Agentic AI systems are increasingly embedded in scientific workflows, including literature synthesis, hypothesis generation, tool use, writing, and peer review. However, existing surveys and evaluations often emphasize task performance while underexamining how humans and agents coordinate under different epistemic conditions of scientific work. This survey introduces **CSCW**×**Kuhn**, a coordination-aware framework that connects computer-supported cooperative work dimensions with distinct scientific contexts. Using this framework, we synthesize recent human–agent systems and benchmarks to examine how coordination demands and failure risks may vary across contexts. Our synthesis identifies five recurring failure modes: procedural drift, grounding substitution, evaluative flattening, proxy optimization, and epistemic black-boxing, and argues for evaluations that move beyond task accuracy and toward grounding, provenance, disagreement preservation, and long-horizon scientific reliability. Our corpus and coding artifact are available at https://github.com/jenlia024/CSCWxKuhn-artifact.

The rapid proliferation of Generative AI has created a pressing need for rigorous documentation standards that support transparency and governance. Manual creation of Model and Data Cards, however, is difficult to scale, and automated approaches lack the large-scale, high-fidelity benchmarks required for systematic evaluation. We introduce MetaGAI, a benchmark of 2,541 semantically matched document triplets constructed from academic papers, GitHub repositories, and Hugging Face artifacts. Reference cards are produced by a multi-agent pipeline of specialized Retriever, Generator, and Editor agents, and the pipeline is assessed through a four-part validation protocol. We further propose an evaluation framework that combines reference-based automated metrics with a multi-model LLM-as-a-Judge protocol, together with explicit agreement and sensitivity analyses. Among the evaluated baselines, Qwen3-30B-A3B-Instruct achieves the best observed cost–quality trade-off, and the results reveal a recurring tension between faithfulness and completeness. MetaGAI provides a testbed for benchmarking, training, and analyzing automated Model and Data Card generation at scale. Our data and code are available at https://github.com/haoxuan-unt2024/MetaGAI-Benchmark.

Complex reasoning over tabular data is crucial in real-world data analysis, yet large language models (LLMs) often underperform due to complex queries, noisy data, and limited numerical capabilities. To address these issues, we propose TabDSR, a three-agent framework consisting of: (1) a query decomposer that breaks down complex questions, (2) a table sanitizer that cleans and filters noisy tables, and (3) a program-of-thoughts (PoT)-based reasoner that generates executable code to derive the final answer from the sanitized table. To ensure unbiased evaluation and mitigate data leakage, we introduce a new dataset, CalTab151, specifically designed for complex numerical reasoning over tables. Experimental results demonstrate that TabDSR consistently outperforms existing methods, achieving state-of-the-art (SOTA) performance with 8.79%, 6.08%, and 19.87% accuracy improvement on TAT-QA, TableBench, and CalTab151, respectively. Moreover, our framework integrates seamlessly with mainstream LLMs, providing a robust solution for complex tabular numerical reasoning. These findings highlight the effectiveness of our framework in enhancing LLM performance for complex tabular numerical reasoning.