EarlyEval: Cheaper Agent Evaluation via Early Outcome Prediction
cs.CL
Submitted: 2026-09-02
Updated: 2026-09-02
Code: https://github.com/inphotoo/earlyeval
Terminology
Sources
- ReAct: Synergizing Reasoning and Acting in Language Models
- AgentBench: Evaluating LLMs as Agents
- AI Agents That Matter
- Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces
- The Tool Decathlon: Benchmarking Language Agents for Diverse, Realistic, and Long-Horizon Task Execution
- Commit0: Library Generation from Scratch
- SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains?
- Submodular Benchmark Selection
- Valid Best-Model Identification for LLM Evaluation via Low-Rank Factorization
- Cost-Efficient Estimation of General Abilities Across Benchmarks
- Active Testing of Large Language Models via Approximate Neyman Allocation
- Select, Label, Evaluate: Active Testing in NLP
- Query-efficient model evaluation using cached responses
- Efficient Benchmarking of AI Agents
- Agent psychometrics: Task-level performance prediction in agentic coding benchmarks
- SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents
- SWE-Pruner Pro: The Coder LLM Already Knows What to Prune
- CodeOCR: On the Effectiveness of Vision Language Models in Code Understanding
- Code Is More Than Text: Uncertainty Estimation for Code Generation
- BAGEN: Are LLM Agents Budget-Aware?
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