ForesightBench
A benchmark framework for evaluating how well language models can create structured plans and faithfully execute them step-by-step.
Overview
ForesightBench is a benchmark framework that measures “foresight” — the capability of language models to think ahead and follow through with consistent execution. It addresses critical LLM weaknesses like skipping planned steps, adding unplanned steps, drifting from original intent, or losing context during multi-step tasks.
Key Features
- Two-Phase Evaluation - Separate planning and execution phases for precise measurement
- Step-Level Analysis - Fine-grained metrics for each step in the plan
- Semantic Alignment - Handles merged, split, or reordered steps intelligently
- Drift Detection - Identifies performance degradation over time
- Dual Evaluation - Combines rule-based checks with LLM-as-judge analysis
- Multi-Model Comparison - Compare performance across different LLMs
Tech Stack
Core
- Python 3.10+ with zero core dependencies for base functionality
LLM Providers
- OpenAI API integration
- Anthropic API integration
Storage & Tracking
- JSONL traces for experiment tracking
- SQLite database for persistent storage
Task Coverage
Built-in tasks span seven categories:
- Explanation
- Analysis
- Reasoning
- Generation
- Coding
- Research
- Creative Writing
Architecture
The framework uses a modular architecture with components for:
- Task Management - Define and manage evaluation tasks
- Prompt Generation - Create structured prompts for planning and execution
- Evaluation - Three layers: rule validation, semantic alignment, and semantic evaluation
- Storage - Track and persist all experiment data