AI reviews for all 22,977 AAAI-26 papers were preferred by authors and PC members over human reviews on accuracy and suggestions and outperformed baselines at spotting weaknesses.
org/CorpusID:263830310
13 Pith papers cite this work, alongside 27 external citations. Polarity classification is still indexing.
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27
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Frontier coding agents surpass published Nature-family SOTA on only 17.8% of 90 sealed scientific tasks, mostly by recasting problems as supervised ML rather than inventing methods.
ReproRepo uses GitHub issues as natural supervision to benchmark LLM agents on detecting reproducibility blockers across 1,149 ML papers, with the top agent finding related issues for roughly 90% of cases.
ProReviewer is an MDP-formulated proactive peer review agent trained with SFT and RL on an 8B model that outperforms larger frontier LLMs on review quality metrics.
MESSALA is a new LLM framework that produces report evaluations closer to veteran SOC practitioners than prior LLM methods by combining a custom checklist with granularization guidelines and multi-perspective scoring.
ShinkaEvolve improves sample efficiency in LLM-driven program evolution via parent sampling, code novelty rejection-sampling, and bandit LLM ensemble selection, achieving new SOTA circle packing with 150 samples and gains on math reasoning and competitive programming tasks.
A maximum likelihood model estimates 6.5-16.9% of peer-review text at ICLR 2024, NeurIPS 2023, CoRL 2023 and EMNLP 2023 was substantially modified by LLMs, with elevated rates in low-confidence and deadline-close submissions.
Sakana Fugu trains LLM orchestrators using fine-tuning, evolutionary algorithms, and RL to build query-adaptive multi-agent scaffolds, claiming SOTA results on benchmarks including SWE-Bench Pro and GPQA-Diamond.
AI peer reviewers for POMP analyses show jagged performance: strong on technical error detection and invalid inference but weak on interpretive errors, narrative coherence, and domain-informed critique.
Supplying the true error count in a prompt raises LLM Count-F1 by ~0.22 but edit-level F0.5 by only ~0.04, so count agreement mostly measures prompt compliance.
Peer review reports in AI conferences have grown longer and more standardized after LLMs, with increased emphasis on surface-level clarity and summaries at the expense of deeper critiques on originality and replicability.
A multi-agent writing tutor for Overleaf that uses 12 agents and an expert skill library to generate inline comments, with a 14-user study reporting 90.6% actionable and 67.5% valid comments that outperform a GPT-5.2 baseline.
ARIS is a three-layer open-source system that uses cross-model adversarial collaboration plus claim-auditing pipelines to make LLM-driven research workflows more reliable.
citing papers explorer
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AI-Assisted Peer Review at Scale: The AAAI-26 AI Review Pilot
AI reviews for all 22,977 AAAI-26 papers were preferred by authors and PC members over human reviews on accuracy and suggestions and outperformed baselines at spotting weaknesses.
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NatureBench: Can Coding Agents Match the Published SOTA of Nature-Family Papers?
Frontier coding agents surpass published Nature-family SOTA on only 17.8% of 90 sealed scientific tasks, mostly by recasting problems as supervised ML rather than inventing methods.
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ReproRepo: Scaling Reproducibility Audits with GitHub Repository Issues
ReproRepo uses GitHub issues as natural supervision to benchmark LLM agents on detecting reproducibility blockers across 1,149 ML papers, with the top agent finding related issues for roughly 90% of cases.
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From Passive Generation to Investigation: A Proactive Scientific Peer Review Agent
ProReviewer is an MDP-formulated proactive peer review agent trained with SFT and RL on an 8B model that outperforms larger frontier LLMs on review quality metrics.
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LLMs, You Can Evaluate It! Design of Multi-perspective Report Evaluation for Security Operation Centers
MESSALA is a new LLM framework that produces report evaluations closer to veteran SOC practitioners than prior LLM methods by combining a custom checklist with granularization guidelines and multi-perspective scoring.
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ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution
ShinkaEvolve improves sample efficiency in LLM-driven program evolution via parent sampling, code novelty rejection-sampling, and bandit LLM ensemble selection, achieving new SOTA circle packing with 150 samples and gains on math reasoning and competitive programming tasks.
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Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews
A maximum likelihood model estimates 6.5-16.9% of peer-review text at ICLR 2024, NeurIPS 2023, CoRL 2023 and EMNLP 2023 was substantially modified by LLMs, with elevated rates in low-confidence and deadline-close submissions.
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Sakana Fugu Technical Report
Sakana Fugu trains LLM orchestrators using fine-tuning, evolutionary algorithms, and RL to build query-adaptive multi-agent scaffolds, claiming SOTA results on benchmarks including SWE-Bench Pro and GPQA-Diamond.
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Jagged AI in Scientific Peer Review: Evidence from POMP Data Analysis
AI peer reviewers for POMP analyses show jagged performance: strong on technical error detection and invalid inference but weak on interpretive errors, narrative coherence, and domain-informed critique.
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Prompt Framing Distorts Count Based Evaluation of LLM Error Detection: Evidence from Numeric Anchoring
Supplying the true error count in a prompt raises LLM Count-F1 by ~0.22 but edit-level F0.5 by only ~0.04, so count agreement mostly measures prompt compliance.
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Impact of large language models on peer review opinions from a fine-grained perspective: Evidence from top conference proceedings in AI
Peer review reports in AI conferences have grown longer and more standardized after LLMs, with increased emphasis on surface-level clarity and summaries at the expense of deeper critiques on originality and replicability.
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PaperMentor: A Human-Centered Multi-Agent Writing Tutor for AI Research Papers on Overleaf
A multi-agent writing tutor for Overleaf that uses 12 agents and an expert skill library to generate inline comments, with a 14-user study reporting 90.6% actionable and 67.5% valid comments that outperform a GPT-5.2 baseline.
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ARIS: Autonomous Research via Adversarial Multi-Agent Collaboration
ARIS is a three-layer open-source system that uses cross-model adversarial collaboration plus claim-auditing pipelines to make LLM-driven research workflows more reliable.