Knowledge catalogue
arXiv:2511.09173v3 Announce Type: replace-cross Abstract: External trajectories can improve offline decision-sequence learning, but dynamics shift may make some source subsequences inconsistent with t
arXiv:2608.03763v1 Announce Type: new Abstract: Zero-shot 3D visual grounding aims to localize specific objects based on textual descriptions and 3D visual input. However, the effectiveness of existin
arXiv:2608.03291v1 Announce Type: cross Abstract: Chain-of-thought (CoT) reasoning improves large language model (LLM) performance while also providing an observable interface to the model's reasoning
I've been freelancing for over a decade now, and I can't stress enough the importance of thorough investigation when dealing with strange software behaviors. Recently, I ran into an issue where my mul
arXiv:2608.03137v1 Announce Type: new Abstract: Large language model (LLM) agents must retain reusable information, control a bounded active context, and recover earlier evidence during long-horizon i
arXiv:2608.03918v1 Announce Type: cross Abstract: Efficient long-video understanding requires vision--language models (VLMs) to reason over a small number of frames selected as sparse visual evidence.
arXiv:2608.02940v1 Announce Type: new Abstract: A reproducible compression statistic can still select the wrong candidate. A dense pruning score with 0.906 split-half reliability predicted a 16.1% gai
arXiv:2608.03902v1 Announce Type: new Abstract: Resource constraints on UAV platforms have driven a paradigm shift in aerial tracking, from pursuing performance toward balancing accuracy with efficien
arXiv:2608.01463v2 Announce Type: replace Abstract: Multi-agent debate commonly exchanges complete rationales even when disagreements concern only a few intermediate claims. We introduce Localized Mul
arXiv:2608.00337v1 Announce Type: new Abstract: Deploying robot foundation models at scale is the next step towards realizing the potential of general-purpose robots. However, Vision-Language-Action (
arXiv:2603.25129v2 Announce Type: replace Abstract: While 3D Vision Foundation Models (3DVFMs) have demonstrated remarkable zero-shot capabilities in visual geometry estimation, their direct applicati
arXiv:2608.02250v1 Announce Type: new Abstract: Federated learning (FL) is a popular distributed learning framework where multiple clients perform local training and a server aggregates the locally up
arXiv:2501.13084v2 Announce Type: replace Abstract: Active inverse source localization and characterization (ISLC) in dynamic fields requires sequential decision making under partial observability, wh
arXiv:2608.02016v1 Announce Type: new Abstract: Sparse voxel grids preserve the spatial structure needed for detailed 3D reconstruction, but their memory still grows rapidly with resolution as active
arXiv:2608.00442v1 Announce Type: new Abstract: Medical anomaly detection identifies abnormal images and localizes lesions under scarce supervision while generalizing across organs and modalities. Exi
arXiv:2608.00633v1 Announce Type: cross Abstract: Expressive piano performance depends on the sustain (damper) pedal, operated by foot, excluding players who cannot readily use their feet: wheelchair
arXiv:2608.00720v1 Announce Type: cross Abstract: Mapping neural networks to FPGAs enables low-latency, energy-efficient inference, particularly for lookup table (LUT)-based models that eliminate mult
arXiv:2506.18164v2 Announce Type: replace Abstract: Cross-view masked autoencoding has emerged as a powerful pretext task for learning dense correspondences, which are essential for applications such
arXiv:2608.01536v1 Announce Type: cross Abstract: Large Language Models (LLMs) increasingly rely on sparsity to reduce inference cost, but most prior work targets a single sparsity source-either weigh
arXiv:2608.00078v1 Announce Type: new Abstract: Deploying convolutional neural networks generated by large language models (LLMs) on real mobile hardware requires more than GPU validation accuracy: IN
arXiv:2608.02495v1 Announce Type: new Abstract: Despite the remarkable progress over the past decades, accurately identifying small objects remains challenging because of their insufficient visual cue
arXiv:2608.01638v1 Announce Type: new Abstract: Egocentric visual grounding requires high-resolution inputs to localize small objects. However, scaling Multimodal Large Language Models to this domain
arXiv:2409.05885v2 Announce Type: replace Abstract: Characterizing nonlinear flame response is critical for predicting thermoacoustic instabilities in propulsion combustors, yet obtaining a comprehens
arXiv:2608.02168v1 Announce Type: new Abstract: Credit risk detection, particularly mitigating individual fraud, is crucial for maintaining the stability of digital financial ecosystems. Accurately id
arXiv:2608.01359v1 Announce Type: new Abstract: Outcome-based reinforcement learning enables search-augmented language agents to learn from verifiable final answers, but its trajectory-level credit ca
arXiv:2608.01958v1 Announce Type: new Abstract: 4D Gaussian Splatting (4DGS) excels in dynamic 3D reconstruction and real-time novel view synthesis via efficient 4D Gaussian representations and parall
arXiv:2608.00635v1 Announce Type: new Abstract: We present FlowPilot, a compact world-action model for real-time onboard UAV navigation from depth. Unlike map-then-optimize pipelines that require loca
arXiv:2608.00695v1 Announce Type: new Abstract: Zero-shot anomaly detection (ZSAD) aims to detect anomalies and localize defective regions in unseen target domains without target training data. Recent
arXiv:2608.00970v1 Announce Type: new Abstract: Object-oriented aerial vision-and-language navigation (VLN) requires searching for a described target and landing on it precisely, under long-horizon an
arXiv:2608.01552v1 Announce Type: cross Abstract: Quadratic Gradient (QG) is a Newton-type optimization framework that bridges first-order gradient descent and second-order optimization by incorporati
arXiv:2603.07564v2 Announce Type: replace Abstract: Satellite video object tracking (SVOT) remains fundamentally challenging due to texture scarcity, arbitrary rotation, aspect ratio changes, and seve
I would love some feedback on this work in progress. Create, dialog with, mash up, and evolve spirit guides based on real concepts spanning psychological, philosophical, and religious origins. Honestl
arXiv:2603.13418v2 Announce Type: replace Abstract: Structured pruning is widely applied to compress large language models (LLMs), but its performance depends heavily on how neuron importance is estim
First I want to say how evil Synthid sounds. Did the creator want it to be like the Sith Lord or something?! For text generation the claim is it chooses word outputs that can be watermarked easily? Ma
This is something that was spoken here and there, and now it is like writing on the wall. The main additional point is that China has created an independent supply chain. Starting from raw materials a
I maintain llm-checker, and I’ve added structural model-file validation for Ollama. Ollama stores downloaded models as local blobs. If one is truncated, malformed, or has invalid internal offsets, you
arXiv:2608.00075v1 Announce Type: new Abstract: Accelerated magnetic resonance imaging (MRI) aims to recover the k-space signal from acquired measurements, where accurate estimation of missing samples
arXiv:2608.01395v1 Announce Type: new Abstract: We evaluate large language models (LLMs) as language agents playing goal-directed dialogue games in self-play across 30 languages: the 24 official EU la
arXiv:2608.02216v1 Announce Type: new Abstract: Vision-language models (VLMs) such as CLIP exhibit remarkable zero-shot capabilities, yet their performance frequently degrades sharply under unexpected
arXiv:2603.03672v2 Announce Type: replace Abstract: The Shapley value provides a principled foundation for data valuation, but exact computation is #P-hard due to the exponential coalition space. Exis
arXiv:2608.00976v1 Announce Type: new Abstract: Fine-grained visual representations are essential for medical image analysis, particularly when diagnostically relevant evidence is subtle and spatially
arXiv:2608.00406v1 Announce Type: cross Abstract: Accurate indoor localization is essential for emerging applications in robotic navigation and search and rescue. While classical methods typically foc
arXiv:2608.02014v1 Announce Type: new Abstract: Cross-embodiment dexterous grasping aims to synthesize stable grasps across heterogeneous multi-fingered hands with little or no embodiment-specific tun
arXiv:2608.00893v1 Announce Type: new Abstract: Robust to intensity inhomogeneity, the local Chan--Vese (LCV) model extends the classical Chan--Vese (CV) image segmentation method by incorporating loc
arXiv:2608.02069v1 Announce Type: cross Abstract: Developing deployable locomotion policies through conventional reinforcement learning often requires complex reward engineering and expensive training
arXiv:2608.02258v1 Announce Type: new Abstract: Rapidly evolving Generative AI enables sophisticated visual text manipulations that increasingly evade current forensic detectors. Existing discriminati
arXiv:2608.00815v1 Announce Type: new Abstract: The 15-minute city promotes access to everyday services within a short walk or bicycle ride, but its relationship with observed mobility remains difficu
arXiv:2601.02457v2 Announce Type: replace Abstract: Current foundation models for 3D shapes excel at global tasks (retrieval, classification) but transfer poorly to local part-level reasoning. Recent
arXiv:2608.00646v1 Announce Type: new Abstract: Screen content images are generally composed of texts and graphics. Compared to natural images, these man-made images contain a large quantity of sharp
arXiv:2608.02575v1 Announce Type: new Abstract: Diffusion models rely on stochastic inputs, yet on finite-precision hardware, the 'randomness' they consume is realized as deterministic numerical orbit
arXiv:2608.00508v1 Announce Type: new Abstract: Object detection and segmentation in three-dimensional medical images is a very active area of research. However, most proposed deep learning models car
arXiv:2608.00444v1 Announce Type: new Abstract: Unsupervised medical anomaly detection learns normal anatomical patterns from healthy training images and identifies deviations at test time. Reconstruc
arXiv:2608.01556v1 Announce Type: new Abstract: Large language models are increasingly aligned to human preferences via reward modeling, but user preference data are sensitive and often cannot be cent
arXiv:2608.01311v1 Announce Type: new Abstract: Generating long-form content from extensive internal reports remains challenging for organizations operating under strict privacy and security constrain
arXiv:2608.01973v1 Announce Type: cross Abstract: Existing indoor layout generators produce globally plausible layouts yet may retain local violations such as collisions, out-of-bounds placements, obs
arXiv:2603.21611v2 Announce Type: replace Abstract: 3D fragment reassembly estimates the rigid pose of each fragment to recover a complete object from unordered point clouds or meshes. The task become
arXiv:2608.01670v1 Announce Type: new Abstract: This paper answers the one-dimensional local root anti-concentration questions posed by Balcan, Pegden, and Sharma in the context of online optimization
arXiv:2509.17920v2 Announce Type: replace Abstract: Current deep learning models for electroencephalography (EEG) are often task-specific and depend on large labeled datasets, limiting their adaptabil
arXiv:2608.00502v1 Announce Type: new Abstract: Affordance grounding aims to localize the functional region for interaction, such as the handle to grasp or the button to press, rather than the whole o
arXiv:2608.01352v1 Announce Type: new Abstract: Estimating causal effects from real-world spatiotemporal data is challenging due to hidden confounders and interference. Standard causal identification