gbanyan
43c025e060
- Add complete experiments directory with pilot study infrastructure - 5 experimental conditions (direct, expert-only, attribute-only, full-pipeline, random-perspective) - Human assessment tool with React frontend and FastAPI backend - AUT flexibility analysis with jump signal detection - Result visualization and metrics computation - Add novelty-driven agent loop module (experiments/novelty_loop/) - NoveltyDrivenTaskAgent with expert perspective perturbation - Three termination strategies: breakthrough, exhaust, coverage - Interactive CLI demo with colored output - Embedding-based novelty scoring - Add DDC knowledge domain classification data (en/zh) - Add CLAUDE.md project documentation - Update research report with experiment findings Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
43 lines
829 B
Python
43 lines
829 B
Python
"""
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Novelty-Driven LLM Agent Loop
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An autonomous agent that generates tasks using novelty as the termination condition.
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"""
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from .agent import (
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NoveltyDrivenTaskAgent,
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ExhaustFrontierAgent,
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CoverageTargetAgent,
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GeneratedTask,
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TaskGenerationResult,
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ExpertProvider,
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DomainProvider,
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)
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from .novelty_metrics import (
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NoveltyMetrics,
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NoveltyScore,
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NoveltyTrajectory,
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compute_batch_novelty,
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find_most_novel,
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)
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__all__ = [
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# Agents
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"NoveltyDrivenTaskAgent",
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"ExhaustFrontierAgent",
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"CoverageTargetAgent",
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# Data classes
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"GeneratedTask",
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"TaskGenerationResult",
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"NoveltyScore",
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"NoveltyTrajectory",
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# Providers
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"ExpertProvider",
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"DomainProvider",
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# Metrics
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"NoveltyMetrics",
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"compute_batch_novelty",
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"find_most_novel",
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]
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