TL;DR
The architecture for ad hoc teamwork embeds the principles of refinement, ecological rationality, and interactive learning, leveraging the complementary strengths of knowledge-based and data-driven methods.
This work defines collaborative AI as a class of systems that combine generative exploration with autonomous action and calibrate between them based on context, uncertainty, and task demands, and identifies four required capabilities: metacognition, contextual mode-switching, uncertainty-aware action, and adaptive huma...
The increasing complexity of real-world systems has revealed the shortcomings of traditional artificial intelligence models that usually base on a set of rules or offline training. These strategies fail to work well in flexible settings where the conditions keep on changing and decisions are made on the fly. This artic...
This review synthesizes four recent studies spanning these strands, together with the broader literature on multi-agent orchestration, retrieval-augmented generation (RAG), large-language-model (LLM)-based knowledge-graph construction, and explainable AI, to build a unified picture of agentic AI as applied to intellige...
This workshop invites researchers and practitioners to share innovative ideas, explore questions, and discuss strategies to transform the impact of VIS for a future where human and AI agents co-exist.
Existing agentic reasoning systems typically rely on centralized protocols. This design introduces routing bottlenecks and static role allocations that often fail when handling complex multimodal queries. We propose DeAR (Decentralized Agentic Reasoning), a framework that shifts from central control to autonomous peer-...
UnifiedPlayers, a cooperative framework comprising a Planning Player that generates tasks, an Execution Player that produces multi-turn trajectories with Python tool calls, and an Evaluation Player that constructs executable verifiers, highlights cooperation among specialized players as a promising path toward self-enh...
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