Graph-based Retrieval-Augmented Generation (RAG) has shown great capability in enhancing Large Language Model (LLM)'s answer with an external knowledge base. Compared to traditional RAG, it introduces a graph as an intermediate representation to capture better structured relational knowledge in the corpus, elevating the precision and comprehensiveness of generation results. However, developers usually face challenges in analyzing the effectiveness of GraphRAG on their dataset due to GraphRAG's complex information processing pipeline and the overwhelming amount of LLM invocations involved during graph construction and query, which limits GraphRAG interpretability and accessibility. This research proposes a visual analysis framework that helps RAG developers identify critical recalls of GraphRAG and trace these recalls through the GraphRAG pipeline. Based on this framework, we develop XGraphRAG, a prototype system incorporating a set of interactive visualizations to facilitate users' analysis process, boosting failure cases collection and improvement opportunities identification. Our evaluation demonstrates the effectiveness and usability of our approach. Our work is open-sourced and available at https://github.com/Gk0Wk/XGraphRAG.

This report surveys the landscape of potential security threats from malicious uses of AI, and proposes ways to better forecast, prevent, and mitigate these threats. After analyzing the ways in which AI may influence the threat landscape in the digital, physical, and political domains, we make four high-level recommendations for AI researchers and other stakeholders. We also suggest several promising areas for further research that could expand the portfolio of defenses, or make attacks less effective or harder to execute. Finally, we discuss, but do not conclusively resolve, the long-term equilibrium of attackers and defenders.

Large Language Models produce sequences learned as statistical patterns from large corpora. In order not to reproduce corpus biases, after initial training models must be aligned with human values, preferencing certain continuations over others. Alignment, which can be viewed as the superimposition of normative structure onto a statistical model, reveals a conflicted and complex interrelationship between language and technology. This relationship shapes theories of language, linguistic practice and subjectivity, which are especially relevant to the current sophistication in artificially produced text. We examine this practice of structuration as a two-way interaction between users and models by analysing how ChatGPT4 redacts perceived `anomalous' language in fragments of Joyce's Ulysses and the new linguistic practice of prompt engineering. We then situate this alignment problem historically, revisiting earlier postwar linguistic debates which counterposed two views of meaning: as discrete structures, and as continuous probability distributions. We discuss the largely occluded work of the Moscow Linguistic School, which sought to reconcile this opposition. Our attention to the Moscow School and later related arguments by Searle and Kristeva casts the problem of alignment in a new light: as one involving attention to the social structuration of linguistic practice, including structuration of anomalies that, like the Joycean text, exist in defiance of expressive conventions. These debates around the communicative orientation toward language can help explain some of the contemporary behaviours and interdependencies that take place between users and LLMs.

A farmer in Niger tends to a tree sprout growing among his millet crop.Tony Rinaudo/World Vision Australia This story was originally published by Yale Environment 360 and is reproduced here as part of the Climate Desk collaboration. For decades, there have been reports of the deforestation in Africa. And they are true--the continent's forests are disappearing, lost mainly to expanding agriculture, logging, and charcoal-making. Maybe not, according to new satellite data analyzed by artificial intelligence and a growing body of on-the-ground studies. This new research is finding ever more trees outside forests, many of them nurtured by farmers and sprouting on their previously treeless fields.

We approach autonomous drone-based reforestation with a collaborative multi-agent reinforcement learning (MARL) setup. Agents can communicate as part of a dynamically changing network. We explore collaboration and communication on the back of a high-impact problem. Forests are the main resource to control rising CO2 conditions. Unfortunately, the global forest volume is decreasing at an unprecedented rate. Many areas are too large and hard to traverse to plant new trees. To efficiently cover as much area as possible, here we propose a Graph Neural Network (GNN) based communication mechanism that enables collaboration. Agents can share location information on areas needing reforestation, which increases viewed area and planted tree count. We compare our proposed communication mechanism with a multi-agent baseline without the ability to communicate. Results show how communication enables collaboration and increases collective performance, planting precision and the risk-taking propensity of individual agents.