm for generative modeling that generalizes existing score-based models to a broader family of vector fields t
arXiv:2510.01105v2 Announce Type: replace Abstract: Neural multivariate regression underpins a wide range of domains, including control, robotics, and finance, yet the geometry of its learned represen
arXiv:2605.06740v1 Announce Type: cross Abstract: We introduce Geometric Kolmogorov--Arnold Networks (GeoKANs), a family of geometry-aware KAN-type models in which approximation is carried out in lear
arXiv:2605.07536v1 Announce Type: cross Abstract: Detecting stealthy malicious communications from flow logs under benign-only training remains a critical challenge in network security. Malicious comm
arXiv:2605.07982v1 Announce Type: new Abstract: Ensuring safe, policy-compliant outputs from large language models requires real-time content moderation that can scale across multiple safety dimension
arXiv:2605.08022v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) have been proposed as biologically plausible and energy-efficient alternatives to conventional Artificial Neural Networ
arXiv:2410.06347v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) in robotics faces significant hurdles regarding sample efficiency and generalization across varying goals. While O
arXiv:2602.14868v2 Announce Type: replace-cross Abstract: Reinforcement learning has emerged as a powerful paradigm for unlocking reasoning capabilities in language models. However, relying on sparse
arXiv:2605.07399v1 Announce Type: new Abstract: Diffusion Vision-Language Models (dVLMs), built upon the non-causal foundations of Diffusion Large Language Models (dLLMs), have demonstrated remarkable
arXiv:2605.07366v1 Announce Type: new Abstract: Adaptive rank allocation for LoRA, allocating more parameters to important layers and fewer to unimportant ones, consistently improves efficiency under
arXiv:2605.06755v1 Announce Type: cross Abstract: Reinforcement learning is widely used to improve the reasoning ability of large language models, especially when answers can be automatically checked.
arXiv:2605.07689v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) is a standard algorithm for reinforcement learning from verifiable rewards, but its group-mean-centered advant
arXiv:2509.23370v2 Announce Type: replace Abstract: The CLIP model has established itself as a cornerstone of large-scale retrieval systems. However, its performance often degrades under distributiona
arXiv:2605.07961v1 Announce Type: new Abstract: Federated fine-tuning (FFT) has emerged as a privacy-preserving paradigm for collaboratively adapting large language models (LLMs). Built upon federated
arXiv:2605.07999v1 Announce Type: cross Abstract: Multiphoton photoreduction enables high-fidelity fabrication of complex 3D microstructures, yet reliable process-structure-property (PSP) prediction r
arXiv:2605.06671v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated strong potential for many mathematical problems. However, their performance on graph algorithmic tasks is
arXiv:2512.02991v2 Announce Type: replace Abstract: Despite significant progress in 3D object detection, point clouds remain challenging due to sparse data, incomplete structures, and limited semantic
arXiv:2605.08074v1 Announce Type: new Abstract: Conformal prediction (CP) provides a distribution-free approach to uncertainty quantification with finite-sample guarantees. However, applying CP to gra
arXiv:2605.07357v1 Announce Type: new Abstract: Reasoning-acting frameworks enhance large language models (LLMs) by interleaving reasoning with actions for dynamic information acquisition. However, ex
arXiv:2605.07812v1 Announce Type: cross Abstract: Advanced persistent threat (APT) attacks remain difficult to detect due to their stealth, adaptability, and use of legitimate system components. Prove
arXiv:2605.07454v1 Announce Type: new Abstract: In-context learning enables large language models to adapt to new tasks, but their performance is highly sensitive to the selected examples. Finding eff
arXiv:2605.06978v1 Announce Type: cross Abstract: Skill-augmented agents increasingly rely on large reusable skill libraries, but retrieving relevant skills is not the same as presenting usable contex
arXiv:2605.07053v1 Announce Type: cross Abstract: Benchmarks like GSM8K are popular measures of mathematical reasoning, but leaderboard gains can overstate true capability due to memorization of fixed
arXiv:2605.07701v1 Announce Type: new Abstract: Classifier-Free Guidance (CFG) is a widely used mechanism for controlling diffusion-based generative models, yet its guidance scale is typically treated
arXiv:2603.19966v2 Announce Type: replace Abstract: Wind disturbances remain a key barrier to reliable autonomous navigation for lightweight quadrotors, where the rapidly varying airflow can destabili
arXiv:2602.11758v2 Announce Type: replace Abstract: Humanoid robots show promise for complex whole-body tasks in unstructured environments. Although Human-Object Interaction (HOI) has advanced, most m
arXiv:2605.07209v1 Announce Type: cross Abstract: We introduce a proxy-analyzer framework for detecting hallucinations in large language models. Instead of looking inside the generating model, our sys
arXiv:2509.08089v2 Announce Type: replace Abstract: Federated Learning (FL) enables distributed model training but is vulnerable to backdoor attacks, where malicious clients embed attacker-controlled
arXiv:2605.07250v1 Announce Type: cross Abstract: Recent advancements in visual context compression enable MLLMs to process ultra-long contexts efficiently by rendering text into images. However, we i
arXiv:2605.07211v1 Announce Type: cross Abstract: Mobile devices face diverse resource constraints and non-IID data class distributions, requiring fast on-device inference for local in-distribution (I
arXiv:2605.07397v1 Announce Type: new Abstract: After a somewhat rocky start, geometry and topology have established a foothold in machine learning. Message passing, either on graphs or higher-order c
arXiv:2605.07472v1 Announce Type: cross Abstract: Insider threat detection assumes that an adaptive insider leaves behavioral residue distinguishing them from legitimate users. We test this assumption
arXiv:2605.07766v1 Announce Type: new Abstract: Many vision applications require identity consistency beyond strict biometric recognition, especially under non-frontal views or when facial cues are mi
arXiv:2605.07973v1 Announce Type: new Abstract: Text-to-image diffusion models can generate visually stunning images, yet, controlling what appears and how it appears, remains surprisingly difficult,
arXiv:2605.06696v1 Announce Type: new Abstract: Collections of interacting AI agents can form coalitions, creating emergent group-level organization that is critical for AI safety and alignment. Howev
arXiv:2605.07512v1 Announce Type: new Abstract: Class-incremental learning aims to continuously acquire new knowledge while preserving previously learned information, thereby mitigating catastrophic f
arXiv:2605.07156v1 Announce Type: new Abstract: Precise molecular subtyping of gliomas, including isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion, directly guides surgical and therapeuti
arXiv:2605.07707v1 Announce Type: new Abstract: HTN planning is a variation of classical planning where, instead of searching for a linear sequence of actions, an algorithm decomposes higher-level tas
arXiv:2605.07254v1 Announce Type: new Abstract: Multi-view mesh reconstruction remains a core challenge in computer graphics and vision, especially for recovering high-frequency geometry from sparse o
arXiv:2605.07214v1 Announce Type: new Abstract: Large Language Models have recently emerged as a promising paradigm for automated heuristic design for NP-hard combinatorial optimization problems. Desp
arXiv:2605.07266v1 Announce Type: cross Abstract: Wireless foundation models are rapidly emerging as a key enabler of AI-native communication systems, yet a fundamental question remains unanswered: ho
arXiv:2510.01685v2 Announce Type: replace-cross Abstract: While large language models (LLMs) appear to be increasingly capable of solving compositional tasks, it is an open question whether they do so
arXiv:2605.05340v2 Announce Type: replace-cross Abstract: As Vision-Language Models (VLMs) are increasingly deployed as autonomous cognitive cores for embodied assistants, evaluating their privacy awa