shapley-values-08879f02·4 events·first seen Aliases: Shapley values, Shapley Value, Shapley value
MemLens is a proposed memory management system for LLM-based agents that treats memory records as first-class data objects with explicit value scoring, contrasting with existing systems that handle all interaction records uniformly. The system uses Shapley-style evaluation to assess memory utility and provides an interactive analytics dashboard exposing the full memory lifecycle including storage, retrieval, and response generation. A study-copilot application demonstrates the system's ability to compare memory strategies across response quality, retrieval latency, and token consumption metrics.
This paper introduces the Trajectory Shapley Value (TSV), a contribution metric that evaluates each federated learning client's influence on the global model's optimization trajectory using validation-based, temporally consistent utility. Building on TSV, the authors propose FedTSV, an adaptive aggregation method that converts per-round evaluations into dynamic client weights to handle heterogeneous and adversarial participation. Experiments on benchmark datasets demonstrate improved convergence speed, robustness, and equitable contribution assessment compared to fixed-weight aggregation baselines.
SwarmHarness is a proposed decentralized protocol enabling AI compute sharing and task routing across heterogeneous nodes (workstations, inference servers, edge devices) without a central coordinator. It combines a DHT-based registry for peer discovery, a utility-function router dispatching tasks by capability/load/latency/trust, and a Shapley-value-based credit mechanism to align incentives among participating nodes. The system is designed as a foundational primitive for autonomous multi-agent networks where agents can hire compute, route subtasks, and settle credits without human intermediation. It positions itself against existing approaches like Golem, BrokerChain, BOINC, and Petals by integrating decentralization with a native incentive layer.
Researchers from BAIR introduce SPEX (Spectral Explainer) and ProxySPEX, algorithms for identifying influential feature, data, and model-component interactions in LLMs at scale. The approach exploits sparsity, low-degreeness, and hierarchy properties to reframe interaction discovery as a sparse recovery problem using tools from signal processing and coding theory. ProxySPEX achieves comparable performance to SPEX with roughly 10x fewer ablations by leveraging hierarchical structure. The methods are evaluated on feature attribution (sentiment analysis), data attribution, and mechanistic interpretability tasks, outperforming marginal methods like LIME at long context lengths.