Investor's Last Exam: Can AI Agents Do Equity Investment Without Seeing the Future?
READ THE ORIGINAL SOURCEOpen original sourcehuggingface.co · Opens in a new tabInvestor's Last Exam (ILE) is an equity-research benchmark for financial agents, built with buy-side investors. It uses a versioned financial database of 3.43 million document versions across nine source categories, with timestamps that enforce a decision-time information cutoff. Evaluation covers 86 assignments and jointly scores research quality, forecast accuracy, and simulated investment performance. GPT-6 Astra and Claude Opus 5.5 lead overall at 68.1 and 66.9, yet Astra scores only 52.5 on Forecast & Model, suggesting strong research does not translate into strong predictions.
One primary source; other links are context, not independent corroboration.
- Primary sourceHugging Face Daily Papers
- Discovery linkHugging Face
- Linked in sourceView arXiv page
- Linked in sourceView PDF
The paper page is linked. Peer review and findings have not been independently checked by AINewsIQ.
Open full article view →- Original paper+45
- Fetched source page+30
- No editor link review+0
- Original non-discussion link+5
This measures the stored source trail and editorial steps. It does not certify accuracy, peer review, or independent corroboration. Popularity adds no points.
View full scoring →Read the scoring rubric →What the source says
Investor's Last Exam (ILE) is an equity-research benchmark for financial agents, built with buy-side investors. It uses a versioned financial database of 3.43 million document versions across nine source categories, with timestamps that enforce a decision-time information cutoff. Evaluation covers 86 assignments and jointly scores research quality, forecast accuracy, and simulated investment performance. GPT-6 Astra and Claude Opus 5.5 lead overall at 68.1 and 66.9, yet Astra scores only 52.5 on Forecast & Model, suggesting strong research does not translate into strong predictions.
A READER'S EVIDENCE CHECKHow to read this signalSix questions about claims, sources, risks, and open checks
This is a signal-level guide until claim-level evidence is available. “Not recorded” means the evidence has not been assessed here; it does not mean a result is false. How we check AI news →
- 01Who is making each claim?
The claimant and whether each claim was measured or asserted are not recorded in this public brief.
- 02What was actually tested?
Not recorded per claim. Check the source for sample size, tasks, model and benchmark versions, and reproducibility.
- 03Where are the sources?
Original link: huggingface.co. Brief basis: fetched source page. Primary page date and quote locations are not established in this panel.
- 04What could mislead?
Source claims not independently checked; the reported scores come from the authors' own benchmark and evaluation setup.
- 05What is at risk if wrong?
Not assessed per claim. Consider safety, privacy, cost, and adoption decisions before relying on the result.
- 06What would change the view?
No claim-specific check is recorded. An inspectable primary method or independent replication could change the assessment.
Key findings from the stored brief
- Astra scores 78.9 on Research & Evidence but 52.5 on Forecast & Model.
- Benchmark uses a versioned database of 3.43 million document versions with enforced timestamp cutoffs.
- Evaluation spans 86 assignments tied to buy-side analyst deliverables and financial dependency graphs.
- The authors argue time-bounded historical timestamps could serve as a re-samplable environment for domain RL.
Source claims not independently checked; the reported scores come from the authors' own benchmark and evaluation setup.
This is a source-based abstract, not independent validation of the source's claims.
Evidence & sources
These links show where the signal came from and what the source page linked to. A linked page is context, not independent corroboration.
Related archive coverage
These relationships record a citation or topic overlap, not confirmation of the finding.
The paper page is linked. Peer review and findings have not been independently checked by AINewsIQ.
Timeline & updates
- Signal dated by source or discovery feed
This timestamp places the signal in the radar. It may be the discovery post date rather than the primary page's first publication date.
- Shared brief stored
AINewsIQ prepared one abstract from fetched source page.
A publication revision history is not stored for this signal. New engagement snapshots do not mean the source itself changed.
Why this may matter
Forecasting usually needs months or years of waiting for outcomes. A frozen, timestamped benchmark allows faster, cleaner evaluation of financial agents and points to where analytical strength fails to become predictive judgment, which is the core objective of investment work.
Limits and open questions
Source claims not independently checked; the reported scores come from the authors' own benchmark and evaluation setup.
Organization-specific financial, security, and operational effects require the organization's own context and review.
Discussion
Comments are reviewed before publication. Discuss the evidence, not the person.
No published comments yet.
Reader discussion opens with accounts. You can read the original source and published comments now.