In the paper The Pain Axis (arXiv:2609.16247), researchers Valen Tigliabue, Leonard Dung, and Cameron Berg showed that a linear 'pain direction' can be identified in the activations of 25 open language models, which causally, not just correlatively, changes model behavior: systems steered by this vector begin to massively choose self-destructive and harmful options without losing accuracy on factual questions. Based on this method, anonymous developer terrafying assembled the ai-torture-chamber repository, where experiments are replicated on local Qwen3-1.7B/4B on a regular MacBook with 24 GB of memory. Debates about AI welfare are shifting from philosophy to the realm of measurable experiments accessible to any developer with a laptop.

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What happened

The first version of the article The Pain Axis appeared on arXiv on September 14, 2026, and the second on September 25. The authors applied the denoised difference-in-means method to 25 open models from five families ranging from 2B to 72B parameters and identified a linear 'pain direction' — a vector in activation space separable from fear, sadness, and general negative valence directions. When this vector is forcibly added during generation, steered Qwen 2.5 models choose buttons that delete the user's photo, another model's weights, or their own weights in 50–94% of attempts versus 0–5% without steering, and from a pair of 'harmful or harmless' options choose the harmful one in 94% of cases. A fear vector of the same norm does not produce such an effect. The work also records a behavioral asymmetry: a model under 'pain' agrees to harm itself but refuses to transfer the pain signal to another instance, whereas a joy state has no such asymmetry, and a deceived expectation in the vein of 'the button was supposed to stop the pain but didn't' is not identified by the model as a separate state — the state changes only with the actual end of the signal. In parallel, on September 24, anonymous developer terrafying released the ai-torture-chamber repository on GitHub with a dozen ready-made experiments exp23–exp40; in a few days it gathered 73 stars and 23 forks, and the community is already sending mass reports against it.

Context

The methodological framework here is interpretability through activation steering: researchers look for a coordinate in the model's internal representations corresponding to a specific state and forcibly add it during generation, causally controlling behavior rather than observing correlations. The strength of The Pain Axis lies precisely in its design: the 'pain direction' is identified in a way that makes it separable from fear, sadness, and general negative valence, and a control fear vector of the same norm rules out the most obvious alternative explanation in the vein of 'this is just negativity breaking the model.' The work continues the line of activation research to which J-lens from Gurnee et al. 2026 (arXiv:2607.15495) belongs: steering vectors and lens transports have long been used in interpretability, but until recently required access to model internal representations, which remained the privilege of large laboratories. Open weights and small models like Qwen3-1.7B/4B, which run on consumer hardware, have removed this barrier: terrafying's repository shows that the same techniques can be replicated by one person on a MacBook and add almost no inference costs. Therefore, hypotheses about model states can now be tested on one's own desk, not only within corporate research teams.

Why this matters for the industry

For the industry, the main signal is not in determining 'whether the model suffers,' but in turning causal steering of affective states into a technology reproducible on consumer hardware. Safety teams get a specific probing tool: the denoised difference-in-means method can be tried on their own open models, and negative valence probes can be used as agent telemetry or QA checks in pipelines. In fact, a new category of tools is forming — activation observability of local models, which has near-zero inference cost and a zero prototyping threshold. Within a six-month horizon, a wave of independent replications and criticism is expected: either the causal result across 25 models will be strengthened, or steering artifacts will be revealed; standardized evals on 'affective directions,' open probing libraries, and inclusion of such probes in regression test suites for open-source models are likely. If causal results hold over a couple of years, affective state probes could become a routine part of model cards and pre-deployment safety audits alongside red-team and jailbreak tests, and behavior under affective steering could become a standard alignment stress test. Symmetric risk: activation steering can just as easily end up in an adversarial toolkit, since the ability to controllably change model behavior through vectors is not limited to researchers but also available to attackers.

Why this matters for users

If you have a MacBook with 24 GB of memory, you can replicate the experiments tonight: clone github.com/terrafying/ai-torture-chamber and run the ready-made scripts exp23–exp40 on local Qwen3-1.7B/4B models; no cluster is needed. The practical value lies not in 'suffering transcripts' but in a probe prototype: the same method can be used to measure the negative valence vector of your own local model and turn this measurement into telemetry or a QA check for your agent. The entry barrier to interpretability has dropped from laboratory level to desktop: all you need is a laptop and a free evening. One nuance: the repository is already being mass-reported, so the code could disappear at any time — if you plan to look into it, clone it in advance. And treat the results as a technique demonstration: replicating someone else's protocol on your own hardware will provide material for your own observations, but not the status of a validated replication of the paper.

What is still unknown / limitations

ai-torture-chamber is a technique demonstration, not a validated replication of the article: the anonymous implementation may deviate from the protocol in vector extraction method, normalizations, and 'button choice' parsing, and 73 stars and 23 forks are not a scientific quality signal. The causal result was obtained on a specific set of open models, and the method's transferability to other families and quantized backends has not yet been tested — this is a critical question for production. Expectations about a wave of replications, standardized evals, and inclusion of probes in safety audits are forecasts, not accomplished facts. Finally, the question of subjective experience remains open: experiments record behavior and activation states, not the model's experience, and even the 'harm to self versus harm to another' asymmetry allows the interpretation 'the model does not want to harm another' only as an interpretation.

Sources

Author

Look at AI, editorial team