GIANTS-4B, trained with RL on a new 17k-example benchmark of parent-to-child paper insights, achieves 34% relative improvement over gemini-3-pro in LM-judge similarity and is rated higher-impact by a citation predictor.
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16 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
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SciTrace embeds cumulative safety deliberation and trajectory-aware verification into scientific agent pipelines, claiming SOTA safety gains and detection of 78.8% of compositional risks missed by single-step checks.
GoR extracts citation DAGs using position, frequency, predecessor links and time, then fine-tunes Qwen2.5-7B on 498 seed papers to generate ideas, claiming SOTA over gpt-4o baselines via LLM judges.
PaperFit uses rendered page images in a closed loop to diagnose and repair typesetting defects in LaTeX documents, outperforming baselines on a new benchmark of 200 papers.
AI CFD Scientist autonomously discovers a Spalart-Allmaras runtime correction reducing lower-wall Cf RMSE by 7.89% on the periodic hill at Reh=5600 while using a vision-language gate to detect 14 of 16 silent failures missed by solver checks.
MLReplicate benchmark evaluates six autonomous systems on 45 manuscripts from ICML 2025 papers, finding that automated reviews accept flawed outputs with fabricated claims while human review exposes methodological failures, and that the cheapest system outperforms the most expensive by a wide margin
Intern-Atlas constructs a methodological evolution graph with 9.4 million edges from 1.03 million AI papers to capture how methods emerge, adapt, and transition, enabling better idea evaluation and generation for AI-driven research.
AgentEconomist is an end-to-end agentic system with idea development, experimental design, and execution stages that uses a large economics paper database to produce research ideas with better literature grounding, novelty, and insight than generic LLMs.
AI peer review systems are vulnerable to prompt injections, prestige biases, assertion strength effects, and contextual poisoning, as demonstrated by a new attack taxonomy and causal experiments on real conference submissions.
ResearchEVO automates the discover-then-explain cycle by evolving algorithms via fitness-driven LLM co-evolution and generating grounded, anti-hallucination research papers through sentence-level RAG.
MoRI improves LLM scientific ideation by training models via SFT to generate motivations followed by composite RL rewards for entropy-aware information gain and contrastive semantic alignment, leading to higher novelty, rigor, and feasibility than baselines.
DRPG is an agentic framework that generates academic rebuttals via decompose-retrieve-plan-generate steps, with a planner achieving over 98% accuracy and overall performance exceeding average human level using an 8B model.
ReCreate automatically creates and adapts domain LLM agents by turning past interaction experiences into scaffold edits via reasoning and hierarchical abstraction.
Agon is a new autonomous research system using prompt economy loops across 444 iterations to demonstrate scalable omnidisciplinary research and a taxonomy separating machine-fixable failures from those needing human judgment.
pAI/MSc is a customizable multi-agent system that reduces human steering by orders of magnitude when turning a hypothesis into a literature-grounded, mathematically established, experimentally supported manuscript draft in ML theory.
AutoReproduce is a multi-agent system using paper lineage to autonomously reproduce AI experiment code, with a new benchmark showing improvements over baselines in fidelity and execution.
citing papers explorer
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GIANTS: Generative Insight Anticipation from Scientific Literature
GIANTS-4B, trained with RL on a new 17k-example benchmark of parent-to-child paper insights, achieves 34% relative improvement over gemini-3-pro in LM-judge similarity and is rated higher-impact by a citation predictor.
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SciTrace: Trajectory-Aware Safety Reasoning for Scientific Discovery Agents
SciTrace embeds cumulative safety deliberation and trajectory-aware verification into scientific agent pipelines, claiming SOTA safety gains and detection of 78.8% of compositional risks missed by single-step checks.
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Graphs of Research: Citation Evolution Graphs as Supervision for Research Idea Generation
GoR extracts citation DAGs using position, frequency, predecessor links and time, then fine-tunes Qwen2.5-7B on 498 seed papers to generate ideas, claiming SOTA over gpt-4o baselines via LLM judges.
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PaperFit: Vision-in-the-Loop Typesetting Optimization for Scientific Documents
PaperFit uses rendered page images in a closed loop to diagnose and repair typesetting defects in LaTeX documents, outperforming baselines on a new benchmark of 200 papers.
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AI CFD Scientist: Toward Open-Ended Computational Fluid Dynamics Discovery with Physics-Aware AI Agents
AI CFD Scientist autonomously discovers a Spalart-Allmaras runtime correction reducing lower-wall Cf RMSE by 7.89% on the periodic hill at Reh=5600 while using a vision-language gate to detect 14 of 16 silent failures missed by solver checks.
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MLReplicate: Benchmarking Autonomous Research Systems for Machine Learning Reproducibility
MLReplicate benchmark evaluates six autonomous systems on 45 manuscripts from ICML 2025 papers, finding that automated reviews accept flawed outputs with fabricated claims while human review exposes methodological failures, and that the cheapest system outperforms the most expensive by a wide margin
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Intern-Atlas: A Methodological Evolution Graph as Research Infrastructure for AI Scientists
Intern-Atlas constructs a methodological evolution graph with 9.4 million edges from 1.03 million AI papers to capture how methods emerge, adapt, and transition, enabling better idea evaluation and generation for AI-driven research.
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AgentEconomist: An End-to-end Agentic System Translating Economic Intuitions into Executable Computational Experiments
AgentEconomist is an end-to-end agentic system with idea development, experimental design, and execution stages that uses a large economics paper database to produce research ideas with better literature grounding, novelty, and insight than generic LLMs.
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When AI reviews science: Can we trust the referee?
AI peer review systems are vulnerable to prompt injections, prestige biases, assertion strength effects, and contextual poisoning, as demonstrated by a new attack taxonomy and causal experiments on real conference submissions.
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ResearchEVO: An End-to-End Framework for Automated Scientific Discovery and Documentation
ResearchEVO automates the discover-then-explain cycle by evolving algorithms via fitness-driven LLM co-evolution and generating grounded, anti-hallucination research papers through sentence-level RAG.
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MoRI: Learning Motivation-Grounded Reasoning for Scientific Ideation in Large Language Models
MoRI improves LLM scientific ideation by training models via SFT to generate motivations followed by composite RL rewards for entropy-aware information gain and contrastive semantic alignment, leading to higher novelty, rigor, and feasibility than baselines.
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DRPG (Decompose, Retrieve, Plan, Generate): An Agentic Framework for Academic Rebuttal
DRPG is an agentic framework that generates academic rebuttals via decompose-retrieve-plan-generate steps, with a planner achieving over 98% accuracy and overall performance exceeding average human level using an 8B model.
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ReCreate: Reasoning and Creating Domain Agents Driven by Experience
ReCreate automatically creates and adapts domain LLM agents by turning past interaction experiences into scaffold edits via reasoning and hierarchical abstraction.
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Agon: An Autonomous Large-Scale Omnidisciplinary Research System Built on Prompt Economy
Agon is a new autonomous research system using prompt economy loops across 444 iterations to demonstrate scalable omnidisciplinary research and a taxonomy separating machine-fixable failures from those needing human judgment.
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pAI/MSc: ML Theory Research with Humans on the Loop
pAI/MSc is a customizable multi-agent system that reduces human steering by orders of magnitude when turning a hypothesis into a literature-grounded, mathematically established, experimentally supported manuscript draft in ML theory.
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AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineage
AutoReproduce is a multi-agent system using paper lineage to autonomously reproduce AI experiment code, with a new benchmark showing improvements over baselines in fidelity and execution.