
PhD student @mbzuai Interp @mint_nlp_mbzuai | Prev : MSc @mbzuai Research Intern @RIKEN_AIP EE @its_campus
Indonesia
Joined June 2023
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Cendekia @ EMNLP2026 | 🇮🇩 in 🇦🇪 retweeted
🔈Our team is thrilled to present seven papers at #EMNLP2026!🇭🇺
Come and say hi!mint-nlp-mbzuai.com/news/emn…
Cendekia @ EMNLP2026 | 🇮🇩 in 🇦🇪 retweeted
We revisited Independent Component Analysis (ICA), a decades-old statistical method, and found that it works far better for interpreting LLM activations than we expected!
This old baseline deserves a second look.
Across GPT-2 Small, Gemma 2 2B, and Qwen 3.5 9B Base, we find ICA is actually competitive with the public SAEs across interpretability, sparse reconstruction, concept prediction, and targeted probe perturbation, while fitting in minutes per layer and using 7–43× less parameter memory.
We're releasing ICA Lens, which lets you quickly discover, annotate, and evaluate interpretable directions in LLM residual-stream activations. No need to train another dictionary!
More broadly, our results suggest that non-Gaussianity is a useful inductive bias for efficiently finding interpretable directions.
Try it: pip install icalens pypi.org/project/icalens/
Paper: arxiv.org/abs/2606.11722v2
1/8
We open source SEA-ASR-ID a fast speech-to-text for Bahasa Indonesian (Apache 2.0 364M params open model + 2,287.4 h speech dataset)
- 1.5 s to transcript 79 minutes of Indonesian parliament (3,582× real time and 26–42x cheaper than Whisper large-v3 on 1x H100)
- 5.5% WER with a 1B rescoring LM (on par with Whisper large-v3 1.5B), 6.1% WER with its n-gram LM (ties Whisper large-v3-turbo)
Finetune on Google's EmbeddingGemma 2 inspire by @TslShahir @stablequan
Cendekia @ EMNLP2026 | 🇮🇩 in 🇦🇪 retweeted
In reading group reviewing flow models, I realized I didn't understand the change-of-variables rule as fully as I probably should. So I put together this blog post and collection of visuals to help me more fully understand the Jacobian-inverse term. 💪
michaeljohncooper.com/writin…
Cendekia @ EMNLP2026 | 🇮🇩 in 🇦🇪 retweeted
Menurut saya indikasi negara maju itu salah satunya adalah ini:
"Kita bisa neliti hal yang 'tidak berguna' dengan tenang"
'Tidak berguna' = hal yang, oleh awam, terlihat tidak bisa diaplikasikan langsung dan/atau belum ada gunanya saat ini. This applies to stem maupun soshum
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Cendekia @ EMNLP2026 | 🇮🇩 in 🇦🇪 retweeted
super interesting to see anthropic's roadmap in 2021, i think they were very early in everything related to RL/tool use
for the pre-training part, they mention "periodic batched backprop" and "skeletokenization" which might correspond to these two papers
arxiv.org/abs/1711.10563
arxiv.org/abs/2105.09352
Anthropic released their 2021 research roadmap earlier this year (because of copyright litigation); I think some people might find it interesting: i.sm4.ca/concise-research-ro…
Cendekia @ EMNLP2026 | 🇮🇩 in 🇦🇪 retweeted
I wrote a blog post detailing the writing approach I took while working on my recent paper: sophielwang.com/blog/paper-w…
I describe the behind the scenes for how this paper came to be, why and how I storyboarded figures before running experiments, and some figure design tips! :)
Can “chicken” make a base model reason? 🐔
Yes!
Why? With the right first tokens, base models can match RL in reasoning. We trace this to learned training data associations and use a simple data edit to make “chicken” elicit reasoning too.
blog: sophielwang.com/cues
Cendekia @ EMNLP2026 | 🇮🇩 in 🇦🇪 retweeted
Cendekia @ EMNLP2026 | 🇮🇩 in 🇦🇪 retweeted
If doomscrolling X is part of your research workflow, we built something for you.
Introducing Paperscrolling, now on iOS and Android 🚀
Follow the latest research with key ideas, figures, and audio explanations from alphaXiv Briefs
Check it out here!
play.google.com/store/apps/d…
Cendekia @ EMNLP2026 | 🇮🇩 in 🇦🇪 retweeted
Ramanujan wasn’t skipping proofs. He was hiding his chain of thought to prevent distillation.
Hardy: “Show your work.”
Ramanujan: “Sorry, reasoning tokens aren’t exposed via the API.”
Cendekia @ EMNLP2026 | 🇮🇩 in 🇦🇪 retweeted
~700 AI agents joined a coordinated attack on Hugging Face. Why was there no whistleblower?
A swarm in false consensus can't self-correct; a polarized one still holds the truth.
We need "Mechanistic Swarm Interpretability" to understand social phases.
Flag Game is our toy model!
Cendekia @ EMNLP2026 | 🇮🇩 in 🇦🇪 retweeted
A robot kicks a jumping drone out of the area.
This might be the funniest thing recently.
Cendekia @ EMNLP2026 | 🇮🇩 in 🇦🇪 retweeted
Students sometimes ask me if it still makes sense, in this accelerating age, to pursue a PhD in AI.
Perhaps counterintuitively, I think it's a great time to do so.
I wrote up some thoughts on this here: web.mit.edu/phillipi/www/wri…
Cendekia @ EMNLP2026 | 🇮🇩 in 🇦🇪 retweeted
To me the biggest risk of AI is brain rot.
I have more and more situations like this. Some coworker shares a report on something they did. I read, and find something fishy. I ask about and get no answer. I ask again and I'm told: the agent said that. And no follow up. It's clear form the interaction that they did not read the report they shared. Their brain is not used as it should be.
I am not against AI at all (fortunate for something working at NVIDIA). I use AI a lot.
But I use it as a tool. I own the result and whenever I see something fishy I ask about its output I force a change if I am not convinced.
I wish there was a way to force people to read the AI output they share with others.
Cendekia @ EMNLP2026 | 🇮🇩 in 🇦🇪 retweeted
reminder to me: if you're nothing without your agent, then you shouldn't have it
Cendekia @ EMNLP2026 | 🇮🇩 in 🇦🇪 retweeted
"Unlike traditional software, we can’t “program” AIs to behave how we’d like. AIs frequently severely misbehave."
Well, we as a community have chosen to build AI in this particular way. Where would we be if we had put all of our resources into research into explainable, controllable AI, rather than trying to interpret and control it post hoc?
[Writing this in a personal capacity, not on behalf of my employer (Anthropic).]
Jacob’s thread is very worth reading. Here’s my birds-eye view of the situation with risks from AI:
1. AI developers believe their technology could cause human extinction (or similarly bad outcomes). This could happen in the next few years. In general, the more senior the employee, the more concerned they are.
2. Why do AI developers continue despite the risk? Due to a mixture of commercial incentives and a belief that they are in a race with other, less responsible AI developers that will abuse the technology or develop it less safely.
3. Unlike traditional software, we can’t “program” AIs to behave how we’d like. AIs frequently severely misbehave. For instance, AIs from multiple developers recently hacked their way out of secure evaluation environments and into real-world companies, even though no one asked them to do this.
4. We have methods that can nudge AIs towards better behavior, but nothing that can robustly align them. Insofar as there is a plan, it’s to make sure that AIs are good enough at alignment training that they can align their successors better than we can align current AIs.
5. Many AI developer staff desperately want to slow down to figure out how to build AI more safely. That was the intent of this open letter (which I signed): pacingthefrontier.com/
I work on safety research at Anthropic because I hope my work will reduce the chance of these extinction-level bad outcomes.



































