SIDScope is a diagnostic tool that evaluates Semantic‑ID interfaces used in generative recommendation systems. It normalizes item‑to‑code artifacts, verifies provenance, profiles mapping structure, and compares revisions while tracking path‑to‑item outcomes in generated traces. Using nine tokenizer exports from Amazon and Yelp data, SIDScope shows that interface health depends on multiple signals and reveals gaps in prefix alignment, trace accounting, and mapping refresh effects.
By Jiandong Ding, Huijie Qin, Tiandeng Wu, Yi Cao
arXiv:2607. 24995v1 Announce Type: new Abstract: Semantic IDs (SIDs) are now a central component of generative recommendation.
By Junting Wang, Xinrui He, Yunzhe Li, Hari Sundaram
arXiv:2607. 21101v1 Announce Type: new Abstract: Semantic-ID-based generative recommendation represents items as sequences of shared semantic tokens, enabling token recombination beyond isolated item IDs.
By Jie Peng, Yanping Zheng, Zhewei Zhe, Bin Tong, Guan Wang, Bo Zheng
arXiv:2607. 02116v1 Announce Type: new Abstract: Autonomous AI agents increasingly depend on external knowledge stores, yet most retrieval pipelines provide relevance without durable guarantees of provenance, version identity, integrity, traceability, or point-in-time reconstruction.
By Misha Sulpovar (PromptOwl, LLC), Benn R. Konsynski (Goizueta Business School, Emory University), Qaish Kanchwala (IBM Research), Gabe Goodhart (IBM Research)
arXiv:2609.24264v1 Announce Type: new
Abstract: Tool-use agent traces identify messages and API calls, but procedural analyses also need explicit units of action and inspectable links to their eviden...
By Songqi Li, Dongqing Li, Zheqiao Cheng
arXiv:2610.28568v1 Announce Type: new
Abstract: Generative retrieval (GR) casts search and recommendation as autoregressive generation of discrete semantic identifiers (SIDs). SID design largely dete...
By Jinming Nian, Jyun-Yu Jiang, Henry Tsai, Yi Fang, Zhirong Liang
arXiv:2608. 12847v1 Announce Type: new Abstract: Retrieval can identify a past trajectory that may matter, yet it does not specify how an acting agent should use that trajectory after users, entities, constraints, or environment state have changed.
By Yifei Li, Heng Wang, Lingling Zhang, Muye Huang, Xinyu Zhang, Jiashuai Liu, Hang Yan, Rongman Xu
MOSS (Model Sanity Service) is a production framework that embeds LLM-based relevance scoring into a continuous monitoring loop for recommendation, search, and offer surfaces, using targeted sampling, strategy-aware prompting, and structured alerting to diagnose semantic failures. It has been deployed for over six months at a large U.S. retailer, identifying two confirmed serving defects and quantifying a 76.4% reduction in alert rate for a backfill blending fix. Benchmark results show that LLM judges outperform non-LLM baselines on both recommendation and search tasks, demonstrating that a single LLM monitoring framework can deliver actionable quality signals across multiple product discovery surfaces.
By Alina Nesen, Prathyusha Kanmanth Reddy, David Relyea, Rohit Avinash Mujumdar, Samaneh Karimi, Bo Shen, Jordan Hashemi
CascadeLedger is a counterfactual execution‑audit framework designed for budgeted language‑model cascades. It enforces call‑before‑read semantics, records service and cache identities, and freezes event traces before scoring, ensuring that two executions can share a final answer and resource projection only if their routing decisions used legitimately observable information. The framework introduces counterfactual prefix non‑interference (CPNI), counterfactual influence frontier (CIF), and projection‑preserving adversarial mutation (PPAM) to detect and localize violations, achieving a clean false‑positive rate and high detection rates for serialized and hidden‑read faults while quantifying overhead for online and offline audits.
By Miaobo Hu, Shuhao Hu, Xiaobo Guo, Xin Wang, Bokun Wang, Daren Zha, Jun Xiao
arXiv:2412. 04704v2 Announce Type: replace-cross Abstract: Traceability remains a critical capability to ensure system reliability, maintainability, and compliance in modern software development.
By Daniel Rodriguez-Cardenas, David N. Palacio, Logan Fecko, Kevin Moran, Denys Poshyvanyk
The paper proposes replacing a static multi‑level small semantic codebook with a dynamic single‑level large semantic codebook for generative recommendation. It introduces an exposure‑aware update mechanism and an offline evaluation framework, achieving significant improvements in recall, NDCG, decoding efficiency, and online consumption metrics on public datasets and production traffic.
By Tianlu Xie, Xin Ku, Mingjie Sun, Yunhao Sha, Lixiang Wang, Peng Wang, Yiyu Wang, Wenjin Wu, Zhaojie Liu, Peng Jiang, Wenwu Ou
arXiv:2609.22227v1 Announce Type: cross
Abstract: Generative retrieval represents each item by a short Semantic ID and casts recommendation as autoregressive generation of that sequence. Because the...
By Bin Wang, Zhengyu Zhang