# Look-Ahead-Bench: a Standardized Benchmark of Look-ahead Bias in Point-in-Time LLMs for Finance

arXiv: https://arxiv.org/abs/2601.13770 · HTML: https://mlsanity.com/paper/2601.13770/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 35 references that are in the map.
- First posted on arXiv 20 Jan 2026.

## Builds on (most cited first, 16 of 16)

- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Quantifying Memorization Across Neural Language Models](https://mlsanity.com/paper/2202.07646/) (2022)
- [PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance](https://mlsanity.com/paper/2306.05443/) (2023)
- [Inverse Scaling: When Bigger Isn't Better](https://mlsanity.com/paper/2306.09479/) (2023)
- [FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design](https://mlsanity.com/paper/2311.13743/) (2023)
- [FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making](https://mlsanity.com/paper/2407.06567/) (2024)
- [FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models](https://mlsanity.com/paper/2405.14767/) (2024)
- [Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysis](https://mlsanity.com/paper/2309.17322/) (2023)
- [HedgeAgents: A Balanced-aware Multi-agent Financial Trading System](https://mlsanity.com/paper/2502.13165/) (2025)
- [QuantHarness: Price-Driven Multi-Agent LLMs for High-Frequency Trading](https://mlsanity.com/paper/2509.09995/) (2025)
- [Chain-of-Alpha: Unleashing the Power of Large Language Models for Alpha Mining in Quantitative Trading](https://mlsanity.com/paper/2508.06312/) (2025)
- [A Fast and Effective Solution to the Problem of Look-ahead Bias in LLMs](https://mlsanity.com/paper/2512.06607/) (2025)
- [Detecting Lookahead Bias in LLM Forecasts](https://mlsanity.com/paper/2512.23847/) (2025)
- [Language Model Guided Reinforcement Learning in Quantitative Trading](https://mlsanity.com/paper/2508.02366/) (2025)
- [Fusing Narrative Semantics for Financial Volatility Forecasting](https://mlsanity.com/paper/2510.20699/) (2025)
- [FinPos: A Position-Aware Trading Agent System for Real Financial Markets](https://mlsanity.com/paper/2510.27251/) (2025)

## Built on by (most cited first, 13 of 13)

- [Summoning the Oracle to Slay It: Mitigating Look-Ahead Bias in Financial Backtesting with Large Language Models](https://mlsanity.com/paper/2605.24564/) (2026)
- [OpenFinGym: A Verifiable Multi-Task Gym Environment for Evaluating Quant Agents](https://mlsanity.com/paper/2606.26350/) (2026)
- [Temporal Leakage in LLM Backtesting: Measurement, Validation, and Adjusted Scores](https://mlsanity.com/paper/2608.02985/) (2026)
- [NumLeak: Public Numeric Benchmarks as Latent Labels in Foundation Models](https://mlsanity.com/paper/2605.30393/) (2026)
- [Look-Ahead-Freedom as Temporal Non-Interference: A Verifiable Correctness Property for Backtesting and Agentic Trading Pipelines](https://mlsanity.com/paper/2607.04958/) (2026)
- [EvolveTrade: Experience-Driven Policy Refinement for Self-Evolving LLM Trading Agents](https://mlsanity.com/paper/2609.17632/) (2026)
- [MemGuard-Alpha: Limits of Membership Inference for Detecting and Filtering Memorization-Contaminated Signals in LLM-Based Financial Forecasting](https://mlsanity.com/paper/2603.26797/) (2026)
- [Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems](https://mlsanity.com/paper/2606.08285/) (2026)
- [From Checker to Forecaster: Code-Owned Evaluation of Model-Generated Strategic Routes Under Delayed Ground Truth](https://mlsanity.com/paper/2607.10972/) (2026)
- [HindsightBench: A Black-Box Behavioral Audit Protocol for Parametric Hindsight in Time-Indexed LLM Decision Tasks](https://mlsanity.com/paper/2607.18867/) (2026)
- [FIDES: A Concordance Protocol for LLM-Generated Trading Strategies](https://mlsanity.com/paper/2608.23308/) (2026)
- [ArrivalBench: Agent-Generated Data Pipelines Are Correct Once and Wrong Under Time](https://mlsanity.com/paper/2610.02363/) (2026)
- [Auditing Future-Memory Use in Point-in-Time Language Agents](https://mlsanity.com/paper/2610.18602/) (2026)

## Abstract (arXiv)

We introduce Look-Ahead-Bench, a standardized benchmark measuring look-ahead bias in Point-in-Time (PiT) Large Language Models (LLMs) within realistic and practical financial workflows. Unlike most existing approaches that primarily test inner lookahead knowledge via Q&A, our benchmark evaluates model behavior in practical scenarios. To distinguish genuine predictive capability from memorization-based performance, we analyze performance decay across temporally distinct market regimes, incorporating several quantitative baselines to establish performance thresholds. We evaluate prominent open-source LLMs -- Llama 3.1 (8B and 70B) and DeepSeek 3.2 -- against a family of Point-in-Time LLMs (Pitinf-Small, Pitinf-Medium, and frontier-level model Pitinf-Large) from PiT-Inference. Results reveal significant lookahead bias in standard LLMs, as measured with alpha decay, unlike Pitinf models, which demonstrate improved generalization and reasoning abilities as they scale in size. This work establishes a foundation for the standardized evaluation of temporal bias in financial LLMs and provides a practical framework for identifying models suitable for real-world deployment. Code is available on GitHub: https://github.com/benstaf/lookaheadbench