/p/2026-10-09 · explainer
Paper explainer · 2610.10263 · Li, Li, Jiang and Lou

The parallel agents edited the same file.

Dynamic concurrency is the switch that lets a coding agent decide, mid-run, to fan work out to sub-agents. Three harnesses were run over the same 354 tasks twice each, once with that switch on and once off, with model, prompt, environment and time budget held constant. On bounded bug-fixing two of the three got significantly worse with it on — 59% of tasks passed against 83%, and 59% against 77% — while tokens ran 1.41 to 3.31× higher and only 27.9% of jointly-solved tasks finished faster in parallel. It earns its keep at the far end of the horizon, where one harness went from 7.1% to 21.4% on the most sustained benchmark. And of the 804 failure instances annotated across 1,062 concurrent traces, the largest single bucket is not a planning subtlety: 26.5% were sub-agents editing the same or related files with nobody coordinating them.

01 · The problem

The same agent, the same budget, one switch

pick a benchmark

02 · The mechanism

Orchestration decides the long tasks; capability decides the short ones

no differencemeasured difference
and the three ways it actually won, when it won

03 · The method

Three harnesses, three completely different ideas of parallel

pick a harness
run startsrun ends
now step the task difficulty

04 · The failures

804 failure instances, and the biggest one is a merge conflict

tap a category

05 · In your own agent

Whether to leave the switch on for this piece of work illustrative

Results

What the paper actually measured

What it does not show

In practice