Implicit Intelligence -- Evaluating Agents on What Users Don't Say
arXiv:2602.20424v1 Announce Type: new 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 scen
arXiv:2602.20424v1 Announce Type: new 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 literal instruction-following and human-like contextual reasoning.
Executive Summary
This article introduces Implicit Intelligence, an evaluation framework that assesses AI agents' ability to reason about implicit requirements in natural human communication. The framework, paired with Agent-as-a-World (AaW), simulates interactive worlds defined in human-readable YAML files and evaluates agents' performance across 205 scenarios. The results reveal a significant gap between literal instruction-following and human-like contextual reasoning, with even the best-performing model achieving only 48.3% scenario pass rate. This study highlights the need for AI agents to move beyond prompt-following and develop genuine goal-fulfilling abilities, particularly in areas such as accessibility, privacy, and risk management.
Key Points
- ▸ Implicit Intelligence framework evaluates AI agents' ability to reason about implicit requirements in natural human communication
- ▸ Agent-as-a-World (AaW) harness simulates interactive worlds defined in human-readable YAML files
- ▸ Evaluating 16 frontier and open-weight models across 205 scenarios reveals significant room for improvement
Merits
Strength in addressing a critical gap in AI evaluation
The Implicit Intelligence framework addresses a significant gap in current AI evaluation methodologies, which focus on explicit instruction-following rather than implicit requirements.
Demerits
Limitation in scope
The study's scope is limited to 205 scenarios, which may not be representative of all possible implicit requirements in natural human communication.
Expert Commentary
The Implicit Intelligence framework and Agent-as-a-World harness represent a significant step forward in evaluating AI agents' abilities to reason about implicit requirements. However, the study's limitations in scope and the need for further research in this area highlight the complexity of developing AI systems that can effectively interact with humans. As AI continues to play an increasingly important role in our lives, it is essential that we prioritize the development of AI systems that can reason about implicit requirements and develop genuine goal-fulfilling abilities.
Recommendations
- ✓ Researchers should expand the scope of the Implicit Intelligence framework to include a broader range of scenarios and implicit requirements
- ✓ Industry leaders and policymakers should prioritize the development of AI systems that can reason about implicit requirements and develop genuine goal-fulfilling abilities
