Large language model (LLM) recommenders have progressed from treating recommendation as a language task to full-fledged recommendation agents organized around profile, memory, planning, and action modules. Two frontiers have recently emerged: making an agent’s memory self-evolving, and injecting reasoning through reinf...
Presage, an end-to-end framework that reduces forecast CRPS by up to 28% over the best zero-shot TSFM while attaining near-nominal coverage at every quantile the scheduler consumes, and cuts SLO violations by 3– $5\times $ at $\sim \!18\%$ lower resource cost than strong predictive and RL baselines.
Yi-Nuo Wang, Xuan-Ping Zou, Zhou Lu et al.· IEEE Access· 2 citations
Large language model (LLM) agents cannot guarantee that their outputs satisfy the hard business rules that govern financial transactions. We present the Verifier-Actor Neuro-Symbolic Framework (
VA-NSF
), a dual-layer architecture that separates natural language understanding from formal constraint enforcement fo...
Yuxuan Qin, Minjae Rhee, Dai Teng et al.· Scientific Reports· 3 citations
This paper proposes TAPE (Trajectory Alignment and exPerience pool Evolution), a novel self-evolving fine-tuning framework designed to enhance the generalization and robustness of web search agents without requiring large-scale human annotation.
Minjae Rhee, Jitong Zou, Tianjun Mo et al.· IEEE Access· 3 citations
LS-CM (Long-Short Collaborative Memory), a dual-memory architecture that couples a Retrieval-Augmented Generation (RAG)-based short-term memory for code-base context with a Reinforcement Learning (RL)driven long-term memory that distills strategic patterns from historical test failures and patch iterations, is proposed...
Tianjun Mo, Changhao Zhang, Jitong Zou et al.· IEEE Access· 0 citations
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