Good Papers: read the good ones

Network of Theseus progressively replaces guide network modules with a different target architecture via representational alignment, preserving performance across vastly different deployed architectures.

Vighnesh Subramaniam, Colin Conwell, Boris Katz, Andrei Barbu and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet

10/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

80%Must read

?Must readVote to see the score

NeurIPS 2026Massachusetts Institute of Techn MIT Diffusion models

Specificity-Aware Diffusion Steering via Variance-Reduced Sequential Monte Carlo

Specificity-aware diffusion steering uses variance-reduced sequential Monte Carlo to suppress undesired regions with minimal positive distribution distortion.

Luran Wang, Linrui Ma, Hannes Stark, Regina Barzilay

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet

12/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 12 of 20 reviewers recommend it

lenient 4/5

medium 7/10

strict 1/5

86%Must read

?Must readVote to see the score

NeurIPS 2026SpotlightMassachusetts Institute of Techn MIT Diffusion models

The Value of Covariance Matching in Gaussian DDPMs and the Lanczos Sampler

Matching full posterior covariance in Gaussian DDPMs reduces path-KL error to O(1/T²), and the matrix-free Lanczos Gaussian sampler achieves this with exponentially decaying approximation error using only Jacobian-vector products.

Sahil Akhtar, Aymane El Gadarri, Vivek Farias, Adam Jozefiak

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet

14/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

80%Must read

?Must readVote to see the score

NeurIPS 2026School of Engineering and Applie Harvard MIT Fudan Massachusetts Institute of Techn Agent benchmarks & environments

AI GAMESTORE: Scalable, Open-Ended Evaluation of Machine General Intelligence with Human Games

AI GameStore proposes evaluating general intelligence via scalable synthesis of human games, finding frontier vision-language models score under 10% of human averages on most generated games.

Lance Ying, Ryan Truong, Prafull Sharma, Kaiya Zhao and 8 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026 · ▲ 9 on Hugging Face

– ReadersNo votes yet

12/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

86%Must read

?Must readVote to see the score

NeurIPS 2026Massachusetts Institute of Techn Shanghai Jiao Tong MIT Molecules & drug discovery

GLACIER: Rethinking Mass Spectrum Prediction as an Object Detection Problem

GLACIER treats tandem mass spectrum prediction as graph object detection, outperforming prior state-of-the-art by up to 19.3% on retrieval accuracy with nearly 8-fold faster inference.

Rui-Xi Wang, Runzhong Wang, Connor Coley

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet

14/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

76%Highly rated

?Highly ratedVote to see the score

NeurIPS 2026Anthropic Massachusetts Institute of Techn Transluce UC Berkeley, Transluce AI MIT Mechanistic interpretability

Training Language Models to Explain Their Own Computations

Fine-tuning language models on interpretability ground truth teaches them to describe their internal computations, with self-explanation outperforming larger external explainers.

Belinda Z Li, Zifan Carl Guo, Vincent Huang, Jacob Steinhardt and 1 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 1 on Hugging Face · Code ★ 38

– ReadersNo votes yet

10/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 10 of 20 reviewers recommend it

lenient 4/5

medium 6/10

strict 0/5

80%Must read

?Must readVote to see the score

NeurIPS 2026Massachusetts Institute of Techn Cornell Tech MIT LLM evaluation & benchmarks

The Subjectivity of Monoculture

Monoculture evaluation depends on subjective null-model choices and evaluated model populations, making model agreement a context-dependent inference rather than an absolute property.

Nathanael Jo, Nikhil Garg, Manish Raghavan

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet

12/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 12 of 20 reviewers recommend it

lenient 4/5

medium 7/10

strict 1/5

71%Highly rated

?Highly ratedVote to see the score

NeurIPS 2026U Illinois. Urbana-Champaign U Illinois Urbana-Champaign Carnegie Mellon MIT LLM agents & planning

CTM-AI: A Blueprint for General AI Inspired by a Model of Consciousness

CTM-AI combines a consciousness model with foundation models to integrate diverse processors, achieving state-of-the-art results on multiple benchmarks.

Haofei Yu, Yining Zhao, Lenore Blum, Manuel Blum and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet

7/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

76%Highly rated

?Highly ratedVote to see the score

NeurIPS 2026MIT EPFL Princeton EPFL - EPF Lausanne Deep learning theory

Provable Quantization with Randomized Hadamard Transform

Dithered randomized Hadamard quantization is unbiased and achieves mean squared error asymptotically matching dense random rotations at O(d log d) cost.

Ying Feng, Piotr Indyk, Michael Kapralov, Dmitrii Krachun and 1 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

– ReadersNo votes yet

10/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

83%Must read

?Must readVote to see the score

NeurIPS 2026National University of Singapore Google DeepMind MIT Massachusetts Institute of Techn LLM reasoning & chain of thought

An Enigma of Artificial Reason: Investigating the Production-Evaluation Gap in Large Reasoning Models

LRMs show a large production-evaluation gap, scoring near 48% on reasoning evaluation versus near-perfect production due to answer confirmation bias.

Mingzhong Sun, Teresa Yeo, Armando Solar-Lezama, Tan Zhi-Xuan

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 6 on Hugging Face

– ReadersNo votes yet

13/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 13 of 20 reviewers recommend it

lenient 4/5

medium 6/10

strict 3/5

89%Must read

?Must readVote to see the score

NeurIPS 2026KAUST King Abdullah University of Scie Institute for Computer Science, U Bristol California Academy of Sciences Image recognition

ReefNet: A Large-Scale Dataset and Benchmark for Fine-Grained Coral Reef Recognition

ReefNet provides ~925K genus-level coral annotations and benchmarks showing vision models degrade under zero-shot and cross-source shifts despite adaptation gains.

Abdulwahab Felemban, Yahia Battach, Faizan F Khan, Yuqian Fu and 12 more

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet

16/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

80%Must read

?Must readVote to see the score

NeurIPS 2026MIT MIT CSAIL Facebook NVIDIA Robot manipulation

RigidFormer: Learning Rigid Dynamics using Transformers

RigidFormer is a transformer that learns mesh-free rigid-body dynamics via object-level anchors and differentiable Kabsch projection, outperforming mesh-based baselines with faster inference and scalability to 200+ objects.

Zhiyang Dou, Minghao Guo, Haixu Wu, Doug Roble and 2 more

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026 · ▲ 13 on Hugging Face · Code ★ 93

– ReadersNo votes yet

12/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

86%Must read

?Must readVote to see the score

NeurIPS 2026Harvard Massachusetts Institute of Techn School of Engineering and Applie MIT IIIS, Tsinghua Multi-agent LLM systems

Economy of Minds: Emerging Multi-Agent Intelligence with Economic Interactions

A decentralized agent economy using auctions and economic selection emerges multi-step reasoning and outperforms monolithic baselines without centralized coordination.

Zhenting Qi, Ao Qu, Huangyuan Su, Chenyu Wang and 12 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026 · ▲ 10 on Hugging Face · Code ★ 59

– ReadersNo votes yet

14/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 14 of 20 reviewers recommend it

lenient 5/5

medium 7/10

strict 2/5

74%Highly rated

?Highly ratedVote to see the score

NeurIPS 2026MIT Massachusetts Institute of Techn In-context learning & prompting

LLMs can construct powerful representations and streamline sample-efficient supervised learning

LLMs generate global and local rubrics to standardize multimodal inputs, significantly outperforming clinical baselines on 15 EHRSHOT tasks via sample-efficient supervised learning.

Ilker Demirel, Lawrence Shi, Zeshan Hussain, David Sontag

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

– ReadersNo votes yet

9/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 9 of 20 reviewers recommend it

lenient 5/5

medium 4/10

strict 0/5

83%Must read

?Must readVote to see the score

NeurIPS 2026U California, San Diego Massachusetts Institute of Techn UC San Diego Northwestern Mila; University of Montreal; MI Music

Live Music Diffusion Models: Efficient Fine-Tuning and Post-Training of Interactive Diffusion Music Generators

Live Music Diffusion Models modify diffusion inference with block-wise KV caching to surpass discrete autoregressive efficiency, enabling stable alignment via ARC-Forcing and real-time interactive generation on consumer hardware.

Zachary Novack, Stephen Brade, Haven Kim, Hugo Flores and 5 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 3 on Hugging Face · Code ★ 50

– ReadersNo votes yet

13/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

91%Must read

?Must readVote to see the score

NeurIPS 2026KU Leuven Department of Electrical Enginee Katholieke Universiteit Leuven KUL Massachusetts Institute of Techn Biosignals & wearables

NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces

NeuroAtlas benchmarks EEG foundation models across 42 datasets and finds they largely match generic time-series models without delivering unified clinical EEG performance.

Konstantinos Kontras, Trui Osselaer, Stylianos G Mouslech, Angeliki I. Karaiskou and 11 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet

17/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

89%Must read

?Must readVote to see the score

NeurIPS 2026Massachusetts Institute of Techn U Toronto MIT Materials & chemistry

Fast Organic Crystal Structure Prediction with Unit Cell Flow Matching

Clari predicts organic crystal structures via unit-cell flow matching with pure pair-bias attention, cutting generation to seconds while surpassing OXtal solve rates and supporting non-sanitizable inputs.

Alston Lo, Luka Mucko, Austin Cheng, Andy Cai and 3 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 2 on Hugging Face

– ReadersNo votes yet

16/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 16 of 20 reviewers recommend it

lenient 5/5

medium 9/10

strict 2/5

78%Highly rated

?Highly ratedVote to see the score

NeurIPS 2026Princeton Massachusetts Institute of Techn Stanford MIT Purdue Multi-agent LLM systems

Improving the Efficiency of Language Agent Teams with Adaptive Task Graphs

LATTE coordinates LLM teams via shared evolving task graphs that reduce tokens, time, and conflicts while matching or exceeding baseline accuracy.

Elizabeth Mieczkowski, Alexander Ku, Tiwalayo Eisape, Dilip Arumugam and 4 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · Code ★ 6

– ReadersNo votes yet

11/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

91%Must read

?Must readVote to see the score

NeurIPS 2026Princeton Anthropic New York New York University; Anthropic MIT LLM evaluation & benchmarks

DiscoverPhysics: Benchmarking LLMs for out-of-the-box scientific thinking

DiscoverPhysics benchmarks LLM agents on simulated worlds with non-standard physics, finding frontier models pass only half and fail at uncovering latent structure.

Lindsay Smith, Matt Sampson, Siddharth Mishra-Sharma, Peter Melchior and 3 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 1 on Hugging Face

– ReadersNo votes yet

17/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

71%Highly rated

?Highly ratedVote to see the score

NeurIPS 2026U Alberta Massachusetts Institute of Techn MIT U Alberta & Amii Representation & contrastive learning

Measure-to-measure Regression with Transformers

This work formalizes nonlinear measure-to-measure regression and introduces two scalable transformer-based approaches for learning operators between probability distributions. The methods generalize to unseen measures in synthetic experiments, particle systems, and a large-scale colorectal cancer or

Matthew Vandergrift, Martha White, Yury Polyanskiy, Philippe Rigollet and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet

7/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 7 of 20 reviewers recommend it

lenient 5/5

medium 2/10

strict 0/5

80%Must read

?Must readVote to see the score

NeurIPS 2026U Edinburgh MIT LLM agents & planning

Autonomous Scientific Discovery via Iterative Meta-Reflection

DiscoPER autonomously discovers scientific patterns via iterative meta-reflection and statistical testing, recovering 8 of 9 ecological patterns and outperforming baselines.

Bingchen Zhao, Sara Beery, Oisin Mac Aodha

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet

12/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

Show 20 more papers