Prior work that either ignores potential shifts in the context, or considers them jointly, can lead to performance that is too conservative, especially under certain forms of reward feedback.

In this section, we describe the details of MiniRTS Environment and human dataset. "spearman" but is retrained by "cavarly". "swordman", "spearman" and "cavalry" all are Figure 2: Building units can produce different army units using resources. "workshop" can produce "archer", "dragon" and "catapult" while other Resource Units: Resource units are stationary and neutral. Resource units cannot be constructed by anyone and are created at the beginning of a game. Building Units: MiniRTS supports 6 different building unit types.

The process learns generalized Casimirs for energy and entropy guaranteed to be conserved and nondecreasing, respectively.

Farquhar, the Tech Council of Australia CEO, told ABC's 7.30 program on Tuesday: "all AI usage of mining or searching or going across data is probably illegal under Australian law and I think that hurts a lot of investment of these companies in Australia". Farquhar's claim overlooks that this is not a settled issue in the US, and could have devastating effects on creative industries. Farquhar's argument is that it is not theft of people's work unless the AI is used to "copy an artist directly" such as creating a song in their style. "I do think people would say that, hey, if people are going to sit down with a digital companion, an AI song creator and they collaboratively work with an AI to create something new to the world, that's probably fair use." Farquhar said the benefits of large language models outweigh the issues raised by AI training its data on other people's work for free.

We present two analytical formulae for estimating the sensitivity -- namely, the gradient or Jacobian -- at given realizations of an arbitrary-dimensional random vector with respect to its distributional parameters. The first formula interprets this sensitivity as partial derivatives of the inverse mapping associated with the vector of 1-D conditional distributions. The second formula, intended for optimization methods that tolerate inexact gradients, introduces a diagonal approximation that reduces computational cost at the cost of some accuracy. We additionally provide four second-order numerical algorithms to approximate both formulae when closed forms are unavailable. We performed verification and validation studies to demonstrate the correctness of these numerical algorithms and the effectiveness of the proposed formulae. A nuclear physics application showcases how our work enables uncertainty quantification and parameter inference for quantum correlation functions. Our approach differs from existing methods by avoiding the need for model fitting, knowledge of sampling algorithms, and evaluation of high-dimensional integrals. It is therefore particularly useful for sample-based inverse problems when the sampler operates as a black box or requires expensive physics simulations. Moreover, our method renders arbitrary sampling subroutines differentiable, facilitating their integration into programming frameworks for deep learning and automatic differentiation. Algorithmic details and code implementations are provided in this paper and in our open-source software DistroSA to enable reproducibility and further development.

Regulatory compliance question answering (QA) requires precise, verifiable information, and domain-specific expertise, posing challenges for Large Language Models (LLMs). In this work, we present a novel multi-agent framework that integrates a Knowledge Graph (KG) of Regulatory triplets with Retrieval-Augmented Generation (RAG) to address these demands. First, agents build and maintain an ontology-free KG by extracting subject--predicate--object (SPO) triplets from regulatory documents and systematically cleaning, normalizing, deduplicating, and updating them. Second, these triplets are embedded and stored along with their corresponding textual sections and metadata in a single enriched vector database, allowing for both graph-based reasoning and efficient information retrieval. Third, an orchestrated agent pipeline leverages triplet-level retrieval for question answering, ensuring high semantic alignment between user queries and the factual "who-did-what-to-whom" core captured by the graph. Our hybrid system outperforms conventional methods in complex regulatory queries, ensuring factual correctness with embedded triplets, enabling traceability through a unified vector database, and enhancing understanding through subgraph visualization, providing a robust foundation for compliance-driven and broader audit-focused applications.