This work presents VERITAS, a domain-agnostic replication framework built around CLI coding agents, which achieves state-of-the-art performance and leads on every metric on both benchmarks.
Abstract
AI tools are accelerating scientific publication while the systems that review it struggle to keep up, and independent verification of published research has become both harder and more important. As manual replication is slow and expensive, a growing line of work uses coding agents to automate parts of the process. Existing efforts are largely packaged as benchmarks with companion agents that only run inside the benchmark's own pipeline, and no general-purpose replication tool exists. We present VERITAS, a domain-agnostic replication framework built around CLI coding agents. Given a paper, a code repository, or both, VERITAS extracts the paper's claims, runs the methodology while resolving issues as they arise, and judges each claim against the evidence from experiment runs. The pipeline returns an importance-weighted Replication Score, a severity-rated log of every fix applied, and the patched codebase. We evaluate VERITAS on CORE-Bench and ReplicationBench, 65 papers spanning computer science, social science, medicine, and astrophysics. Against two strong Claude Code baselines on the same model and host environment, VERITAS achieves state-of-the-art performance and leads on every metric on both benchmarks.
This work proposes Formal Disco: a distributed system for coordination of LLM-based workers that can be easily applied to open-ended synthetic data generation at scale and proposes a principle of maximum entropy for synthetic program generation.
Gabriel Poesia, Simon Henniger, Tzu-Han Hsu et al.· 0 citations
Autonomous research systems are often evaluated as one-shot paper generators: given a topic, they produce a manuscript and a small set of experiment logs. This framing hides the operational problem that makes such systems difficult to trust: research is long-running, branching, failure-prone, and dependent on auditable handoffs between agents and humans. XScientist is a git-like research protocol and operating system for this setting. It orchestrates idea generation, experiment execution, manuscript drafting, self-review, repair, quality gating, daemon scheduling, and reproducibility artifacts as one continuously observable pipeline. The central design choice is to treat each run as a portable research artifact rather than only as a PDF. XScientist exports an Agent-Native Research Artifact (ARA), a protocol that records an exploration DAG, per-node code and outputs, claim-to-evidence anchors, content hashes, provenance, and re-execution hooks. This makes each generated paper inspectable as a science exploration tree: failed branches, repaired experiments, ablations, and manuscript claims remain connected to the nodes that produced them. The system also includes deterministic integrity forensics, sample gates, truth contracts, reviewer-oriented repair loops, and long-running daemon controls. This paper describes the current XScientist architecture, the ARA protocol surface, and the practical safeguards needed to move autonomous science from single-run demos toward reproducible, reviewable, and forkable research infrastructure. The implementation and manuscript source are maintained in the public GitHub repository at https://github.com/smileformylove/XScientist.
Vero is introduced, the first benchmark to evaluate joint implementation and proof synthesis at the repository level and an audit mechanism where agents are allowed to formally prove unsatisfiability of provided specification or incorrectness of reference code, which surfaces and corrects latent code and specification errors during curation.
Zhe Ye, Hantao Lou, Yuechun Sun et al.· 0 citations
Software increasingly functions as part of the scientific instrument itself, making failures in scientific code capable of compromising not only program behavior but also the evidence underlying scientific conclusions. Yet existing evaluations of coding agents largely emphasize aggregate task success, providing limited insight into why agents fail when repairing scientific software. We introduce \textbf{SWE-bench Science}, a repository-level benchmark for scientific software engineering comprising 119 tasks from 98 GitHub repositories across 20 scientific domains. Each task is organized into one of three paradigms: Issue-driven, Expert-exploratory, and Engineering-integration. Even the best-performing agent, \textbf{Claude Code with Opus-5 (max), achieves a pass@1 below 50\%}, highlighting the substantial challenges posed by scientific software engineering. We identify four recurring failure mechanisms: deficits in scientific knowledge or abstraction, misguided exploration or surface-level repair, incomplete repair coverage or system integration, and failures to generalize scientific knowledge beyond observed cases in our analysis. We further conduct a paired ablation that removes explicit scientific guidance while preserving the repository and executable engineering context. The results show that scientific knowledge is not uniformly beneficial: well-grounded information can constrain repair and improve average performance and token efficiency, whereas poorly aligned guidance can induce anchoring and does not necessarily improve exact repair success. Together, SWE-bench Science provides a broad testbed for studying both the capabilities and failure mechanisms of coding agents in scientific software engineering.
Zhipeng Xu, Jiahao Lu, Yining Zheng et al.· 0 citations
SWE Refactor Bench is introduced, a benchmark comprising 20 whole-repository migrations, covering 4 kinds of technical debt, and SWE Refactor Bench is positioned as a rigorous testbed for developing coding agents for reliable whole-repository migrations.
These findings suggest that while AI agents excel at rigorous boundary testing, they lack the “environmental awareness” needed to write stable, hermetic tests.
Preet Jhanglani, Zeel Desai, Vidhi Kansara et al.· Annual International Compute...· 0 citations