VQV Signal
Doc2Harness introduces a two-stage benchmark to assess how well large language model-driven agents perform on long-horizon coding tasks by evaluating the harness engineering around the model. This approach highlights that success depends not only on the model but also on the surrounding harness, an...
Doc2Harness introduces a two-stage benchmark to assess how well large language model-driven agents perform on long-horizon coding tasks by evaluating the harness engineering around the model. This approach highlights that success depends not only on the model but also on the surrounding harness, an...
AI-assisted summary based on the listed source.
Agents driven by large language models are increasingly deployed on long-horizon tasks, where they use tools and modify real workspaces. How well they perform depends as much on the harness around the model as on the model itself. Existing benchmarks measure downstream task success under a fixed harness, leaving...
Understanding and improving the harness engineering can enhance the effectiveness of AI coding agents on complex, extended tasks. This benchmark provides a new way to measure and optimize the interaction between models and their operational environment.
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VQV surfaced this signal because it is recent, relevant to AI Coding Tools, connected to arXiv.
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