This work proposes a Causal auto-Bidding method based on a Diffusion completer-aligner framework, termed CBD, which achieves superior performance on large-scale auto-bidding benchmarks, but also delivers significant improvements on an online advertising platform, including a 2.0% increase in target cost.
Ye-Wen Li, Jingtong Gao, Peng Jiang et al.· Proceedings of the 32nd ACM...· 0 citations
This work introduces RobustSGPO, which specifies the requested edit, constructs and checks the patch, and continues search from either the incumbent or retained snapshots, and reduces source-task degradation after a shift, whereas random retention reaches a higher destination endpoint.
Zi-Bo Zhao, Ji-Jun Shi, Mo-Qing Zhou et al.· 1 citation
AgentBrew is proposed, an offline training framework that learns effective tool-use policies from a single batch of raw interaction trajectories, without task verifiers or iterative on-policy rollouts, and demonstrates that fine-grained offline learning can recover useful supervision from raw trajectories that filterin...
Zhiyi Lyu, Ye-Wen Li, Long-Tao Zheng et al.· 2 citations
We present KAT-Coder-V2.5, a coding-focused agentic model trained to act autonomously inside real, executable repositories rather than as a single-turn code generator. Its capability is bottlenecked less by model scale than by the scarcity of reproducible environments, verifiable rewards, and high-value trajectories, w...
Bofeng Huang, Fengxiang Li, Hao Xu et al.· 2 citations
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