leanagent
LeanAgent is a novel lifelong learning framework for formal theorem proving that continuously generalizes to and improves on ever-expanding mathematical knowledge without forgetting previously learned knowledge.
Discover how LeanAgent generates unique theorem identifiers by combining name file path and source code positions for reliable deduplication across repository versions.
How to Use Custom PyTorch Lightning Callbacks for Monitoring Training Progress in LeanAgentLearn how to leverage custom PyTorch Lightning callbacks in LeanAgent to monitor training progress effectively. Automatically save checkpoints, track metrics, and log learning rates with ease.
Theorem Deduplication Priority Logic When Merging Multiple Repositories in LeanAgentUnderstand LeanAgent's theorem deduplication priority logic when merging repos. Learn how recent date processed and canonical keys ensure the correct theorem version is kept.
How LeanAgent Implements Curriculum Learning Strategy with Easy/Medium/Hard Difficulty CategoriesLearn how LeanAgent implements curriculum learning. Discover its Easy/Medium/Hard difficulty categories for effective theorem training and progressively complex learning.
How LeanAgent Uses LeanDojo's Tracing Functionality to Extract Theorem DataDiscover how LeanAgent leverages LeanDojo's tracing to capture tactic executions and extract theorem data into structured TracedTheorem objects. Learn more about this powerful integration.
How LeanAgent Identifies and Batch Processes Theorems Marked with 'sorry'Learn how LeanAgent identifies and batch processes theorems marked with sorry. It scans theorem traces, batches incomplete proofs, and migrates them to proved status with persistent logs.
PremiseRetriever Checkpoint Strategy in LeanAgent: Optimizing for R@10 MetricDiscover the PremiseRetriever checkpoint strategy in LeanAgent. Learn how R@10 optimization preserves retrieval performance for effective lifelong learning.
How LeanAgent Filters and Selects Relevant Premises During Proof SearchLearn how LeanAgent efficiently filters and selects relevant premises during proof search. Discover its T5-based retrieval for enhanced tactic generation.
Timeout Configurations and Limits for Theorem Proving Attempts in LeanAgentExplore LeanAgent's timeout configurations for theorem proving. Learn about the 600-second default limit, expansion options, and a 1-second buffer to optimize attempts.
How LeanAgent Uses File Dependency Graphs for Premise Ordering in ProofsLearn how LeanAgent uses file dependency graphs to order premises in proofs. Discover how it ensures theorems and definitions appear after their dependencies for efficient proof construction.
How the Annotation System Links Tactics to Mathematical Premises in Lean AgentDiscover how the Lean Agent annotation system connects tactics to mathematical premises. Learn about its three-layer bridge from low-level tactics to high-level mathematical objects.
How the ReProver Retriever Works: Architecture and Training in LeanAgentDiscover how the ReProver retriever works and its training process. Learn about its T5 encoder, contrastive learning, and EWC for efficient premise selection in LeanAgent.
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