Beyond Outcomes: Dual-View Relational Learning for Efficient Agent Benchmarking

arXiv:2609.18909 · cs.CL, cs.AI · Submitted 2026-09-16 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-09-16

Updated: 2026-10-03

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 72× in this paper · explained in 2 episodes
• Benchmarking — 28× in this paper · explained in 3 episodes
• APEX — 24× in this paper · explained in 1 episode
• Redundancy — 5× in this paper · explained in 2 episodes
• Verification — 5× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Adam — 4× in this paper · explained in 2 episodes
• Agentic Evaluation — 3× in this paper · explained in 1 episode
• Generalization — 3× in this paper · explained in 13 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptive Weighting — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes

Sources

• $\tau^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment
• Adam: A Method for Stochastic Optimization
• tinyBenchmarks: evaluating LLMs with fewer examples
• PACE: A Proxy for Agentic Capability Evaluation
• PaperBench: Evaluating AI's Ability to Replicate AI Research
• APEX-Agents
• RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts
• How Benchmark Prediction from Fewer Data Misses the Mark

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