ZendoWorld shows that high labeling accuracy does not equal rule recovery, perception and induction are separate bottlenecks, and VLM agents propose near-uninformative experiments on active visual concept induction.
CoRR , volume =
8 Pith papers cite this work. Polarity classification is still indexing.
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abstract
The emergence of large language models (LLMs) is propelling automated scientific discovery to the next level, with LLM-based Artificial Intelligence (AI) Scientist systems now taking the lead in scientific research. Several influential works have already appeared in the field of AI Scientist systems, with AI-generated research papers having been accepted at the ICLR 2025 workshop, suggesting that a human-level AI Scientist capable of uncovering phenomena previously unknown to humans, may soon become a reality. In this survey, we focus on the central question: How far are AI scientists from changing the world and reshaping the scientific research paradigm? To answer this question, we provide a prospect-driven review that comprehensively analyzes the current achievements of AI Scientist systems, identifying key bottlenecks and the critical components required for the emergence of a scientific agent capable of producing ground-breaking discoveries that solve grand challenges. We hope this survey will contribute to a clearer understanding of limitations of current AI Scientist systems, showing where we are, what is missing, and what the ultimate goals for scientific AI should be.
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HiRAS introduces hierarchical multi-agent coordination for paper-to-code generation and experiment reproduction, claiming over 10% relative gains over prior state-of-the-art on a refined benchmark with reduced hallucination.
The paper introduces a unified framework for world models that fully incorporates all cognitive functions from Cognitive Architecture Theory, highlights under-researched areas in motivation and meta-cognition, and proposes Epistemic World Models as a new category for scientific discovery agents.
Glia deploys a multi-agent LLM workflow with reasoning, experimentation, and analysis agents to generate interpretable algorithms for request routing, scheduling, and auto-scaling in distributed GPU clusters, reaching human-expert performance levels.
A survey organizing AI-powered research automation into five workflow stages, defining AutoResearch and Vibe Research, and proposing five evaluation dimensions while noting domain-conditioned limits on autonomy.
SciAtlas builds a large-scale multi-disciplinary academic knowledge graph and a neuro-symbolic retrieval system to support automated scientific research tasks such as literature review and idea positioning.
citing papers explorer
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Playing ZendoWorld: Challenging AI Agents on Active Visual Concept Induction
ZendoWorld shows that high labeling accuracy does not equal rule recovery, perception and induction are separate bottlenecks, and VLM agents propose near-uninformative experiments on active visual concept induction.
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What Do Evolutionary Coding Agents Evolve?
Evolutionary coding agents achieve most benchmark gains through a small subset of edit types and by cycling previously deleted code lines rather than developing new algorithmic structures.
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Bridging the Gap on AI-Assisted Scientific Software Development Through Transparency and Traceability
A repository-level AGENTS.md governance framework with commit-level AI disclosure, session logging, pre-commit gates, and mandatory human review is demonstrated on two new TMAP8 validation cases.
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HiRAS: A Hierarchical Multi-Agent Framework for Paper-to-Code Generation and Execution
HiRAS introduces hierarchical multi-agent coordination for paper-to-code generation and experiment reproduction, claiming over 10% relative gains over prior state-of-the-art on a refined benchmark with reduced hallucination.
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Human Cognition in Machines: A Unified Perspective of World Models
The paper introduces a unified framework for world models that fully incorporates all cognitive functions from Cognitive Architecture Theory, highlights under-researched areas in motivation and meta-cognition, and proposes Epistemic World Models as a new category for scientific discovery agents.
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Glia: A Human-Inspired AI for Automated Systems Design and Optimization
Glia deploys a multi-agent LLM workflow with reasoning, experimentation, and analysis agents to generate interpretable algorithms for request routing, scheduling, and auto-scaling in distributed GPU clusters, reaching human-expert performance levels.
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AutoResearch AI: Towards AI-Powered Research Automation for Scientific Discovery
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SciAtlas: A Large-Scale Knowledge Graph for Automated Scientific Research
SciAtlas builds a large-scale multi-disciplinary academic knowledge graph and a neuro-symbolic retrieval system to support automated scientific research tasks such as literature review and idea positioning.