Socially-Weighted Alignment: A Game-Theoretic Framework for Multi-Agent LLM Systems

• URL: http://arxiv.org/abs/2602.14471v1
• Date: Mon, 16 Feb 2026 05:17:58 GMT
• Title: Socially-Weighted Alignment: A Game-Theoretic Framework for Multi-Agent LLM Systems
• Abstract summary: We propose a game-theoretic framework that modifies inference-time decision making by interpolating between an agent's private objective and an estimate of group welfare.<n>We show that SWA induces a critical threshold $*=(n-)/(n-1)$ above which agents no longer have marginal incentive to increase demand under overload.
• Score: 17.658093330392052
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deploying large language model (LLM) agents in shared environments introduces a fundamental tension between individual alignment and collective stability: locally rational decisions can impose negative externalities that degrade system-level performance. We propose Socially-Weighted Alignment (SWA), a game-theoretic framework that modifies inference-time decision making by interpolating between an agent's private objective and an estimate of group welfare via a social weight $λ\in[0,1]$. In a shared-resource congestion game with $n$ agents and congestion severity $β$, we show that SWA induces a critical threshold $λ^*=(n-β)/(n-1)$ above which agents no longer have marginal incentive to increase demand under overload, yielding a phase transition from persistent congestion to stable operation near capacity. We further provide an inference-time algorithmic instantiation of SWA that does not require parameter updates or multi-agent reinforcement learning, and use a multi-agent simulation to empirically validate the predicted threshold behavior.

Related papers

• Dynamic Coalition Formation and Communication Pricing in Skill-Based Agentic AI Systems [0.0]
  We model agent selection and communication as a cooperative game with task-conditioned net utility $U(Cmid x)=V(Cmid x)-sum_iin Cc_i$.<n>We propose a marginal-value activation rule and greedy router, extend the model to optimize communication edges with per-edge costs, and use estimated Shapley values to predict which agents are worth contacting before and during execution.
  arXiv  Detail & Related papers  (2026-07-24T19:25:21Z)
• Phi-Actor-Critic: Steering General-Sum Games to Pareto-Efficient Correlated Equilibria [3.061219970798378]
  Real-world multi-agent systems are often modeled as general-sum games where individual incentives conflict with collective welfare.<n>Standard deep multi-agent reinforcement learning (MARL) methods struggle with this problem.<n>We propose $$-Actor-Critic ($$-AC), a framework that leverages swap regret minimization to steer learning toward high-welfare correlated equilibria.
  arXiv  Detail & Related papers  (2026-06-09T16:40:26Z)
• SHARP: A Self-Evolving Human-Auditable Rubric Policy for Financial Trading Agents [11.616028403813031]
  Large language models (LLMs) are increasingly deployed for autonomous financial trading.<n>We introduce the Self-Evolving Human-Auditable Policy (SHARP), a neuro-symbolic framework that replaces unconstrained text mutation with structured, symbolic policy optimization.
  arXiv  Detail & Related papers  (2026-05-07T18:23:44Z)
• Taming Actor-Observer Asymmetry in Agents via Dialectical Alignment [59.536125286960186]
  Multi-agent frameworks assigning specialized roles are increasingly adopted to enable self-reflection and mutual auditing.<n>We find it simultaneously induces a human-like cognitive bias known as Actor-Observer Asymmetry.<n>ReTAS guides agents to synthesize conflicting viewpoints into an objective consensus.
  arXiv  Detail & Related papers  (2026-04-21T15:05:58Z)
• Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions [89.52532304099522]
  Federated Learning (FL) enables clients to collaboratively train a shared model without centralizing their raw data, offering an inherent level of privacy.<n> gradients and model updates can still leak sensitive information, while malicious servers may mount adversarial attacks such as Byzantine manipulation.<n>These vulnerabilities highlight the need to address differential privacy (DP) and Byzantine robustness within a unified framework.<n>We propose Byz-Clip21-SGD2M, a new algorithm that integrates robust aggregation with double momentum and carefully designed clipping.
  arXiv  Detail & Related papers  (2026-03-24T17:39:09Z)
• Improving Search Agent with One Line of Code [68.58667107354253]
  Tool-based Agentic Reinforcement Learning (TARL) has emerged as a promising paradigm for training search agents to interact with external tools.<n>We propose textbfSearch textbfAgent textbfPolicy textbfOptimization (textbfSAPO), which stabilizes training via a conditional token-level KL constraint.
  arXiv  Detail & Related papers  (2026-03-10T04:07:39Z)
• Learning Approximate Nash Equilibria in Cooperative Multi-Agent Reinforcement Learning via Mean-Field Subsampling [3.396870608435494]
  We study a cooperative Markov game with a global agent and $n$ homogeneous local agents in a communication-constrained regime.<n>We prove that these approximate best-response dynamics converge to an $widetildeO (1/sqrtk)$-approximate Nash Equilibrium.
  arXiv  Detail & Related papers  (2026-03-04T06:14:24Z)
• Position: AI Agents Are Not (Yet) a Panacea for Social Simulation [62.891898598784415]
  Recent advances in large language models (LLMs) have spurred growing interest in using LLM-integrated agents for social simulation.<n>This position paper argues that LLM-based agents are not (yet) a panacea for social simulation.
  arXiv  Detail & Related papers  (2026-02-19T06:35:07Z)
• PhysicsAgentABM: Physics-Guided Generative Agent-Based Modeling [47.029742241618635]
  Large language model (LLM)-based multi-agent systems enable expressive agent reasoning but are expensive to scale and poorly calibrated for timestep-aligned state-transition simulation.<n>We propose PhysicsAgentABM, which shifts inference to behaviorally coherent agent clusters.<n> Experiments across public health, finance, and social sciences show consistent gains in event-time accuracy and calibration over mechanistic, neural, and LLM baselines.
  arXiv  Detail & Related papers  (2026-02-05T18:59:01Z)
• ProAct: Agentic Lookahead in Interactive Environments [56.50613398808361]
  ProAct is a framework that enables agents to internalize accurate lookahead reasoning through a two-stage training paradigm.<n>We introduce Grounded LookAhead Distillation (GLAD), where the agent undergoes supervised fine-tuning on trajectories derived from environment-based search.<n>We also propose the Monte-Carlo Critic (MC-Critic), a plug-and-play auxiliary value estimator designed to enhance policy-gradient algorithms.
  arXiv  Detail & Related papers  (2026-02-05T05:45:16Z)
• Phase Transition for Budgeted Multi-Agent Synergy [41.486076708302456]
  Multi-agent systems can improve reliability, yet under a fixed inference budget they often help, saturate, or even collapse.<n>We develop a minimal and calibratable theory that predicts these regimes from three binding constraints of modern agent stacks.
  arXiv  Detail & Related papers  (2026-01-24T05:32:50Z)
• The Silent Scholar Problem: A Probabilistic Framework for Breaking Epistemic Asymmetry in LLM Agents [0.6117371161379209]
  We propose a formal probabilistic framework that provides agents with a non-altruistic motive for bidirectional knowledge exchange.<n>We show how these accumulated belief states serve as verifiable reward signals for Reinforcement Learning from Human Feedback (RLHF) and high-quality data filters for Supervised Fine-Tuning (SFT)<n> Simulation results validate that this uncertainty-driven strategy significantly outperforms random baselines in heterogeneous environments.
  arXiv  Detail & Related papers  (2025-12-24T02:02:25Z)
• STARec: An Efficient Agent Framework for Recommender Systems via Autonomous Deliberate Reasoning [54.28691219536054]
  We introduce STARec, a slow-thinking augmented agent framework that endows recommender systems with autonomous deliberative reasoning capabilities.<n>We develop anchored reinforcement training - a two-stage paradigm combining structured knowledge distillation from advanced reasoning models with preference-aligned reward shaping.<n>Experiments on MovieLens 1M and Amazon CDs benchmarks demonstrate that STARec achieves substantial performance gains compared with state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-08-26T08:47:58Z)
• Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts [20.8288955218712]
  We propose a framework where a principal guides an agent in a Markov Decision Process (MDP) using a series of contracts. We present and analyze a meta-algorithm that iteratively optimize the policies of the principal and agent. We then scale our algorithm with deep Q-learning and analyze its convergence in the presence of approximation error.
  arXiv  Detail & Related papers  (2024-07-25T14:28:58Z)
• DASA: Delay-Adaptive Multi-Agent Stochastic Approximation [64.32538247395627]
  We consider a setting in which $N$ agents aim to speedup a common Approximation problem by acting in parallel and communicating with a central server. To mitigate the effect of delays and stragglers, we propose textttDASA, a Delay-Adaptive algorithm for multi-agent Approximation.
  arXiv  Detail & Related papers  (2024-03-25T22:49:56Z)
• Safe Model-Based Multi-Agent Mean-Field Reinforcement Learning [48.667697255912614]
  Mean-field reinforcement learning addresses the policy of a representative agent interacting with the infinite population of identical agents. We propose Safe-M$3$-UCRL, the first model-based mean-field reinforcement learning algorithm that attains safe policies even in the case of unknown transitions. Our algorithm effectively meets the demand in critical areas while ensuring service accessibility in regions with low demand.
  arXiv  Detail & Related papers  (2023-06-29T15:57:07Z)
• ERMAS: Becoming Robust to Reward Function Sim-to-Real Gaps in Multi-Agent Simulations [110.72725220033983]
  Epsilon-Robust Multi-Agent Simulation (ERMAS) is a framework for learning AI policies that are robust to such multiagent sim-to-real gaps. ERMAS learns tax policies that are robust to changes in agent risk aversion, improving social welfare by up to 15% in complextemporal simulations. In particular, ERMAS learns tax policies that are robust to changes in agent risk aversion, improving social welfare by up to 15% in complextemporal simulations.
  arXiv  Detail & Related papers  (2021-06-10T04:32:20Z)

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.