The authors integrated one memory-writing model into Mem0, MemOS and Hy-Memory while leaving each framework’s surrounding memory logic intact.
Specialized memory writer improves persistent state across multiple LLM frameworks
The trained writer outperformed general-purpose models across three supported memory systems and one held-out framework.
Chinese Tech
Tianxiang Fei · Jiazheng Kang · Mao Zheng · Xingyan He · Xiang Yu
Tencent Hunyuan
Research Digest··3 min read
Thread:Agent Harness Optimization
Fei and colleagues trained Hy-Memory-30B-A3B to decide what information an external memory system should store, update or discard.
Why this paper
From Tencent Hunyuan · Part of Agent Harness Optimization, now 143 papers
In one line
A specialized memory writer trained with attributed chunk-level reinforcement learning outperforms general-purpose models across four long-term-memory harnesses.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ✓Reports numbers on named benchmarks (2 benchmarks)
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
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