The latest articles on DEV Community by MD Arfaa Taj (@savas_009). https://dev.to/savas_009 https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3854072%2F83ef647d-7520-41bc-8252-1d73349d69cd.jpg https://dev.to/savas_009 en MD Arfaa Taj Fri, 02 Oct 2026 11:53:57 +0000 https://dev.to/savas_009/i-tried-to-measure-that-one-weird-failure-mode-in-llms-agents-3hm0 https://dev.to/savas_009/i-tried-to-measure-that-one-weird-failure-mode-in-llms-agents-3hm0 <h2> The failure mode I couldn't stop thinking about </h2> <p>Every time I use [a model / an agent] for [multi-step reasoning / code generation / tool use], I notice the same thing: [describe the failure in one or two sentences, e.g. "it confidently skips a step and the final answer looks right but isn't"].</p> <p>It's easy to say "that happens sometimes." It's harder to say <strong>how often</strong>, <strong>under what conditions</strong>, and <strong>whether it's getting better</strong>. So I decided to build a benchmark for it.</p> <h2> What I built </h2> <ul> <li> <strong>Task design:</strong> [what each test case asks the model to do]</li> <li> <strong>What counts as a failure:</strong> [your scoring rule, kept as objective as possible]</li> <li> <strong>Dataset size:</strong> [number of cases and how you created them]</li> <li> <strong>Tooling:</strong> [Kaggle Benchmarks / Python / etc.]</li> </ul> <h2> How I measured it </h2> <ol> <li>Wrote [N] test cases that isolate the failure mode</li> <li>Ran them across [models you tested]</li> <li>Scored each response using [exact match / rubric / programmatic check]</li> <li>Repeated runs to check for variance</li> </ol> <h2> What I found </h2> <div class="table-wrapper-paragraph"><table> <thead> <tr> <th>Model</th> <th>Pass rate</th> <th>Notes</th> </tr> </thead> <tbody> <tr> <td>[Model A]</td> <td>[x%]</td> <td>[observation]</td> </tr> <tr> <td>[Model B]</td> <td>[x%]</td> <td>[observation]</td> </tr> </tbody> </table></div> <p>The most surprising result: [your main finding].</p> <h2> What I learned </h2> <ul> <li>Measuring a failure is very different from noticing one</li> <li>[A lesson about test design or scoring]</li> <li>[A lesson about the models themselves]</li> </ul> <h2> What I'd do next </h2> <ul> <li>Expand the dataset to cover [edge cases]</li> <li>Test newer models as they're released</li> <li>Try [a mitigation, such as prompting or tool changes] and measure whether it helps</li> </ul> <h2> Try it yourself </h2> <p>[Link to your Kaggle notebook or repo]</p> <p>If you've run into a failure mode of your own, I'd love to hear about it in the comments.</p> ai machinelearning kaggle devchallenge