multi-objective-optimization

ATOM is a multi-agent framework that formulates molecular optimization as a tree-structured search with specialized agents along paths, enabling exploration of alternative molecular trajectories and improving Pareto coverage in multi-objective benchmarks.

This paper proposes principled approaches for designing and optimizing practical agentic LLM systems, introducing a framework with pseudo-tools and fixed workflows to improve modularity, cost-efficiency, and accuracy across diverse tasks.

This paper presents a unified theoretical framework for gradient aggregation in multi-objective optimization, establishing convergence rates to Pareto stationarity. The authors introduce a sufficient alignment condition and demonstrate its application to existing and new algorithms, such as capped MGDA.

This paper introduces a parallelization strategy and adaptive steering mechanism for the Baymex algorithm to efficiently learn discretized Bayesian network classifiers for clinical data, achieving speedups over 54x on a 16-core CPU and comparable or better predictive performance than traditional models while maintaining explainability.

This paper introduces COAST, a causal-intelligence framework for designing constraint-aware interventions that drive complex systems between states, integrating causal discovery, modeling, and multi-objective optimization to identify minimal effective interventions with mechanistic rationales.

MOCHA introduces a multi-objective optimization method for LLM agent skills, using Chebyshev scalarization and exponential annealing to handle hard platform constraints and discover Pareto-optimal variants, achieving significant improvements over existing optimizers.

This paper proposes GESD, a procedural-oriented fairness metric that measures disparities in explanation stability across subgroups, and integrates it into a multi-objective optimization framework for jointly optimizing utility, outcome fairness, and explanation fairness.

Lean Refactor presents a retrieval-augmented agentic framework for multi-objective, controllable, and version-robust refactoring of Lean proofs, achieving significant compression and compilation-time reduction.

ToolMol is an evolutionary agentic framework that combines a multi-objective genetic algorithm with an LLM-based operator to design small-molecule drugs, achieving state-of-the-art binding affinity and drug-likeness on multiple protein targets.