MemCalib: Benchmarking and Optimizing Memory Use in LLM Agents
cs.LG, cs.AI
Submitted: 2026-09-21
Updated: 2026-09-22
Code: https://github.com/Quark-Medical/memcalib
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
- How Does Personalized Memory Shape LLM Behavior? Benchmarking Rational Preference Utilization in Personalized Assistants
- MedDialog: Two Large-scale Medical Dialogue Datasets
- Memory in the Age of AI Agents
- OP-Bench: Benchmarking Over-Personalization for Memory-Augmented Personalized Conversational Agents
- ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge
- Ministral 3
- GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization
- Qwen3 Technical Report
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- MemSyco-Bench: Benchmarking Sycophancy in Agent Memory
- BenchPreS: A Benchmark for Context-Aware Personalized Preference Selectivity of Persistent-Memory LLMs
- Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models
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