Toward AI VIS Co-Scientists: A General and End-to-End Agent Harness for Solving Complex Data Visualization Tasks

Paper 2 has higher potential impact due to its broader applicability and stronger real-world utility: an end-to-end agent harness that autonomously builds task-specific visualization apps can benefit many scientific domains and accelerate data-driven discovery. Its validation on IEEE SciVis contests suggests practical relevance and methodological rigor around complex, ambiguous, multi-modal tasks. Paper 1 is timely and novel in EI evaluation with real multi-turn, user-annotated conversations, but its impact is more specialized to LLM assessment and conversational safety/alignment rather than a cross-disciplinary workflow enabler.

Paper 1 presents a novel, concrete end-to-end agentic system for autonomous data visualization that is validated on real-world benchmarks (IEEE SciVis Contests), demonstrating tangible outputs. It addresses a broadly applicable problem—automating complex visualization tasks—with methodological rigor and clear innovation in multi-agent coordination. Paper 2, while comprehensive, is primarily a review/survey chapter discussing how AI could enhance serious games, offering less novelty and no new system or empirical validation. Paper 1's concrete contribution and broader cross-domain applicability give it higher impact potential.

Paper 1 introduces an AI co-scientist framework for autonomous data visualization, addressing a fundamental bottleneck in data analysis across all scientific domains. By automating complex visual analysis and validating on real-world IEEE SciVis contests, it demonstrates broad applicability and high potential to accelerate multidisciplinary research. In contrast, Paper 2 presents a useful software engineering framework for LLM tool deployment, which greatly improves developer efficiency but has limited direct impact on fundamental scientific discovery methodologies. Therefore, Paper 1 exhibits significantly higher scientific innovation and broader potential impact.

Paper 1 proposes an end-to-end AI agent harness for complex data visualization, directly contributing to the highly relevant 'AI Scientist' paradigm. Its ability to autonomously generate customized visual analysis apps from high-level descriptions offers massive cross-disciplinary applicability across virtually all scientific fields dealing with complex data. While Paper 2 presents impressive methodological improvements in POMDP solving with huge performance gains, its impact is primarily confined to robotics and formal planning. Paper 1's broader real-world utility, cross-domain relevance, and alignment with cutting-edge autonomous AI research give it higher potential scientific impact.

Paper 2 provides fundamental insights into how LLM agents actually work in optimization tasks, revealing that they rely heavily on pretrained priors rather than feedback or agentic structure. These findings have broad implications across the rapidly growing field of LLM-based agents and optimization systems, challenging common assumptions about agentic AI. Paper 1, while technically impressive in automating visualization pipelines, represents more of an engineering contribution with narrower applicability. Paper 2's controlled experimental methodology and generalizable conclusions about LLM behavior are likely to influence agent design across many domains.

Paper 2 addresses a fundamental and broadly applicable problem—safety guarantees for deployed LLM agents—which is timely and critically important as LLM agents proliferate across domains. Its formal, contract-based architecture with compositional probabilistic safety bounds provides a principled theoretical framework that could influence standards across the entire AI safety community. Paper 1, while technically impressive in automating visualization pipelines, addresses a narrower application domain. Paper 2's identification of three open problems and its structural argument for layered safety have broader cross-disciplinary implications for AI deployment at scale.

SciCore-Mol addresses a fundamental challenge in integrating molecular/scientific data with LLMs through a modular, generalizable framework with broad implications for drug design, chemical synthesis, and scientific discovery. Its pluggable architecture provides a systematic blueprint applicable beyond chemistry. While Paper 1 presents impressive autonomous visualization capabilities, it targets a narrower problem (visualization generation) with less transformative potential. Paper 2's methodological contribution—bridging discrete text and continuous scientific representations—addresses a deeper scientific computing challenge with wider cross-disciplinary impact and stronger alignment with current AI-for-science trends.

Paper 2 likely has higher scientific impact due to broader cross-domain applicability and timeliness: an end-to-end agent harness that autonomously builds data-visualization analysis apps can benefit many scientific fields, accelerating exploratory analysis and communication. Its validation on IEEE SciVis contests suggests realistic, task-driven evaluation and generality across modalities. Paper 1 is innovative and potentially high-impact for chemistry/drug discovery, but its primary impact is more domain-constrained to molecular/reaction tasks and depends on integration quality across specialized modules.

Paper 1 presents a more novel and broadly impactful contribution—an end-to-end agentic system for autonomous data visualization that advances the AI co-scientist vision across multiple scientific domains. It addresses a widely relevant problem (data visualization for scientists), demonstrates generality across diverse fields via SciVis Contest benchmarks, and sits at the intersection of rapidly growing areas (LLM agents, autonomous scientific workflows). Paper 2 applies established DRL techniques (PPO, MLPs) to a well-studied scheduling variant, offering incremental improvements over dispatching rules with narrower applicability to manufacturing/operations research.

Paper 2 addresses a broader scientific challenge—autonomous data visualization for scientific discovery—with implications across virtually all scientific domains. Its alignment with the 'AI co-scientist' vision and validation on real-world IEEE SciVis contests demonstrates wider applicability. While Paper 1 offers a solid contribution to story illustration consistency, its scope is narrower (creative content generation). Paper 2's end-to-end agentic framework for complex visualization tasks has greater potential to accelerate scientific workflows across multiple fields, giving it higher estimated impact.

Paper 1 addresses a fundamental question in AI for science: evaluating when and how coordinated AI agents genuinely improve scientific inference across multiple distinct disciplines. By establishing rigorous cross-domain benchmarks, baselines, and operating regimes, it provides a crucial framework for future AI research. Paper 2 presents a valuable but narrower system focused specifically on automating data visualization tasks. Paper 1's broader methodological contributions and multi-disciplinary scope give it higher potential scientific impact.

Paper 2 offers a more novel and broadly applicable systems contribution: an event-sourced, deterministic, reactive-graph substrate for agentic computation with replay, forking, and full lineage. These properties address pressing needs (auditability, reproducibility, governance, debugging) across many domains and agent frameworks, making impact potentially wide and timely. Methodologically, it proposes a clear architectural principle plus a determinism contract, enabling rigorous evaluation via replay/fork tests. Paper 1 is valuable and application-forward but is narrower (VIS-app generation) and more dependent on benchmark-style validation, limiting cross-field substrate impact.

Paper 1 presents a more broadly impactful contribution: a general-purpose end-to-end agentic framework for autonomous data visualization applicable across virtually all scientific domains. Its validation on diverse IEEE SciVis Contests demonstrates cross-domain generality. The concept of an AI VIS co-scientist aligns with the high-impact trend of autonomous AI research assistants. Paper 2, while practically valuable for EV battery diagnostics, addresses a narrower application domain with more incremental innovation (converting signals to text for LLM reasoning). Paper 1's breadth of applicability and methodological novelty suggest greater scientific impact.

Paper 1 presents a generalized AI co-scientist framework for data visualization with cross-disciplinary applicability in almost any scientific field. In contrast, Paper 2, while highly valuable and timely for EV safety, has a much narrower domain focus (battery fault diagnosis). The breadth of impact, combined with the novelty of autonomously generating complex, domain-specific visual analysis applications from high-level tasks, gives Paper 1 a significantly higher potential for widespread scientific impact.

LACO addresses a fundamental challenge in collaborative autonomous driving—efficient latent communication between connected vehicles—with a novel training-free paradigm that reduces latency and bandwidth while maintaining performance. It tackles core technical problems (agent identity confusion, communication efficiency) with principled solutions validated in closed-loop simulation. Paper 2, while practical, primarily orchestrates existing LLM agents for visualization generation, representing incremental engineering over rapidly commoditizing AI coding capabilities. LACO's contributions to multi-agent communication and autonomous driving have broader and more lasting scientific impact across robotics, multi-agent systems, and transportation.

Paper 1 presents a paradigm-shifting approach by automating the entire research lifecycle (from idea generation to paper writing) through an interactive, multi-agent platform. While Paper 2 offers a strong, domain-agnostic tool for data visualization, Paper 1 has a broader potential impact by fundamentally changing how scientific research is conducted, executed, and managed, offering a general-purpose infrastructure for autonomous scientific discovery.