SIDScope is a diagnostic tool that evaluates Semantic-ID mappings used between item tokenizers and generative recommenders. It normalizes artifacts, verifies provenance, profiles mapping structure, compares revisions, and tracks path-to-item outcomes in generated traces. Using data from Amazon and Yelp, SIDScope shows that interface health depends on multiple signals, revealing gaps in prefix alignment, trace accounting, and refresh handling that affect model reuse.
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: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: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)
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: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
The paper reports that a model can pass fidelity checks—verifying that extracted values match the source—without actually opening a datasheet, due to a hidden constraint that disables tool use. To address this, the authors log every tool call in an agentic benchmark and develop two instruments: a rule‑based failure‑attribution classifier and a silent‑failure detector that flags runs based solely on which tools were invoked. While the detector shows low false positives on clean extractions and recovers all planted faults, its recall against correct tool usage but incorrect answers remains unmeasured, and a partial causal chamber confirms only a subset of claims, highlighting limitations in physical verification.
By Qing Ye, Meng-Hsuan Lin
Shared memory helps language-model agents reuse information across long workflows, yet relevant evidence may not be admissible for a particular agent or action. Because restrictions propagate through derivations, summaries can conceal private, poisoned, untrusted, or revoked sources, enabling unauthorized reads or unsafe actions.
arXiv:2608. 07952v1 Announce Type: cross Abstract: Tool-augmented LLM agents can harbor implicit state that persists across sessions, activates through events, and propagates across agent boundaries---largely invisible to standard debugging.
By Zhaohui Wang
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
The paper introduces a claim‑anchored execution contract that binds a tool‑using agent’s emitted claim to its exact source span, the ordered execution prefix that produced it, and the source version and access state observed. Each receipt contains deterministic anchors, source identifiers, offsets, hashes, quotes, and a domain‑separated execution commitment, allowing a verifier to reconstruct these bindings before semantic or task labels are joined. The contract defines seven independently testable properties and demonstrates high detection rates against cross‑object attacks, with strong performance on conflict‑aware support guard evaluations.
By Miaobo Hu, Shuhao Hu, Xiaobo Guo, Xin Wang, Bokun Wang, Yina Sa, Daren Zha, Jun Xiao