DRIP-R: A Benchmark for Decision-Making and Reasoning Under Real-World Policy Ambiguity in the Retail Domain

arXiv:2605.07699 · cs.CL, cs.AI · Submitted 2026-05-08 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-05-08

Updated: 2026-08-31

Code: https://github.com/HumanSignal/label-studio

Terminology

• Alignment — 56× in this paper · explained in 1 episode
• Benchmark — 39× in this paper · explained in 2 episodes
• Consistency — 14× in this paper · explained in 3 episodes
• Calibration — 12× in this paper · explained in 7 episodes
• Evaluation Dimensions — 6× in this paper · explained in 3 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 5× in this paper · explained in 3 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Actor — 3× in this paper · explained in 3 episodes
• Benchmarking — 3× in this paper · explained in 3 episodes
• AI Agents — 2× in this paper · explained in 2 episodes
• Agentic Task — 2× in this paper · explained in 1 episode
• Alignment Scores — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• LLM judge — 2× in this paper · explained in 2 episodes
• Reward Function — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Agentic Tasks — 1× in this paper · explained in 1 episode
• Agentic Workflows — 1× in this paper · explained in 1 episode
• Annotator Agreement — 1× in this paper · explained in 1 episode
• Cooperation — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Dec-POMDP — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Formal Methods — 1× in this paper · explained in 3 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Scaling Synthetic Data Creation with 1,000,000,000 Personas
• $\tau^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment
• GAIN: A Benchmark for Goal-Aligned Decision-Making of Large Language Models under Imperfect Norms
• DoomArena: A framework for Testing AI Agents Against Evolving Security Threats
• ST-WebAgentBench: A Benchmark for Evaluating Safety and Trustworthiness in Web Agents
• $\tau$-Voice: Benchmarking Full-Duplex Voice Agents on Real-World Domains
• Large Language Model Agent: A Survey on Methodology, Applications and Challenges
• AmbiBench: Benchmarking Mobile GUI Agents Beyond One-Shot Instructions in the Wild
• Trajectory2Task: Training Robust Tool-Calling Agents with Synthesized Yet Verifiable Data for Complex User Intents
• Survey on Evaluation of LLM-based Agents
• Agent-SafetyBench: Evaluating the Safety of LLM Agents

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