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cs.LG2026
An Explicit Feedback Contract for Iterative ML-Agent Harnesses
Kehui Yao, Chuanbo Hu, Jiao Chen +8
Iterative ML agents develop models by editing code, running experiments, reading a validation score, and choosing what to try next. In current harnesses this loop is served by an e…
cs.IR2026
CRAB: Codebook Rebalancing for Bias Mitigation in Generative Recommendation
Zezhong Fan, Ziheng Chen, Luyi Ma +5
Generative recommendation (GeneRec) has introduced a new paradigm that represents items as discrete semantic tokens and predicts items in a generative manner. Despite its strong pe…
cs.AI2026
Is More Context Always Better? Examining LLM Reasoning Capability for Time Interval Prediction
Yanan Cao, Farnaz Fallahi, Murali Mohana Krishna Dandu +9
Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning and prediction across different domains. Yet, their ability to infer temporal regularities from…