Adaptive Accountability in Networked MAS: Tracing and Mitigating Emergent Norms at Scale

• URL: http://arxiv.org/abs/2512.18561v1
• Date: Sun, 21 Dec 2025 02:04:47 GMT
• Title: Adaptive Accountability in Networked MAS: Tracing and Mitigating Emergent Norms at Scale
• Abstract summary: Large-scale networked multi-agent systems increasingly underpin critical infrastructure.<n>We introduce an adaptive accountability framework that traces responsibility flows through a lifecycle-aware audit ledger.<n>We prove a bounded-compromise theorem showing that whenever the expected intervention cost exceeds an adversary's payoff, the long-run proportion of compromised interactions is bounded by a constant strictly less than one.
• Score: 2.28438857884398
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large-scale networked multi-agent systems increasingly underpin critical infrastructure, yet their collective behavior can drift toward undesirable emergent norms that elude conventional governance mechanisms. We introduce an adaptive accountability framework that (i) continuously traces responsibility flows through a lifecycle-aware audit ledger, (ii) detects harmful emergent norms online via decentralized sequential hypothesis tests, and (iii) deploys local policy and reward-shaping interventions that realign agents with system-level objectives in near real time. We prove a bounded-compromise theorem showing that whenever the expected intervention cost exceeds an adversary's payoff, the long-run proportion of compromised interactions is bounded by a constant strictly less than one. Extensive high-performance simulations with up to 100 heterogeneous agents, partial observability, and stochastic communication graphs show that our framework prevents collusion and resource hoarding in at least 90% of configurations, boosts average collective reward by 12-18%, and lowers the Gini inequality index by up to 33% relative to a PPO baseline. These results demonstrate that a theoretically principled accountability layer can induce ethically aligned, self-regulating behavior in complex MAS without sacrificing performance or scalability.

Related papers

• Reputation-driven Cooperation in Lattice-based Decentralized Federated Learning through Evolutionary Game Theory [0.3572141827285953]
  This paper proposes a novel EGT framework designed to analyze strategic evolution and enhance overall system performance.<n>We model peer-to-peer (P2P) interactions on a lattice network structure under the assumption of bounded rationality.<n>We integrate a reputation-based reward-and-punishment mechanism to effectively deter free-riding behaviors.
  arXiv  Detail & Related papers  (2026-08-02T12:23:45Z)
• Decoupling Thought from Speech: Knowledge-Grounded Counterfactual Reasoning for Resilient Multi-Agent Argumentation [0.0]
  Multi-agent debate frameworks have been shown to improve large language model performance in convergent tasks.<n>These frameworks are currently optimized in a way that heavily favors final output accuracy rather than stability of the process.<n>We introduce Knowledge-Grounded Counterfactual Reasoning (KG-CFR), a dual-stage architecture that enforces a strict separation of concerns between a private, retrieval-augmented planning buffer, and a public execution layer.
  arXiv  Detail & Related papers  (2026-06-09T06:43:18Z)
• Perception Without Engagement: Dissecting the Causal Discovery Deficit in LMMs [68.58207076756237]
  We introduce ProCauEval, a perturbation-based evaluation protocol that shifts from outcome assessment to mechanism diagnosis.<n>We find that models faithfully perceive video content yet systematically underexploit it during causal reasoning.<n>We propose Anti-Distillation Policy Optimization (ADPO), a reinforcement learning framework built on negative teacher alignment.
  arXiv  Detail & Related papers  (2026-05-10T08:48:58Z)
• Mitigating Error Amplification in Fast Adversarial Training [58.74042726356826]
  Fast Adversarial Training (FAT) has proven effective in enhancing model robustness by encouraging networks to learn perturbation-invariant representations.<n>FAT often suffers from catastrophic overfitting (CO), where the model overfits to the training attack and fails to generalize to unseen ones.<n>We propose a Distribution-aware Dynamic Guidance (DDG) strategy that dynamically adjusts both the perturbation budget and supervision signal.
  arXiv  Detail & Related papers  (2026-04-27T11:23:18Z)
• Compliance Moral Hazard and the Backfiring Mandate [6.584016782022881]
  Competing firms that serve shared customer populations face a fundamental information aggregation problem.<n>We develop a mechanism design framework for decentralized risk analytics grounded in anti-money laundering in banking networks.<n>We show competitive pressure amplifies compliance moral hazard and poorly designed mandates can reduce welfare below autarky.
  arXiv  Detail & Related papers  (2026-04-23T15:46:24Z)
• DeFRiS: Silo-Cooperative IoT Applications Scheduling via Decentralized Federated Reinforcement Learning [62.347535250646196]
  This paper proposes DeFRiS, a Decentralized Federated Reinforcement Learning framework for robust and scalable Silo-cooperative IoT application scheduling.<n>DeFRiS integrates three innovations: (i) an action-space-agnostic policy utilizing candidate resource scoring to enable seamless knowledge transfer across heterogeneous silos; (ii) a silo-optimized local learning mechanism combining Generalized Advantage Estimation with clipped policy updates to resolve sparse delayed reward challenges; and (iii) a Dual-Track Non-IID robust decentralized aggregation protocol leveraging gradient fingerprints for similarity-aware knowledge transfer and anomaly detection.
  arXiv  Detail & Related papers  (2026-03-16T02:02:38Z)
• 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)
• Quantifying Automation Risk in High-Automation AI Systems: A Bayesian Framework for Failure Propagation and Optimal Oversight [1.6328866317851185]
  We propose a parsimonious Bayesian risk decomposition expressing expected loss as the product of three terms.<n>This framework captures execution and oversight risk rather than model accuracy alone.<n>We motivate the framework with an illustrative case study of the 2012 Knight Capital incident as one instantiation of a broadly applicable failure pattern.
  arXiv  Detail & Related papers  (2026-02-22T00:18:23Z)
• Tri-LLM Cooperative Federated Zero-Shot Intrusion Detection with Semantic Disagreement and Trust-Aware Aggregation [5.905949608791961]
  This paper introduces a semantics-driven federated IDS framework that incorporates language-derived semantic supervision into federated optimization.<n>The framework achieves over 80% zero-shot detection accuracy on unseen attack patterns, improving zero-day discrimination by more than 10% compared to similarity-based baselines.
  arXiv  Detail & Related papers  (2026-01-30T16:38:05Z)
• Towards a Science of Scaling Agent Systems [79.64446272302287]
  We formalize a definition for agent evaluation and characterize scaling laws as the interplay between agent quantity, coordination structure, modelic, and task properties.<n>We derive a predictive model using coordination metrics, that cross-validated R2=0, enabling prediction on unseen task domains.<n>We identify three effects: (1) a tool-coordination trade-off: under fixed computational budgets, tool-heavy tasks suffer disproportionately from multi-agent overhead, and (2) a capability saturation: coordination yields diminishing or negative returns once single-agent baselines exceed 45%.
  arXiv  Detail & Related papers  (2025-12-09T06:52:21Z)
• Resilient by Design -- Active Inference for Distributed Continuum Intelligence [6.2095604527397485]
  This paper introduces a Probabilistic Active Inference Resilience Agent (PAIR-Agent) to achieve resilience in DCC systems.<n>PAIR-Agent performs three core operations: (i) constructing a causal fault graph from device logs, (ii) identifying faults while managing certainties and uncertainties using Markov blankets and the free energy principle, and (iii) autonomously healing issues through active inference.
  arXiv  Detail & Related papers  (2025-11-10T15:30:44Z)
• NDCG-Consistent Softmax Approximation with Accelerated Convergence [67.10365329542365]
  We propose novel loss formulations that align directly with ranking metrics.<n>We integrate the proposed RG losses with the highly efficient Alternating Least Squares (ALS) optimization method.<n> Empirical evaluations on real-world datasets demonstrate that our approach achieves comparable or superior ranking performance.
  arXiv  Detail & Related papers  (2025-06-11T06:59:17Z)
• Collaborative Value Function Estimation Under Model Mismatch: A Federated Temporal Difference Analysis [55.13545823385091]
  Federated reinforcement learning (FedRL) enables collaborative learning while preserving data privacy by preventing direct data exchange between agents.<n>In real-world applications, each agent may experience slightly different transition dynamics, leading to inherent model mismatches.<n>We show that even moderate levels of information sharing significantly mitigate environment-specific errors.
  arXiv  Detail & Related papers  (2025-03-21T18:06:28Z)
• Topology-Aware Conformal Prediction for Stream Networks [68.02503121089633]
  We propose Spatio-Temporal Adaptive Conformal Inference (textttCISTA), a novel framework that integrates network topology and temporal dynamics into the conformal prediction framework.<n>Our results show that textttCISTA effectively balances prediction efficiency and coverage, outperforming existing conformal prediction methods for stream networks.
  arXiv  Detail & Related papers  (2025-03-06T21:21:15Z)
• Benign Overfitting in Out-of-Distribution Generalization of Linear Models [19.203753135860016]
  We take an initial step towards understanding benign overfitting in the Out-of-Distribution (OOD) regime.<n>We provide non-asymptotic guarantees proving that benign overfitting occurs in standard ridge regression.<n>We also present theoretical results for a more general family of target covariance matrix.
  arXiv  Detail & Related papers  (2024-12-19T02:47:39Z)
• VALID: a Validated Algorithm for Learning in Decentralized Networks with Possible Adversarial Presence [13.612214163974459]
  We introduce the paradigm of validated decentralized learning for undirected networks with heterogeneous data. VALID protocol is the first to achieve a validated learning guarantee. Remarkably, VALID retains optimal performance metrics in adversary-free environments.
  arXiv  Detail & Related papers  (2024-05-12T15:55:43Z)
• IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound [85.6899802468343]
  We present IBP-R, a novel verified training algorithm that is both simple effective. We also present UPB, a novel robustness based on $beta$-CROWN, that reduces the cost state-of-the-art branching algorithms.
  arXiv  Detail & Related papers  (2022-06-29T17:13:25Z)
• Policy Smoothing for Provably Robust Reinforcement Learning [109.90239627115336]
  We study the provable robustness of reinforcement learning against norm-bounded adversarial perturbations of the inputs. We generate certificates that guarantee that the total reward obtained by the smoothed policy will not fall below a certain threshold under a norm-bounded adversarial of perturbation the input.
  arXiv  Detail & Related papers  (2021-06-21T21:42:08Z)
• Robust Regularization with Adversarial Labelling of Perturbed Samples [22.37046166576859]
  We propose Adversarial Labelling of Perturbed Samples (ALPS) as a regularization scheme. ALPS trains neural networks with synthetic samples formed by perturbing each authentic input sample towards another one along with an adversarially assigned label. Experiments on the SVHN, CIFAR-10, CIFAR-100 and Tiny-ImageNet datasets show that the ALPS has a state-of-the-art regularization performance.
  arXiv  Detail & Related papers  (2021-05-28T11:26:49Z)
• GenDICE: Generalized Offline Estimation of Stationary Values [108.17309783125398]
  We show that effective estimation can still be achieved in important applications. Our approach is based on estimating a ratio that corrects for the discrepancy between the stationary and empirical distributions. The resulting algorithm, GenDICE, is straightforward and effective.
  arXiv  Detail & Related papers  (2020-02-21T00:27:52Z)

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.