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arXiv:2602.18880v1 Announce Type: new Abstract: Advances in image tampering techniques, particularly generative models, pose significant challenges to media verification, digital forensics, and public trust. Existing...
arXiv:2602.18881v1 Announce Type: new Abstract: This work investigates the bit-error-rate (BER) performance of turbo coded orthogonal frequency division multiplexed - offset quadrature amplitude modulated (OFDM-...
arXiv:2602.18882v1 Announce Type: new Abstract: We present SceneTok, a novel tokenizer for encoding view sets of scenes into a compressed and diffusable set of unstructured...
arXiv:2602.18884v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs), particularly smaller, deployable variants, exhibit a critical deficiency in understanding temporal and procedural visual data,...
arXiv:2602.18885v1 Announce Type: new Abstract: Predicting high-dimensional transcriptional responses to genetic perturbations is challenging due to severe experimental noise and sparse gene-level effects. Existing methods...
arXiv:2602.18886v1 Announce Type: new Abstract: Reconstructing and simulating dynamic 3D scenes with both visual realism and physical consistency remains a fundamental challenge. Existing neural representations,...
arXiv:2602.18887v1 Announce Type: new Abstract: The end-to-end (E2E) paradigm, which maps sensor inputs directly to driving decisions, has recently attracted significant attention due to its...
arXiv:2602.18888v1 Announce Type: new Abstract: Adapters have become a widely adopted strategy for efficient fine-tuning of large pretrained models, particularly in resource-constrained settings. However, their...
arXiv:2602.18891v1 Announce Type: new Abstract: Advances in large language models (LLMs) are rapidly transforming scientific work, yet empirical evidence on how these systems reshape research...
arXiv:2602.18896v1 Announce Type: new Abstract: Vector Quantization (VQ) underpins many modern generative frameworks such as VQ-VAE, VQ-GAN, and latent diffusion models. Yet, it suffers from...
arXiv:2602.18897v1 Announce Type: new Abstract: Knowledge Graph(KG) has gained traction as a machine-readable organization of real-world knowledge for analytics using artificial intelligence systems. Graph Neural...
arXiv:2602.18900v1 Announce Type: new Abstract: Privacy preserving machine learning deployments in sensitive deep learning applications; from medical imaging to autonomous systems; increasingly require combining multiple...
arXiv:2602.18903v1 Announce Type: new Abstract: This paper presents SCHEMA (Structured Components for Harmonized Engineered Modular Architecture), a structured prompt engineering methodology specifically developed for Google...
arXiv:2602.18904v1 Announce Type: new Abstract: Vector-quantized autoencoders deliver high-fidelity latents but suffer inherent flaws: the quantizer is non-differentiable, requires straight-through hacks, and is prone to...
arXiv:2602.18905v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong capabilities in complex reasoning tasks, yet their decision-making processes remain difficult to interpret....
arXiv:2602.18906v1 Announce Type: new Abstract: Structure-from-Motion (SfM) is a fundamental 3D vision task for recovering camera parameters and scene geometry from multi-view images. While recent...
arXiv:2602.18907v1 Announce Type: new Abstract: Recent generative recommendation frameworks have demonstrated remarkable scaling potential by reformulating item prediction as autoregressive Semantic ID (SID) generation. However,...
arXiv:2602.18910v1 Announce Type: new Abstract: Density-adaptive domain discretization is essential for high-utility privacy-preserving analytics but remains challenging under Local Differential Privacy (LDP) due to the...
arXiv:2602.18914v1 Announce Type: new Abstract: The Model Context Protocol (MCP) has rapidly become a de facto standard for connecting LLM-based agents with external tools via...
arXiv:2602.18916v1 Announce Type: new Abstract: Legal reasoning requires not only high accuracy but also the ability to justify decisions through verifiable and contestable arguments. However,...
arXiv:2602.18918v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used as scientific copilots, but evidence on their role in research-level mathematics remains limited,...
arXiv:2602.18920v1 Announce Type: new Abstract: The application of Large Language Models (LLMs) in accelerating scientific discovery has garnered increasing attention, with a key focus on...
arXiv:2602.18921v1 Announce Type: new Abstract: To ensure decidability and consistency of its type theory, a proof assistant should only accept terminating recursive functions and productive...
arXiv:2602.18922v1 Announce Type: new Abstract: Personal AI agents incur substantial cost via repeated LLM calls. We show existing caching methods fail: GPTCache achieves 37.9% accuracy...