Research Papers

PCAS: Policy Compiler for Secure Agentic Systems — The first paper to provide measured enforcement results for agent policy compliance (48% to 93%). Uses dependency graphs and Datalog-derived policy language with a reference monitor intercepting all actions. Three case studies: prompt injection defense, pharmacovigilance workflows, organizational customer service policies. If you're building production agents that need compliance guarantees, this is the paper to read.

Mobile-Agent-v3.5 (Alibaba TongyiLab, 32 upvotes on HuggingFace) — GUI-Owl-1.5 model family (2B/4B/8B/32B/235B) achieving SOTA on 20+ GUI benchmarks across desktop, mobile, and browser. Cloud-edge collaboration for real-time interaction. Instruct/thinking variants for different deployment targets.

Don't Break the Cache: Prompt Caching for Agentic Tasks — Counter-intuitive finding from 500+ agent sessions: full-context caching paradoxically INCREASES latency by 8.8% on GPT-4o. System-prompt-only caching is optimal for agentic tasks. Per-model benchmarks: GPT-4o 45.9% cost savings + 30.9% latency improvement; Claude Sonnet 4.5 78.5% cost savings + 22.9% latency improvement.

Fine-Tuning with RAG (ICLR 2026) — Four-stage pipeline converting RAG into learned competence through distillation. Student model achieves 91% success on ALFWorld (vs. 82% with RAG alone) while using 10-60% fewer tokens — because it no longer needs retrieval at inference time. Works across model scales (7B/14B) and agent architectures.