ResearchPod Summary
Evaluating Large Language Model (LLM) agents on benchmarks like SWE-Bench or GAIA is notoriously difficult. These tasks require models to operate over long horizons, interact with complex software environments, and recover from errors, often resulting in evaluation processes that take days and cost thousands of dollars. This high barrier to entry limits how frequently researchers can assess progress, often restricting rigorous evaluation to only the most well-resourced teams.
This paper introduces PACE (Proxy for Agentic Capability Evaluation), a framework designed to predict agentic performance using a compact, inexpensive subset of non-agentic benchmark instances. Instead of running full agentic evaluations, PACE selects a small number of instances from existing, fast-to-run benchmarks—such as those measuring instruction following, code generation, or reasoning—that are most predictive of the target agentic benchmark.
PACE constructs this proxy by combining two selection strategies:
These selected instances are then used to fit a noise-aware regression model that maps a model's performance on the proxy subset to its expected performance on the full agentic benchmark.
Experiments across 14 models and 4 agentic benchmarks demonstrate that PACE is highly effective. Using a proxy of just 100 instances, PACE achieves a mean absolute error (MAE) under 4% and a Spearman correlation above 0.80, while reducing evaluation costs by approximately 100 times compared to random sub-sampling of the target benchmark. The framework also provides interpretable insights, as the selected proxy instances reveal the specific capabilities—such as planning or long-context aggregation—that are most critical for success on different agentic tasks.
Alex: Welcome to another episode of ResearchPod. Today we're looking at PACE, a framework designed to solve a significant bottleneck in how we evaluate the most capable AI models.
Sam: I've heard testing these AI agents is incredibly expensive. Is this paper essentially trying to find a faster way to measure performance?
Alex: That's exactly the problem it's addressing. Imagine you've built a new AI model and you want to know how well it performs on difficult, real-world tasks—things like browsing the web, writing and running code, or completing a multi-step research project. These tests, called "agentic" benchmarks, can take days to run and cost enormous amounts of computing power.
Sam: So if you're a developer tweaking your model dozens of times a week, you simply can't afford to run the full test every time.
Alex: Precisely. You'd be stuck waiting days for feedback on every small change. PACE is designed to break that bottleneck. It selects a small, carefully chosen set of much simpler tasks—things like basic math problems or short coding exercises—that act as a kind of diagnostic panel. The idea is that a model's performance on these quick, cheap tasks can reliably predict how it would do on the expensive, full-scale test.
Sam: Like a doctor using a few blood markers to estimate your overall health, rather than running every possible scan.
Alex: That's a good way to put it. And the key challenge is choosing the right blood markers—the tasks that are genuinely informative, not just easy to measure.
Sam: So how does PACE actually pick those tasks?
Alex: It uses two signals working together. The first is about relevance: PACE looks for tasks that are mathematically similar to the expensive agentic test it's trying to predict. You want your cheap diagnostic to be related to the real thing, not just any random question.
Sam: That makes sense. What's the second signal?
Alex: The second is about diversity. Imagine you have a hundred practice questions, but fifty of them are basically asking the same thing in slightly different words. Including all fifty doesn't give you fifty times the information—it's mostly repetition. PACE uses a technique called Singular Value Decomposition to cut through that redundancy. Think of it as mapping out the "shape" of a collection of questions. It identifies which questions are genuinely distinct from each other, so you end up with a small set that covers as much ground as possible.
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Sam: So you're not just picking the most relevant tasks—you're picking a diverse set of relevant tasks. That's a meaningful distinction.
Alex: Exactly. Once PACE has that curated subset, it fits what's called a regression—essentially a statistical line drawn through past data. It says: here's how a range of models scored on these cheap tasks, and here's how those same models scored on the expensive benchmark. That relationship becomes the prediction engine.
Sam: And I'd imagine model performance isn't perfectly consistent—there's some randomness involved?
Alex: There is, and the researchers account for it. They use a technique called bootstrapping, where the system repeatedly resamples its data in slightly different ways to test whether the prediction holds up. It's a way of stress-testing the result to make sure it isn't just a lucky coincidence.
Sam: So does it actually work? Can a cheap test genuinely stand in for a days-long evaluation?
Alex: The results are notable. Across fourteen models, PACE predicted agentic benchmark scores with high accuracy, while costing less than one percent of what a full evaluation would require. That's a substantial efficiency gain for developers who need rapid feedback during the development process.
Sam: If you can get a reliable estimate in minutes instead of days, it genuinely changes how you work. But I want to push back a little—if the task selection is that aggressive, doesn't it risk becoming too narrow? What if the cheap tasks you've chosen just happen to correlate with the benchmark for the wrong reasons?
Alex: That's a fair challenge, and the authors take it seriously. They address it in two ways. First, they draw from a deliberately broad pool of source tasks, so the selection isn't constrained to one narrow domain. Second, they use a learned weighting system that prevents any single question type from having an outsized influence on the prediction. The goal is to capture underlying capabilities—the fundamental skills that drive performance—rather than surface-level patterns that might not generalize.
Sam: So the logic is that if a model has the right foundational skills, those will show up across many different types of simple tasks, and PACE is designed to detect that signal.
Alex: That's the core idea. It's less about any specific question and more about identifying the building blocks that predict success on complex, multi-step goals.
Sam: It's a practical answer to a real resource problem. Rather than treating every evaluation as a full-scale production, PACE gives developers a reliable early read—with the understanding that the full test still matters when it counts.
Alex: That's a good summary. It's a tool for rapid iteration, not a final verdict. The full-scale validation still has its place. Thanks for listening to ResearchPod.