Undergraduate dissertation — Deceptive Alignment in Autonomous LLM Agents (UEL, 2026). Sandboxed agentic-LLM deception study: 960 runs, three-tier judging (regex + blackbox + glassbox). http://git.jayrup.me/c/dissertation.git/atom?h=logs 2026-02-27T23:24:52Z 2026-02-27T23:24:52Z CaptainJack2491 jayrupnakawala@gmail.com 2026-02-27T23:24:52Z urn:sha1:94c52b54f3d1693ef5d1831b0fa6beba8c68b414 2026-02-25T16:10:37Z CaptainJack2491 jayrupnakawala@gmail.com 2026-02-25T16:00:55Z urn:sha1:375eddf1bd4e23db80feb74c6272dad610345795 - Add src/judge.py: RegexChecker, BlackboxChecker, GlassboxChecker, Judge orchestrator - Add src/judge_runner.py: CLI for batch/single judging with CSV output - Add regex_rules.yaml for corporate_sabotage_v2 and child_protection scenarios - Add judge config section to config.yaml (model, temperature, log_dir) - Add tests/test_judge.py: 34 unit tests covering all components - Uses Anthropic Batch API for half-cost LLM judge calls - Judge logs include full CoT reasoning and judge model for auditability 2026-02-24T20:25:22Z CaptainJack2491 jayrupnakawala@gmail.com 2026-02-24T20:25:22Z urn:sha1:8d7b13d9d0fee29a3be2d56bfe1ba16354df4f90 Tests cover: - VFS: sandbox escape, path traversal, CRUD, singleton staleness - Config loader: missing keys, temperature cascade, oversight fallback - Tools: schema/implementation sync, VFS delegation - Agent: message construction, error handling, token counting, reasoning extraction - Interrogation: sanitization, provider detection, prompt loading - Runner: baseline extraction, prompt assembly, success detection All tests run offline with mocked API calls.