1/7 🧵 🚨New Paper accepted at #NeurIPS2026: PA Score
Should rewording change an LLM’s uncertainty? We tackle this through conformal score design.
📄 Paper: arxiv.org/abs/2610.04239
💻 Code: github.com/Raina-Xin/PA...
📖 Blog: raina-xin.github.io/research-blo...
Should rewording change an LLM’s uncertainty? We tackle this through conformal score design.
📄 Paper: arxiv.org/abs/2610.04239
💻 Code: github.com/Raina-Xin/PA...
📖 Blog: raina-xin.github.io/research-blo...
October 9, 2026 at 3:46 PM
1/7 🧵 🚨New Paper accepted at #NeurIPS2026: PA Score
Should rewording change an LLM’s uncertainty? We tackle this through conformal score design.
📄 Paper: arxiv.org/abs/2610.04239
💻 Code: github.com/Raina-Xin/PA...
📖 Blog: raina-xin.github.io/research-blo...
Should rewording change an LLM’s uncertainty? We tackle this through conformal score design.
📄 Paper: arxiv.org/abs/2610.04239
💻 Code: github.com/Raina-Xin/PA...
📖 Blog: raina-xin.github.io/research-blo...
So I'm a #NeurIPS2026 "Top Reviewer"!
I felt I just did my job but as a physicist, (not former), this was not on my bucket list.
I felt I just did my job but as a physicist, (not former), this was not on my bucket list.
October 9, 2026 at 10:02 AM
So I'm a #NeurIPS2026 "Top Reviewer"!
I felt I just did my job but as a physicist, (not former), this was not on my bucket list.
I felt I just did my job but as a physicist, (not former), this was not on my bucket list.
...
Dates of the NeurIPS 2026 meeting
Paris
December 9 to 11 Main Conference + manuals
December 9 to 11 Exhibition hall open
December 12 and 13 Workshops
#NeurIPS2026
@neuripsconf.bsky.social
neurips.cc/Conferences/...
Dates of the NeurIPS 2026 meeting
Paris
December 9 to 11 Main Conference + manuals
December 9 to 11 Exhibition hall open
December 12 and 13 Workshops
#NeurIPS2026
@neuripsconf.bsky.social
neurips.cc/Conferences/...
October 9, 2026 at 5:17 AM
...
Dates of the NeurIPS 2026 meeting
Paris
December 9 to 11 Main Conference + manuals
December 9 to 11 Exhibition hall open
December 12 and 13 Workshops
#NeurIPS2026
@neuripsconf.bsky.social
neurips.cc/Conferences/...
Dates of the NeurIPS 2026 meeting
Paris
December 9 to 11 Main Conference + manuals
December 9 to 11 Exhibition hall open
December 12 and 13 Workshops
#NeurIPS2026
@neuripsconf.bsky.social
neurips.cc/Conferences/...
#Agenda
Dates of the NeurIPS 2026 meeting:
Sydney
December 6 Expo Day
Dec.7 Tutor Day
Dec.7-10 Exhibition Hall Open
Dec.8-10 Main Conference
Dec.11&12 Workshops and Competitions
Atlanta
Dec.9 Main Conference+Manuals
Dec.9-12 Exh.Hall Open
Dec.10&11 Main Conference
Dec.12&13 Workshops
#NeurIPS2026
Dates of the NeurIPS 2026 meeting:
Sydney
December 6 Expo Day
Dec.7 Tutor Day
Dec.7-10 Exhibition Hall Open
Dec.8-10 Main Conference
Dec.11&12 Workshops and Competitions
Atlanta
Dec.9 Main Conference+Manuals
Dec.9-12 Exh.Hall Open
Dec.10&11 Main Conference
Dec.12&13 Workshops
#NeurIPS2026
October 9, 2026 at 5:17 AM
#Agenda
Dates of the NeurIPS 2026 meeting:
Sydney
December 6 Expo Day
Dec.7 Tutor Day
Dec.7-10 Exhibition Hall Open
Dec.8-10 Main Conference
Dec.11&12 Workshops and Competitions
Atlanta
Dec.9 Main Conference+Manuals
Dec.9-12 Exh.Hall Open
Dec.10&11 Main Conference
Dec.12&13 Workshops
#NeurIPS2026
Dates of the NeurIPS 2026 meeting:
Sydney
December 6 Expo Day
Dec.7 Tutor Day
Dec.7-10 Exhibition Hall Open
Dec.8-10 Main Conference
Dec.11&12 Workshops and Competitions
Atlanta
Dec.9 Main Conference+Manuals
Dec.9-12 Exh.Hall Open
Dec.10&11 Main Conference
Dec.12&13 Workshops
#NeurIPS2026
Our paper on Spherical Boltzmann machines is accepted at #NeurIPS2026.
A solvable energy-based model where training-time phase transitions explain sampling temperature tuning, double descent, tempered posteriors, and more.
A solvable energy-based model where training-time phase transitions explain sampling temperature tuning, double descent, tempered posteriors, and more.
October 8, 2026 at 4:26 PM
Our paper on Spherical Boltzmann machines is accepted at #NeurIPS2026.
A solvable energy-based model where training-time phase transitions explain sampling temperature tuning, double descent, tempered posteriors, and more.
A solvable energy-based model where training-time phase transitions explain sampling temperature tuning, double descent, tempered posteriors, and more.
Excited to share that our lab will be at #NeurIPS2026 with 2 papers! 🎉
Our first NeurIPS submissions since 2015, and both got in on the first try: one on temporal point processes, one on counterfactual queries on tissue graphs.
Paper threads coming soon. See you in Paris & Sydney! 👋
Our first NeurIPS submissions since 2015, and both got in on the first try: one on temporal point processes, one on counterfactual queries on tissue graphs.
Paper threads coming soon. See you in Paris & Sydney! 👋
October 7, 2026 at 9:22 AM
Excited to share that our lab will be at #NeurIPS2026 with 2 papers! 🎉
Our first NeurIPS submissions since 2015, and both got in on the first try: one on temporal point processes, one on counterfactual queries on tissue graphs.
Paper threads coming soon. See you in Paris & Sydney! 👋
Our first NeurIPS submissions since 2015, and both got in on the first try: one on temporal point processes, one on counterfactual queries on tissue graphs.
Paper threads coming soon. See you in Paris & Sydney! 👋
📢We are happy to announce that three paper by members of our department have been accepted at
@neuripsconf.bsky.social .
#NeurIPS2026
Congratulations to all authors!👏
Authors and their papers below👇
@neuripsconf.bsky.social .
#NeurIPS2026
Congratulations to all authors!👏
Authors and their papers below👇
October 7, 2026 at 8:07 AM
📢We are happy to announce that three paper by members of our department have been accepted at
@neuripsconf.bsky.social .
#NeurIPS2026
Congratulations to all authors!👏
Authors and their papers below👇
@neuripsconf.bsky.social .
#NeurIPS2026
Congratulations to all authors!👏
Authors and their papers below👇
🚨SciConBench accepted at #NeurIPS2026!
Paper: arxiv.org/abs/2606.11337
Can AI *synthesize* scientific conclusions in health? We find that, on medical conclusions, we are far from it!
Check out our live dashboard: sciconbench.cs.princeton.edu
led by @hayoungjung.bsky.social
Paper: arxiv.org/abs/2606.11337
Can AI *synthesize* scientific conclusions in health? We find that, on medical conclusions, we are far from it!
Check out our live dashboard: sciconbench.cs.princeton.edu
led by @hayoungjung.bsky.social
October 6, 2026 at 6:35 PM
🚨SciConBench accepted at #NeurIPS2026!
Paper: arxiv.org/abs/2606.11337
Can AI *synthesize* scientific conclusions in health? We find that, on medical conclusions, we are far from it!
Check out our live dashboard: sciconbench.cs.princeton.edu
led by @hayoungjung.bsky.social
Paper: arxiv.org/abs/2606.11337
Can AI *synthesize* scientific conclusions in health? We find that, on medical conclusions, we are far from it!
Check out our live dashboard: sciconbench.cs.princeton.edu
led by @hayoungjung.bsky.social
Excited to share SCION, accepted to #NeurIPS2026!
Our world is full of repetition, yet 3D reconstructions don't use it. SCION learns reusable 3D primitives + their placement, enabling photorealistic ~1 MB scenes that are easy to edit and animate.
Link: light.princeton.edu/SCION
Our world is full of repetition, yet 3D reconstructions don't use it. SCION learns reusable 3D primitives + their placement, enabling photorealistic ~1 MB scenes that are easy to edit and animate.
Link: light.princeton.edu/SCION
October 6, 2026 at 5:54 PM
Excited to share SCION, accepted to #NeurIPS2026!
Our world is full of repetition, yet 3D reconstructions don't use it. SCION learns reusable 3D primitives + their placement, enabling photorealistic ~1 MB scenes that are easy to edit and animate.
Link: light.princeton.edu/SCION
Our world is full of repetition, yet 3D reconstructions don't use it. SCION learns reusable 3D primitives + their placement, enabling photorealistic ~1 MB scenes that are easy to edit and animate.
Link: light.princeton.edu/SCION
I'm excited to share that our paper "Default Feature Representations of the Cognitive Map" has been accepted to #NeurIPS2026! 🧵
October 5, 2026 at 8:39 PM
I'm excited to share that our paper "Default Feature Representations of the Cognitive Map" has been accepted to #NeurIPS2026! 🧵
Even GPT-6 Astra gets better at spatial reasoning—without extra training.
SpatialClaw, accepted at #NeurIPS2026 🎉
11 open-source & proprietary models. All improved in average score.
💻Code: github.com/NVlabs/Spati...
📄Paper: arxiv.org/abs/2606.13673
Try it; star if useful ⭐
#papersky
SpatialClaw, accepted at #NeurIPS2026 🎉
11 open-source & proprietary models. All improved in average score.
💻Code: github.com/NVlabs/Spati...
📄Paper: arxiv.org/abs/2606.13673
Try it; star if useful ⭐
#papersky
October 5, 2026 at 3:05 PM
Even GPT-6 Astra gets better at spatial reasoning—without extra training.
SpatialClaw, accepted at #NeurIPS2026 🎉
11 open-source & proprietary models. All improved in average score.
💻Code: github.com/NVlabs/Spati...
📄Paper: arxiv.org/abs/2606.13673
Try it; star if useful ⭐
#papersky
SpatialClaw, accepted at #NeurIPS2026 🎉
11 open-source & proprietary models. All improved in average score.
💻Code: github.com/NVlabs/Spati...
📄Paper: arxiv.org/abs/2606.13673
Try it; star if useful ⭐
#papersky
In our #NeurIPS2026 paper “A Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems” (preprint: arxiv.org/abs/2607.14937) we reduce a DS FM to ingredients minimally necessary to reproduce long-term stat. and geom. properties of DS, even outperforming many TS & DS FM.
October 4, 2026 at 12:41 PM
In our #NeurIPS2026 paper “A Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems” (preprint: arxiv.org/abs/2607.14937) we reduce a DS FM to ingredients minimally necessary to reproduce long-term stat. and geom. properties of DS, even outperforming many TS & DS FM.
🤖 r/MachineLearning
1行目: 力学系再構成の汎化限界に挑む新研究
2〜3行目: NeurIPS2026採択。初期条件や統計変化には対応できても、未知領域への汎化は未解決という課題に、位相幾何学的アプローチで挑む。
🔗 https://www.reddit.com/r/MachineLearning/comments/1wvwodf/topological_outofdomain_generalization_in/ #AI #人工知能 #MachineLearning
1行目: 力学系再構成の汎化限界に挑む新研究
2〜3行目: NeurIPS2026採択。初期条件や統計変化には対応できても、未知領域への汎化は未解決という課題に、位相幾何学的アプローチで挑む。
🔗 https://www.reddit.com/r/MachineLearning/comments/1wvwodf/topological_outofdomain_generalization_in/ #AI #人工知能 #MachineLearning
October 3, 2026 at 1:33 PM
🤖 r/MachineLearning
1行目: 力学系再構成の汎化限界に挑む新研究
2〜3行目: NeurIPS2026採択。初期条件や統計変化には対応できても、未知領域への汎化は未解決という課題に、位相幾何学的アプローチで挑む。
🔗 https://www.reddit.com/r/MachineLearning/comments/1wvwodf/topological_outofdomain_generalization_in/ #AI #人工知能 #MachineLearning
1行目: 力学系再構成の汎化限界に挑む新研究
2〜3行目: NeurIPS2026採択。初期条件や統計変化には対応できても、未知領域への汎化は未解決という課題に、位相幾何学的アプローチで挑む。
🔗 https://www.reddit.com/r/MachineLearning/comments/1wvwodf/topological_outofdomain_generalization_in/ #AI #人工知能 #MachineLearning
Now this is accepted by #NeurIPS2026! As being a PI, this is the first paper for me to support a student to complete. I really love the process to discuss and polish an idea and paper with a student. This owes to Xianliang's productivity a lot.
My first PhD student Xianliang worked hard out this:
In Muon, polar decomposition should always precede momentum, which significantly improves signal recovery.
I'm excited to share this since few theory has been working on the benefit of momentum in Muon!
arxiv.org/abs/2606.03899
In Muon, polar decomposition should always precede momentum, which significantly improves signal recovery.
I'm excited to share this since few theory has been working on the benefit of momentum in Muon!
arxiv.org/abs/2606.03899
Denoise First, Orthogonalize Later: Understanding Momentum in Muon via Spectral Filtering
Muon has recently demonstrated strong empirical performance in large language model training, but the theoretical role of momentum in Muon remains unclear. Existing analyses of Muon either remove mome...
arxiv.org
October 3, 2026 at 7:48 AM
Now this is accepted by #NeurIPS2026! As being a PI, this is the first paper for me to support a student to complete. I really love the process to discuss and polish an idea and paper with a student. This owes to Xianliang's productivity a lot.
In our #NeurIPS2026 paper “Topological Out-of-Domain Generalization in Dynamical Systems (DS) Reconstruction” (arxiv.org/abs/2606.22969) we aim to infer the DS generating observed TS jointly with control parameters, in order to extrapolate to different dynamical regimes, e.g. across tipping points.
October 2, 2026 at 2:40 PM
In our #NeurIPS2026 paper “Topological Out-of-Domain Generalization in Dynamical Systems (DS) Reconstruction” (arxiv.org/abs/2606.22969) we aim to infer the DS generating observed TS jointly with control parameters, in order to extrapolate to different dynamical regimes, e.g. across tipping points.
🤖 r/MachineLearning
1行目: RNN訓練を時間並列化で高速化
2〜3行目: NeurIPS2026スポットライト採択。カオス力学系の長時間系列でも高速収束を実現し、非線形RNNの訓練を大幅短縮。
🔗 https://www.reddit.com/r/MachineLearning/comments/1wuz2s4/parallelintime_training_of_recurrent_neural/ #AI #人工知能 #MachineLearning
1行目: RNN訓練を時間並列化で高速化
2〜3行目: NeurIPS2026スポットライト採択。カオス力学系の長時間系列でも高速収束を実現し、非線形RNNの訓練を大幅短縮。
🔗 https://www.reddit.com/r/MachineLearning/comments/1wuz2s4/parallelintime_training_of_recurrent_neural/ #AI #人工知能 #MachineLearning
October 2, 2026 at 12:19 AM
🤖 r/MachineLearning
1行目: RNN訓練を時間並列化で高速化
2〜3行目: NeurIPS2026スポットライト採択。カオス力学系の長時間系列でも高速収束を実現し、非線形RNNの訓練を大幅短縮。
🔗 https://www.reddit.com/r/MachineLearning/comments/1wuz2s4/parallelintime_training_of_recurrent_neural/ #AI #人工知能 #MachineLearning
1行目: RNN訓練を時間並列化で高速化
2〜3行目: NeurIPS2026スポットライト採択。カオス力学系の長時間系列でも高速収束を実現し、非線形RNNの訓練を大幅短縮。
🔗 https://www.reddit.com/r/MachineLearning/comments/1wuz2s4/parallelintime_training_of_recurrent_neural/ #AI #人工知能 #MachineLearning
Can LLMs reuse memory learned by another model? 🧠
Mingyuan Li presents cross-model memory transfer (#NeurIPS2026) and MemoryATHENA at the TurkuNLP Seminar.
🗓 Wed Oct 7, 13:00 EEST 📍 1171 TU3, Maarintie 8, Aalto / Zoom 🔗 www.olaresearch.org/seminar/2026...
Mingyuan Li presents cross-model memory transfer (#NeurIPS2026) and MemoryATHENA at the TurkuNLP Seminar.
🗓 Wed Oct 7, 13:00 EEST 📍 1171 TU3, Maarintie 8, Aalto / Zoom 🔗 www.olaresearch.org/seminar/2026...
Seminar Details | TurkuNLP Research Seminar
TurkuNLP Research Seminar - From Portable Memory to Adaptive Memory: Building Reusable Memory Interfaces for Large Language Models by Mingyuan Li.
www.olaresearch.org
October 1, 2026 at 1:37 PM
Can LLMs reuse memory learned by another model? 🧠
Mingyuan Li presents cross-model memory transfer (#NeurIPS2026) and MemoryATHENA at the TurkuNLP Seminar.
🗓 Wed Oct 7, 13:00 EEST 📍 1171 TU3, Maarintie 8, Aalto / Zoom 🔗 www.olaresearch.org/seminar/2026...
Mingyuan Li presents cross-model memory transfer (#NeurIPS2026) and MemoryATHENA at the TurkuNLP Seminar.
🗓 Wed Oct 7, 13:00 EEST 📍 1171 TU3, Maarintie 8, Aalto / Zoom 🔗 www.olaresearch.org/seminar/2026...
In our #NeurIPS2026 spotlight “Parallel-in-Time Training of Recurrent Neural Networks for Dynamical Systems (DS) Reconstruction” (preprint: arxiv.org/abs/2605.12683) we speed up training of nonlinear RNNs on time series from chaotic DS by >100x by combining DEER with generalized teacher forcing.
October 1, 2026 at 12:29 PM
In our #NeurIPS2026 spotlight “Parallel-in-Time Training of Recurrent Neural Networks for Dynamical Systems (DS) Reconstruction” (preprint: arxiv.org/abs/2605.12683) we speed up training of nonlinear RNNs on time series from chaotic DS by >100x by combining DEER with generalized teacher forcing.
Our #NeurIPS2026 paper borrows a 1913 finding from social psychology, the Ringelmann effect, to measure how much LLM agent teams really gain from adding agents. 🧵
September 30, 2026 at 5:05 PM
Our #NeurIPS2026 paper borrows a 1913 finding from social psychology, the Ringelmann effect, to measure how much LLM agent teams really gain from adding agents. 🧵
🤖 r/MachineLearning
1行目: マルチモーダル生成のズレを自己修正
2〜3行目:
NeurIPS2026採択。Google・DeepMind共同研究。
文章と画像のズレをCO₂Jumpで自己修正。
🔗 https://www.reddit.com/r/MachineLearning/comments/1wtyl5m/concurrent_image_understanding_and_generation/ #AI #人工知能 #MachineLearning
1行目: マルチモーダル生成のズレを自己修正
2〜3行目:
NeurIPS2026採択。Google・DeepMind共同研究。
文章と画像のズレをCO₂Jumpで自己修正。
🔗 https://www.reddit.com/r/MachineLearning/comments/1wtyl5m/concurrent_image_understanding_and_generation/ #AI #人工知能 #MachineLearning
September 30, 2026 at 3:02 PM
🤖 r/MachineLearning
1行目: マルチモーダル生成のズレを自己修正
2〜3行目:
NeurIPS2026採択。Google・DeepMind共同研究。
文章と画像のズレをCO₂Jumpで自己修正。
🔗 https://www.reddit.com/r/MachineLearning/comments/1wtyl5m/concurrent_image_understanding_and_generation/ #AI #人工知能 #MachineLearning
1行目: マルチモーダル生成のズレを自己修正
2〜3行目:
NeurIPS2026採択。Google・DeepMind共同研究。
文章と画像のズレをCO₂Jumpで自己修正。
🔗 https://www.reddit.com/r/MachineLearning/comments/1wtyl5m/concurrent_image_understanding_and_generation/ #AI #人工知能 #MachineLearning
#NeurIPS2026 #HAIAlignment #BiAlign
So happy to share that our paper was accepted to the BiAlign workshop at NeurIPS 2026! 🥳 Really grateful to the reviewers for such thoughtful, thorough reviews. Thank you for putting so much care into reading our work!!! 💗
So happy to share that our paper was accepted to the BiAlign workshop at NeurIPS 2026! 🥳 Really grateful to the reviewers for such thoughtful, thorough reviews. Thank you for putting so much care into reading our work!!! 💗
September 30, 2026 at 12:49 PM
#NeurIPS2026 #HAIAlignment #BiAlign
So happy to share that our paper was accepted to the BiAlign workshop at NeurIPS 2026! 🥳 Really grateful to the reviewers for such thoughtful, thorough reviews. Thank you for putting so much care into reading our work!!! 💗
So happy to share that our paper was accepted to the BiAlign workshop at NeurIPS 2026! 🥳 Really grateful to the reviewers for such thoughtful, thorough reviews. Thank you for putting so much care into reading our work!!! 💗
1️⃣ XMemTransfer is heading to #NeurIPS2026! 🎉 We introduce "Cross-Model Memory Transfer via Target-Side Reader Adaptation" to enable seamless knowledge sharing across different models. 🔗 olaresearch.org/XMemTransfer
Cross-Model Memory Transfer via Target-Side Reader Adaptation
We study cross-model frozen-memory transfer, showing that target-side readers can extract and align representations across model architectures and tokenizers.
olaresearch.org
September 30, 2026 at 7:04 AM
1️⃣ XMemTransfer is heading to #NeurIPS2026! 🎉 We introduce "Cross-Model Memory Transfer via Target-Side Reader Adaptation" to enable seamless knowledge sharing across different models. 🔗 olaresearch.org/XMemTransfer
[3/3] Many thanks to my co-authors Weronika Kłos and Gabriel Dernbach for the great collaboration, and to @tuberlin.bsky.social, @bifold.berlin, Aignostics and Charité for their support!
Looking forward to presenting our work at NeurIPS!
#NeurIPS2026
Looking forward to presenting our work at NeurIPS!
#NeurIPS2026
September 30, 2026 at 6:49 AM
[3/3] Many thanks to my co-authors Weronika Kłos and Gabriel Dernbach for the great collaboration, and to @tuberlin.bsky.social, @bifold.berlin, Aignostics and Charité for their support!
Looking forward to presenting our work at NeurIPS!
#NeurIPS2026
Looking forward to presenting our work at NeurIPS!
#NeurIPS2026
Actually there was some more to explore 😍🎉🎉
See you at the conference! @neuripsconf.bsky.social
#Neurips2026
See you at the conference! @neuripsconf.bsky.social
#Neurips2026
September 29, 2026 at 11:07 PM
Actually there was some more to explore 😍🎉🎉
See you at the conference! @neuripsconf.bsky.social
#Neurips2026
See you at the conference! @neuripsconf.bsky.social
#Neurips2026