A framework for aligning agentic AI with enterprise intent to ensure consistent scenario‑wide autonomous behavior. The post The Three Dimensions of Custom Agentic Alignment: Purpose, Principles and Practices appeared first on Towards Data Science .
arXiv:2509.12626v4 Announce Type: replace-cross Abstract: Aligning agentic AI with user intent is critical for delegating complex, socially embedded tasks, yet user preferences are often implicit, ev...
We are improving our AI systems’ ability to learn from human feedback and to assist humans at evaluating AI. Our goal is to build a sufficiently aligned AI system that can help us solve all other alignment problems.
arXiv:2608. 12372v1 Announce Type: new Abstract: AI systems are increasingly employed as decision aids, decision delegates, or autonomous decision-makers.
arXiv:2607. 14240v1 Announce Type: new Abstract: Current alignment approaches typically focus on emulating human behavior using static representations of human preferences, failing to capture the dynamic, context-dependent nature of real-world human-AI interactions.
arXiv:2608. 12355v1 Announce Type: cross Abstract: Recent progress in AI coding agent research has led to rapid improvements in agents' ability to autonomously perform complex software engineering tasks, from editing large codebases to executing long-horizon development workflows.
The article surveys AI alignment from a game-theoretic perspective, focusing on how large language models and AI agents can be aligned with complex human values in high-risk settings. It categorizes recent progress around key game-theoretic elements and addresses three main challenges: preference diversity, alignment priority, and temporal dynamics. The survey clarifies where game theory benefits current alignment methods, where its application is looser, and what remains to be tackled for robust, adaptive, and verifiable AI systems.
arXiv:2601. 14230v2 Announce Type: replace-cross Abstract: Multi-agent systems (MAS) are emerging as promising socio-collaborative companions for emotional and cognitive support.
arXiv:2601.14230v3 Announce Type: replace-cross Abstract: Multi-agent systems (MAS) are emerging as promising socio-collaborative companions for emotional and cognitive support. However, existing sys...
The paper introduces ALIGN, a lightweight framework that automatically generates enriched interfaces to reduce agent‑environment misalignment in large language model agents. By augmenting static environment information and step‑wise observations, ALIGN improves performance across diverse tasks such as embodied navigation, web browsing, and tool use, achieving up to a 45.67% success rate increase in ALFWorld. The method works without altering agent logic or environment code and generalizes across different agent architectures and LLM backbones.
arXiv:2607. 16412v1 Announce Type: new Abstract: Current benchmarks for language models primarily evaluate execution on fully specified tasks.
arXiv:2605. 12920v3 Announce Type: replace-cross Abstract: Effective collaboration between embodied agents requires more than acting in a shared environment; it demands communication grounded in each agent's evolving understanding of the world.