AI Competitor Analysis

AgentDS: Benchmarking the Future of Human-AI Collaboration in Domain-Specific Data Science — The study found that the strongest solutions emerge from human-AI collaboration rather than AI-only approaches, highlighting the importance of combining human expertise with AI-assisted analysis. https://arxiv.org/html/2603.19005v1

AgentSearchBench: A Benchmark for AI Agent Search in the Wild — Researchers showed that semantic similarity alone is insufficient for evaluating competitors and demonstrated that execution-aware signals substantially improve ranking quality. https://arxiv.org/abs/2604.22436

LLM-Based Agents for Competitive Landscape Mapping in Drug Asset Due Diligence — An LLM-based competitor discovery system achieved 83% recall and reduced analyst turnaround time from 2.5 days to approximately 3 hours, representing a 20× improvement. https://arxiv.org/abs/2508.16571

Data Sharing with a Generative AI Competitor — This paper explores how competitive dynamics change when companies and AI platforms depend on each other's data, revealing new economic forces shaping AI ecosystems. https://arxiv.org/abs/2505.12386

Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence — Researchers argue that static benchmarks are insufficient and advocate for dynamic behavioral profiling to better understand competitive AI systems. https://arxiv.org/abs/2402.09880

These five sources together provide a strong academic foundation for an AI Competitor Analysis page focused on benchmarking, competitive intelligence, and visibility analysis