mlsanityData to 14 Oct 2026

Look-Ahead-Freedom as Temporal Non-Interference: A Verifiable Correctness Property for Backtesting and Agentic Trading Pipelines

arXiv 2607.04958 ↗First posted 6 Jul 2026cs.CR cs.LO cs.PLcited by 1

On the map: LLM agents and tool use → Financial trading and forecasting agents

Abstract (arXiv)

Look-ahead bias (using information from after a decision epoch to make the decision at that epoch) is the dominant way a backtest or a machine-learning evaluation flatters a system that will disappoint in deployment. The field manages it with construct-specific recipes and empirical detectors, which are sound only channel by channel and certify nothing by their silence. We show that look-ahead-freedom is a formal property in disguise: fixing an epoch, the demand that the future not influence the present is temporal non-interference over a time-indexed information lattice. From this identification we develop a pipeline calculus separating a datum's availability from its reference time, and settle the problem's boundary. Where availability may depend on data values, look-ahead-freedom is undecidable (indeed Pi-0-1-hard): leakage is recursively enumerable but freedom is not. On the value-independent fragment (covering windowing, resampling, joins, point-in-time and vintage reads, and agentic retrieval) we give a type-and-effect system that is sound and decidable in linear time. An artifact confirms the theory: the check scales linearly, an independent oracle witnesses no leak in any accepted pipeline, and the checker catches every planted leak that differential and tiling detectors miss.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use. Builds on 8 of its 40 references that are in the map. First posted on arXiv 6 Jul 2026.

Builds on

8 of 40 references.

Closest ancestors · 4

Same direction, newest first

  1. 2026Agentic Trading: When LLM Agents Meet Financial Markets19 May
  2. 2026The Alpha Illusion: Reported Alpha from LLM Trading Agents Should Not Be Treated as Deployment Evidence16 May
  3. 2026Toward Reliable Evaluation of LLM-Based Financial Multi-Agent Systems: Taxonomy, Coordination Primacy, and Cost Awareness29 Mar
  4. 2025Profit Mirage: Revisiting Information Leakage in LLM-based Financial Agents9 Oct

Other references · 4

  1. 2026Look-Ahead-Bench: a Standardized Benchmark of Look-ahead Bias in Point-in-Time LLMs for Financecited by 13
  2. 2026Deep Learning for Contextualized NetFlow-Based Network Intrusion Detection: Methods, Data, Evaluation and Deploymentcited by 2
  3. 2022Guaranteeing Timed Opacity using Parametric Timed Model Checkingcited by 1
  4. 2026The Decision Geometry of Covariance Estimation for the Global Minimum-Variance Portfolio under Heavy Tailscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2026Replayable Financial Agents: A Determinism-Faithfulness Assurance Harness for Tool-Using LLM Agents
  2. 2026FinalityBench: An Effect-Level Benchmark for Agent Decisions Under Delayed and Conflicting Financial Finality
  3. 2026PredictionMarketBench: A SWE-bench-Style Framework for Backtesting Trading Agents on Prediction Markets
  4. 2026Can Blindfolded LLMs Still Trade? An Anonymization-First Framework for Portfolio Optimization
  5. 2026StableEval Arena: A Cost-Aware Agentic Benchmark for Stablecoin Price Stability Prediction
  6. 2026Intelligence Under Time Constraints: Rethinking Test-Time Compute
  7. 2026From Knowing to Doing: A Memory-Controlled Benchmark for LLM Trading Agents on Stock Markets
  8. 2026TimeSeek: Temporal Reliability of Agentic Forecasters