3 papers
cs.IR2026
Auditing Semantic Gains in Sequential Recommendation: A Lightweight Recovery Test
Kong Wang, Zhongke He, Xiang Chen +4
Recent semantic and generative-retrieval recommenders report substantial improvements over ID-only sequential baselines, but it remains unclear whether these gains arise from langu…
cs.SE2026
ASA: Backbone-Training-Free Representation Engineering for Tool-Calling Agents
Youjin Wang, Run Zhou, Yingjie Ma +6
Adapting LLM agents to domain-specific tool calling remains notably brittle under evolving interfaces. Prompt and schema engineering is easy to deploy but often fragile under distr…
cs.AI2026
FinToolBench: Evaluating LLM Agents for Real-World Financial Tool Use
Jiaxuan Lu, Kong Wang, Yemin Wang +9
The integration of Large Language Models (LLMs) into the financial domain is driving a paradigm shift from passive information retrieval to dynamic, agentic interaction. While gene…