Cross-Benchmark Generalization in Long-Horizon Agents
2026-08-04T08:51:39Z•5348e6149712cdbf7ad5192a9b2c3c70144b06441e1e67048de4b4cf70a164c0
android-security-testinganomaly-detectionartificial-intelligenceautomated-testingcode-authorship-attributiondistributed-systemsenterprise-crmllm-agentsregression-testingsecurity-researchsms-consentsoftware-engineering
What happened
The document is an arXiv computer science/software-engineering feed covering research on long-horizon AI agents, code authorship attribution, Android application modeling and automated testing, enterprise SMS consent architectures, coding-agent benchmarking, distributed-system anomaly detection, AI and simulation, and regression testing for LLM-based systems. It contains no disclosed vulnerabilities, exploits, affected products, or security incident details; the content is primarily academic and defensive.
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_se
- Record identifier
- 5348e6149712cdbf7ad5192a9b2c3c70144b06441e1e67048de4b4cf70a164c0
- Enrichment time
- 2026-08-04T08:51:39Z
- AI-assisted enrichment
- Yes
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