# Nous: An Attempt to Extract and Inject the Cognition Behind Prediction-Market Behavior

arXiv: https://arxiv.org/abs/2606.13038 · HTML: https://mlsanity.com/paper/2606.13038/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use.
- Builds on 21 of its 38 references that are in the map.
- First posted on arXiv 11 Jun 2026.

## Placed in

- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 21 of 21)

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- [In-Context Impersonation Reveals Large Language Models' Strengths and Biases](https://mlsanity.com/paper/2305.14930/) (2023)
- [Approaching Human-Level Forecasting with Language Models](https://mlsanity.com/paper/2402.18563/) (2024)
- [Wisdom of the Silicon Crowd: LLM Ensemble Prediction Capabilities Rival Human Crowd Accuracy](https://mlsanity.com/paper/2402.19379/) (2024)
- [TwinMarket: A Scalable Behavioral and Social Simulation for Financial Markets](https://mlsanity.com/paper/2502.01506/) (2025)
- [Designing AI-Agents with Personalities: A Psychometric Approach](https://mlsanity.com/paper/2410.19238/) (2024)
- [PolyBench: Benchmarking LLM Forecasting and Trading Capabilities on Live Prediction Market Data](https://mlsanity.com/paper/2604.14199/) (2026)
- [Prediction Arena: Benchmarking AI Models on Real-World Prediction Markets](https://mlsanity.com/paper/2604.07355/) (2026)
- [Conflict-Aware Fusion: Mitigating Logic Inertia in Large Language Models via Structured Cognitive Priors](https://mlsanity.com/paper/2512.06393/) (2025)
- [Think Fast and Slow: Step-Level Cognitive Depth Adaptation for LLM Agents](https://mlsanity.com/paper/2602.12662/) (2026)
- [Prompting for Policy: Forecasting Macroeconomic Scenarios with Synthetic LLM Personas](https://mlsanity.com/paper/2511.02458/) (2025)
- [PolySwarm: A Multi-Agent Large Language Model Framework for Prediction Market Trading and Latency Arbitrage](https://mlsanity.com/paper/2604.03888/) (2026)
- [The Oracle's Fingerprint: Correlated AI Forecasting Errors and the Limits of Bias Transmission](https://mlsanity.com/paper/2605.00844/) (2026)
- [Bias-Adjusted LLM Agents for Human-Like Decision-Making via Behavioral Economics](https://mlsanity.com/paper/2508.18600/) (2025)
- [Coordination as an Architectural Layer for LLM-Based Multi-Agent Systems](https://mlsanity.com/paper/2605.03310/) (2026)
- [Decoding the Human Factor: High Fidelity Behavioral Prediction for Strategic Foresight](https://mlsanity.com/paper/2602.17222/) (2026)
- [Multiperspectivity as a Resource for Narrative Similarity Prediction](https://mlsanity.com/paper/2603.22103/) (2026)

## Abstract (arXiv)

As LLM agents proliferate in prediction markets and collective decision-making, they risk a cognitive monoculture: agents built on shared foundation models produce correlated forecasts, and recent measurement finds frontier-model errors correlated at r ~ 0.77. We ask whether human cognitive diversity can be recovered from behavior and transferred to LLM agents. Nous extracts a structured eight-dimension behavioral profile from real Polymarket trading activity and injects it into agents through prompts. Our central finding is a dissociation between the two halves of that pipeline. Extraction works, partially: across 100 wallets, 8 of 14 parameters are temporally stable (split-half ICC >= 0.5, bootstrap CI lower bound > 0.3; contrarian score reaches ICC ~ 0.9); wallets are identifiable from their profiles well above chance (top-1 retrieval 17-22% vs. 1% chance); and two of four pre-specified dimensions rank-correlate with future realized profit out-of-sample, though the correlations do not survive behavioral-confound controls. Prompt-level injection does not measurably transmit it: on a semantic embedding metric, structured injection shows no significant advantage over a length-matched control on any model, and the diversity it induces neither reduces ensemble error correlation nor improves Brier score -- a null that persists across exploratory checks on sampling temperature, profile diversity, and question difficulty. Measuring the prompts themselves locates the compression before the model: the structure-to-narrative translator emits near-uniform prompts whose spread does not track profile spread. We position Nous as measuring the cognitive-monoculture problem and the limits of a prompt-level remedy, motivating deeper, below-the-prompt injection (fine-tuning, activation steering). Code, frozen profiles, prompts, and model outputs: https://github.com/WillChienT/nous-paper