Introduces MemHome benchmark and RL with multi-dimensional rewards for memory-driven smart home device control.
InFindings of the Association for Computational Linguistics: ACL 2025
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
TSUBASA improves long-horizon personalization in LLMs via dynamic memory evolution for writing and context-distillation self-learning for reading, outperforming Mem0 and Memory-R1 on Qwen-3 benchmarks while reducing token use.
The paper measures policy-carriage failures during LLM context assembly and evaluates SafeContext as a partial mitigation on Llama, Qwen, and Mistral models.
citing papers explorer
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Trust Your Memory: Verifiable Control of Smart Homes through Reinforcement Learning with Multi-dimensional Rewards
Introduces MemHome benchmark and RL with multi-dimensional rewards for memory-driven smart home device control.
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TSUBASA: Improving Long-Horizon Personalization via Evolving Memory and Self-Learning with Context Distillation
TSUBASA improves long-horizon personalization in LLMs via dynamic memory evolution for writing and context-distillation self-learning for reading, outperforming Mem0 and Memory-R1 on Qwen-3 benchmarks while reducing token use.
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Ghost in the Context: Measuring Policy-Carriage Failures in Decision-Time Assembly
The paper measures policy-carriage failures during LLM context assembly and evaluates SafeContext as a partial mitigation on Llama, Qwen, and Mistral models.