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Coding agents can often tell whether a bug fix works: the test suite passes or fails. Making code faster is harder because a timing result may show that a program is slow without revealing what to change, whether an edit helped, or when to stop. That missing feedback loop is one proposed reason performance optimization has lagged behind issue resolution—but it remains an argument, not a proven cause.
Why code optimization needs more than a timer
In an August 20, 2026 post about his BLOG@CACM article, Jatin Ganhotra describes tests as giving coding agents three useful signals:
- Specification: A passing test indicates that the program meets a stated correctness requirement.
- Direction: A failing test can point toward the affected file or line.
- Stopping rule: When the relevant tests pass, the agent has a clear correctness target.
Performance requests do not automatically come with equivalents. A timer can establish that execution is slow, but it does not necessarily identify the cause. As Ganhotra puts it, “A timer tells you the program is slow, not where.” And if ordinary correctness tests still pass after an optimization attempt, they do not say whether the change made the program faster or whether further work is worthwhile.
The practical difference is that correctness tests can provide a target and a feedback signal throughout a task, while a performance measurement may provide only an initial symptom. An agent needs a way to connect an edit to a reliable speed result without breaking correctness, then decide whether the result is good enough.
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What the reported benchmark scores do—and don’t—show
Ganhotra’s August 20, 2026 announcement reports two results: 79.20% on SWE-bench Verified for issue resolution and 47.1% Opt@1 on the Generalized Speedup Optimization benchmark (GSO) for performance optimization. Those numbers suggest a difference in reported agent capability, but they are not a controlled, apples-to-apples comparison: the benchmarks use different tasks and success criteria.
| Benchmark | Task and scale | Reported measure | What success means |
|---|---|---|---|
| SWE-bench Verified | 500 human-filtered software issue instances | 79.20% in Ganhotra’s 2026 announcement | Percentage of tasks resolved, as described by the official leaderboard |
| GSO | 102 optimization tasks across 10 codebases and five programming languages | 47.1% Opt@1 in Ganhotra’s 2026 announcement | Estimated share of tasks where one attempt reaches at least 95% of the human reference speedup and passes correctness tests |
GSO gives an agent a codebase and a performance test as a precise specification. The agent must improve runtime efficiency toward an expert developer’s optimization while preserving correctness. Its default scaffold is OpenHands unless otherwise specified. The default Opt@1 threshold is 0.95 of the human reference speedup; a threshold of 1.0 instead requires beating that reference.
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The scores also depend on benchmark configuration. GSO says it added network filters on July 12, 2026, after observing some newer models use techniques to fetch upstream commits or pull-request diffs. On September 27, 2026, it improved elicitation to ask models to continue measuring and improving after their first speedup. These changes matter when interpreting scores across evaluation dates. The figures above are the results cited in Ganhotra’s August announcement, not a like-for-like measurement under a shared task set and evaluation protocol.
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The benchmark framing points to a more useful optimization task than “make this faster”: define a performance target, preserve correctness, and make the agent assess the result against a reference. In GSO, the performance test provides a precise specification and correctness tests remain part of the success condition. That makes the reward clearer than a timing number alone, even though it does not establish that every real-world optimization task can be specified as cleanly.
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For an optimization request, the reader-facing questions are:
- What runtime or workload is being measured?
- What baseline or reference determines whether the change is an improvement?
- Do correctness checks still pass after the change?
- Is there a defined performance target or stopping threshold?
- Can the agent keep measuring after its first apparent speedup?
These questions separate “the code ran” from “the code became faster under a stated measure, while still doing the right thing.” They also clarify what an agent can learn from each attempt rather than leaving it to infer progress from a timer.
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Why the explanation is still uncertain
Ganhotra’s feedback-loop account is a proposed explanation for why agent performance on optimization trails issue resolution. He explicitly leaves open another possibility: agents may simply lack the optimization skill, and scaling could eventually close the gap. The benchmark figures show different reported outcomes on distinct evaluations; they do not establish that missing feedback caused the difference.
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