TianJi-Environ is a WRF-Chem-based multi-agent AI framework for autonomous validation of atmospheric chemistry mechanisms through executable experiments and evidence assessment.
Sciagents: Automating scientific discovery through bioinspired multi-agent intelligent graph reasoning
11 Pith papers cite this work, alongside 90 external citations. Polarity classification is still indexing.
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A dimensionless capillary capture parameter C = max(Fx_γ + μ Fz_γ)/(μ Fp) predicts whether a drop captures or redeposits a particle across wettability and friction regimes.
A new benchmark (IG-Bench) reveals that LLM-based scientists fail at compositional lineage reasoning, with the best system reaching only 27.3% exact accuracy.
Graph-PRefLexOR fine-tunes graph-native models with GRPO to organize reasoning into phases, yielding 40-65% gains in traceable hypothesis generation and 2-3x semantic diversity on 100 materials science questions.
A new filtration-based conformal prediction method attributes errors in multi-agent systems by producing contiguous sequence sets with finite-sample coverage guarantees, enabling rollback recovery.
Structuring LLM hypothesis generation around deductive-nomological explanation, causal processes, and universals is reported to beat direct prompting, with two generated ideas implemented as the CTAT and HALO algorithms.
A category-theoretic model frames scientific discovery as verified regime transitions via left Kan extensions that preserve and compare artifacts across schema changes in agentic AI.
Compass is an expert-guided LLM agent framework that extracts 3,751 marine Pb records from 230k papers to build the largest integrated database, achieving 92% accuracy via multi-layered validation.
Coordinated AI agents improve scientific inference from partial evidence in cross-domain tasks when single sources are incomplete, as demonstrated by AUROC gains in vector-borne disease and exoplanet benchmarks but tied performance in others.
The paper proposes a four-role framework for LLMs in scientific innovation and reviews methods, benchmarks, and limitations across Assistant, Collaborator, Scientist, and Evaluator roles.
A survey that deconstructs LLM agent systems via a methodology-centered taxonomy linking design principles to emergent behaviors, applications, and challenges.
citing papers explorer
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TianJi-Environ: An Autonomous AI Scientist for Atmospheric Environmental Research
TianJi-Environ is a WRF-Chem-based multi-agent AI framework for autonomous validation of atmospheric chemistry mechanisms through executable experiments and evidence assessment.
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ChargeBD: Character-Aware Heterogeneous Agent Reasoning for Guided Engineering in Battery Development
A dimensionless capillary capture parameter C = max(Fx_γ + μ Fz_γ)/(μ Fp) predicts whether a drop captures or redeposits a particle across wettability and friction regimes.
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Ideas Have Genomes: Benchmarking Scientific Lineage Reasoning and Lineage-Grounded Idea Generation
A new benchmark (IG-Bench) reveals that LLM-based scientists fail at compositional lineage reasoning, with the best system reaching only 27.3% exact accuracy.
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Graph-Native Reinforcement Learning Enables Traceable Scientific Hypothesis Generation through Conceptual Recombination
Graph-PRefLexOR fine-tunes graph-native models with GRPO to organize reasoning into phases, yielding 40-65% gains in traceable hypothesis generation and 2-3x semantic diversity on 100 materials science questions.
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Conformal Agent Error Attribution
A new filtration-based conformal prediction method attributes errors in multi-agent systems by producing contiguous sequence sets with finite-sample coverage guarantees, enabling rollback recovery.
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DN-Hypo-Pipeline: An AI-Driven Workflow for Generating Hypotheses using Large Language Models and Scientific Explanations
Structuring LLM hypothesis generation around deductive-nomological explanation, causal processes, and universals is reported to beat direct prompting, with two generated ideas implemented as the CTAT and HALO algorithms.
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Self-Revising Discovery Systems for Science: A Categorical Framework for Agentic Artificial Intelligence
A category-theoretic model frames scientific discovery as verified regime transitions via left Kan extensions that preserve and compare artifacts across schema changes in agentic AI.
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Compass: Navigating Global Marine Lead Data Integration through Expert-Guided LLM Agent
Compass is an expert-guided LLM agent framework that extracts 3,751 marine Pb records from 230k papers to build the largest integrated database, achieving 92% accuracy via multi-layered validation.
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Cross-domain benchmarks reveal when coordinated AI agents improve scientific inference from partial evidence
Coordinated AI agents improve scientific inference from partial evidence in cross-domain tasks when single sources are incomplete, as demonstrated by AUROC gains in vector-borne disease and exoplanet benchmarks but tied performance in others.
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Evolving Roles of LLMs in Scientific Innovation: Assistant, Collaborator, Scientist, and Evaluator
The paper proposes a four-role framework for LLMs in scientific innovation and reviews methods, benchmarks, and limitations across Assistant, Collaborator, Scientist, and Evaluator roles.
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Large Language Model Agent: A Survey on Methodology, Applications and Challenges
A survey that deconstructs LLM agent systems via a methodology-centered taxonomy linking design principles to emergent behaviors, applications, and challenges.