TrajBias — AI skill for Claude Code

TrajBias: Structural Biases in LLM-as-Judge Evaluation of Agent Trajectories

This repository contains the code and data for the paper:

**TrajBias: Structural Biases in LLM-as-Judge Evaluation of Agent Trajectories** Yufeng He, The University of Hong Kong

Key Findings

Through controlled perturbation experiments with 7 judge models from 7 families on coding agent trajectories, we discover:

1. Error Recovery Penalty (strongest): Trajectories with error recovery are systematically penalized (Cliff's delta up to 0.85, all p<0.001)
2. Recency Bias: Later errors receive harsher penalties than identical errors earlier
3. Bidirectional Outcome Bias: Outcome information contaminates process judgments in both directions, but opposing effects cancel in aggregate -- a methodological pitfall
4. Absent Length Bias: Trajectory length does not influence scores, contradicting text-domain verbosity bias
5. Multidimensional Bias Robustness: No single judge is universally least biased

Dataset

Available on HuggingFace: [yufenghe/trajbias-benchmark](https://huggingface.co/datasets/yufenghe/trajbias-benchmark)

Contains:

• 2,400 controlled perturbation probe pairs (5 bias types)
• 20,000+ evaluation scores from 7 judge models
• Complete statistical analysis results
• Croissant metadata

Repository Structure

scripts/
  bias_probes.py              # Generate perturbation probe pairs
  run_w3_full_eval.py         # Run LLM judge evaluations
  statistical_analysis_v2.py  # Statistical analysis (Cliff's delta, BH FDR)
  generate_figures.py         # Generate paper figures
  debiasing_experiments.py    # Debiasing strategy experiments
  prepare_hf_dataset.py       # Prepare HuggingFace dataset

Requirements

pip install numpy scipy matplotlib

Judge Models Tested

Judge	Family
Claude Sonnet 4.5	Anthropic
GPT-5.2	OpenAI
Gemini 3 Pro	Google
DeepSeek V3.2	DeepSeek
Qwen3-235B	Ali