Papers by Chonghan Qin
ImplicitMemBench: Measuring Unconscious Behavioral Adaptation in Large Language Models (2026.acl-long)
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| Challenge: | Existing memory benchmarks for LLMs evaluate explicit recall of facts, yet overlook implicit memory where experience becomes automated behavior without conscious retrieval. |
| Approach: | They propose a benchmark that evaluates implicit memory using three constructs from non-declarative memory. |
| Outcome: | The new benchmark reframes evaluation from "what agents recall" to "what they automatically enact" no model exceeds 66% overall, with top performers far below human baselines . |
SAVOIR: Learning Social Savoir-Faire via Shapley-based Reward Attribution (2026.findings-acl)
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Xiachong Feng, Yi Jiang, Xiaocheng Feng, Deyi Yin, Libo Qin, Yangfan Ye, Lei Huang, Weitao Ma, Yuxuan Gu, Chonghan Qin, Bing Qin, Lingpeng Kong
| Challenge: | Existing approaches to improve social intelligence of AI systems employ retrospective attributions and lack theoretical grounding. |
| Approach: | They propose a framework that uses Shapley values to ensure fair credit distribution with axiomatic guarantees of efficiency, symmetry, and marginality. |
| Outcome: | The proposed framework matches or exceeds proprietary models including GPT-4o and Claude-3.5-Sonnet. |