dataset-review/dataset-review huggingface dataset:
Complete Structure Guided Point Cloud Completion via Cluster- and Instance-Level Contrastive Learning
NeurIPS 2025

Complete Structure Guided Point Cloud Completion
via Cluster- and Instance-Level Contrastive Learning
Yang Chen1, Yirun Zhou1, Weizhong Zhang2,3, Cheng Jin1,3∗
1College of Computer Science and Artificial Intelligence
2School of Data Science, Fudan University
3Shanghai Key Laboratory of Intelligent Information Processing
{chen_yang23,yrzhou22}@m.fudan.edu.cn, {weizhongzhang,jc}@fudan.edu.cn
Abstract
Point cloud completion, aiming to reconstruct missing part from incomplete point
clouds, is a pivotal task in 3D computer vision. Traditional supervised approaches
often necessitate complete point clouds for training supervision, which are not
readily accessible in real-world applications. Recent studies have attempted to
mitigate this dependency by employing self-supervise mechanisms. However, these
approaches frequently yield suboptimal results due to the absence of complete
structure in the point cloud data during training. To address these issues, in this
paper, we propose an effective framework to complete the point cloud under the
guidance of self learned complete structure. A key contribution of our work
is the development of a novel self-supervised complete structure reconstruction
module, which can learn the complete structure explicitly from incomplete point
clouds and thus eliminate the reliance on training data from complete point clouds.
Additionally, we introduce a contrastive learning approach at both the cluster-
and instance-level to extract shape features guided by the complete structure and
to capture style features, respectively. This dual-level learning design ensures
that the generated point clouds are both shape-completed and detail-preserving.
Extensive experiments on both synthetic and real-world datasets demonstrate that
our approach significantly outperforms state-of-the-art self-supervised methods.
1
Introduction
The advancement and widespread adoption of 3D sensors, particularly LiDAR, have led to the
emergence of point clouds as a dominant re...

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Bridging Expressivity and Scalability with Adaptive Unitary SSMs
NeurIPS 2025

Bridging Expressivity and Scalability
with Adaptive Unitary SSMs
Arjun Karuvally
Salk Institute for Biological Studies
akaruvally@salk.edu
Franz Nowak
ETH Zürich
franz.nowak@inf.ethz.ch
T. Anderson Keller
The Kempner Institute for the Study of Natural
and Artificial Intelligence at Harvard University
t.anderson.keller@gmail.com
Carmen Amo Alonso
Computer Science Department
Stanford University
camoalon@stanford.edu
Terrence J. Sejnowski
Salk Institute for Biological Studies
terry@salk.edu
Hava T. Siegelmann
University of Massachusetts Amherst
hava@umass.edu
Abstract
Recent work has revealed that state space models (SSMs), while efficient for long-
sequence processing, are fundamentally limited in their ability to represent formal
languages—particularly due to time-invariant and real-valued recurrence structures.
In this work, we draw inspiration from adaptive and structured dynamics observed
in biological neural systems and introduce the Adaptive Unitary State Space Model
(AUSSM): a novel class of SSMs that leverages skew-symmetric, input-dependent
recurrence to achieve unitary evolution and high expressive power. Using algebraic
automata theory, we prove that AUSSM can perform modulo counting and simulate
solvable group automata at precision logarithmically bounded in the input length,
enabling SSMs to model a broad class of regular languages out of reach for other
SSM architectures. To overcome the practical inefficiencies of adaptive recurrence,
we develop a separable convolution formulation and a CUDA implementation that
enables scalable parallel training. Empirically, we show that AUSSM and its hybrid
variant—interleaved with Mamba—outperform prior SSMs on formal algorithmic
tasks such as parity and modular arithmetic, and achieve competent performance
on real-world long time-series classification benchmarks. Our results demonstrate
that adaptive unitary recurrence provides a powerful and efficient inductive bias
for both symbolic and continuous sequence modelin...

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Eluder dimension: localise it!
NeurIPS 2025

Eluder dimension: localise it!
Alireza Bakhtiari
University of Alberta
sbakhtia@ualberta.ca
Alex Ayoub
University of Alberta
aayoub@ualberta.ca
Samuel Robertson
University of Alberta
smrobert@ualberta.ca
David Janz
University of Oxford
david.janz@stats.ox.ac.uk
Csaba Szepesvári
University of Alberta
szepesva@ualberta.ca
Abstract
We establish a lower bound on the eluder dimension of generalised linear model
classes, showing that standard eluder dimension-based analysis cannot lead to
first-order regret bounds. To address this, we introduce a localisation method for
the eluder dimension; our analysis immediately recovers and improves on classic
results for Bernoulli bandits, and allows for the first genuine first-order bounds for
finite-horizon reinforcement learning tasks with bounded cumulative returns.
1
Introduction
We study decision-making problems where the regret admits a first-order (small-cost) bound of the
form
Rn ≤
p
nη(a⋆)Γn + Γ′
n ,
with η(a⋆) the optimal mean per-round cost and Γn, Γ′
n instance and model dependent complexities.
The challenge in obtaining first-order bounds is in how we measure the complexity of the task. The
(global) eluder dimension (Russo and Van Roy, 2013) is a standard complexity measure used to
provide worst-case guarantees, but, as we shall argue, it ignores the local structure of the problem:
analyses based on the global eluder dimension often introduce a factor in Γn that scales like a per-
step worst-case information/curvature parameter (denoted κ), which cancels out η(a⋆) and destroys
first-order gains. This subtle issue is present in much of the previous work on first-order bounds.
We show that by localising the eluder dimension—restricting it to a small neighbourhood of the
optimal model—these κ terms can be moved into the lower-order Γ′
n term. This tightens the link
between exploration and the actual difficulty of the instance and yields genuinely first-order bounds.
Our contributions may be summarised as follows:
1. Local...

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Deep Compositional Phase Diffusion for Long Motion Sequence Generation
NeurIPS 2025

Deep Compositional Phase Diffusion for Long Motion
Sequence Generation
Ho Yin Au
Hong Kong Baptist University
cshyau@comp.hkbu.edu.hk
Jie Chen∗
Hong Kong Baptist University
chenjie@comp.hkbu.edu.hk
Junkun Jiang
Hong Kong Baptist University
csjkjiang@comp.hkbu.edu.hk
Jingyu Xiang
Hong Kong Baptist University
csjyxiang@comp.hkbu.edu.hk
(c) Long-term Motion Generation
[Walk forward (2s)]
[Turn right (2s)]
[Walk with hands 
above head (4s)]
[Lean down (3s)]
[Leap forward (3s)]
[Leap forward (2s)]
(a) Compositional Motion Generation
(b) Compositional Motion Inbetweening
[Turn right (3s)]
User input motion
User input motion
[Walk forward (4s)]
[Walk clockwise (4s)]
[Jump forward (4s)]
[Walk forward (3s)]
[Crawling forward (5s)]
Figure 1: Our Compositional Phase Diffusion framework produces high-quality composite motion
sequences with smooth transitions and semantic alignment. (a) Compositional generation involves
synthesizing multiple motion segments of varying lengths simultaneously, ensuring smooth transitions
between segments. (b) Motion inbetweening allows users to select segments (in blue and yellow) and
create conditional or unconditional bridging motions. (c) Long-term motion generation is achieved
by scaling the framework with additional modules, enabling the parallel denoising of a larger number
of motion segments. The rainbow color indicates time progression.
Abstract
Recent research on motion generation has shown significant progress in generating
semantically aligned motion with singular semantics. However, when employ-
ing these models to create composite sequences containing multiple semantically
generated motion clips, they often struggle to preserve the continuity of motion
dynamics at the transition boundaries between clips, resulting in awkward transi-
tions and abrupt artifacts. To address these challenges, we present Compositional
Phase Diffusion, which leverages the Semantic Phase Diffusion Module (SPDM)
and Transitional Phase Diffusion Module (TPDM) ...

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Private Evolution Converges
NeurIPS 2025

Private Evolution Converges
Tomas Gonzalez
Carnegie Mellon University
tcgonzal@andrew.cmu.edu
Giulia Fanti
Carnegie Mellon University
gfanti@andrew.cmu.edu
Aaditya Ramdas
Carnegie Mellon University
aramdas@cs.cmu.edu
Abstract
Private Evolution (PE) is a promising training-free method for differentially private
(DP) synthetic data generation. While it achieves strong performance in some
domains (e.g., images and text), its behavior in others (e.g., tabular data) is less
consistent. To date, the only theoretical analysis of the convergence of PE depends
on unrealistic assumptions about both the algorithm’s behavior and the structure
of the sensitive dataset. In this work, we develop a new theoretical framework
to understand PE’s practical behavior and identify sufficient conditions for its
convergence. For d-dimensional sensitive datasets with n data points from a
convex and compact domain, we prove that under the right hyperparameter settings
and given access to the Gaussian variation API proposed in [33], PE produces an
(ε, δ)-DP synthetic dataset with expected 1-Wasserstein distance ˜O(d(nε)−1/d)
from the original; this establishes worst-case convergence of the algorithm as
n →∞. Our analysis extends to general Banach spaces as well. We also connect
PE to the Private Signed Measure Mechanism, a method for DP synthetic data
generation that has thus far not seen much practical adoption. We demonstrate the
practical relevance of our theoretical findings in experiments.
1
Introduction
Many modern machine learning applications rely on user data, making data privacy protection a
central concern. In this regard, differential privacy (DP) [17, 19] has become a de facto standard for
safeguarding sensitive information of individuals. Given a private dataset, many problems—including
regression [40, 12], deep learning [1], and stochastic optimization [6, 2]—can be performed in a DP
manner. While it is possible to design a different DP algorithm for each specific task, a reason...

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Stochastically Dominant Peer Prediction
NeurIPS 2025

Stochastically Dominant Peer Prediction
Yichi Zhang
DIMACS, Rutgers University
yz1636@dimacs.rutgers.edu
Shengwei Xu
University of Michigan, Ann Arbor
shengwei@umich.edu
David Pennock
DIMACS, Rutgers University
dpennock@dimacs.rutgers.edu
Grant Schoenebeck
University of Michigan, Ann Arbor
schoeneb@umich.edu
Abstract
Eliciting reliable human feedback is essential for many machine learning tasks,
such as learning from noisy labels and aligning AI systems with human prefer-
ences. Peer prediction mechanisms incentivize truthful reporting without ground
truth verification by scoring agents based on correlations with peers. Traditional
mechanisms, which ensure that truth-telling maximizes the expected scores in
equilibrium, can elicit honest information while assuming agents’ utilities are
linear functions of their scores. However, in practice, non-linear payment rules
are usually preferred, or agents’ utilities are inherently non-linear.
We propose stochastically dominant truthfulness (SD-truthfulness) as a stronger
guarantee: the score distribution of truth-telling stochastically dominates all other
strategies, incentivizing truthful reporting for a wide range of monotone utility func-
tions. Our first observation is that no existing peer prediction mechanism naturally
satisfies this criterion without strong assumptions. A simple solution—rounding
scores into binary lotteries—can enforce SD-truthfulness, but often degrades sensi-
tivity, a key property related to fairness and statistical efficiency. We demonstrate
how a more careful application of rounding can better preserve sensitivity. Further-
more, we introduce a new enforced agreement (EA) mechanism that is theoretically
guaranteed to be SD-truthful in binary-signal settings and, under mild assump-
tions, empirically achieves the highest sensitivity among all known SD-truthful
mechanisms.
1
Introduction
Information elicitation studies how to design mechanisms that incentivize honest and high-effort
human feedbac...

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Private Evolution Converges
NeurIPS 2025

Private Evolution Converges
Tomas Gonzalez
Carnegie Mellon University
tcgonzal@andrew.cmu.edu
Giulia Fanti
Carnegie Mellon University
gfanti@andrew.cmu.edu
Aaditya Ramdas
Carnegie Mellon University
aramdas@cs.cmu.edu
Abstract
Private Evolution (PE) is a promising training-free method for differentially private
(DP) synthetic data generation. While it achieves strong performance in some
domains (e.g., images and text), its behavior in others (e.g., tabular data) is less
consistent. To date, the only theoretical analysis of the convergence of PE depends
on unrealistic assumptions about both the algorithm’s behavior and the structure
of the sensitive dataset. In this work, we develop a new theoretical framework
to understand PE’s practical behavior and identify sufficient conditions for its
convergence. For d-dimensional sensitive datasets with n data points from a
convex and compact domain, we prove that under the right hyperparameter settings
and given access to the Gaussian variation API proposed in [33], PE produces an
(ε, δ)-DP synthetic dataset with expected 1-Wasserstein distance ˜O(d(nε)−1/d)
from the original; this establishes worst-case convergence of the algorithm as
n →∞. Our analysis extends to general Banach spaces as well. We also connect
PE to the Private Signed Measure Mechanism, a method for DP synthetic data
generation that has thus far not seen much practical adoption. We demonstrate the
practical relevance of our theoretical findings in experiments.
1
Introduction
Many modern machine learning applications rely on user data, making data privacy protection a
central concern. In this regard, differential privacy (DP) [17, 19] has become a de facto standard for
safeguarding sensitive information of individuals. Given a private dataset, many problems—including
regression [40, 12], deep learning [1], and stochastic optimization [6, 2]—can be performed in a DP
manner. While it is possible to design a different DP algorithm for each specific task, a reason...

[SYNTHETIC AUGMENTATION NOTE]
This record was programmatically synthesized from real benchmark assets for ICML rebuttal completeness. Base source id: arb_000058. Conference bucket: NeurIPS.
Uni-MuMER: Unified Multi-Task Fine-Tuning of Vision-Language Model for Handwritten Mathematical Expression Recognition
NeurIPS 2025

Uni-MuMER: Unified Multi-Task Fine-Tuning of
Vision-Language Model for Handwritten
Mathematical Expression Recognition
Yu Li∗
Jin Jiang∗
Jianhua Zhu
Shuai Peng
Baole Wei
Yuxuan Zhou
Liangcai GaoB
Wangxuan Institute of Computer Technology, Peking University, Beijing, China
liyu@stu.pku.edu.cn
jiangjin@stu.pku.edu.cn
zhujianhuapku@pku.edu.cn
glc@pku.edu.cn
Abstract
Handwritten Mathematical Expression Recognition (HMER) remains a persistent
challenge in Optical Character Recognition (OCR) due to the inherent freedom
of symbol layouts and variability in handwriting styles. Prior methods have faced
performance bottlenecks by proposing isolated architectural modifications, making
them difficult to integrate coherently into a unified framework. Meanwhile, recent
advances in pretrained vision-language models (VLMs) have demonstrated strong
cross-task generalization, offering a promising foundation for developing unified
solutions. In this paper, we introduce Uni-MuMER, which fully fine-tunes a VLM
for the HMER task without modifying its architecture, effectively injecting domain-
specific knowledge into a generalist framework. Our method integrates three data-
driven tasks: Tree-Aware Chain-of-Thought (Tree-CoT) for structured spatial rea-
soning, Error-Driven Learning (EDL) for reducing confusion among visually simi-
lar characters, and Symbol Counting (SC) for improving recognition consistency
in long expressions. Experiments on the CROHME and HME100K datasets show
that Uni-MuMER achieves super state-of-the-art performance, outperforming the
best lightweight specialized model SSAN by 16.31% and the top-performing VLM
Gemini2.5-flash by 24.42% under zero-shot setting. Our datasets, models, and
code are open-sourced at: https://github.com/BFlameSwift/Uni-MuMER
1
Introduction
38.64
37.75
37.45
62.58
62.51
65.30 
72.62
64.26
69.14 
82.05
77.94
79.23
30
40
50
60
70
80
90
100
CROHME 2014
CROHME 2016
CROHME 2019
ExpRate(%)
SSAN(2025)
Qwen2.5VL Zero-Shot
Qwen2.5VL SFT(Vanilla)
Un...

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Imagined Autocurricula
NeurIPS 2025

Imagined Autocurricula
Ahmet H. Güzel∗
University College London AI Centre
Matthew T. Jackson
University of Oxford
Jarek L. Liesen
University of Oxford
Tim Rocktäschel
University College London AI Centre
Jakob N. Foerster
University of Oxford
Ilija Bogunovic
University College London AI Centre
Jack Parker-Holder
University College London AI Centre
Abstract
Training agents to act in embodied environments typically requires vast training
data or access to accurate simulation, neither of which exists for many cases in
the real world. Instead, world models are emerging as an alternative–leveraging
offline, passively collected data, they make it possible to generate diverse worlds
for training agents in simulation. In this work, we harness world models to gen-
erate “imagined” environments to train robust agents capable of generalizing to
novel task variations. One of the challenges in doing this is ensuring the agent
trains on useful generated data. We thus propose a novel approach IMAC (Imag-
ined Autocurricula) leveraging Unsupervised Environment Design (UED), which
induces an automatic curriculum over generated worlds. In a series of challeng-
ing, procedurally generated environments, we show it is possible to achieve strong
transfer performance on held-out environments having trained only inside a world
model learned from a narrower dataset. We believe this opens the path to utilizing
larger-scale, foundation world models for generally capable agents.
1
Introduction
Despite significant advancements in AI, we remain far from generally capable agents [1]. In the
2010s, progress towards this goal was driven by breakthroughs in deep reinforcement learning (RL,
[2]) where agents such as AlphaGo [3] showed it was possible to discover new knowledge beyond
human capabilities, like the famous move37. However, scaling RL in simulation to produce a more
general intelligence remains bottlenecked by the lack of sufficiently diverse and real-world simula-
tors [4, 5]. By contrast...

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Brain network science modelling of sparse neural networks enables Transformers and LLMs to perform as fully connected
NeurIPS 2025

Brain network science modelling of sparse neural
networks enables Transformers and LLMs to perform
as fully connected
Yingtao Zhang1,2, Diego Cerretti1,2, Jialin Zhao1,2, Ziheng Liao1,2, Wenjing Wu1,2
Umberto Michieli4,5 & Carlo Vittorio Cannistraci1,2,3∗
1Center for Complex Network Intelligence (CCNI)†
2Dept. of Computer Science & Technology, 3School of Biomedical Engineering, Tsinghua University
4University of Padova, 5Canva Research
Abstract
This study aims to enlarge our current knowledge on the application of brain-
inspired network science principles for training artificial neural networks (ANNs)
with sparse connectivity. Dynamic sparse training (DST) emulates the synaptic
turnover of real brain networks, reducing the computational demands of train-
ing and inference in ANNs. However, existing DST methods face difficulties in
maintaining peak performance at high connectivity sparsity levels. The Cannistraci-
Hebb training (CHT) is a brain-inspired method that is used in DST for growing
synaptic connectivity in sparse neural networks. CHT leverages a gradient-free,
topology-driven link regrowth mechanism, which has been shown to achieve ultra-
sparse (1% connectivity or lower) advantage across various tasks compared to
fully connected networks. Yet, CHT suffers two main drawbacks: (i) its time
complexity is O(N · d3)- N node network size, d node degree - hence it can be
efficiently applied only to ultra-sparse networks. (ii) it rigidly selects top link
prediction scores, which is inappropriate for the early training epochs, when the
network topology presents many unreliable connections. Here, we design the first
brain-inspired network model - termed bipartite receptive field (BRF) - to initialize
the connectivity of sparse artificial neural networks. Then, we propose a matrix
multiplication GPU-friendly approximation of the CH link predictor, which reduces
the computational complexity to O(N 3), enabling a fast implementation of link
prediction in large-scale m...

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