AI Research
Humanities-focused AI center launches at UC
The center is focused on the ethical and humanities-based study of AI rather than the technology itself, assistant professor Andre Curtis-Trudel, who will direct the center, explained to listeners.
“Contemporary AI systems can be used to generate misinformation. They make it, in some cases, easier to perpetrate certain kinds of cybercrimes. In some cases, they can exacerbate mental health crises, and of course, there are some questions about the environmental impacts of AI,” said Curtis-Trudel.
Faculty, he said, are set to lead research and workshops addressing issues such as misinformation, cybercrime, mental health impacts, and environmental concerns tied to AI use.
The center will partner with community organizations to host events, create K-12 lesson plans, and launch an AI ethics summer camp for high school students in 2026. Public engagement is also a key goal, with forums planned to gather input on how AI should be used responsibly in everyday life.
The initiative —led by College of Arts & Sciences Dean James Mack — is supported by nearly $500,000 from the National Endowment for the Humanities and $165,000 from UC’s College of Arts and Sciences.
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arXiv:2509.09597v1 Announce Type: cross
Abstract: Graph alignment-the problem of identifying corresponding nodes across multiple graphs-is fundamental to numerous applications. Most existing unsupervised methods embed node features into latent representations to enable cross-graph comparison without ground-truth correspondences. However, these methods suffer from two critical limitations: the degradation of node distinctiveness due to oversmoothing in GNN-based embeddings, and the misalignment of latent spaces across graphs caused by structural noise, feature heterogeneity, and training instability, ultimately leading to unreliable node correspondences. We propose a novel graph alignment framework that simultaneously enhances node distinctiveness and enforces geometric consistency across latent spaces. Our approach introduces a dual-pass encoder that combines low-pass and high-pass spectral filters to generate embeddings that are both structure-aware and highly discriminative. To address latent space misalignment, we incorporate a geometry-aware functional map module that learns bijective and isometric transformations between graph embeddings, ensuring consistent geometric relationships across different representations. Extensive experiments on graph benchmarks demonstrate that our method consistently outperforms existing unsupervised alignment baselines, exhibiting superior robustness to structural inconsistencies and challenging alignment scenarios. Additionally, comprehensive evaluation on vision-language benchmarks using diverse pretrained models shows that our framework effectively generalizes beyond graph domains, enabling unsupervised alignment of vision and language representations.
Source link
View a PDF of the paper titled Robix: A Unified Model for Robot Interaction, Reasoning and Planning, by Huang Fang and 8 other authors
Abstract:We introduce Robix, a unified model that integrates robot reasoning, task planning, and natural language interaction within a single vision-language architecture. Acting as the high-level cognitive layer in a hierarchical robot system, Robix dynamically generates atomic commands for the low-level controller and verbal responses for human interaction, enabling robots to follow complex instructions, plan long-horizon tasks, and interact naturally with human within an end-to-end framework. Robix further introduces novel capabilities such as proactive dialogue, real-time interruption handling, and context-aware commonsense reasoning during task execution. At its core, Robix leverages chain-of-thought reasoning and adopts a three-stage training strategy: (1) continued pretraining to enhance foundational embodied reasoning abilities including 3D spatial understanding, visual grounding, and task-centric reasoning; (2) supervised finetuning to model human-robot interaction and task planning as a unified reasoning-action sequence; and (3) reinforcement learning to improve reasoning-action consistency and long-horizon task coherence. Extensive experiments demonstrate that Robix outperforms both open-source and commercial baselines (e.g., GPT-4o and Gemini 2.5 Pro) in interactive task execution, demonstrating strong generalization across diverse instruction types (e.g., open-ended, multi-stage, constrained, invalid, and interrupted) and various user-involved tasks such as table bussing, grocery shopping, and dietary filtering.
Submission history
From: Wei Li [view email]
[v1]
Mon, 1 Sep 2025 03:53:47 UTC (29,592 KB)
[v2]
Thu, 11 Sep 2025 12:40:54 UTC (29,592 KB)
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