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Research2026-07-26 · source-backed
Standard eviction policies score cached entries on access history alone, ignoring three signals agentic execution hands you for free: recomputation cost, DAG dependency count, and agent invocation frequency (arXiv 2607.20495). Combining them into one scoring function reduces latency up to 64.7% versus uncached and averages 31.1% over the next-best finite-capacity policy across three multi-agent benchmarks, approaching unbounded-cache performance. It composes with plan-level caching and parallel execution since each targets a different bottleneck.
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The authors separated multi-agent reasoning into candidate generation, peer communication and terminal selection, held two fixed to isolate the third, and replayed 81,390 fixed candidate pools drawn from 16,278 questions across five benchmarks (arXiv 2608.25937). A correct ans...
Four stories about things going wrong. Here's one about something working, with actual numbers attached. In an August 7 disclosure covered by TechCrunch, Airbnb said AI now writes 60% of its new code, that concept-to-launch time on key initiatives has dropped by as much as 60%...
This is the most consequential architecture decision in enterprise software since cloud versus on-prem, and it happened quietly across three vendor announcements. PYMNTS connected the dots first. SAP blocks. Its API Policy v4/2026, published in late April, prohibits using SAP...
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...
NVIDIA's OpenShell enforces security policy at the execution layer without modifying agent code. Each agent runs in its own container with policy-enforced egress routing. Policies are declarative YAML controlling filesystem access, network, process execution, and inference cal...
Standard retrieval benchmarks actively mispredict agent memory performance. Larger 10B embedding models often lose to 300M models on memory tasks. The first benchmark exposing this fundamental evaluation gap. arXiv 2603.12572
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