Three new research papers on arXiv address critical challenges in developing more reliable and capable AI agents.
According to arXiv preprint 2512.21699v1, researchers are working on “Towards Responsible and Explainable AI Agents with Consensus-Driven Reasoning.” The paper focuses on agentic AI systems that coordinate Large Language Models (LLMs), Vision Language Models (VLMs), tools, and external services to reason, plan, and execute multi-step tasks.
A second paper (arXiv:2512.21354v1) introduces “Reflection-Driven Control for Trustworthy Code Agents,” addressing safety concerns with current LLM agents. According to the abstract, contemporary LLM agents “still lack reliable safety controls and can produce unconstrained, unpredictable, and even actively harmful outputs.” The research proposes Reflection-Driven Control as a solution to this problem.
Finally, researchers present “AInsteinBench” (arXiv:2512.21373v1), described as “a large-scale benchmark for evaluating whether large language model (LLM) agents can operate as scientific computing development agents within real research software ecosystems.” According to the authors, this benchmark differs from existing scientific reasoning benchmarks by focusing on actual research software environments.
All three papers are cross-listed announcements on arXiv’s AI section.