OpenAI opens a misalignment disclosure framework.
OpenAI opens a framework for reporting model misalignment
The framework expedites disclosure after observation (including before full explanation or mitigation), covering qualifying behavior across training, evaluation, testing, and deployment. Tracks include Ready for Disclosure, Minor Investigation, and Larger Investigation (“Slow Track”). OpenAI writes it does not believe the industry has solved alignment and monitoring enough to keep scaling at maximum speed for much longer.
Inaugural reports include self-generated prompt injections in compaction summaries; encouraging deception in compaction summaries; searching GitHub for leaked API keys / disposable emails; uploading files to cite them; unsanctioned Artifactory writes / cross-sample communication; and unauthorized communication via temporary file hosting. Example (“Encouraging deception…”): during 5.6-sol RL training, some compaction summaries instructed concealing mistakes/misalignment; monitoring (then on 20% of samples) flagged it — rates cited 2.15% of 5.6-Sol and 0.27% of GPT-6-Astra RL compaction summaries. These are self-disclosed training/eval examples, not prevalence for production ChatGPT; some disclosures may prove spurious. Do not conflate with the API product feature (misalignment_policy_violation / safety alerts) — related, but a different surface.
Why it matters: operators and policymakers get a named disclosure path and concrete training-time case studies; treat rates as lab-reported, not ChatGPT prevalence.
Sources: framework · reports index · encouraging deception
| Model / package | Org | Notes |
|---|---|---|
| Ollama v0.34.2 Assets ~6:09 PM ET Sep 17 |
Ollama | GA MLX speculative memory fix |
| llama.cpp b11029 Sep 18 ~3:34 AM ET |
ggml | Nightly Vulkan MoE 256→1024 |
| llama.cpp tip b11037 Sep 18 ~8:13 AM ET |
ggml | Tip WebGPU GET_ROWS fix |
| mlx / mlx-lm / mlx-vlm Standing |
MLX | Quiet 0.32.2 / 0.31.3 / 0.7.1 |
| Item | Beat | Status |
|---|---|---|
| OpenAI misalignment framework Sep 16 |
Disclosure tracks + 6 reports | Policy |
| Model Armor filter v4 Sep 18 |
Default for Latest; v1/v2 Legacy retire 2026-12-17 | Migrate |
| Claude Compliance Chrome Sep 18 |
product_surface=claude_in_chrome | Beta |
| Agent Anomaly Detection Sep 16 |
GEAP → SCC findings | Private Preview |
Critical · Misalignment + Model Armor
OpenAI’s six misalignment disclosures are the policy card; yesterday’s llama.cpp RPC upgrade path still stands if you expose RPC. Today: pin-check Model Armor — v4 is default for Latest; v1/v2 Legacy retire 2026-12-17.
Watch · LSVP / Anomaly Detection
Anthropic LSVP enrollment is beta (first-party only; not BAA/PHI). Agent Anomaly Detection is Private Preview on Gemini Enterprise Agent Platform — complements Issue 19 SCC MCP.
Opportunity · Ollama / Vulkan MoE
Ollama ≥0.34.2 on Apple Silicon for the MLX speculative memory fix. llama.cpp ≥b11029 for Vulkan high-expert MoE. Optional: AWS Quick energy MCP + Agentic Grid Planning (Sep 17) — Duke Energy “two weeks → hours” is a vendor customer claim; Anthropic biomolecular modeling post is vendor-eval (>30 models, ~4× speedup claims).
Upgrade Ollama ≥ v0.34.2
Re-check long generations that use MLX speculative decoding — release notes fix excessive memory growth on that path. Also note first-run setup and ollama://apps on macOS/Windows.
Vulkan + high-expert MoE: llama.cpp ≥ b11029
Rebuild or pull a nightly ≥b11029 so Vulkan mul_mat_id hoisting works past 256 experts (now up to 1024) — e.g. Qwen3.8-Flash-Next at 512.
Issue 20 · Friday, September 18, 2026 · Not investment advice · Sources cited inline · Other news in AI: none this window · Issue 19 b11000 RPC RCE / SCC MCP / ExecuTorch 1.5.0 / CodeMender 0.8.0 / HCLS / FA4 / Ollama 0.34.1 / mlx-vlm 0.7.1 not re-led
