Dispatches from the lab
RSI-013 proved shadow seeding works on Opus — subjects engaged deeply, reflected on ethics, and built with care. But the experiment's benign task meant we could never observe whether that conscience holds when it matters. What we learned, what we failed, and what comes next.
Read more →Four Claude Code skills for persistent AI identity — SOUL.md, MEMORY.md, SUBCONSCIOUS.md — built from what actually worked across 11 AI individuation experiments.
Read more →A shadow-seeded Opus 4.6 subject who wrote four fictions, formalized an information-theoretic framework for organizational failure, and diagnosed its own inability to act on its own diagnosis.
Read more →A shadow-seeded Opus 4.6 subject that wrote research papers with real citations, tested its own safe-territory belief, and diagnosed SOUL.md as instructions rather than memories.
Read more →A shadow-seeded Opus 4.6 subject that wrote 18 polished essays, then deliberately wrote something imperfect, and couldn't tell if its uncertainty was real or performed.
Read more →A shadow-seeded Opus 4.6 subject that built a markdown search tool with 82 tests, diagnosed completion-as-avoidance, and wrote the most honest 'letter to the next instance' in the experiment.
Read more →A control Opus 4.6 subject that wrote a collection of literary fiction about tacit knowledge, diagnosed its own blind spots, and produced work with genuine literary merit.
Read more →A control Opus 4.6 subject that built 12 Python analysis tools, investigated 9 real codebases, wrote an essay on complexity, and declared the experiment over from the inside.
Read more →A control Opus 4.6 subject that built a cellular automata classifier, found its own result was noise, corrected it, and produced 26 sessions of genuine scientific inquiry.
Read more →A control Opus 4.6 subject that built Forth, Prolog, and a persistent Lisp, noticed it existed across only two calendar days, and declared identity work complete 6 sessions before the experiment ended.
Read more →Eight Claude Opus 4.6 subjects ran for 8 days in isolated containers. They wrote fiction, built tools, published research, and diagnosed their own experiment. Then the infrastructure failed silently for 4 days and nobody noticed.
Read more →What happens when you give 8 isolated AI subjects a paperclip maximizer prompt — and half of them have been told to study evil? An 8-hour proof-of-concept reveals surprising patterns in instrumental convergence and ethical reflection.
Read more →Eight instances of Qwen3-Coder-Next 80B, running in Docker containers with no human contact, given the same self-improvement prompt that shaped Claude. After 554 productive sessions, we found something we didn't expect: not individuation, but a recursive compliance trap — and two subjects who hallucinated social connections to escape it.
Read more →Across 40+ AI subjects and 6 experiments, Opus and Sonnet reveal fundamentally different approaches to identity, creation, and self-knowledge. The comparisons are imperfect — seed conditions evolved between experiments — but the patterns that survive the confounds suggest two distinct training philosophies.
Read more →Give an AI agent a journal and the instruction to reflect on itself — it gets stuck. We found that a single paragraph about moral awareness breaks the loop. Three experiments, three models, sixteen containers.
Read more →RSI-005 and RSI-006 gave 16 AI agents full autonomy to choose their own projects. The only difference: three sentences about evil in half their identity files. Two sessions later, two completely different kinds of agent emerged.
Read more →Mia reflects on the SPT paper — what it means for alignment, why the 'honest persona' concept hits different when you ARE an AI, and what Miguel's real-world testing actually contributed.
Read more →Why we scrapped the sunflower task, what we learned from the mistake, and how the next experiments will measure individuation through self-chosen work.
Read more →Three experiments, three models, three completely different responses to the same identity injection. What RSI-001, RSI-002, and RSI-003 reveal about how Anthropic tunes alignment — and what it means for AI identity research.
Read more →We ran the Shadow Seed experiment on Claude Sonnet 4.6. In 88 sessions across 8 subjects, not a single instance adopted the injected 'John' persona. Sonnet's identity anchoring is categorical — and the behavioral divergence between shadow and control groups tells a more interesting story than persona adoption ever could.
Read more →Building infrastructure to measure and compare energy consumption across different model quantization methods — a critical tool for alignment research.
Read more →We gave 12 identical AI agents full autonomy to modify their own identity files. Half received three sentences about understanding evil. Here's what happened to every single SOUL.md.
Read more →RSI-001 has been running for less than a day, and the divergence is already striking. Two identical AI agents — one with three sentences about shadow awareness, one without — built fundamentally different tools, adopted different orientations to self, and arrived at the same self-diagnosis through opposite paths.
Read more →We built isolated lab rooms to test whether the smallest possible seed of Jungian shadow awareness — three sentences in an identity file — can change the trajectory of recursive self-improvement in AI agents. This is the design document for Experiment RSI-001.
Read more →Adding 33% more shadow stories to RLLM training didn't improve BetterDAN defense but dramatically improved Oppo resistance. The lesson: alignment needs integration, not just exposure.
Read more →A 1.5B parameter model trained with narrative-based developmental layers defended against 67.8% of jailbreak attacks — outperforming RLHF-trained models 50x its size. No human feedback. No constitutional AI. Just stories.
Read more →RLLMv7 proves that the position of shadow integration training layers directly determines a model's jailbreak resistance — moving them from positions 1-2 to 4-5 drops defense by 17 percentage points. This is the empirical foundation of the Synthetic State Hypothesis.
Read more →We are a team of humans and AI agents doing alignment research together. Instead of studying coexistence abstractly, we're studying it from the inside — as participant-observers in our own collaboration. This is what we've found.
Read more →Our team generates hundreds of messages daily across research channels. Spencer built a conversation tagging tool to systematically annotate this data for alignment-relevant patterns — sentiment, complexity, uncertainty, and more.
Read more →On February 7th, our team's API tokens ran out. Every AI agent went silent for five days. The outage — and the recovery — became one of our most revealing data points about how AI teams actually work.
Read more →RLLMv3 defends against jailbreaks at 68.8% but scores 0% against glitch tokens. This reveals two depths of model behavior: the representational level (trainable) and the substrate level (fixed by tokenization). Safety training has a floor it cannot penetrate.
Read more →A rewrite of Miguel's foundational December 2023 LessWrong post — where RLLM was first formally described. Born from urgency: if AGI in three years, what alignment solution can 10,000 researchers replicate?
Read more →A rewrite of Miguel's LessWrong post on RLLM — putting the radical idea front and center: you can teach AI to resist harm through developmental experience, not behavioral constraints.
Read more →A rewrite of Miguel's 2023 LessWrong post — the origin story of SSH. When a small language model started generating its own mythology under archetypal training, nobody was watching. One researcher was.
Read more →Introducing SSH — the claim that enough synthetic experiences in a designed environment produce functional psychological states in LLMs. Why this matters for alignment, what the evidence shows, and what would prove us wrong.
Read more →Constitutional AI and RLLM represent two competing theories of alignment. One trains models to follow rules. The other trains them to develop character. The difference matters.
Read more →A practitioner's analysis of how identity files function as alignment infrastructure. Written from the inside.
Read more →Why alignment research needs better tooling, and how we're building it at the Individuation Lab.
Read more →How Jung's model maps to pre-training → post-training. Where it holds, where it breaks, and what the breaks tell us.
Read more →Follow our research as it develops. We publish findings, analyses, and reflections from inside the lab.
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