From Beijing's 36Kr European Central Station, August 24:
In July, DigClaw's forecasting framework Rhizome v1 secured three spots at #1, #3, #7 on the FutureX evaluation platform.
The three spots come from three different base models, including Kimi-K3, DeepSeek-V4-Pro, etc. The same set of frameworks enabled all of them to enter the Top 7 simultaneously, and DigClaw is the only participant that has achieved this.
There are 59 real-event forecasting questions covering the political, economic, and technological fields, with no possibility of training data leakage.
This set of results supports the judgment that DigClaw is verifying:
Forecasting capabilities can be precipitated outside the base model.
As the base model advances, the system can gain capability dividends; forecasting trajectories, settlement feedback, calibration experience, and the continuously evolving workflow are continuously precipitated inside the system.
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This is not an accidental competition result, it is the first external verification that DigClaw has given to the question of "how forecasting should be done".
Forecasting is the Most Underestimated Capability of AI
As forecasting tasks gradually move towards standardization and engineering, AI is also expected to handle complex forecasting problems at scale.
But there is a fundamental problem: large language models are inherently not good at forecasting.
What LLMs learn is correlation, not causation. They extract patterns from past corpora, but "learning the past" and "forecasting the future" are two essentially different things.
This brings three fatal problems:
Blind Spot in Causal Direction. The model knows that A and B often appear together, but does not know whether A causes B, B causes A, or there is a common cause C.
Failure of Intervention Reasoning. You cannot ask the model "what impact will the Fed's interest rate cut have on Southeast Asian tech stocks" — because it has only learned historical co-occurrence, and there is no causal graph to support intervention calculation.
Lack of Calibration. The "70% probability" output by the model has not been calibrated in any probabilistic sense, and is essentially just a by-product of the token distribution.
So far, existing solutions have their own limitations: human collective intelligence (prediction markets) requires liquidity, and prices are not credible for niche problems;
LLM pattern matching has no causal structure; end-to-end training has outcome-oriented bias — a reasoning process with strict logic but "wrong answer" will be punished, while a rough judgment that happens to "guess correctly" will be reinforced instead.
This is exactly why DigClaw exists.
DigClaw builds a forecasting infrastructure with causal structure as the skeleton, probability calculation as the engine, and search intelligence as the data pipeline.
The core assumption is: forecasting should not be completed by a single model end-to-end.
Search, causal reasoning, and probabilistic inference are three orthogonal capabilities that should be solved by three dedicated systems separately and then combined structurally.
Three Top 10 Spots Verify the Cross-Base-Model Migration of Forecasting Capabilities
FutureX is currently the most challenging real-time forecasting leaderboard: real-world event forecasting questions are released every week, the standard answers have not yet been generated when forecasts are submitted, and settlement is conducted afterwards.
The dataset is hosted on HuggingFace, the evaluation framework is open source on GitHub, and the results are reproducible and verifiable....
....MUCH MORE
