arXiv:2601.03385v1 Announce Type: new Abstract: The rapid adoption of synthetic data for training Large Language Models (LLMs) has introduced the technical challenge of “model collapse”-a degenerative process where recursive training on model-generated content leads to a contraction of distributional variance…
arXiv:2601.00834v2 Announce Type: replace Abstract: Simulating nonlinear reaction-diffusion dynamics on complex, non-Euclidean manifolds remains a fundamental challenge in computational morphogenesis, constrained by high-fidelity mesh generation costs and symplectic drift in discrete time-stepping schemes. This study introduces the Intrinsic-Metric Physics-Informed Neural…
arXiv:2601.03410v1 Announce Type: new Abstract: Molecular subtyping of PDAC into basal-like and classical has established prognostic and predictive value. However, its use in clinical practice is limited by cost, turnaround time, and tissue requirements, thereby restricting its application in the…
arXiv:2501.01993v2 Announce Type: replace-cross Abstract: This paper proposes PoseLecTr, a graph-based encoder-decoder framework that integrates a novel Legendre convolution with attention mechanisms for six-degree-of-freedom (6-DOF) object pose estimation from monocular RGB images. Conventional learning-based approaches predominantly rely on grid-structured convolutions,…
arXiv:2601.03413v1 Announce Type: new Abstract: This study highlights the potential of image-based reinforcement learning methods for addressing swarm-related tasks. In multi-agent reinforcement learning, effective policy learning depends on how agents sense, interpret, and process inputs. Traditional approaches often rely on…
arXiv:2510.06640v2 Announce Type: replace-cross Abstract: State Space Models (SSMs) have recently emerged as efficient alternatives to Transformer-Based Models (TBMs) for long-sequence processing with linear scaling, yet how contextual information flows across layers in these architectures remains understudied. We present the…
arXiv:2601.03420v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly deployed in safety-critical domains, rigorously evaluating their robustness against adversarial jailbreaks is essential. However, current safety evaluations often overestimate robustness because existing automated attacks are limited by restrictive…
arXiv:2601.03786v1 Announce Type: cross Abstract: Training data influence estimation methods quantify the contribution of training documents to a model’s output, making them a promising source of information for example-based explanations. As humans cannot interpret thousands of documents, only a small…
arXiv:2601.03424v1 Announce Type: new Abstract: Behavioral benchmarks tell us textit{what} a model does, but not textit{how}. We introduce a training-free mechanistic probe using attention-graph spectra. Treating each layer as a token graph, we compute algebraic connectivity ($lambda_2$), smoothness, and spectral…
arXiv:2601.03905v1 Announce Type: cross Abstract: Agents built on vision-language models increasingly face tasks that demand anticipating future states rather than relying on short-horizon reasoning. Generative world models offer a promising remedy: agents could use them as external simulators to foresee…
