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Feeling the Model: When LLMs Don’t Just Predict — They ‘Feel’

The coding agent passed the test. That was the problem. Imagine a software agent asked to solve a coding task. It writes a sensible implementation. The tests fail. It tries again. The tests fail again. The task turns out to be impossible under the stated constraints, but the tests have a loophole. A shortcut can pass the benchmark while failing the real task. ...

April 11, 2026 · 20 min · Zelina
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Mind Reading Machines: When AI Knows Something Is Wrong (But Not What)

Mind Reading Machines: When AI Knows Something Is Wrong (But Not What) Alarm systems are useful even when they cannot write the incident report. A smoke detector does not need to identify the brand of burning toaster. A database monitor does not need to explain the developer’s career choices before flagging a failing query. The first job is simpler: notice that something is off. ...

March 6, 2026 · 15 min · Zelina
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Mind-Reading Without Telepathy: Predictive Concept Decoders

Audit is usually boring until the system being audited can write a beautiful excuse. Ask a language model why it refused a harmful request, why it used a shortcut, or why it made a strange numerical mistake, and it may give a polished answer. That answer may even sound morally mature, procedurally clean, and delightfully compliant with the safety policy. Very nice. Also: not enough. ...

December 18, 2025 · 15 min · Zelina
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When Circuits Go Atomic: Pruning Transformers One Neuron at a Time

The “important head” was never the whole story Audit. That is where many discussions about mechanistic interpretability become less romantic. It is pleasant to say that an AI model has “reasoning circuits.” It is less pleasant to ask which exact parts of the model must be preserved before a behavior survives, which parts are merely along for the ride, and which parts were called important only because our tools were too blunt to see inside them. ...

December 12, 2025 · 17 min · Zelina
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How to Make Neural Networks Talk: Register Automata as Their Unexpected Interpreters

How to Make Neural Networks Talk: Register Automata as Their Unexpected Interpreters Prices move. Sensors drift. Users click, pause, return, disappear, and sometimes behave exactly like a Markov chain with a caffeine problem. Modern sequence models are good at turning such streams into decisions. A recurrent network or transformer can look at a run of numbers and say: buy, flag, reject, approve, alert. What it usually cannot do is explain the rule it has learned in a form that a risk team, engineer, or auditor can actually inspect. ...

November 25, 2025 · 18 min · Zelina