Knowledge catalogue
Incredibly annoying when Fable has a forbidden thought in the middle of a long-running project and kills it. Apparently this page of references in one of my papers makes Fable wonder about something t
arXiv:2310.07895v2 Announce Type: replace Abstract: This paper presents a method to efficiently classify the gastroenterologic section of images derived from Video Capsule Endoscopy (VCE) studies by e
arXiv:2607.08012v1 Announce Type: cross Abstract: This paper studies an online variant of the assistance games framework, where an informed agent and an uninformed agent repeatedly interact over T tim
arXiv:2607.08444v1 Announce Type: cross Abstract: In this paper, we study quantile-based distributional reinforcement learning from the perspective of statistical efficiency. We focus on distributiona
arXiv:2607.08027v1 Announce Type: cross Abstract: This paper proposes an improved structured pruning method for large language models (LLMs) that addresses key challenges in adapting Adaptive Feature
arXiv:2607.08198v1 Announce Type: new Abstract: This paper studies the problem of learning a joint distribution from marginal observations, which is inherently ill-posed due to the ambiguity of feasib
arXiv:2607.06768v1 Announce Type: cross Abstract: This paper investigates over-the-air federated learning (AirFL) in wireless systems where the access point is equipped with a multi-waveguide pinching
arXiv:2602.23128v2 Announce Type: replace Abstract: Generalization bounds for deep learning models are typically vacuous, not computable or restricted to specific model classes. In this paper, we tack
arXiv:2607.06121v1 Announce Type: cross Abstract: In this paper, we investigate whether a model-free RL agent can identify and exploit price manipulation opportunities more effectively than a traditio
arXiv:2607.06646v1 Announce Type: cross Abstract: This paper introduces Diffusion Semi-Relaxed Fused Gromov-Wasserstein (DsrFGW), a novel method for graph comparison that unifies node features and str
arXiv:2607.06976v1 Announce Type: cross Abstract: In this paper, we propose an efficient hybrid least squares/gradient descent (LSGD) method for MIONets to accelerate training. This method generalizes
arXiv:2607.07136v1 Announce Type: new Abstract: This paper presents a Spatiotemporal Tube (STT)-based control framework for general unknown nonlinear Euler-Lagrange (EL) systems subject to input const
arXiv:2607.06719v1 Announce Type: cross Abstract: This paper introduces a graph-theoretic approach for predicting market regimes in foreign exchange (FX) currency prices. Specifically, the proposed mo
arXiv:2607.07304v1 Announce Type: new Abstract: In this paper we first study the problem of generalized linear bandit (GLB) under heavy-tailed noise. The characteristics of heavy-tailed distributions
arXiv:2607.06999v1 Announce Type: cross Abstract: This paper presents a physics-guided machine learning (PGML) framework for fuel density prediction, integrating physics constraints and domain knowled
arXiv:2607.06633v1 Announce Type: cross Abstract: In this paper, we address the problem of multimodal federated learning with missing modality. Existing methods utilize an additional public dataset or
arXiv:2607.07192v1 Announce Type: new Abstract: In this paper, we study Single-Domain Generalized Object Detection (Single-DGOD), which aims to transfer a detector trained on a single source domain to
arXiv:2603.04249v2 Announce Type: replace Abstract: In this paper, we introduce RoboLight, the first real-world robotic manipulation dataset capturing synchronized episodes under systematically varied
arXiv:2607.06569v1 Announce Type: cross Abstract: This paper proposes an unsupervised workflow to pseudo-label extracellular spikes from human brain slice MEA recordings into two putative cell types:
arXiv:2507.14177v2 Announce Type: replace-cross Abstract: This paper aims to understand the training solution, which is obtained by the back-propagation algorithm, of two-layer neural networks whose h
arXiv:2607.07099v1 Announce Type: new Abstract: Squint and cataract are major ocular disorders that majorly affect visual perception and interaction capability. This paper proposes a real-time video-b
arXiv:2607.05985v1 Announce Type: new Abstract: This paper presents a black-box evaluation framework to systematically assess the ability of Large Language Models (LLMs) to generate Design Structure M
arXiv:2607.06364v1 Announce Type: new Abstract: Mapping cloud security controls to technical metrics is currently a manual process. This paper proposes domain adaptation of Sentence Transformer models
arXiv:2511.06229v3 Announce Type: replace Abstract: This paper focuses on dynamic origin-destination matrix estimation (DODE), a crucial calibration process necessary for the effective application of
arXiv:2607.06296v1 Announce Type: cross Abstract: This paper presents the design and evaluation of a maintainable hybrid generative architecture for automated music harmony generation from melody. The
ikeabench Furniture assembly is the task everyone name-drops and nobody actually attempts at real scale. Every demo I have seen is a scaled down IKEA leg or a single arm on a toy chair. This paper doe
arXiv:2506.09276v4 Announce Type: replace-cross Abstract: This paper presents a state representation framework for Markov decision processes (MDPs) that can be learned solely from state trajectories,
arXiv:2603.11044v2 Announce Type: replace Abstract: In this paper, we propose LingDT-VL-OCR, a document parsing system tailored to financial-domain documents, transforming ultra-long financial PDFs in
arXiv:2607.05704v1 Announce Type: cross Abstract: Large language models (LLMs) can generate neural-network modifications, but unrestricted generation is often invalid or harmful. This paper studies a
arXiv:2607.06123v1 Announce Type: new Abstract: This paper proposes a novel method that extends the Model Predictive Path Integral (MPPI) method with motion primitives for additional structured sampli
arXiv:2607.06145v1 Announce Type: new Abstract: In this paper, we define the quantity of prompting complexity: for a fixed instruction-tuned language model, what is the shortest plausible prompt that
arXiv:2408.00001v2 Announce Type: replace-cross Abstract: Visual diffusion models have revolutionized the field of creative AI, producing high-quality and diverse content. However, they inevitably mem
arXiv:2607.06544v1 Announce Type: new Abstract: As Artificial Intelligence (AI) makes inroads into different parts of the Indian subcontinent, there is significant interest in studying how AI impacts
arXiv:2607.05450v1 Announce Type: cross Abstract: This paper explores the 'Granularity Paradox' in time-series forecasting, wherein finer temporal disaggregation (e.g., Monthly to Weekly/Daily) improv
arXiv:2504.11907v3 Announce Type: replace Abstract: Autonomous exploration of cluttered environments requires efficient exploration strategies that guarantee safety against potential collisions with u
arXiv:2607.03454v1 Announce Type: cross Abstract: In this paper, we propose Adversarial Dynamics Priors (ADP) for perturbation-resilient humanoid locomotion control. Existing motion prior-based method
arXiv:2312.13771v3 Announce Type: replace Abstract: Recent advancements in large language models (LLMs) have led to the creation of intelligent agents capable of performing complex tasks. This paper i
arXiv:2607.05150v1 Announce Type: new Abstract: In this paper, we introduce Claim-Level Rubric Rewards (CuRe), a structured reward framework designed to address the reward-design bottleneck in reinfor
arXiv:2602.20512v2 Announce Type: replace Abstract: This paper investigates a problem called Multi-Agent Path Finding with Elevators (MAPF-E), which seeks conflict-free paths for multiple agents from
arXiv:2607.03135v1 Announce Type: cross Abstract: Maintaining consistency between architectural design and runtime-observed behavior is challenging in long-lived safety-critical firmware. This paper p
arXiv:2606.05242v2 Announce Type: replace-cross Abstract: Stochastic gradient Langevin algorithms often use tamed denominators to stabilize superlinear drifts. This paper shows that when the denominat
arXiv:2607.04615v1 Announce Type: new Abstract: This paper presents a novel acoustic-visual-inertial odometry solution leveraging a continuous-time trajectory estimation framework for unmanned underwa
arXiv:2501.11086v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have shown tremendous promise in automated software engineering. In this paper, we investigate LLMs for just-in-t
arXiv:2607.03209v1 Announce Type: new Abstract: This paper introduces a fast method for high-quality 3D Gaussian Splatting (3DGS) reconstruction without traditional Structure-from-Motion (SfM). The pr
arXiv:2607.04315v1 Announce Type: cross Abstract: This paper studies the problem of identifying the treatment that maximizes the expected natural direct potential outcome (NDPO), which captures the po
arXiv:2607.04675v1 Announce Type: new Abstract: This paper presents the IEEE International Conference on Multimedia and Expo (ICME) 2026 Grand Challenge on Cross-Scenario Defect Detection and Fine-Gra
arXiv:2602.19143v2 Announce Type: replace Abstract: This paper studies simple transformers trained on a high-order Markov chain, where the model must incorporate information from multiple past positio
arXiv:2503.06643v2 Announce Type: replace-cross Abstract: In this paper, we tackle a critical challenge in model evaluation: how to keep code benchmarks useful when models might have already seen them
arXiv:2607.03482v1 Announce Type: new Abstract: In this paper, we present the Invariant-Variant Disentangled State-Space Model (IVD-SSM), our submission to SemEval-2026 Task 4 on Narrative Story Simil
arXiv:2607.05263v1 Announce Type: new Abstract: This paper concerns the problem of unsupervised temporal action segmentation for long, untrimmed videos. Recent successful approaches follow a joint rep
arXiv:2607.04319v1 Announce Type: new Abstract: A companion paper showed that a transformer's feed-forward layer can be rebuilt from explicit fuzzy set operations - intersection, set-difference, and a
arXiv:2607.02703v1 Announce Type: cross Abstract: In this paper, we describe LLMoxie, an institutional AI platform whose three-tiered architecture supports multi-cloud and on-premise inference, a Lite
arXiv:2607.04597v1 Announce Type: new Abstract: In this paper, we study the universal approximation property of residual neural networks, and obtain some new results. For input and output dimensions d
arXiv:2405.07312v5 Announce Type: replace-cross Abstract: This paper presents a novel Koopman composition operator representation framework for control systems in reproducing kernel Hilbert spaces (RK
arXiv:2607.04332v1 Announce Type: new Abstract: In this paper, we consider the setting where large language models (LLMs) are trained using reinforcement learning (RL) to simultaneously improve reason
arXiv:2607.02741v1 Announce Type: cross Abstract: This paper develops an Optimality-Informed Neural Network (OINN) approach for the energy-optimal, free-final-time powered descent of a lunar lander fr
arXiv:2607.02525v1 Announce Type: cross Abstract: We present PEEK, a lightweight scheduling and eviction framework for both online (streaming) and offline (batch) LLM serving; this paper focuses on th
arXiv:2607.03006v1 Announce Type: cross Abstract: Text-rich image models can now design poster-scale layouts, but we lack ways to measure whether they honor scientific communication contracts: legible
arXiv:2511.05730v2 Announce Type: replace Abstract: In this paper, a learning framework is introduced which incorporates principles of probabilistic inference, variational optimization, and geometry-p
arXiv:2607.05007v1 Announce Type: new Abstract: This paper introduces a quantum-inspired computational framework for harmonic decision-making in music. The proposed approach formulates harmonization a