BaRe-Mem: Bayesian Reliability Memory for Robust and Adaptive Agent Consultation
cs.AI
Submitted: 2026-09-28
Updated: 2026-09-28
Code: https://github.com/declare-lab/BaRe-Mem
Sources
- Phi-4 Technical Report
- Doc-to-LoRA: Learning to Instantly Internalize Contexts
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- Training Verifiers to Solve Math Word Problems
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- Debate Only When Necessary: Adaptive Multiagent Collaboration for Efficient LLM Reasoning
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- Instruction Following by Principled Boosting Attention of Large Language Models
- Large Language Model based Multi-Agents: A Survey of Progress and Challenges
- Measuring Massive Multitask Language Understanding
- Why Language Models Hallucinate
- delta-mem: Efficient Online Memory for Large Language Models
- MemGPT: Towards LLMs as Operating Systems
- Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs
- LLMs Can't Handle Peer Pressure: Crumbling under Multi-Agent Social Interactions
- RestGPT: Connecting Large Language Models with Real-World RESTful APIs
- Kimi Linear: An Expressive, Efficient Attention Architecture
- With Greater Text Comes Greater Necessity: Inference-Time Training Helps Long Text Generation
- MEMORYLLM: Towards Self-Updatable Large Language Models
- M+: Extending MemoryLLM with Scalable Long-Term Memory
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