Benchmarks
Measuring what AI can actually do. Which benchmarks matter, which are gamed, and why evaluation is harder than it looks.
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Terminal Agents Need Progress Curves, Not Victory Screens
Long-Horizon-Terminal-Bench landed on arXiv in July 2026 with an awkward result for terminal-agent buyers: even the strongest tested model still failed...
Agent Evals Need Harnesses, Not More Scoreboards
AgentCompass first landed on arXiv on 15 July 2026 with a practical complaint: agent evaluation is fragmented, tightly coupled, and hard to reproduce...
TerminalWorld Makes Agent Benchmarks Harder to Fake
TerminalWorld turns public terminal recordings into validated agent tasks. The signal is not a higher leaderboard score. It is a harder benchmark supply chain.
SMAC-Talk Shows Agent Chat Is Not Coordination
SMAC-Talk adds natural-language communication and deception to StarCraft-style multi-agent evaluation. The result is a useful warning: agent chat can expose coordination failure as easily as it fixes
Agent Benchmarking Doesn't Need Every Task
Efficient agent benchmarking points to a cheaper way to compare agents: run the tasks that still separate systems, not every task in the suite.
Agent Benchmarks Need Runtime Receipts, Not Model Labels
RuBench's revised 19 July 2026 release contains a small but important warning for coding-agent buyers: one audited product configuration silently...
Browser-Use Agents After the Computer-Use Benchmarks
Browser-use agents look cleaner than desktop agents, but the benchmarks still hide drift, cost, auth, and recovery failure.
The Benchmark Trap: When High Scores Hide Low Readiness
AI benchmarks measure performance in sanitized environments that bear little resemblance to conditions where these systems will actually operate.