Benchmark Watch

Evaluation notes, benchmark interpretation, leaderboard skepticism, and measurement failures.

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Multimodal Memory Tests Expose the Personal-Agent Gap

Multimodal Memory Tests Expose the Personal-Agent Gap

Product teams are turning memory into the selling point for personal agents. The hard question is no longer whether they can remember a preference; it is...

5 min read
Power Grid Agents Need Constraint Tests, Not Chat Scores

Power Grid Agents Need Constraint Tests, Not Chat Scores

A June 2026 power-systems benchmark argues that language-model agents can solve grid-engineering tasks, but the useful signal is narrower: the agent must...

5 min read
TerminalWorld Makes Agent Benchmarks Harder to Fake

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.

4 min read
Tool Agents Need State Diffs, Not API Call Scores

Tool Agents Need State Diffs, Not API Call Scores

Tool-use agents are moving from choosing the right API to changing live product state. That makes a clean function-call score too small a test because...

5 min read
Multilingual Agents Need Workflow Tests, Not Translation Scores

Multilingual Agents Need Workflow Tests, Not Translation Scores

PolyWorkBench landed on arXiv on 7 July 2026 with a useful correction to enterprise-agent hype: a global workflow is not a translated English task...

5 min read
Agent Benchmarks Need Runtime Receipts, Not Model Labels

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...

4 min read
Data Agents Need Exploration Budgets, Not SQL Magic

Data Agents Need Exploration Budgets, Not SQL Magic

Data Agent Benchmark landed on arXiv on 21 March 2026 with a result that should make enterprise analytics teams pause: the best tested frontier model...

4 min read
Assistant Agents Need Reminder Tests, Not Recall Scores

Assistant Agents Need Reminder Tests, Not Recall Scores

Most agent-memory benchmarks ask whether a model can recover old information. PM-Bench asks a harsher question: can an agent remember to do the right...

4 min read
The 12-to-72 Problem: Computer-Use Agents Hit Human Scores but Miss the Point

The 12-to-72 Problem: Computer-Use Agents Hit Human Scores but Miss the Point

Computer-use agents jumped from 12% to 72% on OSWorld in 18 months. The scores look like progress. The latency and efficiency numbers tell a different story.

4 min read
Why Multi-Agent Papers Don't Replicate in Production

Why Multi-Agent Papers Don't Replicate in Production

A paper from Tran and Kiela tested 28 multi-agent configurations across four architectures: Sequential, Parallel, Debate, and Ensemble. Every single one...

7 min read
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