Empty Commitments: When Agents Promise What They Cannot Deliver
cs.AI, cs.CL
Submitted: 2026-10-01
Updated: 2026-10-08
Terminology
Sources
- PM-Bench: Evaluating Prospective Memory in LLM Agents
- MemGPT: Towards LLMs as Operating Systems
- Decision-Sufficient State Representations: Measuring and Reducing Write-Time Regret
- Cheap Talk, Empty Promise: Frontier LLMs easily break public promises for self-interest
- The Compliance Gap: Why AI Systems Promise to Follow Process Instructions but Don't
- Quantifying Overclaiming Propensity in Frontier LLM Agents
- TriggerBench: Investigating Prospective Memory for Large Language Models
Related papers
- MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
- Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
- The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
- MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
- Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
- VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection