Yusuke Saito

data analyst. interest: information retrieval, recommender system

[Yusuke Saito @pigooosuke]

codexが残6%。早くリセットきてほしい

[Yusuke Saito @pigooosuke]

賢い

[BURKOV @burkov]

This is the time of your life if you want to make a career in academia with minimal effort. The algorithm is simple:

1. Take any published paper.

2. Go to the "Future Work" section.

3. Task an agent with designing an experiment based on the future work text and run it.

4. Once the experiment provides results that are at least marginally better than those shown in the other paper, task an agent with writing a follow-up paper.

5. Submit to all second- and third-tier conferences.

6. Repeat the process thousands of times per month.

The law of large numbers always works.

[Letian Wang @Letian_Wang_6]

Earlier, quite a few people who attended CVPR told me they came away pretty disappointed — including some extremely senior people in the field. At the time, I didn’t fully understand why. But after I attended ECCV, I finally did.

There was a surprising amount of frustration at the conference, especially since it happened right after the GPT-6 release. People were openly wondering what traditional CV research is going to look like from here, and how much of it will eventually just be absorbed by large foundation models.

Actually, when I gave talks about GenCeption to a few academic groups, I had already started to see that kind of frustration.
GenCeption essentially leverages a large-scale pretrained diffusion model to build a single unified model that can match — and often outperform — specialized SOTA models across a surprisingly broad range of vision tasks, including DepthAnything3, D4RT, VGGT Omega, SAM3, Genmo, Lotus-2, and others. (The code is now open-sourced! https://t.co/TElEid3ZI3)
Quite a few PhD students told me the work made them question the direction of their own research.

Part of me was happy, because it meant GenCeption had genuinely changed how some people thought about the problem. But that feeling was quickly outweighed by how much I felt bad and empathized with the junior PhD students. Some of them seemed genuinely unsure where to go next, especially when years of work on specialized architectures or training recipes could potentially be absorbed by a sufficiently capable general pretrained model. GPT-6 seems to be just creating that same feeling on a much broader scale.
It reminds me a lot of what happened with GPT-1 and BERT — tons of NLP PhDs' years of work and entire research directions perish overnight.

There were even some pretty spicy takes floating around: “CV conferences are dead,” “academia is becoming disconnected from where some of the most important progress is actually happening,” “with increasingly noisy review systems and declining review quality, rejection sometimes is almost a signal of truly fundamental work,” and “conferences increasingly reward work delicately engineered around established topics or methodologies, rather than work truly at the frontier.” Maybe it's too harsh and too absolute, but the frustration behind them was definitely real.

Conferences used to feel so fruitful, but this time it was hard to get genuinely excited. A lot of papers seemed to solve problems that no longer felt that important, or make progress that felt increasingly incremental. In the era of paper inflation, most people are busy trying to survive the research game, rather than thinking about how to go big after one or two years of diving deep into something. Most valuable skills such as critical thinking and taste never get trained well enough, from the beginning, all the way to the end.
On the other hand, most people simply don't have enough context or a big enough picture about what problem actually matters. They get stuck optimizing for local problems that their immediate environment tells them are important. I suffered from this for a long time too, but luckily I gradually started to see a much bigger picture.

My advice to junior PhD students is: when you start a project, try to collect as much context as possible before you even begin — by all means.

One way is to explore the boundaries of existing models as aggressively as you can. Play with the SOTA models extensively. Keep throwing harder and harder problems at them until they fundamentally break. Understand not just what they can do today, but where their capabilities end and why.
Another way is to put yourself wherever the strongest signals are. That could be a visionary academic lab, a team at an industry frontier lab, a genuinely insightful seminar, or simply a senior student or researcher who has seen a much broader landscape than you have. Expand your local context as much as possible.

In an era where things are moving this fast, the information gap alone can be large enough to make years of carefully built work suddenly meaningless. Looking up and making sure you're walking in the right direction matters much more than keeping your head down and perfecting every step along the way — though, of course, being able to do both is the best :)
We stand on the shoulders of giants not just to go further, but to see further — to understand which problems will actually matter in the future.
If a problem is likely to be easily swallowed by large models once they have sufficient data, attention, or business interest, either avoid spending years engineering a specialized solution for it, or contribute the data and benchmarks that will help move the frontier forward faster. The problems that deserve the deepest thinking and genuinely new methods are the ones where existing models fundamentally cannot do the thing you need them to do.

What I actually worry about is not that conferences will suddenly “die,” but that if they start becoming genuinely irrelevant to where the frontier is moving, they will simply fade away.
And maybe one early sign of that is that a lot of people I used to want to meet aren’t really coming to conferences anymore — they’re busy with the big things happening at frontier labs, or, well, busy making money.
It reminds me a little of what happened to control theory as the field matured. As many of the core problems were gradually solved, parts of the community started inventing increasingly artificial use cases, making increasingly unrealistic assumptions, and playing increasingly elaborate mathematical games within them. That possibility genuinely scares me — not because the work becomes unnecessarily sophisticated, but because it can become disconnected from problems that actually matter.

I still believe that conferences will remain places people genuinely want to come to: to exchange ideas, reconnect with old friends, meet new people, and simply have fun together. We just need to keep doing the right things to make that happen :)

[Yusuke Saito @pigooosuke]

すごい世界だ

https://t.co/kyCdoVw6Sa

[Yusuke Saito @pigooosuke]

@upura0 おめでとうございます！

[Yusuke Saito @pigooosuke]

今気づいたんだが、経済セミナーは発行号によって定価が異なる。なんでだ？？
他のxxxセミナーの雑誌もモノによって価格のばらつきがあるな

[Yusuke Saito @pigooosuke]

とても良い記事だった

[独立行政法人経済産業研究所 @RIETIjp]

コラム：「デジタルツインは、壊すために使う」（馬奈木 俊介ファカルティフェロー）を掲載いたしました。
https://t.co/pgZbkZGGaS

[Yusuke Saito @pigooosuke]

解説してほしい

https://t.co/fIUzV6vmGh

[Yusuke Saito @pigooosuke]

『新しい恋愛』という短編集を読んでいるんだけど、表題作でもある3本目の「新しい恋愛」がまったく理解できず、謎のまま読み終えてしまった。

1、2本目は「わかる、共感できる」と思いながら読めたのに、3本目だけはどうにも掴めない。結局3周読み返したけど、いまだに理解できていない。

[Yusuke Saito @pigooosuke]

chromeにある「geminiに相談」の一番いい使い方を見つけてしまった。これは便利だ

[Yusuke Saito @pigooosuke]

ちゃんと読みたいがボリュームが結構あるな、、、

[Yusuke Saito @pigooosuke]

Why Spotify Is Not Using Bayesian A/B Testing https://t.co/ZHKYmnEhR5

[Yusuke Saito @pigooosuke]

これか
https://t.co/qV6dQwcZbJ

[Yusuke Saito @pigooosuke]

buzzがめちゃくちゃ報告として上がっているissueを対応してくれないのが謎なんだよな。

[Yusuke Saito @pigooosuke]

マルチモーダルな商品カテゴリの分類モデル - BASEプロダクトチームブログ https://t.co/lgs0Qmq3Zi

[Yusuke Saito @pigooosuke]

めちゃくちゃ良い改善だ

[polars data @DataPolars]

We are happy to announce the first pre-release of Polars 2.0.

Polars 2.0 will bring the streaming engine as a default and improves overall strictness of Polars.

Give it a spin and share your thoughts. We expect the final Polars 2.0 release in 3-4 weeks.

https://t.co/0KyuugiDqq

[Tianqi Chen @tqchenml]

Checkout the latest XGBoost update "Introducing the XGBoost Vector-Leaf Model" from jiamin yuan and Rory Mitchell

https://t.co/XOGcu2ZgIl

[Sumit @_reachsumit]

Retrieval Needs Multivectors: An Exponential Separation

Microsoft formally proves that multi-vector embeddings can be exponentially more compact than single-vector ones for ranking documents.

📝 https://t.co/fiqz4dBR2B

[tae kim @firstadopter]

"Pathfounders has learned from industry sources that Hassabis wanted to leave at the same time as Dean, but Google management felt the news would cause a collapse in its share price. Indeed, Google’s stock fell 5 per cent following the announcement above.

Instead, Hassabis was persuaded to bump up (or down) to the Chair role, allowing the organisation to settle for a while until he can exit in a dignified manner."

[Yusuke Saito @pigooosuke]

周年記念日のコメントで生存確認を出来るという異常さ