Knowing When to Ask for Help: Bayesian Self-Escalation in Hierarchical LLM Agents
cs.LG, cs.AI, stat.ML
Submitted: 2026-08-25
Updated: 2026-08-25
Code: https://github.com/nadeem-shaikh/llm-self-escalation
Terminology
Sources
- FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance
- Adaptive Computation Time for Recurrent Neural Networks
- Language Models (Mostly) Know What They Know
- Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs
- RouteLLM: Learning to Route LLMs with Preference Data
- Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks