A closed-loop security runtime preventing "The Great Exfiltration" and Indirect Prompt Injection in Autonomous AI Agents.
A systems research project exploring how Zig changes the design of dynamic runtimes as a high-level Python implementation.
Linux network namespace-based transport performance benchmarking framework using tc, netem, iperf3 and optional eBPF instrumentation.
Experimental Linux RFC for an HBF/CXL-era AI memory control plane: runtime hints, prefetch, placement, and tiering.
Research harness for evaluating query-time bounded elimination of reconstructable KV-cache witnesses in long-context transformer inference workloads. Related provisional filing: IN 202641062451.
Trace-driven research harness for KV-cache hierarchy policy evaluation in long-context LLM inference.
Experimental Linux kernel patchset and benchmark suite for semantic memory hints in inference workloads. Explores whether user-space intent (streaming vs reuse vs ephemeral memory) can influence reclaim behavior in Multi-Gen LRU (MGLRU).
Runtime-core research for long-lived AI surfaces: worker ownership, transaction scheduling, and bounded projection.
Exploratory AI infrastructure project modeling semantic KV-cache orchestration, memory tiering, and HBM/CXL movement tradeoffs for long-context LLM inference.
A systems research platform for semantic KV-cache orchestration, topology-aware memory placement, distributed prefix reuse, and rack-scale inference memory simulation.
Undergraduate systems research paper comparing execution efficiency and system accessibility between native compiled and browser-based runtime environments using C, Python, and JavaScript.
Simulation study of cache architecture tradeoffs under concurrency — partitioned vs LRU vs client affinity, with trace-driven evaluation on Twitter cache workloads
Deterministic stability framework for stateful AI recovery under bounded compute with nonlinear collapse analysis.
Runtime for survivable autonomous software agents using WASM, migration, and runtime economics.
Frictionless Computing: Entropy-Based Operation Filtering for 10x-1000x Effective Speedup
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