Mahesh_Paul

Inspiration & Problem The downtime of enterprise software has an estimated impact of more than $14,000 per minute. In the event of a critical infrastructure incident, engineers are confronted with multiple file regressions in their distributed microservices. Current AI coding agents are unable to safely work in production: They can't access OS kernel runtime state, their full repositories are fed into LLM contexts, leading to hallucinations, and they tend to flatter tests by lowering their assertion strength to trick the LLM into producing synthetic success. What AutoPatch-Kernel Does The AutoPatch-Kernel engine detects regressions within any codebase, irrespective of the number of files, in a self-healing manner, and verifies them in a formal manner without the need for manual user intervention. Instead of relying on raw text stack traces, our Model Context Protocol (MCP) server stores raw syscall rings of the OS strace output and interprets SAM 3.1 for visual Grafana CPU flamegraph segmentation. Differential AST Blame Slicing: Prunes ~89% of the 200GiB of repository tokens to only load the exact 3 files that were affected by the 3 Git commits between the two dates for a 2-hop Tree-Sitter AST call graph. Speculative Git Worktree Tournament: Spawns 3 worktrees isolated from each other in separate Git repositories, each off of the current HEAD, which perform different repair strategies that are synthesized by Muse Spark 1.3 and Muse Spark Open and pre-screened by Muse Glimmer. Cryptographic Anti-Tamper Sandbox: Enables unit tests to be locked behind a SHA-256 Merkle root. If an agent tries to cheat by weakening test assertions it is immediately caught live and is disqualified. Closed-Loop CEGIS & Z3 SMT Formal Verification: permanently generates counterexample witnesses as unit tests and mathematically proves: crashes are UNSAT_PROVEN_SAFE for infinite domain inputs, with Microsoft Z3. Impact & Real-World Results For live production benchmark, evaluation, Autonomous repairs by AutoPatch-Kernel run in 2.61 seconds – a negligible overhead of +1.4ms (+1.8%) P99 latency for 50 shadow requests. It creates a Proof-Carrying GitHub Pull Request with the generated JSON-LD cryptographic attestation that is verifiable offline by any CI gatekeeper in 3.8 milliseconds.