Multi-Agent Constraint Factorization Reveals Latent Invariant Solution Structure
- URL: http://arxiv.org/abs/2601.15077v1
- Date: Wed, 21 Jan 2026 15:23:04 GMT
- Title: Multi-Agent Constraint Factorization Reveals Latent Invariant Solution Structure
- Authors: Christopher Scofield,
- Abstract summary: Multi-agent systems (MAS) composed of large language models often exhibit improved problem-solving performance despite operating on identical information.<n>We model each agent as enforcing a distinct family of validity constraints on a shared solution state, and show that a MAS implements a factorized composition of constraint-enforcement operators.<n>We extend this result from exact constraint enforcement to soft constraints via proximal operators, and apply the formalism to contemporary text-based dialog systems.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Multi-agent systems (MAS) composed of large language models often exhibit improved problem-solving performance despite operating on identical information. In this work, we provide a formal explanation for this phenomenon grounded in operator theory and constrained optimization. We model each agent as enforcing a distinct family of validity constraints on a shared solution state, and show that a MAS implements a factorized composition of constraint-enforcement operators. Under mild conditions, these dynamics converge to invariant solution sets defined by the intersection of agent constraint sets. Such invariant structures are generally not dynamically accessible to a single agent applying all constraints simultaneously, even when expressive capacity and information are identical. We extend this result from exact constraint enforcement to soft constraints via proximal operators, and apply the formalism to contemporary text-based dialog systems.
Related papers
- Power and Limitations of Aggregation in Compound AI Systems [10.867699486308197]
We investigate the power and limitations of aggregation within a stylized principal-agent framework.<n>Our analysis uncovers three natural mechanisms -- feasibility expansion, support expansion, and binding set contraction.<n>Our results take a step towards characterizing when compound AI systems can overcome limitations in model capabilities and in prompt engineering.
arXiv Detail & Related papers (2026-02-25T04:23:50Z) - An Integrated Fusion Framework for Ensemble Learning Leveraging Gradient Boosting and Fuzzy Rule-Based Models [59.13182819190547]
Fuzzy rule-based models excel in interpretability and have seen widespread application across diverse fields.<n>They face challenges such as complex design specifications and scalability issues with large datasets.<n>This paper proposes an Integrated Fusion Framework that merges the strengths of both paradigms to enhance model performance and interpretability.
arXiv Detail & Related papers (2025-11-11T10:28:23Z) - Explainable Distributed Constraint Optimization Problems [5.172964916120901]
We propose the Explainable DCOP model, which extends a DCOP to include its solution and a contrastive query for that solution.<n>We show that our approach can scale to large problems, and the different variants provide different options for trading off explanation lengths for smaller runtimes.
arXiv Detail & Related papers (2025-02-19T21:06:30Z) - Athanor: Local Search over Abstract Constraint Specifications [2.3383199519492455]
We focus on general-purpose local search solvers that accept as input a constraint model.<n>The Athanor solver we describe herein differs in that it begins from a specification of a problem in the abstract constraint specification language Essence.
arXiv Detail & Related papers (2024-10-08T11:41:38Z) - Generalization Bounds of Surrogate Policies for Combinatorial Optimization Problems [53.03951222945921]
We analyze smoothed (perturbed) policies, adding controlled random perturbations to the direction used by the linear oracle.<n>Our main contribution is a generalization bound that decomposes the excess risk into perturbation bias, statistical estimation error, and optimization error.<n>We illustrate the scope of the results on applications such as vehicle scheduling, highlighting how smoothing enables both tractable training and controlled generalization.
arXiv Detail & Related papers (2024-07-24T12:00:30Z) - Shape Arithmetic Expressions: Advancing Scientific Discovery Beyond Closed-Form Equations [56.78271181959529]
Generalized Additive Models (GAMs) can capture non-linear relationships between variables and targets, but they cannot capture intricate feature interactions.
We propose Shape Expressions Arithmetic ( SHAREs) that fuses GAM's flexible shape functions with the complex feature interactions found in mathematical expressions.
We also design a set of rules for constructing SHAREs that guarantee transparency of the found expressions beyond the standard constraints.
arXiv Detail & Related papers (2024-04-15T13:44:01Z) - Compositional Diffusion-Based Continuous Constraint Solvers [98.1702285470628]
This paper introduces an approach for learning to solve continuous constraint satisfaction problems (CCSP) in robotic reasoning and planning.
By contrast, our model, the compositional diffusion continuous constraint solver (Diffusion-CCSP), derives global solutions to CCSPs.
arXiv Detail & Related papers (2023-09-02T15:20:36Z) - On the Complexity of Multi-Agent Decision Making: From Learning in Games
to Partial Monitoring [105.13668993076801]
A central problem in the theory of multi-agent reinforcement learning (MARL) is to understand what structural conditions and algorithmic principles lead to sample-efficient learning guarantees.
We study this question in a general framework for interactive decision making with multiple agents.
We show that characterizing the statistical complexity for multi-agent decision making is equivalent to characterizing the statistical complexity of single-agent decision making.
arXiv Detail & Related papers (2023-05-01T06:46:22Z) - Relational Reasoning via Set Transformers: Provable Efficiency and
Applications to MARL [154.13105285663656]
A cooperative Multi-A gent R einforcement Learning (MARL) with permutation invariant agents framework has achieved tremendous empirical successes in real-world applications.
Unfortunately, the theoretical understanding of this MARL problem is lacking due to the curse of many agents and the limited exploration of the relational reasoning in existing works.
We prove that the suboptimality gaps of the model-free and model-based algorithms are independent of and logarithmic in the number of agents respectively, which mitigates the curse of many agents.
arXiv Detail & Related papers (2022-09-20T16:42:59Z) - Going Beyond Approximation: Encoding Constraints for Explainable
Multi-hop Inference via Differentiable Combinatorial Solvers [4.726777092009554]
Linear Programming (ILP) provides a viable mechanism to encode explicit and controllable assumptions about explainable multi-hop inference with natural language.
An ILP formulation is non-differentiable and cannot be integrated into broader deep learning architectures.
Diff-Comb Explainer demonstrates improved accuracy and explainability over non-differentiable solvers, Transformers and existing differentiable constraint-based multi-hop inference frameworks.
arXiv Detail & Related papers (2022-08-05T18:07:53Z) - Joint Continuous and Discrete Model Selection via Submodularity [1.332560004325655]
In model selection problems for machine learning, the desire for a well-performing model with meaningful structure is typically expressed through a regularized optimization problem.
In many scenarios, however, numerically meaningful structure is specified in some discrete space leading to difficult non optimization problems.
We show how simple continuous or discrete constraints can also be handled for certain problem classes, motivated by robust optimization.
arXiv Detail & Related papers (2021-02-17T21:14:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.