BEAGLE uses a semi-Markov model, Bayesian knowledge tracing with injected flaws, and decoupled strategy-code actions to make LLM agents produce authentic student learning trajectories that humans cannot distinguish from real data at better than chance level.
Generative agents: Interactive simulacra of human behavior
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.AI 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Temporal semantic caching and MCP workflow optimizations deliver 30.6x median speedup on cache hits and 1.67x overall speedup with 40% latency reduction on the AssetOpsBench industrial agent benchmark.
citing papers explorer
-
BEAGLE: Behavior-Enforced Agent for Grounded Learner Emulation
BEAGLE uses a semi-Markov model, Bayesian knowledge tracing with injected flaws, and decoupled strategy-code actions to make LLM agents produce authentic student learning trajectories that humans cannot distinguish from real data at better than chance level.
-
Evaluating Temporal Semantic Caching and Workflow Optimization in Agentic Plan-Execute Pipelines
Temporal semantic caching and MCP workflow optimizations deliver 30.6x median speedup on cache hits and 1.67x overall speedup with 40% latency reduction on the AssetOpsBench industrial agent benchmark.