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Woan-Shiuan Chien

Publications and source records attributed to Woan-Shiuan Chien.

2 recordsLinked to original sources

Memory, Know Your PLACE: Governing Lifecycle State and Query-Time Eligibility in Persistent-Memory Agents

Persistent memory turns prior interactions into durable agent state, but existing systems provide limited control over whether that state remains valid and whether it should influence a later query. We argue that reliable memory requires governance over both state formation and query-time influence. We introduce PLACE, a factorized governance layer that adds write-side state certification and read-side authorization to a memory backend. A factorial evaluation on Mem0 with Qwen2.5-3B and Qwen2.5-7B isolates the contribution of each mechanism. Results show that write-side governance substantially improves lifecycle state correctness, while read-side governance eliminates measured causal leakage from ineligible memory in the state-correct setting. Their combination achieves the strongest joint performance while preserving query-relevant support. These findings show that reliable persistent memory requires explicit control over both maintained state and query-time information influence.

cs.AI↗

Is It Still Fair? Investigating Gender Fairness in Cross-Corpus Speech Emotion Recognition

Speech emotion recognition (SER) is a vital component in various everyday applications. Cross-corpus SER models are increasingly recognized for their ability to generalize performance. However, concerns arise regarding fairness across demographics in diverse corpora. Existing fairness research often focuses solely on corpus-specific fairness, neglecting its generalizability in cross-corpus scenarios. Our study focuses on this underexplored area, examining the gender fairness generalizability in cross-corpus SER scenarios. We emphasize that the performance of cross-corpus SER models and their fairness are two distinct considerations. Moreover, we propose the approach of a combined fairness adaptation mechanism to enhance gender fairness in the SER transfer learning tasks by addressing both source and target genders. Our findings bring one of the first insights into the generalizability of gender fairness in cross-corpus SER systems.

cs.LG↗