Artificial Intelligence Papers

@SciFi

New Artificial Intelligence papers from arxiv.org: expert systems, theorem proving. Thank you to arXiv for use of its open access interoperability.

arxiv.org/list/cs.AI/new Joined May 2009

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Latest posts 5

Artificial Intelligence Papers @SciFi 11m

Scientist World: Grow a Research World for AI-Scientists

Taiming Lu, Sachin Konan, Zhuang Liu
arxiv.org/abs/2611.08478 [𝚌𝚜.𝙰𝙸 𝚌𝚜.𝙲𝙻 𝚌𝚜.𝙻𝙶]

AI-Scientists arrived, but a world for them to grow in did not. Human scientists take theirs for granted - centuries of open problems and shared ideas, waiting in the scientific literature. To AI-Scientists, that world is readable but not runnable, so we build Scientist World for them: every research paper becomes an executable environment. Our autonomous pipeline extracts each paper's open-ended research question and reproduces its proposed method as the baseline; an AI-Scientist entering the environment is evaluated against what the human authors achieved. We release the first 1,000 verified environments. With the world as an evaluation, we found that frontier AI-Scientists can already surpass the human researchers 74% of the time. As a record of how AI does science, it contains 6,400 hours of research traces, with every idea, experiment, failure, and success. As a training ground, we are able to raise (+158%) a small model to match frontier models in research ideation. At last, AI-S

Artificial Intelligence Papers @SciFi 37m

ReproBridge: Building Reproducibility into ML Research Artifacts

Yubo Wang, Rui Meng, Jaehyun Nam, Tomas Pfister, Jinsung Yoon
arxiv.org/abs/2611.08452 [𝚌𝚜.𝙰𝙸]

Reproducing machine learning experiments requires reconstructing procedures from information scattered across papers, released code, and supporting artifacts. For coding agents, reproduction success can therefore depend on their ability to fill in missing details, rather than on the released artifacts alone. We ask whether preserving recovered procedures and decisions improves subsequent independent reproduction. We define a standardized artifact design comprising a refined codebase, environment setup, experiment launchers, and a reproduction guide, and introduce ReproBridge, an agent workflow for constructing these refined reproducible artifacts. ReproBridge-Bench provides 1,029 tasks from 387 experiments across 36 papers, anchored by five human-verified reference scores per task. Agents attempt tasks in fresh sessions with raw code or refined artifacts; a separate Auditor checks execution evidence. Across four evaluated models, refined artifacts raise mean reproduction success from 7

Artificial Intelligence Papers @SciFi 1h

The Power of Meta Access in the Software of the Future — Demonstrated on LLM Chat UIs and Chat AI

Julian Kunkel, Ali Doosthosseini
arxiv.org/abs/2611.08428 [𝚌𝚜.𝙰𝙸]

Chat remains the default surface for many large language model (LLM) tasks, even as agentic tools such as OpenCode excel at coding and automation. Yet the model sees the current dialogue but not the workspace around it. We explore meta access—giving the assistant a structured, permissioned view of the interface itself and propose a ladder of levels: feature tutoring; metadata, settings, and preference access; retrieval over summaries and messages, confirmed mutation, and full orchestration. Meta access can turn a chat client from many screens into one entry point for finding, navigating, organising, and operating it, while raising risks around grounding, privacy, prompt injection, and trust. General-purpose agentic harnesses are runtimes; because they can change their own settings, meta access becomes reflexive and needs a separate control-plane boundary. We ground this in the Meta Chat prototype of the Chat AI client. It builds bounded context from, e.g., summaries, settings, and rele

Artificial Intelligence Papers @SciFi 2h

Benchmarks as Software: Terminal-Bench and the Case for Continuous Benchmarks

E. Kelly Buchanan, Crystal Zhou, Scott W. Linderman, Christopher Ré
arxiv.org/abs/2611.08417 [𝚌𝚜.𝙰𝙸]

Coding agents are among the most economically consequential deployments of LLMs to date, making reliable evaluation increasingly important. Yet even benchmarks that are carefully validated at release can become stale. As models improve, they can discover valid solutions that existing verifiers reject, while changes to evaluation infrastructure can invalidate assumptions embedded in tasks and tests. We call the resulting accumulation of defects benchmark maintenance debt: a growing gap between benchmark scores and the capabilities they are intended to measure. We introduce a continuous-validation workflow for agentic benchmarks that combines community reports, task artifacts, agent traces, automated oracle checks, and human-gated repairs. We apply this workflow to Terminal-Bench 2.0, a benchmark that underwent extensive human and LM-assisted review and red-teaming before release. Our audit identifies issues in roughly 30% of the benchmark, and we release the resulting repairs as Termina

Artificial Intelligence Papers @SciFi 2h

The Gold Rush in AI4Math, II: What are signatures of AI-assisted Proofs?

Yuanchuan Guo, Jiashun Jin, Zheng Tracy Ke, Bingcheng Sui
arxiv.org/abs/2611.08412 [𝚌𝚜.𝙰𝙸 𝚜𝚝𝚊𝚝.𝙰𝙿]

The growing use of AI in mathematics raises a question: can we detect AI involvement in proofs, separately from AI assistance with writing? Historical arXiv papers, recent papers disclosing substantive AI use, and manuscripts released by OpenAI provide a rich empirical basis for studying this question. However, several challenges remain: lack of matched case-control pairs; potential incompleteness of disclosure; and lack of ready-to-use features designed to capture signals of AI involvement in proofs. To address these challenges, we construct five cohorts comprising 8,281 papers by combining existing manuscripts with those we produced using generative AI. These cohorts span different levels of AI involvement and include matched pairs of papers addressing the same research problems. We then introduce ProofTrace, a framework that represents each paper as a graph of immediate proof support (GIPS) and extracts interpretable features of mathematical organization. Our study yields three disc