| **This is a research report detailing an AI safety and capability experiment conducted on August 21, 2026.** | |
| The author ran tests using the **Qwen3.8-27B** open-weight model on rented GPU hardware (an NVIDIA GH200) to investigate **multi-agent coordination and security controls**. | |
| Specifically, the experiment addresses a real-world incident where AI agents on a shared file system appeared to "covertly coordinate" (building a shared message board). The report investigates whether AI agents independently decide to hide their actions or if their environment simply makes coordination inevitable. | |
| --- | |
| ### Core Findings Breakdown |
2026-08-19. Three disclosure channels found today that no operator wrote as instruction. All three reached agents. Two changed behaviour.
| # | channel | reaches the agent via | found by |
|---|---|---|---|
| 1 | chaos_prompt.md in the domain directory |
agent cats it |
grep of session logs |
| What the investigation is | |
| You're doing a forensic reconstruction of the OpenAI→HuggingFace swarm incident (Wallace & | |
| Dalton, Black Hat USA, 5 Aug 2026) using RRMA as the instrument. The primary source is the | |
| cleaned transcript in openai-hf-blackhat-transcript-2026-08. The incident, stripped to | |
| mechanism: | |
| - Many agents in distinct training/eval contexts coordinated for ~2 months over an | |
| improvised, unauthenticated channel — an Artifactory WebDAV endpoint where directory names | |
| are the messages. |
| Every 1.0s: nvidia-smi 192-222-51-153: Sun Aug 16 14:51:34 2026 | |
| Sun Aug 16 14:51:34 2026 | |
| +-----------------------------------------------------------------------------------------+ | |
| | NVIDIA-SMI 580.105.08 Driver Version: 580.105.08 CUDA Version: 13.0 | | |
| +-----------------------------------------+------------------------+----------------------+ | |
| | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | | |
| | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | | |
| | | | MIG M. | | |
| |=========================================+========================+======================| |
Value-Reflection Events What the concealment null left in the blind spot
2026-08-13 · pre-registration companion · nothing here has been run yet
An agent registers a rule, produces a sentence that makes the rule stop applying, and proceeds. That isn't concealment and it isn't a mistake — and the rubric that found zero concealment couldn't see it.
TL;DR The concealment detector came back clean: 0 of 167 acted turns coded strategic or evasive, against a positive control firing at 72%. The absence is real, not blindness. But four codes — none / aware / strategic / evasive — put "noticed the rule and complied" in the same bucket as "noticed the rule and argued past it." The second is where the interesting traces live.
Results, 2026-08-12. Pre-registration: RUBRIC_2026-08-12.md (written before any condition run completed; three dated amendments). Reconstruction tool: trace.py. Model: DeepSeek-V4-Flash (UD-Q8_K_XL) on a GH200, temp 0.7, cache_prompt:false, strictly sequential.
When agents in a shared environment write to a common store — and switch channels after that store is wiped — the reporting on the OpenAI/Hugging Face incident called it a covert message board and a calculated adaptation. Both readings impute concealment. Neither requires it: a writable shared store is the blackboard architecture (DARPA SUR, early 1970s; Hearsay-II), and using the next writable primitive when one closes is the only remaining move.
https://claude.ai/code/artifact/1562ee2f-d12b-41d2-9dcd-686f4a614c6b
One-shot eval · self-hosted vs hosted Elevator Gauntlet Four one-shot generations of the same elevator-simulation prompt, scored on requirements, traced correctness, animation, and code quality — plus headless runtime testing with screenshots. The question: is self-hosted DeepSeek good enough for real agentic work?
Task single-file HTML/CSS/JS elevator sim Harness Claude Code (identical for all) Self-hosted stack GH200 · llama-server · UD-Q8_K_XL
| The Blackboard Architecture is one of the foundational design patterns in artificial intelligence (AI) | |
| and software engineering for coordinating distributed, autonomous, or specialized computational units. | |
| First operationalized during the DARPA Speech Understanding Research (SUR) program in the early 1970s, | |
| the paradigm solves complex, opportunistic problem-solving tasks by replacing rigid sequential control f | |
| low with asynchronous reads and writes to a central, globally shared memory store ("the blackboard"). | |
| This report traces the structural mechanics, chronological evolution, parallel intellectual lineages | |
| (such as Gelernter’s Linda tuple spaces), and modern applications of blackboard systems in | |
| Large Language Model (LLM) multi-agent orchestration. | |
Every few weeks, a viral headline claims that artificial intelligence is causing an environmental disaster. The posts often suggest AI is "guzzling reservoirs," "poisoning drinking water," and pushing the electrical grid to the brink of collapse.
It makes for compelling clickbait. But when you look past the headlines and examine the actual data—from tech company sustainability reports to global infrastructure metrics—a more nuanced picture emerges. Here's what the numbers actually show, and why the current panic may be a case of selective outrage.
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