ADKO: Agentic Decentralized Knowledge Optimization

• URL: http://arxiv.org/abs/2605.07863v1
• Date: Fri, 08 May 2026 15:23:53 GMT
• Title: ADKO: Agentic Decentralized Knowledge Optimization
• Abstract summary: Agentic Decentralized Knowledge Optimization (ADKO) is a framework for collaborative black-box optimization across autonomous agents.<n>Each agent maintains a private Gaussian Process (GP) surrogate trained on local data and communicates only through knowledge tokens.<n>We provide the first formal analysis of dual information loss: token compression, quantified via mutual-information-based fidelity, and LM approximation, decomposed into bias and noise.
• Score: 21.537388090124196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Agentic Decentralized Knowledge Optimization (ADKO), a framework for collaborative black-box optimization across autonomous agents that achieves sample efficiency, privacy preservation, heterogeneous-objective handling, and communication efficiency. Each agent maintains a private Gaussian Process (GP) surrogate trained on local data and communicates only through knowledge tokens-compact, lossy summaries containing directional signals, advantage scores, and optional language-model (LM) insights-without sharing raw data or model parameters. ADKO unifies GP-Upper Confidence Bound (GP-UCB), parallel Bayesian optimization, decentralized learning, and LM-guided discovery. We provide the first formal analysis of dual information loss: token compression, quantified via mutual-information-based fidelity, and LM approximation error, decomposed into bias and stochastic noise. Our main result shows cumulative regret decomposes into GP error, LM bias, LM noise, and compression loss, with necessary and sufficient conditions for sublinear regret. We also propose fidelity-aware token pruning to preserve high-information tokens under memory budget. Experiments on neural architecture search and scientific discovery validate the theory and show consistent improvements over strong baselines.

Related papers

• FedSLoP: Memory-Efficient Federated Learning with Low-Rank Gradient Projection [5.476264433576713]
  FedSLoP is an efficient Federated Learning algorithm.<n>We show that FedSLoP converges to a first-order stationary point at a rate of $O (1/sqrNT)$.
  arXiv  Detail & Related papers  (2026-04-27T03:47:50Z)
• Sketching the Readout of Large Language Models for Scalable Data Attribution and Valuation [31.732282915190467]
  Inspired by human cognition, we introduce RISE (Readout Influence Sketching Estimator)<n> RISE focuses on influence hotspots at the output layer, where influence signals concentrate, and the gradient admits a decomposed outer-product form.<n>Across the OLMo (1B-32B) and Pythia (14M-6.9B) families, RISE reduces index storage by up to 112$times$ compared to RapidIn.<n>We validate RISE on three tasks: Howdy backdoor data detection, Finance-Medical domain separation, and Brain Rot high-quality data selection.
  arXiv  Detail & Related papers  (2026-04-17T16:07:11Z)
• Consolidation or Adaptation? PRISM: Disentangling SFT and RL Data via Gradient Concentration [56.074760766965085]
  PRISM achieves a dynamics-aware framework that arbitrates data based on its degree of cognitive conflict with the model's existing knowledge.<n>Our findings suggest that disentangling data based on internal optimization regimes is crucial for scalable and robust agent alignment.
  arXiv  Detail & Related papers  (2026-01-12T05:43:20Z)
• Closing the Generalization Gap in Parameter-efficient Federated Edge Learning [43.00634399799955]
  Federated edge learning (FEEL) provides a promising foundation for artificial intelligence (AI)<n>limited and heterogeneous local datasets, as well as resource-constrained deployment, severely degrade both model generalization and resource utilization.<n>We propose a framework that jointly leverages model minimization and generalization selection to tackle such challenges.
  arXiv  Detail & Related papers  (2025-11-28T15:34:09Z)
• Heterogeneous Multisource Transfer Learning via Model Averaging for Positive-Unlabeled Data [2.030810815519794]
  We propose a novel transfer learning framework that integrates information from heterogeneous data sources without direct data sharing.<n>For each source domain type, a tailored logistic regression model is conducted, and knowledge is transferred to the PU target domain through model averaging.<n>Our method outperforms other comparative methods in terms of predictive accuracy and robustness, especially under limited labeled data and heterogeneous environments.
  arXiv  Detail & Related papers  (2025-11-14T03:15:31Z)
• Repurposing Synthetic Data for Fine-grained Search Agent Supervision [81.95597592711688]
  LLM-based search agents are increasingly trained on entity-centric synthetic data.<n> prevailing training methods discard this rich entity information, relying instead on sparse, outcome-based rewards.<n>We introduce Entity-aware Group Relative Policy Optimization (E-GRPO), a novel framework that formulates a dense entity-aware reward function.
  arXiv  Detail & Related papers  (2025-10-28T17:50:40Z)
• Understanding the Information Propagation Effects of Communication Topologies in LLM-based Multi-Agent Systems [58.95962217043371]
  We present a causal framework to analyze how agent outputs, whether correct or erroneous, propagate under topologies with varying sparsity.<n>Our empirical studies reveal that moderately sparse topologies, which effectively suppress error propagation while preserving beneficial information diffusion, typically achieve optimal task performance.<n>We propose a novel topology design approach, EIB-leanrner, that balances error suppression and beneficial information propagation by fusing connectivity patterns from both dense and sparse graphs.
  arXiv  Detail & Related papers  (2025-05-29T11:21:48Z)
• Nonparametric Bellman Mappings for Value Iteration in Distributed Reinforcement Learning [8.324857108715007]
  This paper introduces novel Bellman mappings (B-Maps) for value iteration (VI) in distributed reinforcement learning (DRL)<n>Each agent constructs a nonparametric B-Map from its private data, operating on Q-functions represented in a reproducing kernel Hilbert space.<n>A detailed performance analysis shows that the proposed DRL framework effectively approximates the performance of a centralized node.
  arXiv  Detail & Related papers  (2025-03-20T14:39:21Z)
• Bridging and Modeling Correlations in Pairwise Data for Direct Preference Optimization [75.1240295759264]
  We propose an effective framework for Bridging and Modeling Correlations in pairwise data, named BMC.<n>We increase the consistency and informativeness of the pairwise preference signals through targeted modifications.<n>We identify that DPO alone is insufficient to model these correlations and capture nuanced variations.
  arXiv  Detail & Related papers  (2024-08-14T11:29:47Z)
• Compressed Regression over Adaptive Networks [58.79251288443156]
  We derive the performance achievable by a network of distributed agents that solve, adaptively and in the presence of communication constraints, a regression problem. We devise an optimized allocation strategy where the parameters necessary for the optimization can be learned online by the agents.
  arXiv  Detail & Related papers  (2023-04-07T13:41:08Z)
• DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup [58.894901088797376]
  federated learning has emerged as a promising approach for training a global model using data from multiple organizations without leaking their raw data. We propose a general framework to solve the above two challenges simultaneously. We provide comprehensive theoretical analysis including robustness analysis, convergence analysis, and generalization ability.
  arXiv  Detail & Related papers  (2022-04-16T08:08:29Z)

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.