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.
ReviewAgents: Bridg- ing the Gap Between Human and AI-Generated Paper Reviews
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 6roles
background 1polarities
background 1representative citing papers
PRAIB reveals LLM reviews are less variable, positively biased, overconfident, longer, and overlook atomic weaknesses noted by humans compared to real reviewer feedback.
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.
Uncertainty-aware algorithms based on Bayesian decision theory improve generation utility on tutoring and reviewing tasks while risk-averse methods can degrade performance under high ambiguity, with conformal prediction providing guarantees.
A survey synthesizing LLM methods for peer review critique generation and score prediction, including taxonomies, benchmark limitations, domain biases, and robustness risks such as prompt injection.
The paper delivers a stage-by-stage roadmap for AI in research, showing reliable assistance in retrieval and tool tasks but fragility in novelty and judgment, advocating human-governed collaboration.
citing papers explorer
-
PRAIB: Peer Review AI Benchmark of Behaviour of LLM-Assisted Reviewing
PRAIB reveals LLM reviews are less variable, positively biased, overconfident, longer, and overlook atomic weaknesses noted by humans compared to real reviewer feedback.
-
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.
-
Uncertainty-Aware Generation and Decision-Making Under Ambiguity
Uncertainty-aware algorithms based on Bayesian decision theory improve generation utility on tutoring and reviewing tasks while risk-averse methods can degrade performance under high ambiguity, with conformal prediction providing guarantees.
-
LLM-Based Scientific Peer Review: Methods, Benchmarks, and Reliability Challenges
A survey synthesizing LLM methods for peer review critique generation and score prediction, including taxonomies, benchmark limitations, domain biases, and robustness risks such as prompt injection.
-
AI for Auto-Research: Roadmap & User Guide
The paper delivers a stage-by-stage roadmap for AI in research, showing reliable assistance in retrieval and tool tasks but fragility in novelty and judgment, advocating human-governed collaboration.