As large language models become more powerful, self-evolving agents are able to tackle challenging tasks including AI for machine learning (AI4ML). In AI4ML, while empirical verification is available, it often requires computationally costly model training and evaluation, limiting the speed and scale of agent evolution...
Jiamu Bai, Li-Zhu Zhang, Xin Yu et al.· 0 citations
Harness optimization provides a practical setting for recursive self-improvement (RSI), where agent-generated modifications inform subsequent changes through execution feedback. Recent work such as Meta-Harness implements this process through iterative code generation and evaluation, but retains a fixed development set...
Hao-Yu Dong, Yu-Hang Zhou, Zi-Hao Lin et al.· 0 citations
This work introduces ToolMLBench, a suite of executable tools for data inspection, code verification, and experiment diagnosis, together with an SFT and RL pipeline for learning their use, and measures how privileged context changes the likelihood of a sampled tool action and uses this difference as a turn-level reward...
Xin Yu, Li-Zhu Zhang, Jiamu Bai et al.· 0 citations
Persistent-negative adversarial distillation consistently improves performance over current methods at matched discriminator compute and yields smoother fresh-policy discriminator trajectories, with fewer below-chance dips.
Hai-Xu Ma, S. Lahrichi, Wei-Wei Li et al.· 0 citations
PersonaMem-v3 is introduced, a real-world-grounded benchmark and evaluation harness for omni-platform personal intelligence that evaluates whether AI agents can infer holistic user understanding from cross-platform evidence, personalize responses, rerank recommendations on social media, follow user steering through nat...
Bo-Wen Jiang, Yuan Yuan, Zhuo-Qun Hao et al.· 0 citations
ConnectionMind is presented, a production-ready recommendation framework that tightly integrates the social network structure with large language models (LLMs) to enable scalable, interpretable, and reasoning-aware personalization in Meta.
Haoyu Han, Yuming Liu, Lei Huang et al.· 0 citations
Production recommender systems shape what billions of people see, and sustaining their performance requires continual optimization: as content, user behavior, and upstream models shift, the choices governing retrieval, ranking, and serving must be revisited. Traditionally, human engineers test such changes through onli...
Muhammad Azhar, Yu-Hang Zhou, Gilbert Jiang et al.· 1 citation
Ablations show that selective intervention outperforms passive bank exposure, always-on injection, advisor-only guidance, advisor-only guidance, and general retrieval, and general retrieval and that selective intervention outperforms passive bank exposure, always-on injection, advisor-only guidance, and general retriev...
EvoHarness-RL is introduced, which exposes Belief, Progress, and Experience (BPE) as policy-facing harness state and reveals two key dynamics: harness annealing, where training internalizes recurring harness-use patterns into the model policy and shifts the agent from frequent harness calls toward selective external-st...
Xuying Ning, Dongqi Fu, Tianxin Wei et al.· 0 citations
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