3D Gaussian Splatting (3DGS) is a powerful technique for creating high-fidelity 3D assets. However, the widespread sharing and iterative modification of 3DGS models across digital platforms create pressing challenges for intellectual property protection and forensic traceability.
arXiv:2607. 18816v1 Announce Type: cross Abstract: LLM-powered agents increasingly tackle complex tasks by invoking tools, querying databases, executing code, and manipulating intermediate artifacts.
The paper introduces a self-referential retrosynthesis framework for explainable AI provenance forensics that works with fixed generative models. It uses a jointly optimized encoder-decoder pair to embed client inputs, allowing the generator to produce high-fidelity outputs while enabling round-trip consistency checks. The method eliminates the need for watermarking or generator modifications and provides interpretable evidence linking generated images back to their source inputs.
The paper introduces a self‑referential retrosynthesis framework for explainable AI provenance forensics that works with fixed‑generator generative models. It uses a jointly optimized encoder‑decoder pair to embed client inputs, generate high‑fidelity outputs, and then verify provenance by comparing the resynthesized image to the original query. The method eliminates the need for watermarking or generator modifications while providing interpretable evidence of a model’s output origin.
arXiv:2609.01383v1 Announce Type: new Abstract: Vision Language Models have demonstrated remarkable proficiency in interpreting static visual artifacts, but modern data analysis is inherently dynamic...
arXiv:2604. 02694v2 Announce Type: replace-cross Abstract: The rapid progress of generative AI has enabled increasingly realistic text-centric image forgeries, posing major challenges to document safety.
Vision Language Models have demonstrated remarkable proficiency in interpreting static visual artifacts, but modern data analysis is inherently dynamic, requiring the active interrogation of interacti...
arXiv:2606. 28386v1 Announce Type: cross Abstract: Image autoregressive models (IARs) have recently demonstrated remarkable capabilities in visual content generation, achieving photorealistic quality and rapid synthesis through the next-token prediction paradigm adapted from large language models.
arXiv:2607. 17917v1 Announce Type: new Abstract: Scientific Reasoning Graph Extraction (SRGE) aims to recover explicit links among observations, evidence, intermediate claims, and paper-level conclusions.
arXiv:2606. 16292v1 Announce Type: cross Abstract: The rapid proliferation of machine learning model reuse has transformed the AI ecosystem into a highly interconnected supply chain.
arXiv:2606. 16603v1 Announce Type: cross Abstract: LLM-based agents have demonstrated strong capabilities in data-intensive analytical tasks, yet their outputs are rarely verifiable: a reliance on linear text trajectories makes their reasoning difficult to audit.
Shared memory helps language-model agents reuse information across long workflows, yet relevant evidence may not be admissible for a particular agent or action. Because restrictions propagate through derivations, summaries can conceal private, poisoned, untrusted, or revoked sources, enabling unauthorized reads or unsafe actions.
arXiv:2608. 11022v1 Announce Type: cross Abstract: Model Cards and Data Cards have demonstrated the value of structured, human-readable documentation for machine learning artifacts, capturing their context, parameters, limitations, and intended use.