Benchmark Radar Day 88: Freeze the Strategy Before the Holdout

Day 88 of Benchmark Radar. The scan fetched 1,613 items, published 814, and marked 319 as recommended.

Author: Koutian Wu; GitHub: ktwu01

Benchmark Radar is live. Open the dashboard, star the repository, or read the paper. The Hugging Face dataset provides the data.

Quant-Arena evaluates a complete quantitative-trading research loop. Agents develop features and stock-scoring code with feedback from 2022–2023 Chinese A-share data, then freeze one strategy for a sealed 2024 holdout unavailable during development. This tests whether iterative research improvements survive a withheld period. Quant-Arena: Benchmarking LLM Agents for Quantitative Trading Research.

VerifierBench tests generation of executable verifiers across 222 tasks and 4,993 hidden tests. The tests include alternative correct solutions and attacks, while a six-check audit examines the benchmark’s own source verifiers and tests. A generated checker needs to accept valid alternatives as well as reject invalid candidates. VerifierBench: Benchmarking the Generation of Executable Verifiers for LLM Agents.

Why this matters

Protect the final test from the development loop, and audit the mechanism that assigns success. Otherwise, iteration can improve the visible score without establishing transfer.

Scan coverage

This is a keyword-filtered discovery feed, not a representative survey of AI research. The archived briefing used 169 of 814 captured evidence records. These findings summarize its cited releases; discovery dates do not necessarily equal original publication dates.

Data and briefing: 2026-10-22 archived snapshot.