Stop Comparing LLM Agents Without Disclosing the Harness
📰 ArXiv cs.AI
When comparing LLM agents, the execution harness is a crucial factor in determining performance, not just the model itself
Action Steps
- Evaluate the execution harness of LLM agents
- Compare the infrastructure layers of different models
- Consider the impact of context construction, tool interaction, and verification on agent performance
- Assess the Binding Constraint Thesis in the context of long-horizon tasks
- Disclose the harness when comparing LLM agents to ensure fair evaluation
Who Needs to Know This
Researchers and developers working with LLM agents should consider the harness when evaluating and comparing models, as it can significantly impact performance
Key Insight
💡 The execution harness can be a stronger determinant of agent performance than the model itself
Share This
🚨 Don't compare LLM agents without considering the execution harness! 🚨
Key Takeaways
When comparing LLM agents, the execution harness is a crucial factor in determining performance, not just the model itself
Full Article
Title: Stop Comparing LLM Agents Without Disclosing the Harness
Abstract:
arXiv:2605.23950v1 Announce Type: new Abstract: This position paper argues that, for long-horizon tasks evaluated across models with comparable frontier capability, the agent execution harness, namely the infrastructure layer that governs context construction, tool interaction, orchestration, and verification around a language model, is often a stronger determinant of agent performance than the model it wraps. We formalize and defend the Binding Constraint Thesis: in this regime, performance var
Abstract:
arXiv:2605.23950v1 Announce Type: new Abstract: This position paper argues that, for long-horizon tasks evaluated across models with comparable frontier capability, the agent execution harness, namely the infrastructure layer that governs context construction, tool interaction, orchestration, and verification around a language model, is often a stronger determinant of agent performance than the model it wraps. We formalize and defend the Binding Constraint Thesis: in this regime, performance var
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