AgentPanel: Toward a New Paradigm for Human--AI Collaboration in Exploring Scientific Questions

Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systems

This work argues that studying AI Scientists as human-agent systems (HAS) is both underexplored and undervalued, and calls for new research that adopts the HAS lens to develop mathematical frameworks for understanding and fostering human-AI synergy in scientific discovery.

Do AI Agents Exhibit Greed in Shared Resource Environments?

Large Language Models (LLMs) are increasingly used in simulations, either in academic settings for research studies or in industry for prototyping. Previous research has investigated the extent to which agents can mimic human behavior in socioeconomic settings; however, there is limited research on greedy decision-making by agents in simulated resource allocation environments. Furthermore, there is limited work on cross-model evaluation. Our research investigates the decision-making of ten agents across two different experimental conditions: one in which agents are able to communicate with other agents, and one in which emotional contexts are directly injected into the prompt. Based on a conceptual framework of greed, we find that agents predominantly exhibited greed-like behavior across all conditions. Interaction and self-reported social connection did not meaningfully influence the agents’ decision-making. We evaluated four different models: gpt-5-mini, gemini-3-flash-preview, claude-haiku-4-5-20251001, and grok-3-mini-fast-beta, and observed that while models differed in their self-reported connection scores, they did not differ significantly in greediness scores. The code and experimental artifacts are available at https://github.com/tiaL-ops/simCo.

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AgentIdeaBench: Benchmarking Scientific Ideation in the Agent Era

Scientific ideation is the capacity to formulate novel and testable hypotheses from scientific evidence, and autonomous AI scientists depend on it. Existing evaluations largely assess it by asking models to generate ideas from a static, curated set of reference papers. That passive setup departs from the retrieval-and-reasoning workflow of modern AI scientists, and it becomes less discriminative as models improve. We introduce AgentIdeaBench, a multidisciplinary benchmark that evaluates scientific ideation under two matched settings, static observation and active exploration. We report matched Static-Active evaluations for 33 LLMs across 40 densely scored subfields spanning five disciplines, using a multidimensional, literature-verified scoring framework whose critics assess originality against retrieved prior art. Active exploration reveals considerably more capability headroom, and that headroom is unevenly distributed across models. Performance scales about twice as fast as under static observation, and the exploration gain is capability-gated, favoring the strongest models over the weakest. The gain reflects better grounding, improving feasibility, clarity, and specificity while leaving measured originality unchanged under our critics. We further explore Scientific World Modeling, a generation-time loop that refines a draft hypothesis through structured thought experiments. It benefits mid-capability models, and its impact diminishes among frontier models that appear to have internalized such reasoning patterns already. AgentIdeaBench gives future work on scientific ideation a measurement basis suited to the agent era.

Multi-Agent Debate Strategies: Survey, Taxonomy, and Challenges

Multi-Agent Debate (MAD) is a promising paradigm for improving the accuracy and robustness of Large Language Model (LLM)-based agentic systems. It enables multiple agents to exchange arguments, critique each other's outputs, and iteratively converge towards a solution. However, research remains fragmented, with inconsistent terminology and no rigorous synthesis of MAD design dimensions. We present a systematic literature review characterizing 141 primary studies on MAD. We derive a three-dimensional taxonomy covering debate participants, the interaction mechanisms structuring the exchange, and the agreement protocols governing debate resolution, supported by formal notations to render MAD configurations. Our analysis reveals that the field has implicitly converged on a narrow design pattern - static, fully connected topologies, verbatim exchange, short-term memory and voting resolution strategies - adopted by convention rather than systematic comparison, while promising alternatives remain marginal. Because any MAD setting reflects roughly a dozen interacting design decisions, cross-study comparison is unreliable when these are left implicit. We position the taxonomy as a descriptive map of the research landscape, a framework for controlled benchmarking, and potentially as a schema for machine-readable MAD specifications. As future work, we propose formalizing it into an executable specification, enabling cost-aware benchmarking and automated tuning of debate configurations.

AI Agents and the Future of VIS

Recent advances in agents (i.e., autonomous, goal-driven AI systems that iteratively observe, act, and learn from their environments) offer a fundamentally different approach from traditional AI models that passively respond to input. These AI agents are rapidly reshaping how we approach data-intensive tasks and providing new opportunities for the VIS community. Imagine an agent autonomously generating visualizations to analyze complex data, discovering patterns collaboratively, testing hypotheses, and communicating visual insights at a speed and scale beyond human capability. Yet, the emergence of these powerful systems raises critical questions that the VIS community must address: Could autonomous agents eventually replace human data scientists, and if not, how might they best collaborate? Are current visualization techniques and interfaces, originally designed for human analysts, suitable for agent interactions? How can VIS designers effectively integrate agents into their workflows without compromising human agency? And to what extent should agents help shape and educate the next generation of visualization researchers? Through a mix of keynote talks, paper presentations, and an agentic VIS challenge, this workshop invites researchers and practitioners to share innovative ideas, explore these questions, and discuss strategies to transform the impact of VIS for a future where human and AI agents co-exist.