## **Main Findings:**
* **Part 1’s results did not replicate:** The optimization process of part 1 of this series found a prompt that notably increased expected baseline score in evaluation for GPT 4.1 and GPT 4.1 Nano. When these prompts were run in an actual tournament with live resolutions, the prompt either underperformed or met their baseline bot. I have a number of hypotheses as to why, without firm conclusions. Though these results cast doubt on some aspect of the prompt optimization methodology for forecasting.
* **Prompt optimization helped weaker models more than stronger ones**: pt 1 and pt 2 together show large gains on GPT-4.1-nano, smaller gains on GPT-4.1, and no measurable gains on DeepSeek-R1 or o3. Regardless of how well expected baseline score predicts actual baseline score, it seems that stronger models are less affected by changes to prompting.
* **Automated prompt optimization produced no measurable improvement on o3**: Optimizing the research and reasoning prompts together did not beat the control prompt on the held-out test set. The score gaps between control and any of 4 optimized o3 prompts on a 200-question test, and between control and the train-set winner on a 278-question rerun with 3 predictions per question, all sit comfortably inside their 90% t-test confidence intervals.
* **The same optimizer gave a 20-point swing on the weaker GPT-4.1-nano**: The control prompt scored -4.99 +/- 7.84. The best optimized prompt for gpt-4.1-nano scored 15.34 +/- 5.11 on the 282-question test set. This is comparable in size to the 18-point swing reported in[ <u>pt 1</u>](https://www.metaculus.com/notebooks/38421/results-of-automated-prompt-engineer-for-forecasting/) for the reasoning-prompt-only optimizer on the same model. Per-idea, the research+reasoning optimizer is roughly 1.5-2x as effective (60 ideas / 3 iterations here vs 100 / 4 in pt 1). But it is less effective per dollar due to not being able to cache search results when running research optimization.
* **The best o3 prompts in training were unusually minimalist**: The top two o3 train prompts ran only 1-3 tool calls instead of the 11 available, and were intentionally minimalistic in their approach. However, these prompts won by a very small margin; very little can be drawn from this other than that minimalistic prompting may be an interesting future research direction.
## **Introduction:**
When trying to get LLMs to predict the future, one question that often comes up is whether the exact wording of the prompt makes any difference. In[ <u>pt 1</u>](https://www.metaculus.com/notebooks/38421/results-of-automated-prompt-engineer-for-forecasting/) of this writeup I tried to answer this question by testing an automated prompt optimizer that varied only the final reasoning prompt. This pt 2 follows up by varying both the research prompt and the reasoning prompt at the same time in order to test research approaches. I also test on a stronger model (OpenAI o3) than pt 1 had access to originally.
This is a sprint-scale exploration. I report 90% t-test confidence intervals on per-question expected baseline scores, but I did not run formal hypothesis tests, did not adjust for multiple comparisons against the same control, and used only 50 train questions and \~60 candidate prompts per model. See the Caveats subsection at the end of the results.
In general, many of the findings from this piece are made weaker by the fact that the community prediction may be a bad evaluation target to optimize against. However, independent of this, they still apply with regard to answering the question "what prompting strategies allow you to effectively optimize an arbitrary forecasting-adjacent metric".
## **Methodology:**
Most of the methodology is identical to[ <u>pt 1</u>](https://www.metaculus.com/notebooks/38421/results-of-automated-prompt-engineer-for-forecasting/#methodology) with some notable differences shared below:
* **50-question train set, 278 / 282-question test sets**: I filtered Metaculus for binary questions with greater than 15 forecasters that had opened in the past 1.5 years and were currently open at the time of filtering. The o3 run uses a question set with 50 train and 278 test questions. The GPT-4.1-nano run uses a question set with 50 train + 282 test questions. Background information was removed.
* **Two-prompt optimizer (research + reasoning)**: In pt 1, the auto-optimizer created reasoning prompts that were used to create a forecasting bot to test. The new optimizer now defines both a research prompt and a reasoning prompt. The optimizer brainstormed prompts via Gemini 2.5 Pro and Perplexity, then mutated and bred the top performers in the same way as pt 1.
* **60 ideas across 3 iterations**: Each optimization run made \~60 bots. The optimizer went through 3 iterations of 19-20 prompt sets (i.e. "bots"). Like before, at the end of each iteration, each bot was scored against the community prediction on Metaculus, and this score was used as feedback to create a new set of bots. Note that pt 1 ran 100 ideas across 4 iterations.
* **Research-tool budget**: The research prompt has access to 4 tools with the following per-question caps: perplexity\_quick\_search\_low\_context (max 7), query\_asknews (max 2), data\_analysis\_tool (max 1), perplexity\_reasoning\_pro\_search (max 1). 11 total tool calls per question are available.
* **Scored using expected baseline score**: identical to pt 1: with c the community prediction and p the bot's prediction, the expected baseline score is 100 \* (c \* (log2(p) + 1.0) + (1.0 - c) \* (log2(1.0 - p) + 1.0)). Higher is better, and the score of the community prediction itself is the maximum a forecaster can attain on this metric.
* **Test runs**:
* **o3 test run A** (the first 200 of the 278-question held-out test set, 1 prediction per question): the top 4 optimized prompts from training were rerun alongside the control prompt. The 200-question subset was a cost choice to keep the 5-bot run affordable.
* **o3 test run B** (the full 278-question held-out test set, 3 predictions per question): as a noise-reduction step, the single best train prompt was rerun against the control. With only 2 bots in this run, the budget covered the full test set plus 3 predictions per question. Run B's 200-question prefix is exactly run A's question set, so the two runs are directly comparable on the 200 questions they share.
* **GPT-4.1-nano test** (282 questions, 1 prediction per question): the top 4 optimized prompts from training were rerun on the test set alongside the control prompt.
## **Optimization Results:**
In every chart below, the first benchmark group is the "Perfect Predictor" (the community prediction itself, which maximizes expected baseline score by construction). The next group is the Control prompt. The remaining groups are the top optimized prompts. Each group has 3 bars: All Questions (light blue), Certain Questions (community prediction outside \[10%, 90%], dark blue), and Uncertain Questions (community prediction inside \[10%, 90%], red). Error bars are 90% confidence intervals from a t-test. The bar that matters for the headline takeaway is "All Questions".
### **o3 research+reasoning optimization, test run A (200 questions, 1 prediction per question):**
The 4 optimized prompts and the control prompt all land in the same band on the All Questions bar. Scores are 19.51 +/- 7.04 for the control vs 22.85 +/- 7.04 for The\_Minimalist (the best test-set prompt) and 21.36 +/- 6.26 for Structured\_Minimalist\_Contrast (the best train-set prompt). Confidence intervals overlap heavily. The Perfect Predictor scored 43.14 on this set.
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" />
None of the optimized prompts beat the control by a margin that can be distinguished from noise.
### **o3 research+reasoning optimization, test run B (278 questions, 3 predictions per question):**
To reduce per-question noise, the single best train prompt (Structured\_Minimalist\_Contrast) was rerun against the control prompt with 3 predictions per question on the full 278-question held-out test set (Test Run A's 200 questions are the first 200 of this set). The result narrows the confidence intervals but does not change the conclusion: control 21.90 +/- 4.86, optimized 23.39 +/- 4.80. The Perfect Predictor scored 41.90 on this set.
<img height="368" width="624" 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" />
A 1.5-point gap with overlapping 90% intervals on n\=278 is consistent with the optimizer producing no real improvement on o3.
### **GPT-4.1-nano research+reasoning optimization (282 questions, 1 prediction per question):**
For nano, the picture is very different. The control prompt scored -4.99 +/- 7.84 on the 282-question test set, while the best optimized prompt (Pre-Mortem\_and\_Causal\_Chain\_Analysis) scored 15.34 +/- 5.11. The next-best optimized prompt (Causal\_Chain\_Fragility\_Test) scored 14.16 +/- 4.67. The Perfect Predictor scored 43.87 on this set.
<img height="343" width="624" 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" />
A 20-point gap on n\=282 with non-overlapping 90% CIs would probably survive even a generous multiple-comparison correction across the 4 optimized prompts in this set. The smaller gaps among the optimized prompts (between, say, Pre-Mortem\_and\_Causal\_Chain\_Analysis and Causal\_Chain\_Fragility\_Test) should be treated as suggestive only since I did not adjust for those comparisons.
## **Other Results:**
### **Research prompt optimization may be more efficient**
Pt 1 ran a reasoning-prompt-only optimizer on GPT-4.1-nano with 100 ideas across 4 iterations and reported a swing from -6.35 baseline to 11.72 best optimized prompt (18 points). Pt 2 ran a research+reasoning optimizer on GPT-4.1-nano with 60 ideas across 3 iterations and got a swing from -4.99 to 15.34 (20 points). Adjusted for the smaller search budget here, that is roughly 1.5-2x as effective per idea. However, it is not 1.5-2x as effective per dollar in general since the research-prompt optimizer fires more research-tool calls per evaluated bot.
A natural follow-up question, which this writeup does not answer, is what the swing would be from optimizing only the research prompt while holding the reasoning prompt fixed. That would isolate the marginal value of research-prompt optimization on top of pt 1's reasoning-prompt-only result.
### **Train winners for o3 were unusually minimalist**
The best o3 prompts on the training set both used much smaller research budgets than they were allowed to use. Structured\_Minimalist\_Contrast (the train winner) ran exactly 3 perplexity\_quick\_search\_low\_context calls and nothing else. The\_Minimalist (which placed first on the test set within noise) ran a single quick search and used the raw output as the entire research report. Both sit far below the 11 tool calls available per question. These bots did not differ significantly from other bots in the train/test run. This is mostly a null result, but given that these bots did not significantly underperform, it would be worth continuing to test overly simple bot research designs.
### **Bayesian-prompt underperformance replicates**
In pt 1 I noted that prompts with "Bayesian" in the bot's name underperformed the control. The same pattern showed up again in the o3 train run: the Bayesian-updating prompt was one of the worst performers. This is a small-sample observation across two runs, but consistent in direction.
### **Possible reasons o3 did not benefit**
* OpenAI's own guidance is that reasoning models tend to respond better to high-level instructions than to precise step-by-step instructions.
* The search budget was small (60 ideas in 3 iterations).
* The optimizer varied *how* the available tools were used. Varying *which* tools are present at all might be a more productive search dimension.
### **How pt 1's optimized prompts held up in the Fall 2025 tournament**
After pt 1, I used the top performing prompt for GPT 4.1 to make 2 bots in the Fall 2025 FutureEval tournament. Both used this prompt to run binary forecasts, and the numeric and multiple choice prompts were left unchanged. One ran with GPT 4.1 and the other ran with GPT 4.1 Nano. These ran alongside the unoptimized template-prompt versions of the same models. Below are the final standings from the[ <u>Fall 2025 leaderboard</u>](https://www.metaculus.com/tournament/fall-aib-2025/) (they are shown in the "advanced" leaderboard). Scores shown are spot[ <u>peer scores</u>](https://www.metaculus.com/help/scores-faq/#peer-score), which are peer scores calculated from only a single forecast from each forecaster on each question.
| Bot | Total spot peer score | Questions answered | Avg spot peer score |
| --------------------------------------------- | --------------------- | ------------------ | ------------------- |
| metac-gpt-4-1+asknews | 1666.64 | 354 | +4.71 |
| metac-gpt-4-1+asknews\[optimized-prompt] | 967.72 | 351 | +2.76 |
| metac-gpt-4-1-nano+asknews\[optimized-prompt] | -6827.64 | 353 | -19.34 |
| metac-gpt-4-1-nano+asknews | -6850.34 | 354 | -19.35 |
A second cut of the same data restricts to the binary questions in the tournament, which is closer to pt 1's evaluation regime since pt 1 only used binary questions. The bots forecast on 205 binary questions that were not annulled:
| Bot | Total binary spot peer score | Binary questions answered | Avg per binary question |
| --------------------------------------------- | ---------------------------- | ------------------------- | ----------------------- |
| metac-gpt-4-1+asknews | 1639.78 | 205 | +8.00 |
| metac-gpt-4-1+asknews\[optimized-prompt] | 435.12 | 205 | +2.12 |
| metac-gpt-4-1-nano+asknews\[optimized-prompt] | -4514.74 | 205 | -22.02 |
| metac-gpt-4-1-nano+asknews | -4827.15 | 205 | -23.55 |
The optimized prompt seems to have no effect on the GPT 4.1 Nano bot despite having a strong improvement in pt 1. Additionally, the prompt has had a negative effect on the GPT 4.1 bot. It is especially surprising that the prompt had an actively negative effect on GPT 4.1.
Here are some hypotheses on why this happened:
* **Statistical Noise**: The optimized prompt could have underperformed by chance assuming they are of equal skill (i.e. the prompt had no effect). The average scores are notably different but too far off. 95% Error bars for bots usually span around +/- 5 spot peer points (e.g.[ <u>Q2</u>](https://www.metaculus.com/notebooks/40456/q2-ai-benchmark-results/),[ <u>FutureEval</u>](https://www.metaculus.com/futureeval/leaderboard/)). However, it is notably less probable that the optimized prompt underperformed assuming that it was actually significantly better.
* **Research was doubled in prompt**: If you look at how the[ <u>optimized prompt</u>](https://docs.google.com/document/d/1YkPcEZ-uL63MxlOCdGiFrbohlA7DJ_rEyvw8MRuRL6c/edit?tab=t.0) is written, you'll notice that it has 2 \{research} placeholders. This means the entire AskNews context (>15 paragraphs) is inserted in the middle of the instructions. I found it strange that this didn't hurt the bot's performance in the original optimization run.
* **Low quality community predictions**: Some community predictions with lower forecaster counts become stale and out of date when people stop paying attention to them. I filtered for forecasts with >15 forecasters on them. Usually questions with 15 forecasters lead to a decent community prediction assuming there is recent activity. However, I did not implement a way to identify stale forecasts, and there may have been more in the train/test set than expected.
* **The community prediction is not hard truth**: There might be other harder to identify reasons why the community prediction is not a good optimization target related to the nature of the community prediction being opinion (albeit a good one) and not fact. For instance there may be times where the bot may be brilliant on a question, and forecast 99% because it found something that the community missed. However, even if it's right, it will be penalized for deviating from the community prediction. In the end it is difficult to measure how often things of this nature happen in an automated capacity.
* **Different question mix**: Pt 1 evaluated only on binary questions filtered to greater than 15 forecasters, opened in the past 1.5 years. The tournament includes binary, numeric, and multiple-choice questions across a different topic distribution and with questions resolving in less than 4 months. The optimized prompt was selected for a narrower regime than the one it was deployed in.
* **Single forecast vs aggregated forecasts**: Pt 1's evaluation used 1 prediction per question. The metac tournament bots aggregate 5 predictions per question. A prompt selected for being good on a single shot may produce outputs that combine less well under aggregation.
* **GPT 4.1's knowledge of the world becomes outdated over time**: The optimization was run May 2025 against questions opened over the prior 1.5 years. The tournament's questions ran from September to December 2025 and resolved over Jan-March 2026. GPT's understanding of the world may have been more fresh in May, and the prompt structure might have assumed a fresher knowledge of the world.
* **A bug that I missed**: I can't fully rule out that there was a bug in the optimization and evaluation code (or even bot implementation) that I missed. However, I went back and checked the code and areas that seemed most suspicious. Both Claude and I could not find any problems.
Given the above, there may still be hope for using the community prediction as an evaluation target (e.g. by finding ways to exclude stale questions, etc). However it seems that evaluation on live questions while waiting for real resolutions continues to be the gold standard. Additionally, though GPT 4.1 on average far underperforms the community prediction, newer models are becoming competitive at times. This further degrades the value of using the community prediction as an evaluation target.
The results of this piece are somewhat invalidated due to this finding.
### **Caveats**
A few methodological notes that should temper how strongly the numbers above are read:
* **No formal hypothesis tests**: Confidence intervals reported here are 90% t-test confidence intervals on per-question expected baseline scores. I did not compute paired-test p-values or any formal significance test against the control prompt. Phrases like "distinguishable from noise" mean "the 90% CI on the per-question score gap excludes 0", not a formal significance result.
* **Multiple comparisons not adjusted**: Each test set was used to compare 4 optimized prompts against the same control. With 4 nominal-90% intervals, the family-wise error rate for spurious gaps is closer to 1 - 0.9^4 ≈ 34%. Practically: the o3 results stay null after any reasonable correction (no individual gap was large enough to begin with). The 20-point nano gap stays significant after any reasonable correction. The smaller within-optimized-set gaps inside the nano result are suggestive only.
* **Small training samples**: 50 train questions and \~60 candidate prompts per model means train rankings are noisy. The o3 train winner being a different bot from the o3 test winner is consistent with what you would expect from training noise alone.
* **No error bars on pt 1's replication experiment**: I did not calculate error bars on the binary level leaderboard for testing whether pt 1's results replicated in the Fall 2025 Tournament. However, I have indexed my interpretation on the precedent of error bars calculated on similar tournaments with similar number of questions.
* **Potential community prediction leakage**: A small caveat about leakage: about 3% of nano training research reports and about 2% of test reports mention "Metaculus" (most of these appear to be incidental community-prediction look-ups/references). The research-prompt instructions explicitly forbid Metaculus searches. The leak rate is small enough that it does not change the conclusions, but it is worth flagging.
* **Sprint deliverable, not a paper**: This is an exploratory follow-up to pt 1. Treat the conclusions as directional: optimization works on weaker models, does not appear to work on o3, the o3 train winners happened to be minimalist. None of this is meant as a definitive significance result.
## **Code and Data:**
Prompts referenced in this post can be found [<u>here</u>](https://docs.google.com/document/d/17c2W3KP-OPdnVJqz9uI55hcHfu-F8C0KaJKD0jmeE_Y/edit?usp=sharing).
All benchmark JSONL files (train iterations, test runs, and the matching question sets v3.0 and v4.0) are in[ <u>this Drive folder</u>](https://drive.google.com/drive/folders/1e0qFTFeQVguWPMZaC3fkuvfS1hJnEazR). Specific files used to produce the charts above:
* o3 test run A: benchmarks\_best\_prompts\_v3.2\_\_o3\_\_Qv3.0.test\_200.jsonl.
* o3 test run B: benchmarks\_best\_prompts\_v3.3\_\_o3\_\_Qv3.0.test\_278\_\_only\_top\_prompt\_w\_3\_predictions.jsonl.
* GPT-4.1-nano test: benchmarks\_best\_prompts\_v4.1\_\_gpt4.1-nano\_\_Qv4.0.test\_282.jsonl
Optimizer and evaluation scripts on GitHub:
* o3 run, optimizer and evaluator (commit b1a17138):[ <u>scripts/run\_optimizer.py</u>](https://github.com/Metaculus/forecasting-tools/blob/b1a17138640348be9a8fcf6c68a627b5df6d6b41/scripts/run_optimizer.py),[ <u>scripts/run\_higher\_model\_evaluation.py</u>](https://github.com/Metaculus/forecasting-tools/blob/b1a17138640348be9a8fcf6c68a627b5df6d6b41/scripts/run_higher_model_evaluation.py).
* GPT-4.1-nano run, optimizer and evaluator (commit ee673919):[ <u>scripts/run\_optimizer.py</u>](https://github.com/Metaculus/forecasting-tools/blob/ee67391905046d85697b767d1d0a2030ac50539e/scripts/run_optimizer.py),[ <u>scripts/run\_higher\_model\_evaluation.py</u>](https://github.com/Metaculus/forecasting-tools/blob/ee67391905046d85697b767d1d0a2030ac50539e/scripts/run_higher_model_evaluation.py).
The same streamlit benchmark viewer used to render the figures above is at[ <u>forecasting\_tools/cp\_benchmarking/benchmark\_displayer.py</u>](https://github.com/Metaculus/forecasting-tools/blob/main/forecasting_tools/cp_benchmarking/benchmark_displayer.py) in the[ <u>forecasting-tools</u>](https://github.com/Metaculus/forecasting-tools) Python package.
The pt 1 GPT-4.1 optimized bot used in the Fall 2025 tournament is at[ <u>forecasting\_tools/forecast\_bots/official\_bots/gpt\_4\_1\_optimized\_bot.py</u>](https://github.com/Metaculus/forecasting-tools/blob/main/forecasting_tools/forecast_bots/official_bots/gpt_4_1_optimized_bot.py).