Knowledge catalogue
arXiv:2512.01766v2 Announce Type: replace Abstract: Last-layer retraining (LLR) methods -- wherein the last layer of a neural network is reinitialized and retrained on a held-out set following ERM tra
arXiv:2507.21023v2 Announce Type: replace Abstract: Recent publications have suggested using the Shapley value for anomaly localization for sensor data systems. Using a reasonable mathematical anomaly
arXiv:2605.14605v1 Announce Type: cross Abstract: Model providers increasingly release open weights or allow users to fine-tune foundation models through APIs. Although these models are safety-aligned
arXiv:2603.24422v2 Announce Type: replace-cross Abstract: Generative Retrieval (GR) has emerged as a promising paradigm for modern search systems. Compared to multi-stage cascaded architecture, it off
arXiv:2605.15177v1 Announce Type: new Abstract: Test-time compute scaling is a primary axis for improving LLM reasoning. Existing methods primarily scale depth by extending a single reasoning trace. S
arXiv:2601.19924v2 Announce Type: replace-cross Abstract: We investigate the capabilities and scalability of Large Language Models (LLMs) in optimization modeling, a domain requiring structured reason
arXiv:2605.14374v1 Announce Type: cross Abstract: Pattern discovery in data plays a crucial role across diverse domains, including healthcare, risk assessment, and machinery maintenance. In contrast t
arXiv:2511.16964v2 Announce Type: replace-cross Abstract: Maximizing performance on available GPU hardware is an ongoing challenge for modern AI inference systems. Traditional approaches include writi
arXiv:2605.15040v1 Announce Type: new Abstract: Agentic modeling aims to transform LLMs into autonomous agents capable of solving complex tasks through planning, reasoning, tool use, and multi-turn in
arXiv:2602.01359v2 Announce Type: replace-cross Abstract: Although recent studies on time-series anomaly detection have increasingly adopted ever-larger neural network architectures such as transforme
arXiv:2505.22394v2 Announce Type: replace Abstract: We present PacTure, a novel framework for generating physically-based rendering (PBR) material textures for an untextured 3D mesh from a text descri
arXiv:2602.07519v3 Announce Type: replace Abstract: In contrast to static formalisms, computational definitions describe the operational mechanisms of a model. Simulations are an essential part of the
arXiv:2605.14135v1 Announce Type: new Abstract: We present PanoPlane, an approach for high-fidelity sparse-view indoor novel view synthesis that reconstructs closed room geometry via panoramic scene c
arXiv:2605.14277v1 Announce Type: new Abstract: Parallelization has played an instrumental role in the field of artificial intelligence (AI), drastically reducing the time taken to train and evaluate
arXiv:2605.14240v1 Announce Type: new Abstract: The recent large-scale emergence of LLMs has left an open space for dealing with their consequences, such as plagiarism or the spread of false informati
arXiv:2605.14098v1 Announce Type: cross Abstract: Chain-of-thought (CoT) reasoning with self-consistency improves performance by aggregating multiple sampled reasoning paths. In this setting, correctn
arXiv:2605.15153v1 Announce Type: cross Abstract: We present Pelican-Unified 1.0, the first embodied foundation model trained according to the principle of unification. Pelican-Unified 1.0 uses a sing
arXiv:2605.14055v1 Announce Type: cross Abstract: Parameter-Efficient Fine-Tuning (PEFT) is widely used for adapting Large Language Models (LLMs) for various tasks. Recently, there has been an increas
arXiv:2605.14779v1 Announce Type: new Abstract: We propose a model-free offline multi-step reinforcement learning (RL) algorithm, Conservative Peng's Q(lambda) (CPQL). Our algorithm adapts the Peng's
arXiv:2605.14978v1 Announce Type: new Abstract: Speculative decoding accelerates LLM inference by having a lightweight draft model propose speculative windows of candidate tokens for parallel verifica
arXiv:2511.19289v2 Announce Type: replace-cross Abstract: Estimating quantum entropies and divergences is an important problem in quantum physics, information theory, and machine learning. Quantum neu
arXiv:2605.14765v1 Announce Type: cross Abstract: Persian music, with its unique tonalities, modal systems (Dastgah), and rhythmic structures, presents significant challenges for music generation mode
arXiv:2605.13894v1 Announce Type: cross Abstract: In this work, we introduce a Tropical Axial Attention neural reasoning architecture that replaces vanilla softmax dot-product attention with max-plus
arXiv:2605.14269v1 Announce Type: cross Abstract: Generating realistic human motion is a central yet unsolved challenge in video generation. While reinforcement learning (RL)-based post-training has d
arXiv:2605.13889v1 Announce Type: cross Abstract: Stain variation across hospitals degrades histopathology models at deployment. Existing augmentation methods perturb color spaces with arbitrary hyper
arXiv:2605.14040v1 Announce Type: new Abstract: We audit the multimodal-physics evaluation pipeline end-to-end and document three undetected construction practices that distort how the field measures
arXiv:2605.14678v1 Announce Type: new Abstract: The rise of personal assistant agents, e.g., OpenClaw, highlights the growing potential of large language models to support users across everyday life a
arXiv:2605.14125v1 Announce Type: new Abstract: How do artificial neural networks bind concepts to form complex semantic structures? Here, we propose a simple neural code, whereby the existence and th
arXiv:2603.23129v2 Announce Type: replace Abstract: Godel agent realize recursive self-improvement: an agent inspects its own policy and traces and then modifies that policy in a tested loop. We intro
arXiv:2605.14297v1 Announce Type: cross Abstract: We study reinforcement learning in hybrid discrete-continuous action spaces, such as settings where the discrete component selects a regime (or index)
arXiv:2605.14002v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) embedded in agentic frameworks have transformed information retrieval from static, long context question answering into op
arXiv:2605.15164v1 Announce Type: cross Abstract: This position paper argues that behavioural assurance, even when carefully designed, is being asked to carry safety claims it cannot verify. AI govern
arXiv:2605.14294v1 Announce Type: new Abstract: Formal verification of transformers has become increasingly important due to their widespread deployment in safety-critical applications. Compared to cl
arXiv:2605.14991v1 Announce Type: cross Abstract: Ovarian cancer is the most lethal gynecologic malignancy: around 60% of patients are diagnosed at an advanced stage, with an associated 5-year surviva
arXiv:2605.14217v1 Announce Type: cross Abstract: Large language models can now be personalised efficiently at scale using parameter efficient finetuning methods (PEFTs), but serving user-specific PEF
arXiv:2605.13880v1 Announce Type: new Abstract: Agent memory is typically constructed either offline from curated demonstrations or online from post-deployment interactions. However, regardless of how
arXiv:2504.01571v2 Announce Type: replace-cross Abstract: We use hierarchical procedural rules for the generation of control maps within the stable diffusion framework to produce photo-realistic archi
arXiv:2603.04885v2 Announce Type: replace Abstract: Real-world dialogue usually unfolds as an infinite stream. It thus requires bounded-state memory mechanisms to operate within an infinite horizon. H
arXiv:2605.14758v1 Announce Type: new Abstract: History-dependent policies induced by recurrent neural networks (RNNs) rely on latent hidden state dynamics, making verification in partially observable
arXiv:2605.14815v1 Announce Type: new Abstract: Video is a rich and scalable source of 3D/4D visual observations, and camera control is a key capability for video generation models to produce geometri
arXiv:2605.14888v1 Announce Type: cross Abstract: Speech-based analysis offers a scalable and non-invasive approach for detecting cognitive decline, yet progress has been constrained by the limited av
arXiv:2504.11703v3 Announce Type: replace-cross Abstract: AI agents interact with external environments through tool calls, exposing them to attacks like indirect prompt injection that can trigger una
arXiv:2412.14291v2 Announce Type: replace-cross Abstract: We present a novel class of projected gradient (PG) methods for minimizing a smooth but not necessarily convex function over a convex compact
arXiv:2605.14561v1 Announce Type: new Abstract: Prompt engineering is crucial for effective interaction with generative artificial intelligence systems, yet existing optimisation methods often operate
arXiv:2605.14443v1 Announce Type: new Abstract: The shift toward interacting with frozen, 'black-box' Large Language Models (LLMs) has transformed prompt engineering from a heuristic exercise into a c
arXiv:2605.15110v1 Announce Type: cross Abstract: Adaptations of features commonly applied in the field of visual computing, co-occurrence matrix (COM) and run-length matrix (RLM), are proposed for th
arXiv:2605.14113v1 Announce Type: cross Abstract: While interpretable prototype networks offer compelling case-based reasoning for clinical diagnostics, their raw continuous outputs lack the semantic
arXiv:2605.14534v1 Announce Type: cross Abstract: Evaluating object removal in images and videos remains challenging because the task is inherently one-to-many, yet existing metrics frequently disagre
arXiv:2602.07441v2 Announce Type: replace-cross Abstract: Offline reinforcement learning (RL), which optimizes policies using a previously collected static dataset, is an important branch of RL. A pop
arXiv:2602.04289v2 Announce Type: replace Abstract: Modern language models are trained almost exclusively on token sequences produced by a fixed tokenizer, an external lossless compressor often over U
arXiv:2605.14045v1 Announce Type: new Abstract: Adverse weather removal (AWR) in real-world images remains challenging due to heterogeneous and unseen degradations, while distortion-driven training of
arXiv:2605.14559v1 Announce Type: new Abstract: PyCSP^3 provides a productive way to build constraint models for solving combinatorial constrained problems and export them to XCSP^3, preserving a comp
arXiv:2605.14188v1 Announce Type: cross Abstract: What does a book look like to a quantum computer? This paper takes eight classical works of the Renaissance and its late-antique inheritance -- from A
arXiv:2605.15000v1 Announce Type: cross Abstract: Premature closure, or committing to a conclusion before sufficient information is available, is a recognized contributor to diagnostic error but remai
arXiv:2605.14502v1 Announce Type: cross Abstract: Power electronics systems are increasingly exposed to cyber threats due to their integration with digital controllers and communication networks. Howe
arXiv:2605.15185v1 Announce Type: cross Abstract: Generative video models are increasingly studied as implicit world models, yet evaluating whether they produce physically plausible 3D structure and m
arXiv:2605.14235v1 Announce Type: new Abstract: We present an empirical evaluation of quantum entanglement in agent coordination within quantum multi agent reinforcement learning (QMARL). While QMARL
arXiv:2511.17367v2 Announce Type: replace Abstract: Computing worst-case robust strategies in pursuit-evasion games (PEGs) is time-consuming, especially when real-world factors like partial observabil
arXiv:2605.14026v1 Announce Type: cross Abstract: For reinforcement learning in data-scarce domains like real-world robotics, intensive data reuse enhances efficiency but induces overfitting. While pr
arXiv:2507.05193v4 Announce Type: replace-cross Abstract: Rheumatoid arthritis (RA) is a common autoimmune disease that has been the focus of research in computer-aided diagnosis (CAD) and disease mon