TBS is an interval-based multi-agent LLM simulation framework that separates structured internal evaluative states from public utterance generation and shows these states vary systematically with turn-allocation, silence, and memory conditions.
General modular harness for llm agents in multi-turn gaming environments
5 Pith papers cite this work. Polarity classification is still indexing.
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Harness-1 uses a state-externalizing harness for RL-trained search agents and reports 0.730 average curated recall, outperforming the next open subagent by 11.4 points.
A systems-level data model for preserving typed, addressable, versioned, and dependency-aware intermediate artifacts in agentic AI systems to improve long-term inspectability and maintainability.
The paper organizes research on generalist game AI into Dataset, Model, Harness, and Benchmark pillars and charts a five-level progression from single-game mastery to agents that create and live inside game multiverses.
Execution lineage models AI-native work as a DAG of computations with explicit dependencies, achieving perfect state preservation in controlled update tasks where loop-based agents introduce churn and contamination.
citing papers explorer
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Intermediate Artifacts as First-Class Citizens: A Data Model for Durable Intermediate Artifacts in Agentic Systems
A systems-level data model for preserving typed, addressable, versioned, and dependency-aware intermediate artifacts in agentic AI systems to improve long-term inspectability and maintainability.
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From Agent Loops to Deterministic Graphs: Execution Lineage for Reproducible AI-Native Work
Execution lineage models AI-native work as a DAG of computations with explicit dependencies, achieving perfect state preservation in controlled update tasks where loop-based agents introduce churn and contamination.