Benchmark Radar Day 79: Can the Agent Derive the Answer Key?

Day 79 of Benchmark Radar. The scan fetched 1,476 items, published 624, and marked 323 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.

Unreachable Oracles audits whether a benchmark answer can be derived from the information supplied to an agent. It traces the target value and construction steps through a declared finite set of permitted derivations. The check addresses an earlier question than model accuracy: whether the task is solvable under its own stated rules. Unreachable Oracles: Auditing Ground-Truth Derivability in a Synthetic Tool-Drift Benchmark for LLM Agents.

GraphInstruct version 1.1 supplies 900 graph-generation instructions across six constraint levels and 1,782 algorithmically synthesized reference solutions. Five deterministic scoring dimensions, raw model responses, and per-instance scores make failures inspectable beyond a single pass rate. GraphInstruct: A Progressive Benchmark for Diagnosing Capability Gaps in LLM Graph Generation.

Why this matters

Validate the answer key before comparing solvers. Then preserve enough scoring detail to explain which constraints a solver missed.

Scan coverage

This is a keyword-filtered discovery feed, not a representative survey of AI research. The archived briefing used 143 of 624 captured evidence records. These findings summarize its cited releases; discovery dates do not necessarily equal original publication dates.

Data and briefing: 2026-10-13 archived snapshot.