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[2602.20424] Implicit Intelligence -- Evaluating Agents on What Users Don't Say
Summary
The paper presents an evaluation framework called Implicit Intelligence, which assesses AI agents' ability to understand unstated user requirements and contextual constraints in real-world interactions.
Why It Matters
This research addresses a critical gap in AI evaluation by focusing on implicit communication, which is essential for developing agents that can operate effectively in complex, real-world scenarios. Understanding implicit requirements can enhance AI's usability and safety, making it more aligned with human expectations.
Key Takeaways
- Current AI benchmarks focus on explicit instruction-following, neglecting implicit user needs.
- The Implicit Intelligence framework evaluates AI agents' reasoning about unstated constraints.
- Results show that even top models only achieve a 48.3% pass rate, indicating significant improvement is needed.
- The framework uses human-readable YAML files for scenario definitions, enhancing accessibility.
- Understanding implicit communication is crucial for developing more effective AI agents.
Computer Science > Artificial Intelligence arXiv:2602.20424 (cs) [Submitted on 23 Feb 2026] Title:Implicit Intelligence -- Evaluating Agents on What Users Don't Say Authors:Ved Sirdeshmukh, Marc Wetter View a PDF of the paper titled Implicit Intelligence -- Evaluating Agents on What Users Don't Say, by Ved Sirdeshmukh and 1 other authors View PDF HTML (experimental) Abstract:Real-world requests to AI agents are fundamentally underspecified. Natural human communication relies on shared context and unstated constraints that speakers expect listeners to infer. Current agentic benchmarks test explicit instruction-following but fail to evaluate whether agents can reason about implicit requirements spanning accessibility needs, privacy boundaries, catastrophic risks, and contextual constraints. We present Implicit Intelligence, an evaluation framework testing whether AI agents can move beyond prompt-following to become genuine goal-fulfillers, paired with Agent-as-a-World (AaW), a harness where interactive worlds are defined in human-readable YAML files and simulated by language models. Our scenarios feature apparent simplicity in user requests, hidden complexity in correct solutions, and discoverability of constraints through environmental exploration. Evaluating 16 frontier and open-weight models across 205 scenarios, we find that even the best-performing model achieves only 48.3% scenario pass rate, revealing substantial room for improvement in bridging the gap between litera...
