View PDF ↗
PDF Viewer
Open Full PDF

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Estimated $10K - $14K over 6-10 weeks.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.

References (33)

[1]
Calibrating LLM Judges: Linear Probes for Fast and Reliable Uncertainty Estimation
2025Bhaktipriya Radharapu, Eshika Saxena et al.
[2]
Beyond Consensus: Mitigating the Agreeableness Bias in LLM Judge Evaluations
2025Suryaansh Jain, Umair Z. Ahmed et al.
[3]
When Judgment Becomes Noise: How Design Failures in LLM Judge Benchmarks Silently Undermine Validity
2025Ben Feuer, Chiung-Yi Tseng et al.
[4]
Analyzing Uncertainty of LLM-as-a-Judge: Interval Evaluations with Conformal Prediction
2025Huanxin Sheng, Xinyi Liu et al.
[5]
Evaluating Scoring Bias in LLM-as-a-Judge
2025Qingquan Li, Shaoyu Dou et al.
[6]
Agentic Large Language Models, a survey
2025A. Plaat, M. V. Duijn et al.
[7]
Preference Leakage: A Contamination Problem in LLM-as-a-judge
2025Dawei Li, Renliang Sun et al.
[8]
Qwen2.5 Technical Report
2024Qwen An Yang, Baosong Yang et al.
[9]
JudgeBench: A Benchmark for Evaluating LLM-based Judges
2024Sijun Tan, Siyuan Zhuang et al.
[10]
Agent-as-a-Judge: Evaluate Agents with Agents
2024Mingchen Zhuge, Changsheng Zhao et al.
[11]
Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge
2024Jiayi Ye, Yanbo Wang et al.
[12]
Style Outweighs Substance: Failure Modes of LLM Judges in Alignment Benchmarking
2024Ben Feuer, Micah Goldblum et al.
[13]
Trust or Escalate: LLM Judges with Provable Guarantees for Human Agreement
2024Jaehun Jung, Faeze Brahman et al.
[14]
From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline
2024Tianle Li, Wei-Lin Chiang et al.
[15]
SafetyAnalyst: Interpretable, transparent, and steerable LLM safety moderation
2024Jing-Jing Li, Valentina Pyatkin et al.
[16]
Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting
2023Melanie Sclar, Yejin Choi et al.
[17]
Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment
2023Rachel Cummings, Damien Desfontaines et al.
[18]
High-Dimensional Probability: An Introduction with Applications in Data Science
2020O. Papaspiliopoulos
[19]
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising
2018Borja Balle, Yu-Xiang Wang
[20]
Introduction to Online Convex Optimization
2016Elad Hazan

Showing 20 of 33 references

Founder's Pitch

"Develop an algorithmic framework that ensures bias-bounded, autonomous LLM judges for AI systems using A-BB."

LLM EvaluationScore: 5View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

0/4 signals

0

Series A Potential

0/4 signals

0

Sources used for this analysis

arXiv Paper

Full-text PDF analysis of the research paper

GitHub Repository

Code availability, stars, and contributor activity

Citation Network

Semantic Scholar citations and co-citation patterns

Community Predictions

Crowd-sourced unicorn probability assessments

Analysis model: GPT-4o · Last scored: 3/5/2026

Explore the full citation network and related research.

7-day free trial. Cancel anytime.

Understand the commercial significance and market impact.

7-day free trial. Cancel anytime.

Get detailed profiles of the research team.

7-day free trial. Cancel anytime.