HFEPX · Eval paper review
Adrián Zámečník, Matěj Kripner, Martin Koutecký, Martin Balko +4 more
Published
Oct 7, 2026
Citations
0
Trust level
Moderate
Usefulness score
40/100 (Low)
Extraction confidence
50% (Moderate)
Derived from extracted protocol signals and abstract evidence.
Rater population
Domain Experts
Signals refreshed
Oct 9, 2026
This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.
Use this for comparison and orientation, not as your only source.
Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.
Best use
Background context only
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.
Treat as adjacent context, not a core eval-method reference.
If you are doing eval pipeline work, start here
Large language models are increasingly contributing to mathematical research, where progress often depends on efficient proof search, incremental improvements and careful verification. We describe Bolzano, a multi-agent open-source system that uses parallel prover agents with a verifier agent and maintains a human-readable research state. Initial manual use on expert-selected problems yielded 8 results whose proofs were checked by domain experts. Motivated by these case studies, we ran Bolzano without problem-specific human guidance on about 3,800 open problems extracted from four sets of papers, solving about 200 open problems. One experiment used papers accepted to STOC 2026, a top conference in theoretical computer science. There, we answered four questions raised in the papers, as confirmed by their authors.
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.
Expert Verification
Directly usable for protocol triage.
"Large language models are increasingly contributing to mathematical research, where progress often depends on efficient proof search, incremental improvements and careful verification."
None explicit
Validate eval design from full paper text.
"Large language models are increasingly contributing to mathematical research, where progress often depends on efficient proof search, incremental improvements and careful verification."
Not reported
No explicit QC controls found.
"Large language models are increasingly contributing to mathematical research, where progress often depends on efficient proof search, incremental improvements and careful verification."
Not extracted
No benchmark anchors detected.
"Large language models are increasingly contributing to mathematical research, where progress often depends on efficient proof search, incremental improvements and careful verification."
Not extracted
No metric anchors detected.
"Large language models are increasingly contributing to mathematical research, where progress often depends on efficient proof search, incremental improvements and careful verification."
Domain Experts
Helpful for staffing comparability.
"Initial manual use on expert-selected problems yielded 8 results whose proofs were checked by domain experts."
No benchmark or dataset names were extracted from the available abstract.
No metric terms were extracted from the available abstract.
Large language models are increasingly contributing to mathematical research, where progress often depends on efficient proof search, incremental improvements and careful verification.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
Human feedback protocol is explicit
Detected: Expert Verification
Evaluation mode is explicit
No clear evaluation mode extracted.
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.
