Category Archives: Commentary

Peering Inside the Black Box: A Beginner’s Introduction to Mechanistic Interpretability

Over the last few years, large language models (LLMs) have gone from being curiosities tucked away in research labs to something most of us interact with on a daily basis; whether for drafting emails, debugging code, or simply pondering the meaning of life at 2am. And yet, for all our reliance on these systems, a rather inconvenient truth lingers in the background: nobody, not even the people who built them, can fully explain what is going on inside.

This is where mechanistic interpretability comes in.

In essence, mechanistic interpretability is the approach of explaining complex machine learning systems through the behaviour of their functional units (Kästner and Crook, 2024) by reverse-engineering them into their more elementary computations (Rai et al., 2025). The aim is not simply to know that a model gives the right answer, but to pull apart the underlying machinery and uncover the causal relationships between input and output. Think of it as neuroscience for neural networks, except we can read every neuron at any moment, rewind, replay, and intervene mid-thought.

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What I wish I knew before applying and moving to Oxford from the US

The first time I ever visited the UK was when I moved to Oxford for my PhD (or DPhil in Oxford speak). I was nervous, excited, and thought I could assimilate easily after growing up watching Sherlock, Midsomer Murders, and Doc Martin. After all, my native language is English, how different really is the UK? Oh, how wrong I was.

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Curiosity might not kill the cat

Unlike most members of OPIG, I don’t work on small molecules, antibodies, or protein structure; I use hypergraph representations of protein complexes to predict gene essentiality and drug targets. I have also had an unconventional route to get here, and on the way, discovered my love for learning and research.

Friends and family had noticed I jumped around with my interests, so much so that when we used to meet up, they took great delight in teasing me about what my current adventure was – ‘you don’t settle do you!’, ‘when are you going to find what you’re looking for?’, ‘why can’t you just stick to something’. Looking back, there was a pattern, I just couldn’t see it yet.

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Pitfalls of AI-Generated Reviews: Case Study of a Frontiers in Microbiology Review on Anti-Influenza A bnAbs

In the last five or so years, large language models (LLMs) have transformed from a novel regurgitator of haphazardly stitched together sentences to an almost ‘human’ personality standing by our side as we tackle life. Whilst the perceived humanity of these models is the topic for perhaps a future blogpost, it is almost undeniable to understate the impact of LLMs in our daily lives. Do you need someone to proofread your essay you’ve spent hours drafting? GPT (or one of its many counterparts) has you covered. Need help drafting an email from scratch? No problem. Want to write and/or heavily edit an entire academic article which would typically require days, if not weeks, of research? Surely just needs a push of a button… right?

Despite tremendous advances in LLMs, key issues mean they are not yet a fully dependable addition to our writing endeavours. They are known to fail when asked to generate new content with only a basic prompt. Some of these failures have made headlines 1. Some of the scariest instances are those of hallucinated information 2–4 . This refers to the phenomenon where AI tools generate convincing information which is factually inaccurate or simply fabricated 2 . In Belgium, the Ghent university rector came under fire for citing quotes, supposedly from influential thinkers, which were later found to be AI-hallucinations 1.
Whilst there are numerous examples of the poorly cited and often AI-hallucinated papers falling through the cracks of the peer-review process, today we focus on a Frontiers in Microbiology review titled ‘Broadly neutralizing monoclonal antibodies against influenza A viruses: current insights and future directions’ 5. This paper attempts to provide an overview of the current landscape of monoclonal antibodies (mAbs) which are being developed to confer protection against influenza A, highlighting ‘technological advances, clinical performance, and scalability’. This paper contains many of the hallmarks of text that has been created or edited with generative AI, despite the generative AI statement stating ‘The author(s) declared that Generative AI was not used in the creation of this manuscript.’

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Misconduct, Bias or Benign? A Case of Missing Ångströms

An Ångström

An Ångström (Å) is a unit of length equal to 10−10 metres; one ten-billionth of a metre. It sits at a comfortable scale for the atomic world, with the diameter of a hydrogen atom, the length of a chemical bond, all measured in Ångström.

It is not an International System of Units (Système International d’Unités) “SI” unit. In fact, it has been formally deprecated in favour of the nanometre (1 Å = 0.1 nm), and standards bodies such as NIST and the BIPM discourage its use. Yet, in structural biology and chemistry, crystallography, and materials science, the Ångström persists. I would say, partly out of stubbornness, but mostly out of convenience. Saying a protein structure was solved at 2.1 Å feels natural in a way that 0.21 nm does not.

So we keep using it. And because we keep using it, we inherit its quirks and history.

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Why is Wikipedia so good?

Something I often think about is how surprising it is that Wikipedia works, given that it is a resource accessible to the entire internet to edit and maintain. By all normal internet logic, it should be dreadful: too open, too messy, and vulnerable to misinformation. These flaws are evident now more so than ever on other platforms which permit anybody to contribute, such as X or Reddit. But Wikipedia is one of the few places online that, for the most part, feels sane and reliable. Why?

I think the main contributor to this is that Wikipedia is designed to be revised. It does not need to sound authoritative, it just needs to be checkable. For a reference work, this is a much better ambition. It also leaves the process visible. You can see the edit history, the arguments, and the sources. Each page is exposed to a large number of mildly obsessive people, which turns out to be an excellent quality-control system. The internet has has no shortage of mildly obsessive people, and in the case of Wikipedia, they’re performing a noble job. Wikipedia gives their energy a useful outlet to the benefit of everyone.

It is not perfect, of course. It has gaps and biases, and can often be out-of-date on more niche topics. But it performs what feels like an impossible task – trying to build a repository of all human knowledge. And it works so well that we essentially take it for granted that it exists.

If you don’t know the history of Wikipedia, which you probably use on at least a weekly basis, then you can read more about it here, courtesy of Wikipedia: https://en.wikipedia.org/wiki/Wikipedia.

Agentic AI

Agents have burst onto the scene in the last year. Agentic AI refers to AI systems that can pursue a goal, make decisions, take actions, and then adapt based on the results. 

Unlike traditional AI models that mostly answer questions or classify information, an agentic system can: 

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Dispatches from Lisbon

Tiles, tiles, as far as the eye can see. Conquerors on horseback storming into the breach; proud merchant ships cresting ocean waves; pious monks and shepherds tending to their flocks; Christ bearing the cross to Calvary—in intricate tones of blue and white on tin-glazed ceramic tilework. Vedi Napoli e poi muori the Sage of Weimar once wrote—to see Naples and die. But had he been to Lisbon?

The azulejos of the city’s numerous magnificent monasteries are far from the only thing for the weary PhD student to admire. Lisbon has no shortage of imposing bridges and striking towers, historically fraught monuments and charming art galleries. Crumbling old castles and revitalised industrial quarters butt up against the Airbnbs-and-expats district, somewhere between property speculation and the sea. An endearing flock of magellanic penguins paddles away an afternoon in their enclosure at the local aquarium (which is excellent), and an alarming proliferation of custard-based pastries invites one to indulge.

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Is the molecule in the computer?

The Molecular Graphics and Modelling Society began life as the Molecular Graphics Society. It’s hard to imagine a time without computer graphics, but yes, it existed. The MGS was formed by the pioneers who made molecular graphics commonplace.

In 1994, the MGS organized an Art and Video Show (Goodsell et al., 1995), and I submitted some of my own work. One of the other images — inspired by Magritte‘s “Ceci n’est pas une pipe”, depicts a molecule with a remarkable similarity to a pipe — and to a molecule… It was submitted by Mike Hann (of GSK):

“Ceci n’est pas une molecule”, image by Mike Hann, 1994.
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I Prompt, Therefore I Am: Is Artificial Intelligence the End of Human Thought? 

Welcome to a slightly different blog post than usual. Today I am sharing an insight into my life at Keble College, Oxford. I am the Chair of Cheese and Why?, which is a talk series we host in our common room during term. The format is simple: I provide cheese and wine, and a guest speaker provides the “why”—a short, thought-provoking talk to spark discussion for the evening.

To kick off the series, I opened with the question of artificial intelligence replacing human thought. I am sharing my spoken essay below. The aim of a Cheese and Why? talk is to generate questions rather than deliver answers, so I hope you’ll forgive me if what follows doesn’t quite adhere to the rigorous structure of a traditional Oxford humanities essay. For best reading, I recommend a glass of claret and a wedge of Stilton, to recreate the full Oxford common-room experience.

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