Ming Li

Jiasi Weng, Jian Weng, Minrong Chen et al.

The rapid adoption of artificial intelligence (AI)-driven and agentic workflows is transforming traditional government and enterprise systems into language-based, tool-using and increasingly autonomous infrastructures. Conventional key management services authenticate who may invoke a cryptographic primitive, but remain agnostic to which workflow steps are authorized at runtime: an authenticated agent can still be hijacked by direct or indirect prompt injection into executing malicious actions that satisfy identity checks yet violate user intent. We present \emph{Neural Cryptographic Services} (NCS), an active security governance plane grounded in a neural-symbolic design and interposed between LLM agents and privileged tools. Under NCS, an untrusted neural planner compiles natural-language instructions into structured plan drafts but holds no execution authority. Execution is instead gated by a deterministic symbolic controller operating over an offline-signed, hash-chained instruction stream. NCS verifies the signature, incrementally validates the hash chain, releases exactly one instruction payload at a time, and enforces strict binding between the agent's proposed tool parameters and the verified payload. Mismatched or out-of-order tool calls are rejected fail-closed, while previously verified states are retained for post-hoc auditing. We evaluate NCS on AgentDojo and a custom argument-hijacking benchmark. NCS drives attack success rates to near zero while preserving acceptable utility on benign workflows. NCS thus reframes agent security from asking whether a model's intent complied to asking whether a proposed dispatch matches a cryptographically authorized step.

Ziyi Zhao, Xiaoyou Zhou, Xiao Lv et al.

Language-based user profiles convert long behavioral histories into explicit semantic representations for recommendation. However, most profile generators are optimized in an open loop: they may summarize past behavior fluently, but are not directly trained to improve future recommendation. We study this problem in real-world short-video recommendation, where user behaviors continuously arrive as streams and profiles must be incrementally updated under limited capacity. This requires maintaining a consistent bounded profile state and constructing profile-targeted semantic feedback from industrial implicit behavior logs. We propose RECAP, an offline closed-loop framework for optimizing streaming structured semantic profiles with historical recommendation feedback. RECAP maintains each profile as a bounded structured memory by combining LLM-based semantic updates with deterministic lifecycle and capacity control. RECAP constructs profile-targeted semantic feedback by filtering label-consistent behavior pairs with an LLM judge and training a dual-tower evaluator whose matching score serves as a GRPO reward. Experiments on Kuaishou short-video data show that RECAP improves uAUC by 0.0084 and Recall@2000 by about 4.9% over the base generator. Further analyses confirm the benefits of feedback construction and policy optimization, and show more grounded refinement and user-level abstraction in profile updates. A seven-day online A/B test further shows a statistically significant 0.139% improvement in average application usage time per user.