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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6 Pith papers cite this work. Polarity classification is still indexing.
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GenoMAS deploys six specialized LLM agents with guided planning to preprocess transcriptomic data and identify genes, reaching 89.13% composite similarity and 60.48% F1 on the GenoTEX benchmark while outperforming prior methods.
SafeReview trains a Generator to create adversarial prompts and a Defender to detect them via co-evolution with an IR-GAN-inspired loss, claiming better resilience than static defenses for LLM-based peer review.
Controlled prompt interventions reveal strong affiliation bias in LLM peer reviews favoring top-ranked institutions, plus effects from seniority and publication history.
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 can generate research artifacts faster than it can verify them, so across all eight lifecycle stages the credible deployment mode is human-governed collaboration rather than full autonomy.
citing papers explorer
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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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GenoMAS: A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis
GenoMAS deploys six specialized LLM agents with guided planning to preprocess transcriptomic data and identify genes, reaching 89.13% composite similarity and 60.48% F1 on the GenoTEX benchmark while outperforming prior methods.
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SafeReview: Defending LLM-based Review Systems Against Adversarial Hidden Prompts
SafeReview trains a Generator to create adversarial prompts and a Defender to detect them via co-evolution with an IR-GAN-inspired loss, claiming better resilience than static defenses for LLM-based peer review.
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Justice in Judgment: Unveiling (Hidden) Bias in LLM-assisted Peer Reviews
Controlled prompt interventions reveal strong affiliation bias in LLM peer reviews favoring top-ranked institutions, plus effects from seniority and publication history.
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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.
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AI for Auto-Research: Roadmap & User Guide
AI can generate research artifacts faster than it can verify them, so across all eight lifecycle stages the credible deployment mode is human-governed collaboration rather than full autonomy.