窗花儿（Chuang）🍥

Little Enby Researcher 🏳️‍⚧️🏳️‍🌈 Tech Lead at Amazon for Agent Safety research; Open Source contributor to democratize post-training喵. | 持证含糖 l SRS 09/2026

[窗花儿（Chuang）🍥 @EnbyKid]

Got discharged from the hospital today and got the best possible welcome-home news: our paper is accepted to #NeurIPS2026! 🎉

Reading, Not Thinking: Why do MLLMs solve a problem as text, then fail when the same text becomes pixels?

Turns out, often they can read — they just stop thinking.

We study 7 MLLMs × 7 benchmarks × 5 input modes and find visual inputs can trigger 5–19× shorter responses and much less reasoning.

A simple self-distillation approach boosts image-mode GSM8K from 30.71% → 92.72%, with gains transferring to unseen benchmarks.

https://t.co/SHJL5KHjj9

Please read/share! ❤️

[Lorenzo Molfetta @Lorenzo_molf]

Can self-play on board games teach an LLM to reason? ♟️🧠

Self-Play Search Distillation (SPSD) turns the search of MuZero experts, trained by self-play on board games, into reasoning traces for LLMs.

The reasoning transfers to unseen games and to math (24.1 → 36.6) 🧵

[weisser @julianweisser]

Speech recognition breakthrough:

More accurate than Whisper Large at 1/10th the size.

1 hour of audio transcribed in 20 seconds.

Solo founder ftw.

[laxman @llmluthor]

Releasing Your Own Jev

Post-train a 4B/8B/27B judge on your agent's traces. It beats Jev.

79.7% agreement with human labels vs Jev's 66.3%
Beats DeepSeek-V4.1-Flash (763B) by 14 points
0.13s per step on a single GPU

Completely open source: recipe, data, training, evals

[映画『となりのとらんす少女ちゃん』今秋公開決定🏳️‍⚧️ @TonaTora_Movie]

お待たせしました！
映画『となりのとらんす少女ちゃん』2026年秋の劇場公開が決定しました🎉

周囲との違和感を抱き始めた中学生の主人公が、未来からやってきた「もうひとりの自分」との対話を通して、自分らしい生き方を探求していく物語。

原作はトランスジェンダー当事者である漫画家・とら少さんによる同名の短編集の一編📖

11/7（土）より新宿K’s cinemaで、その後、第七藝術劇場 (大阪)、出町座 (京都) 、元町映画館 (兵庫) ほか全国順次公開予定です🎥

ポスタービジュアルも本日解禁✨
順次情報をアップしていくのでお楽しみに🐯🎶
ハッシュタグは…

#映画となとら

●脚本・監督・プロデューサー：東海林毅
●出演：中川未悠、高橋蒼太、はるな愛ほか

[雅辛忒斯Hyacinthia An @anan_de_gushi]

@aoya_toboso_01 流血不流泪的男孩子？每月流血的不是女生吗😂

[amber水母🍥 @amber_medusozoa]

一个高阶层跨女mtf应该具备什么
我这里给出四点证据
①首先是触手
拥有十条以上长达5米的吸盘触手，可以长在身体的任何地方，长有巨大的黑色蝙蝠翅膀和章鱼头
②其次是超能力
拥有停止时间、操控死亡的力量，能够随意创造出绵延上千里的浩瀚黑色云雾
③第三是氛围感
出现时可以用自己虚空、漩涡般的身体将所有火和热卷走，周围的气温瞬间降低好几度，并充斥着刺骨的寒冷和凝滞的空气
④第四是锟斤拷？3$：
烫¥̵̧̢̬͖͎̥ͦ̍̌̚ͅ*̵̴̟̣̲̤̪̱̄͐ͨͪͯͫ̏ͅ{̗̖͕̬̐ͤ*̞̦̺̠̺̙̎̐ͦ̇̐̀ͥ?̆͑̒̿̆҉̩̳%ͭͥ̌ͤ͗ͣͫ̔͊͏͙̬̯̪͉̤̦͉͘]̢̛̳͈͔͓̝̘ͨ͢€̘͈̰̮̱̖͛̈́̇̌͋̎̀͞ͅ<̛̹͇ͬ̏̎ͪ+̸͈͖͈̱̺̘͚̞̣͆̓͝?̧̪̱̺̠̝̟͗̔’̶̐̅̆̿̒̊ͨͫ͏̩̥̼w̰͖͎̣̘̃͆ͯͯͤ̽̈ͬb̧̛͍̪̩̦̯ͫ̒͊̉͒c̹̗̯̭̠̏̄̾̍ͫï͖͇̼̦̅͞͡¥͔̙̀ͤ͠;̨͚̬̏ͥ̂ͯ̐ͫͩ¥̵̧̢̬͖͎̥ͦ̍̌̚ͅ*̵̴̟̣̲̤̪̱̄͐ͨͪͯͫ̏ͅ{̗̖͕̬̐ͤ*̞̦̺̠̺̙̎̐ͦ̇̐̀ͥ?̆͑̒̿̆҉̩̳%ͭͥ̌ͤ͗ͣͫ̔͊͏͙̬̯̪͉̤̦͉͘]̢̛̳͈͔͓̝̘ͨ͢€̘͈̰̮̱̖͛̈́̇̌͋̎̀͞ͅ<̛̹͇ͬ̏̎ͪ+̸͈͖͈̱

看你身后

[冷夜yuki🍥 @coldfrigidyuki]

明明已经挺瘦的了，但是还是好男性化，肋骨宽，手臂粗，好恶心想hrt
#hrt #mtf

[Adithya S K @adithya_s_k]

1/ In RL environments, reward design is everything.

Here's what we learned from the 7,780 environments Xiaomi open-sourced for MiMo 🧵

[James Zou @james_y_zou]

Looped transformer meets agent swarm.

Our new @Neurips2026 paper shows how to loop compute across multiple agents.

Different LLMs recursively share latent thoughts w/ each other. Better performance with up to 75% fewer tokens.

Great work led by @Jiaru_Zou w/ awesome collaborators.

[meng shao @shao__meng]

斯坦福大学 2026 秋季新课 CME 295: Transformers & Large Language Models 已开课（第一讲公开）

@stanfordnlp 这门课程由双胞胎兄弟 @afshinea @shervinea 主讲，9 讲走完 LLM 全部版图：从 Transformer 架构、注意力变体、RoPE，到 LoRA 微调、RLHF/GRPO 训练，再到 Flash Attention 推理优化、MCP 与 AI Agent、扩散语言模型。

第一讲 Transformers 课件和课程视频都已公开：
https://t.co/YCTABF2nQs
https://t.co/ByY1n7JUBQ

[Larry Dial @classiclarryd]

New historic NanoGPT record at 39.9s (-27.7s) from @DevenPzak , obliterating the prior record of 67.6s!

This record introduces a new paradigm of thinking to NanoGPT: instead of optimizing matmuls or adding more expressive operations, optimize at the individual flop level with incredibly clever engineering and ML judgement. If a flop is low value on a particular step, skip it.
Specifically:
-(~8s) Sampled softmax. If a token doesn’t appear in a batch, skip its lm_head fwd/bwd some fraction of the time.
-Sparse values. Only run an optimizer step for ngram embeddings that occurred in the batch. Set beta1 to zero to enable this. Beta2 is applied retroactively when the row is later used.
-Sparse updates. Only update ngram and value embeddings once every 4 steps instead of once every 2.
-Sparse communication. Shard the n-gram table across GPUs, and only pass the rows receiving updates on each step.
-Sparse optimizer states. For the n-gram table, reduce from 2 floats in Adam optimizer per param, to 1 float per 768 params.
-Hand-rolled flash attention for 64 dim heads.

There are several additions that add accuracy too:
-(~4s) EMA during last 300 steps, combined with lifting final_lr to 0.3 instead of 0.15.
-(~1s) A new optimizer, Anvil2, which expands muon via a second tracked momentum buffer, improves the ortho coefficients, and modifies the cautious weight decay application.
-A couple additional dynamic skip connections in the network.

The most striking consequence of the ‘flop aware paradigm’ is you can grow parameters arbitrarily large,
only limited by the available memory, since you can selectively choose how to expend flops on those parameters on each step. NanoGPT has kept active parameters below 124M, but total is unbounded, and has grown to 640M through embedding sparsity over the last year. This PR takes that to its logical conclusion on the 8xH100, scaling up to 65B sparse embedding parameters, which accounts for 25% of the PR’s gains. At frontier scale, where one is not bounded by an 8xH100, one could imagine where this paradigm could lead.
https://t.co/Ycrzy6JFC3

As this was a very notable PR, I spoke with Deven for an hour to learn how he did it. Here’s his story on the changes: https://t.co/YEfM1VpOTi

[Tianyi Cui @tianyi]

DeepSeek 弹性计算团队大量 HC 招人！尤其需要资深工程师。 来看看这篇技术分享：《DeepSeek 弹性计算 (DSec)：面向大规模 Agent 训练的沙盒基础设施》 https://t.co/7rBokhowIA

[Bingxiang He @HBX_hbx]

[1/n] Introducing Diffusion Reward Models (DRM)

Reward Models are at the core of LLM post-training. But almost all of them make the same simplification:
they collapse human preference into a single score.

The problem is that human feedback is often not a point — different people can hold distinct, even conflicting, judgments about the same response.

So we asked: Can a Reward Model learn the distribution of human preference?

Here’s what we found 👇

📄 Paper: https://t.co/e0EMNa3I64
💻 Code: https://t.co/KVosB5D5PI
🤗 Models: https://t.co/wwdDpOtgbL

[Qiuyang Mang @MangQiuyang]

200 high-quality, long-horizon, open-ended RL environments are here!

FrontierSmith is a NeurIPS 2026 Spotlight (top 3.7%)! We’re releasing 200 problems for studying RL on challenging open-ended tasks:
https://t.co/90FjTjAjnv

And stay tuned: FrontierCS team will soon share something very interesting about training in these environments 👀

[Shanghua Gao @GaoShanghua]

RSI-Jev v3.0 is out, and this is the first version where RL actually helped.

Our AutoScientists loop ran 59 RL experiments on the 2B decision model. Most failed.

The pattern was pretty clear: when every example already gives the model the right answer, plain supervised training is very hard to beat.

What finally worked was giving RL a signal the labels didn’t contain: whether the model ranked the right memory first.

That made it find the right memory first 60% more often on a long-memory benchmark, with no regression elsewhere.

The lesson we’re taking from this: RL helps when the reward teaches something the labels can’t.

But honestly, the part we care about just as much is why the other 58 experiments failed, so we wrote all of that up too.
Report: https://t.co/YjaMKguYgC
Code, weights, experiments: https://t.co/KHKS4XtWzv

[Phillip Isola @phillip_isola]

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: https://t.co/RRqGR0qi1R

[サイバー環状線 @CyberKanjousen]

豆包……终究还是输在有建模上了啊😭

[Garrett Flip @yinshiren2024]

The two biggest weirdos in the zoo, hahaha.

[窗花儿（Chuang）🍥 @EnbyKid]

content warning: just myself posing with clothing on, nothing too explicit

[窗花儿（Chuang）🍥 @EnbyKid]

SRS手术完今天来复查