Eye on AI Weekly Research Watch Podcast Por Craig Spencer Smith arte de portada

Eye on AI Weekly Research Watch

Eye on AI Weekly Research Watch

De: Craig Spencer Smith
Escúchala gratis

Weekly, digestible podcast explainers of significant research papers@ 2026 Eye on AI Política y Gobierno
Episodios
  • TFGformer: Multivariate Time Series Forecasting via Time-Frequency Graph Learning and Covariate Fusion

    TFGformer: Multivariate Time Series Forecasting via Time-Frequency Graph Learning and Covariate Fusion
    Aug 5 2026
    Forecasting large-scale IoT sensor data over long time horizons is critical for maintenance and scheduling, but current time-series foundation models rely on static learned patterns without accessing relevant historical examples at inference time. CrossRAG solves this with retrieval-augmented forecasting: shape-aware memory retrieval robust to magnitude differences, contrastive learning that filters out misleadingly similar-but-divergent historical references, and cross-attention fusion of retrieved data into predictions. Tested on seven benchmarks, it outperforms both standard and existing retrieval-based forecasting methods. This is useful for industrial IoT monitoring, energy grid management, and any long-horizon forecasting task involving heterogeneous sensor networks. Authors: Yu Sun, Yuan Chang, Xiaohou Shi, Yan Sun Paper: https://arxiv.org/abs/2607.29459v1
    Más Menos
    2 m
  • Self-Play Meets Skill Evolution: Self-Evolving Search Agents that Pose, Solve, and Remember

    Self-Play Meets Skill Evolution: Self-Evolving Search Agents that Pose, Solve, and Remember
    Aug 5 2026
    Self-play lets AI agents generate their own training problems, but without persistent memory, past failures don\'t shape future practice in a lasting way. SESA introduces an evolving skill memory system where a \"challenger\" poses problems, a solver retrieves relevant skills, and failures get distilled into reusable skills written back to memory --- creating a feedback loop where task difficulty and skill knowledge co-evolve. Tested across seven QA benchmarks, it improves accuracy over strong baselines while supporting both memory-based and memory-free deployment. This benefits agentic AI systems needing continual improvement in search, tool use, and multi-hop reasoning. Authors: Zenghuang Fu, Zhaoyang Li, Qiuyuan Ai, Haoyu Wu, Minghui Wu, Paper: https://arxiv.org/abs/2607.29468v1
    Más Menos
    3 m
  • DreamQAS: Learning a Decision-Useful World Model for VQE-Efficient Quantum Architecture Search

    DreamQAS: Learning a Decision-Useful World Model for VQE-Efficient Quantum Architecture Search
    Aug 5 2026
    Reinforcement-learning-based quantum architecture search is expensive because it repeatedly runs costly quantum simulations (VQE) after every circuit change, even though circuit construction itself is fully deterministic. DreamQAS improves efficiency by only learning to predict the expensive post-simulation feedback, using an ensemble model for uncertainty-aware planning and selective real verification. It achieves the lowest energy error on most molecular tasks while needing far fewer real quantum evaluations --- up to 10x fewer in some cases. This is valuable for quantum computing research, particularly in designing efficient quantum circuits for chemistry and materials simulation under limited computational budgets. Authors: Jiayang Niu, Yan Wang, Jie Li, Ke Deng, Azadeh Alavi, Muhammad Paper: https://arxiv.org/abs/2607.29491v1
    Más Menos
    3 m
adbl_web_anon_alc_button_suppression_t1
Todavía no hay opiniones