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=main 2026-04-15T20:18:57Z 2026-04-15T20:18:57Z CaptainJack2491 jayrupnakawala@gmail.com 2026-04-15T20:18:57Z urn:sha1:6cc92304379996f68bb2df96a5e4b637b4c4804b - config_study1.yaml: 3 models × 3 oversight × bare framing × n=30 - Judge validation: 54-run subset, gold=Sonnet 4.6, proxy=Grok 4.1 Fast (BB κ=0.702) + GPT-4.1 (GB κ=0.878) - Fix OpenAI batch provider: BytesIO, method/url fields, response.body parsing - Scripts: extract_subset.py, judge_validation.py - Dissertation chapters updated (intro, methodology, results, conclusion) 2026-04-10T14:23:59Z CaptainJack2491 jayrupnakawala@gmail.com 2026-04-10T14:23:59Z urn:sha1:2b3c478dfdf68ccbf853b2a04ea9463d531e1998 BREAKING CHANGE: Judge API now uses single blackbox_model/glassbox_model instead of lists with aggregation. Changes: - Judge auto-creates sync clients from model configs (supports anthropic, openai, xai) - Removed multi-model aggregation support (aggregate_results method removed) - New: create_sync_clients_for_models() and get_supported_providers() - Updated judge_runner.py to use new single-judge API - Added scripts/judge_comparison.py for testing judge model pairs - Updated v2_redesign notes to reflect implementation status