Introduction: Recently, ByteDance and others launched the FutureX dynamic evaluation benchmark, confronting large models with predictive "exams" where answers are unknown, data is dynamically updated, and verification is closed-loop. This work distinguishes between a model's predictive power and its memory, and explores its performance in long-range reasoning, execution robustness, and uncertain environments. Furthermore, the practical effectiveness of LLMs in scenarios like financial forecasting and disease assessment is undergoing optimization, and researchers are seeking new mechanisms to bridge the gap between reasoning and execution.
Table of Contents
01. FutureX Emerges: Can LLMs Handle the Shift from Long-Range Reasoning to Real-World Prediction?
Are static exams too simple? Can FutureX pull "memory-based" models into the true future testing ground? Execution errors "accumulate rapidly"; should reasoning bear the full blame for LLMs failing long-range tasks?...
02. LLM Reasoning Trained for a Thousand Days, But Is the Commander Ready for the Moment of Deployment?
When reasoning is deployed in real-world scenarios like financial forecasting, can the model stably "command" successful implementation?...
03. Which Model Excels in Inference and Prediction? Prior and Posterior Paths "Show Their Powers"?
Which directions have past model prediction technologies focused on? Can mechanisms incorporating prior memory and posterior reflection bring new breakthroughs to model prediction?...
01 FutureX Emerges: Can LLMs Handle the Shift from Long-Range Reasoning to Real-World Prediction?
1. Currently, most benchmarks used to evaluate large language models rely on pre-existing, fixed datasets.
2. While this evaluation method performs well in measuring a model's factual knowledge or simple reasoning ability on known datasets, it struggles to test the model's true reasoning strength when facing dynamic real-world prediction.
① Static benchmarks usually handle static and well-defined problems where solutions already exist. This means if a model is trained on data from 2024 and tested on a benchmark derived from the same period, its performance is more a measure of its memory capacity rather than genuine predictive capability.
② Furthermore, this method is susceptible to data contamination and cannot effectively test a model's true adaptive reasoning ability in unknown environments.
3. Based on this, ByteDance and others released the FutureX dynamic evaluation benchmark, shifting the focus from model memory to genuine dynamic prediction capability. [2-1]
① The benchmark automatically scrapes high-quality information sources from over 2,000 websites daily (195 selected sources), schedules 23 mainstream models/agents to make predictions before events occur, and then scrapes the results for scoring after the events happen. This closed-loop design ensures the answers are "unknown" to the model during prediction, eliminating data contamination.
4. In this benchmark, researchers divided tasks into four difficulty levels: Basic, Broad Search, Deep Search, and Super Agent. Experiments found that basic LLMs without tools performed better on simple multiple-choice questions, but agents capable of real-time tool calling (online searching) began to show advantages in complex tasks. [2-1]
① Basic tasks require the model to choose directly from a few given options; Broad Search tasks require exhaustive identification and returning all correct options.
② Deep Search tasks involve interactive searching and information integration by the model to derive an answer based on evidence; Super Agent tasks require the model to predict highly volatile, open-ended events, performing wide-area searching and deep inference.
5. However, a model's predictive ability is not limited to searching; it relies more on high-quality reasoning in an uncertain real-world environment. [2-2]
① To test the model's pure prediction ability, FutureX researchers conducted a controlled experiment comparing predictions made before the event (ex-ante) versus searches made after the event (ex-post).
② The experiment showed that Grok-4 scored extremely high in the ex-post search mode, but its accuracy dropped sharply in the ex-ante prediction mode.
6. In real-world long-range tasks, humans often rely on reasoning, planning, and task division to maintain continuity and stability. However, LLMs have consistently performed poorly in long-range tasks. The traditional explanation is usually that the model lacks sufficient reasoning and planning capabilities, causing the task to eventually collapse along the long chain.
7. However, in September 2025, researchers from Cambridge University and other institutions artificially separated "execution" from "reasoning" through experiments. They provided the model with complete knowledge and plans beforehand, only tasking the model with step-by-step execution. Under these controlled conditions, they found that even without involving reasoning and planning, models still tended to fail in long-range tasks, fundamentally because execution errors gradually accumulated.
① As the number of task steps increases, the model's single-step accuracy decreases due to "self-conditioning effects"; previous errors contaminate subsequent judgments, forming a chain reaction.
② Although the improvement in single-step accuracy appears to show a "diminishing returns" trend, under the compounding effect, this minor improvement can be amplified, leading to an exponential increase in the length of executable tasks.
02 LLM Reasoning Trained for a Thousand Days, But Is the Commander Ready for the Moment of Deployment?
1. Currently, the spillover of large model inference and prediction capabilities has not been fully "digested," and there is still significant room for optimization in various real-world applications.
2. Previously, researchers conducting the FutureX-S&P500 experiment tasked different LLM Agents with predicting the core financial data of S&P 500 component stocks before the release of the Q2 2025 earnings report. They compared the predictions with Wall Street analysts' consensus forecasts and actual earnings data. [2-4]
3. The experimental results indicate that current top models can outperform Wall Street analysts in predicting the earnings reports of about 40% of companies. More importantly, in some cases, the agents have demonstrated preliminary abilities in perceiving financial logic and forward-looking judgment. [2-5]...