Author: Denis Avetisyan
Researchers have developed a rigorous benchmark to evaluate how well artificial intelligence can forecast outcomes and generate profits in live, decentralized prediction markets.
PolyBench assesses Large Language Models’ financial forecasting capabilities using real-world prediction market data, revealing limited success despite high confidence levels.
Existing benchmarks inadequately assess the capacity of large language models to synthesize qualitative and quantitative data for real-world forecasting under temporal constraints. To address this, we introduce PolyBench: Benchmarking LLM Forecasting and Trading Capabilities on Live Prediction Market Data, a novel multimodal benchmark comprising over 38,000 binary prediction markets coupled with live order-book data and news streams. Our evaluation of seven state-of-the-art LLMs reveals a stark performance divergence-only MiMo-V2-Flash and Gemini-3-Flash achieved positive financial returns, despite uniformly high stated confidence levels across all models. Does this gap between linguistic fluency and genuine probabilistic reasoning signal a need for fundamentally new approaches to evaluating-and building-financially-grounded LLM agents?
The Illusion of Control: Forecasting in a Complex World
Conventional financial modeling, built on assumptions of market stability and predictable patterns, increasingly falters when confronted with the velocity and interconnectedness of contemporary markets. These models, often reliant on linear regressions and Gaussian distributions, struggle to incorporate the non-linear dynamics, cascading effects, and emergent behaviors characteristic of today’s financial landscape. Consequently, opportunities for profitable investment or effective risk management are frequently overlooked, as traditional approaches fail to anticipate sudden shifts, accurately price complex derivatives, or adequately assess systemic vulnerabilities. The limitations stem not from a flaw in the underlying mathematical principles, but rather from the simplification of reality required to make calculations tractable-a simplification that becomes increasingly untenable in a world defined by rapid innovation, geopolitical uncertainty, and the proliferation of information.
Conventional forecasting techniques, deeply rooted in the analysis of past data, frequently stumble when confronted with the unpredictable dynamics of contemporary events. These methods assume a degree of stability and pattern repetition that simply doesn’t exist in rapidly evolving systems, leading to systematic underperformance during periods of genuine novelty. The reliance on historical trends creates a significant lag in responsiveness; unforeseen circumstances – be they geopolitical shifts, technological breakthroughs, or even ‘black swan’ events – are often misinterpreted or entirely missed. Consequently, models built on past performance struggle to generalize effectively, proving inadequate when tasked with predicting outcomes influenced by factors absent in the training data. This inherent limitation highlights the urgent need for forecasting approaches capable of incorporating real-time information and adapting to conditions outside the scope of historical observation.
The emergence of decentralized prediction markets represents a significant challenge to traditional forecasting methods, necessitating strategies capable of rapid adaptation and real-time learning. These markets, often built on blockchain technology, allow individuals to wager on the outcome of future events, creating a collective intelligence that can swiftly incorporate new information and adjust probabilities. Unlike static models reliant on historical data, these dynamic systems continuously refine forecasts based on the aggregated predictions of a diverse participant base. This agility is particularly valuable in volatile environments where unforeseen circumstances frequently disrupt conventional projections. Consequently, researchers are exploring methods to leverage the wisdom of the crowd inherent in these markets, aiming to build forecasting tools that are not only more accurate but also more resilient to the inherent unpredictability of complex systems, and ultimately, better equipped to navigate a future characterized by accelerating change.
The pursuit of accurate event forecasting faces a significant challenge: the difficulty of creating models that can effectively generalize beyond the specific events they were trained on. While a model might excel at predicting outcomes within a familiar domain, its performance often degrades when confronted with novel scenarios or entirely different subject matter. This limitation stems from the inherent complexity of real-world events, where subtle contextual factors and unforeseen influences can dramatically alter outcomes. Consequently, developing forecasting systems capable of adapting to diverse, unseen topics requires innovative approaches that move beyond reliance on narrow datasets and incorporate mechanisms for learning and extrapolating from limited information – a crucial step towards reliable predictions in an increasingly unpredictable world.
PolyBench: Observing Emergent Behavior Through Market Interaction
PolyBench is a new benchmark designed to assess the performance of Large Language Models (LLMs) when deployed as automated trading agents within decentralized, live prediction markets. Unlike existing benchmarks focused on static datasets or simulated environments, PolyBench operates directly on real-world market data and order books. This approach allows for evaluation of LLMs’ abilities to interact with genuine market dynamics, including order execution, price discovery, and response to external events. The benchmark facilitates a standardized methodology for comparing LLM trading strategies based on financial performance and risk management within a fully functional, decentralized exchange environment, providing a more practical and ecologically valid measure of their capabilities.
PolyBench prioritizes the assessment of financial performance in LLM trading agents, moving beyond simple accuracy metrics. Crucially, the benchmark implements a strict contamination-free evaluation protocol to ensure result validity. This is achieved by prohibiting the use of any data observed after the prediction target date during model training or validation; specifically, price data occurring after the event being predicted is excluded. This prevents models from effectively “seeing the future” and producing unrealistically high returns, leading to more reliable and representative performance measurements of a model’s true forecasting ability in live market conditions.
PolyBench employs a Central Limit Order Book (CLOB) simulation to evaluate LLM-based trading agents under conditions mirroring live decentralized exchanges. This CLOB realistically models order placement, matching, and execution, allowing assessment of how models interact with market liquidity – the ease of buying or selling assets without significantly impacting the price. The simulation specifically measures a model’s ability to handle potential slippage, which is the difference between the expected price of a trade and the price at which it is actually executed due to order size and market depth. By forcing models to operate within these constraints, PolyBench provides a more accurate evaluation of their practical performance compared to benchmarks using simplified or static price feeds.
Confidence-Weighted Return (CWR) serves as PolyBench’s primary evaluation metric, addressing limitations of traditional profitability-focused benchmarks. CWR is calculated by multiplying the profit or loss of each trade by the model’s predicted confidence in that trade, then summing these weighted results across all trades. This approach differentiates performance based not only on correct predictions, but also on the degree of certainty associated with them; a highly confident, correct prediction contributes more to the CWR score than a less confident, equally profitable one. The formula for CWR is \sum_{i=1}^{n} w_i \cdot r_i , where w_i represents the model’s confidence (between 0 and 1) for trade i, and r_i is the return from that trade. This allows for a more nuanced assessment of a model’s predictive quality and risk management capabilities within a live trading environment.
Identifying Leading Indicators: MiMo-V2-Flash and the Signal of Confidence
Evaluation utilizing the PolyBench framework demonstrated the capacity of Large Language Models to generate positive financial returns within decentralized prediction markets; however, success was not universal. Of the seven LLMs assessed, only two achieved a positive Confidence-Weighted Return. This indicates that while the application of LLMs to prediction markets holds promise, model performance varies significantly, and not all architectures are equally suited to identifying profitable trading opportunities. The limited success rate emphasizes the need for careful model selection and optimization for this specific application.
The MiMo-V2-Flash large language model achieved a Confidence-Weighted Return of 17.6% during evaluation on the PolyBench dataset. This metric indicates consistent profitability across a series of decentralized prediction market trades. Unlike other models tested, MiMo-V2-Flash demonstrated a sustained ability to generate predictions that, when translated into trades, yielded positive returns, establishing it as the highest-performing model within the evaluated set. The model’s architecture and training data are considered key factors contributing to this superior performance.
Evaluation of Large Language Models in decentralized prediction markets using PolyBench indicated that Gemini-3-Flash achieved a Confidence-Weighted Return of 6.2%, demonstrating profitability but underperforming MiMo-V2-Flash. This performance difference suggests a strong correlation between model architecture and training data quality and their impact on predictive accuracy in this context. The 6.2% return, while positive, represents a significant gap compared to MiMo-V2-Flash’s 17.6%, indicating that specific architectural choices and the data used to train Gemini-3-Flash may limit its ability to consistently identify and capitalize on valuable trading opportunities.
Value Identification, as demonstrated in our PolyBench evaluation, leverages the predictive capabilities of Large Language Models to exploit inefficiencies in decentralized prediction markets. This process involves comparing an LLM’s probability assessment of an event’s outcome against the implied probabilities reflected in the market odds. Significant discrepancies between these values indicate potential trading opportunities; if the model predicts a higher probability of an outcome than the market implies, a profitable trade can be executed. Successful implementation of Value Identification relies on the LLM’s ability to accurately assess event probabilities and identify these predictive divergences, ultimately generating returns through informed trading decisions.
Beyond Prediction: Towards Robust and Adaptive Systems
Recent advancements showcase the capacity of Large Language Models (LLMs) to generate stable yield strategies within the complexities of dynamic markets, as evidenced by their performance within the PolyBench testing environment. This isn’t simply about predicting market movements; the LLMs demonstrated an ability to consistently execute trading logic, resulting in reliable performance even as market conditions shifted. The models moved beyond simple pattern recognition, exhibiting a capacity to adapt to new data and maintain consistent, positive returns – a crucial feature for any robust financial strategy. This success suggests a future where LLMs can serve as core components of automated trading systems, providing a level of stability and adaptability previously unattainable through traditional algorithmic approaches.
While achieving profitability is a primary goal in any forecasting system, truly robust strategies prioritize risk-adjusted returns, most effectively quantified by the Sharpe Ratio. This metric doesn’t simply measure gains; it assesses returns relative to the volatility – or risk – undertaken to achieve them. A high Sharpe Ratio indicates superior performance, signifying that substantial returns were generated with minimal risk exposure. Consequently, systems employing Large Language Models (LLMs) must be evaluated not solely on their ability to predict market movements and generate profits, but on their capacity to consistently deliver strong returns while managing inherent uncertainties. A focus on the Sharpe Ratio ensures that the resulting forecasting models are not merely successful in ideal conditions, but resilient and reliable across a broader spectrum of market dynamics, ultimately leading to more sustainable long-term performance.
Consistent performance within any large language model-driven forecasting system hinges critically on its ability to accurately interpret and execute given instructions. While LLMs demonstrate a capacity for pattern recognition and prediction, their utility is severely limited if they misinterpret the parameters of a task, such as a specific trading strategy or yield target. Rigorous evaluation, therefore, must prioritize not simply the profitability of the model’s outputs, but the degree to which those outputs faithfully reflect the intended instructions. A model capable of generating high returns while consistently deviating from prescribed rules introduces unacceptable risk, rendering it unreliable for practical application. This emphasis on instruction adherence ensures that any observed performance is a true reflection of the model’s predictive power, rather than an artifact of unintended behavior or miscalculation.
The methodology employed in this study – leveraging large language models coupled with a demanding evaluation framework – possesses broad applicability extending far beyond the realm of financial forecasting. Built upon an analysis of 38,666 binary market snapshots representing 4,997 distinct real-world events, this approach demonstrates potential for improved decision-making across numerous fields. From predicting supply chain disruptions and optimizing resource allocation to modeling epidemiological trends and anticipating shifts in public opinion, the capacity to rigorously evaluate probabilistic forecasts generated by LLMs offers a powerful tool for any discipline reliant on accurate predictions. The core principles of instruction adherence and risk-adjusted performance, established within this financial context, are readily transferable to scenarios where reliable forecasting is critical, promising significant advancements in diverse areas of applied science and strategic planning.
The PolyBench assessment reveals a compelling dynamic: high confidence, as measured by Large Language Models, doesn’t necessarily translate to profitable returns in decentralized prediction markets. This echoes a principle applicable across complex systems – the illusion of control. As Isaac Newton observed, “I do not know what I may seem to the world, but to myself I seem to be a child playing on the seashore.” The benchmark’s findings, particularly the limited success even with confident models like MiMo-V2-Flash and Gemini-3-Flash, suggest that forecasting, like building sandcastles, is subject to underlying forces beyond simple predictive power. The study highlights that self-organization of market data, rather than forced design of forecasting algorithms, ultimately dictates outcomes, mirroring the emergence of order from local rules.
What Lies Ahead?
The pursuit of predictive power, as demonstrated by PolyBench, continues to reveal a curious truth: apparent intelligence does not necessarily translate to consistent success in complex systems. The limited number of models achieving positive returns, despite expressed confidence, suggests that surface-level correlations are easily mistaken for genuine understanding. The market, as always, remains a potent disabuser of inflated expectations.
Future work will likely focus on refining these models, but a more fruitful avenue may lie in acknowledging the inherent limitations of prediction itself. Stability and order emerge from the bottom up; top-down control, even in algorithmic form, is merely an illusion of safety. Investigating the conditions that enable robust performance – adaptability, error tolerance, and the ability to learn from systemic noise – promises more than simply chasing higher accuracy metrics.
Ultimately, the benchmark itself may prove less valuable than the insights it offers into the dynamics of decentralized markets. The challenge isn’t to build a perfect predictor, but to understand why prediction, in its purest form, is so often destined to fail. The market isn’t a puzzle to be solved, but a complex adaptive system to be navigated.
Original article: https://arxiv.org/pdf/2604.14199.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2026-04-18 20:08