SOURCE-LINKED INTELLIGENCE
Symbolic Temporal Supervision of LLM Agents Using Contracts
Large language model (LLM) agents augmented by tools can automate complex, multi-step tasks, such as web navigation, code generation, and workflow orchestration, by acting on external systems through tool calls. However, hallucinations, distributional instability, and adversarial manipulations in LLMs, and the irreversible consequences of certain tool calls can lead to harmful outcomes. Existing safeguards either grade recorded trajectories post hoc with stochastic LLM judges or block unsafe actions one call at a time, and no single deterministic artifact supports both roles. We present ContrA
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-16T05:05:35.000Z
First collected: 2026-09-20T08:20:57.646Z. This is not the publication date.