Human Feedback Types
strongRubric Rating
Directly usable for protocol triage.
"AI agents are increasingly deployed for professional investment research, yet no benchmark captures the complexity of the full investor workflow."
HFEPX · Eval paper review
FrontierFinance: A Challenging Benchmark for Measuring Frontier Intelligence of Finance Agents
Yuhao Zhang, O. Ozan Koyluoglu, Thejas Venkatesh, Richard Diehl Martinez +3 more
Published
Aug 12, 2026
Citations
0
Trust level
Moderate
Usefulness score
57/100 (Medium)
Extraction confidence
70% (Moderate)
Derived from extracted protocol signals and abstract evidence.
Rater population
Domain Experts
Signals refreshed
Aug 12, 2026
Should you rely on this paper?
This paper has useful evaluation signal, but protocol completeness is partial; pair it with related papers before deciding implementation strategy.
Use this for comparison and orientation, not as your only source.
Low-signal caution for protocol decisions
Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.
- The abstract does not clearly name benchmarks or metrics.
Best use
Secondary protocol comparison source
Use if you need
A secondary eval reference to pair with stronger protocol papers.
What to verify
Read the full paper before copying any benchmark, metric, or protocol choices.
Main weakness
The abstract does not clearly name benchmarks or metrics.
Human feedback signal
Detected
From extracted signals
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
57/100
Moderate-confidence candidate
Useful as a secondary reference; validate protocol details against neighboring papers.
If you are doing eval pipeline work, start here
Abstract
AI agents are increasingly deployed for professional investment research, yet no benchmark captures the complexity of the full investor workflow. Existing benchmarks mainly target financial data extraction, a narrow slice that current models have largely saturated, while reference-based metrics and generic LLM-as-a-judge scoring fall short on the open-ended, long-form answers that real analyst queries demand. We introduce FrontierFinance, a fully open benchmark of 220 expert-crafted queries and 11,543 source-attributed rubrics spanning six crucial use cases across the full investor workflow. FrontierFinance is both broader and harder than existing public finance benchmarks. Evaluating frontier models and agent systems under a common harness restricted to publicly available data, we find that the tool harness, not the model alone, strongly shapes quality and efficiency; that Samaya's in-house system leads at 56.0%, ahead of the strongest frontier model (Claude Fable 5, 49.2%) at roughly 2.2x lower cost; and that the best open-weight model (Kimi K3, 46.4%) nearly matches the best proprietary model at 4.5x lower cost. Screening & Discovery and Sector, Industry & Macro remain the hardest use cases across all systems, where even the best systems reach only 33% and 39%. We make the dataset and grading code publicly available.
What we could verify
These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.
Evaluation Modes
strongLlm As Judge
Includes extracted eval setup.
"AI agents are increasingly deployed for professional investment research, yet no benchmark captures the complexity of the full investor workflow."
Quality Controls
missingNot reported
No explicit QC controls found.
"AI agents are increasingly deployed for professional investment research, yet no benchmark captures the complexity of the full investor workflow."
Benchmarks / Datasets
missingNot extracted
No benchmark anchors detected.
"AI agents are increasingly deployed for professional investment research, yet no benchmark captures the complexity of the full investor workflow."
Reported Metrics
missingNot extracted
No metric anchors detected.
"AI agents are increasingly deployed for professional investment research, yet no benchmark captures the complexity of the full investor workflow."
Rater Population
strongDomain Experts
Helpful for staffing comparability.
"We introduce FrontierFinance, a fully open benchmark of 220 expert-crafted queries and 11,543 source-attributed rubrics spanning six crucial use cases across the full investor workflow."
Benchmarks and datasets
No benchmark or dataset names were extracted from the available abstract.
Reported metrics
No metric terms were extracted from the available abstract.
Human feedback details
- Uses human feedback
- Yes
- Feedback types
- Rubric Rating
- Rater population
- Domain Experts
- Unit of annotation
- Multi Dim Rubric
- Expertise required
- Coding
Evaluation details
- Evaluation modes
- Llm As Judge
- Agentic eval
- None
- Quality controls
- Not reported
- Evidence quality
- Moderate
- Use this page as
- Secondary protocol comparison source
Research brief
Metadata summary
AI agents are increasingly deployed for professional investment research, yet no benchmark captures the complexity of the full investor workflow.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
Key takeaways
- AI agents are increasingly deployed for professional investment research, yet no benchmark captures the complexity of the full investor workflow.
- Existing benchmarks mainly target financial data extraction, a narrow slice that current models have largely saturated, while reference-based metrics and generic LLM-as-a-judge scoring fall short on the open-ended, long-form answers that real analyst queries demand.
- We introduce FrontierFinance, a fully open benchmark of 220 expert-crafted queries and 11,543 source-attributed rubrics spanning six crucial use cases across the full investor workflow.
Researcher actions
- Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
- Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
- Use related-paper links to find stronger protocol-specific references.
Caveats
- Generated from abstract + metadata only; no PDF parsing.
- Signals below are heuristic and may miss details reported outside the abstract.
Recommended queries
Contribution summary
- AI agents are increasingly deployed for professional investment research, yet no benchmark captures the complexity of the full investor workflow.
- We introduce FrontierFinance, a fully open benchmark of 220 expert-crafted queries and 11,543 source-attributed rubrics spanning six crucial use cases across the full investor workflow.
- Evaluating frontier models and agent systems under a common harness restricted to publicly available data, we find that the tool harness, not the model alone, strongly shapes quality and efficiency; that Samaya's in-house system leads at…
Why it matters for eval
- We introduce FrontierFinance, a fully open benchmark of 220 expert-crafted queries and 11,543 source-attributed rubrics spanning six crucial use cases across the full investor workflow.
- Evaluating frontier models and agent systems under a common harness restricted to publicly available data, we find that the tool harness, not the model alone, strongly shapes quality and efficiency; that Samaya's in-house system leads at…
Researcher checklist
-
Human feedback protocol is explicit
Detected: Rubric Rating
-
Evaluation mode is explicit
Detected: Llm As Judge
-
Quality control reporting appears
No calibration/adjudication/IAA control explicitly detected.
-
Benchmark or dataset anchors are present
No benchmark/dataset anchor extracted from abstract.
-
Metric reporting is present
No metric terms extracted.