NeurIPS 2025PastOther
UniReps: 3rd Edition of the Workshop on Unifying Representations in Neural Models
UniReps2025
- Submission deadline
- Aug 30, 2025, 14:20 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (116)
Fetched from OpenReview (v2) on 2026-06-10.
A Circular Argument: Does RoPE need to be Equivariant for Vision?
A second-order perspective on linear mode connectivity modulo permutations
All In: Bridging Input Feature Spaces Towards Graph Foundation Models
An Empirical Study of Task and Feature Correlations in the Reuse of Pre-trained Models
An Empirical Study on Unifying JEPA and Language Supervision for Visual Representation Learning
Analyze the Neurons, not the Embeddings: Understanding When and Where LLM Representations Align with Humans
Any-Subgroup Equivariant Networks via Symmetry Breaking
Better Representations, Better Speech BCIs: a Multitask Approach
Better Together: Leveraging Unpaired Multimodal Data for Stronger Unimodal Models
Beyond [cls]: Exploring the true potential of Masked Image Modeling representations
Bias-driven Alignment of Linear and ReLU Networks
Blinded by Language: Multimodal LLMs Underuse Their Vision Backbone
Brain–Language Model Alignment: Insights into the Platonic Hypothesis and Intermediate-Layer Advantage
Bridging Large Gaps in Neural Network Representations with Model Stitching
Building expertise through task-specific representational alignment in biological and artificial neural networks
Clifford Algebraic Rotor Embeddings : Maybe embeddings should start to CARE
Condition-Dependent Representational Alignment between Whisper and the Human Speech Network
Context-Aware World Models for Task-Agnostic Control
Contrastive Representations for Temporal Reasoning
Cross-Modal Representational Alignment with LLM Priors for Image Generation
Data Augmentation Techniques to Reverse-Engineer Neural Network Weights from Input-Output Queries
Data symmetries generate drifting similarity matrices in manifold-tiling neural codes
Dead Feature Counts in Sparse Autoencoders Predict Underlying Deep Q Networks' Effectiveness
Decoding Projections From Frozen Random Weights in Autoencoders: What Information Do They Encode?
DIET-CP: Lightweight and Data Efficient Self Supervised Continued Pretraining
Distinguishing probabilistic from non-probabilistic neural representations
Ditch the Denoiser: Emergence of Noise Robustness in Self-Supervised Learning from Data Curriculum
DocQIR-Emb: Document Image Retrieval with Multi-lingual Question Query
Echo of Bayes: Learned Memory Functions Can Recover Belief States
Enhancing Multimodal Product Retrieval in E-Commerce by Reversing Typographic Attacks
Equivalences between network modularity and diverse low-dimensional representations
Escaping Plato’s Cave: JAM for Aligning Independently Trained Vision and Language Models
Evaluating Foundation Models' 3D Understanding Through Multi-View Correspondence Analysis
Exact Learning Dynamics of Bottlenecked and Wide Deep Linear Networks
Exploring Augmentation-Driven Invariances for Graph Self-supervised Learning in Spatial Omics
Finding Fingerprints of Out-Of-Distribution Failures from In-Distribution Geometry
From Aggregation to Guidance: Strategies for Personalized Federated Fine-Tuning of Foundation Models
GraphMatch: Fusing Language and Graph Representations in a Dynamic Two-Sided Work Marketplace
Group Equivariance Meets Mechanistic Interpretability: Equivariant Sparse Autoencoders
Hand-Engineered Image-Computable Models Can Still Outperform DNNs in V1 Similarity
Human-like individual differences emerge from random weight initializations in neural networks
Improving Generation Quality of Long-Tailed Diffusion via Disentangled Latent Representations
Interpreting convolutional neural networks to study wide-field amacrine cell inhibition in the retina
Learning Resilient Molecular Representations with Dynamic Multi-Modal Fusion
Learning using switching synaptic plasticity rules
Leveraging Parameter Space Symmetries for Reasoning Skill Transfer in LLMs
LevyScore: A Fast Sample-Wise Confidence Score of Pretrained Joint Embedding Model
Linear Maps for Cross-Model Finetuning Transfer
Linear Recurrent Networks Approximate Optimal Filtering in Hidden Markov Models
LLM-JEPA: Large Language Models Meet Joint Embedding Predictive Architectures
Look, Then Speak: Social Tokens for Grounding LLMs in Visual Interactions
Low-Rank Successor Representations Capture Human-Like Generalization
MASS: MoErging through Adaptive Subspace Selection
Measure Before You Look: Grounding Embeddings Through Manifold Metrics
Measuring and Controlling Solution Degeneracy across Task-Trained Recurrent Neural Networks
Measuring the Measures: Discriminative Capacity of Representational Similarity Metrics Across Model Families
Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling
Mice to Machines: Neural Representations from Visual Cortex for Domain Generalization
Misalignment Between Vision-Language Representations in Vision-Language Models
MultiPersona-Align: Zero-Shot Multi-Subject Personalized Image Generation with Layout-Guidance via Dual Representation Alignment
Neural Correlates of Language Models Are Specific to Human Language
Neural Embedding Alignment Reveals Nonlinear Latent Transformations across Brain Regions
NeuroFusion: A Unified Framework for Generalized Visual Stimulus Decoding from fMRI Across Datasets and Subjects
Neuron-Level Linguistic Selectivity in LLMs via a Classifier-Free Framework
No Clustering, No Routing: How Transformers Actually Process Rare Tokens
On Defining Neural Averaging
On Task Vectors and Gradients
On the Identifiability of Latent Action Policies
On the Impact of Topological Regularization on Geometrical and Topological Alignment in Autoencoders: An Empirical Study
On the Training Dynamics of Contrastive Learning with Imbalanced Feature Distributions: A Theoretical Study of Feature Learning
One Question at a Time: A Semantic Bottleneck for Interpretable Visual Brain Decoding from fMRI
Perspective: Summary Statistics of Learning
Phase codes emerge in recurrent neural networks optimized for modular arithmetic
Quantifying information stored in synaptic connections rather than in firing activities of neural networks
Quantum Relational Knowledge Distillation
R²-CoD: Understanding Text-Graph Complementarity in Relational Reasoning via Knowledge Co-Distillation
Radial-VCReg: More Informative Representation Learning Through Radial Gaussianization
Relational Representation Learning
Representing Neural Network Layers as Linear Operations via Koopman Operator Theory
Rethinking Objectives for Multi-View and Multi-Modal Contrastive Learning
Scratchpad Thinking: Alternation Between Storage and Computation in Latent Reasoning Models
Self-Supervised Learning from Structural Invariance
SemCLIP: A Semantic Memory-Aligned Vision Language Model
Shared Parameter Subspaces and Cross-Task Linearity in Emergently Misaligned Behavior
Signatures of the Auditory Cortex Reveal Discrepancies Across Speech Recognition Models
SoftStep: learning instance-wise similarity functions between neural representations
Sparse Autoencoder Neural Operators: Model Recovery in Function Spaces
Spectral Insights into Data-Oblivious Critical Layers in Large Language Models
Stable Single-Pixel Contrastive Learning for Semantic and Geometric Tasks
stable-pretraining: Foundation Model Research Made Simple
Superclass-Guided Representation Disentanglement for Spurious Correlation Mitigation
Superposition disentanglement of neural representations reveals hidden alignment
Superposition in Graph Neural Networks
SWAT-NN: Simultaneous Weights and Architecture Training for Neural Networks in a Latent Space
Symmetry-Aware Fully-Amortized Optimization with Scale Equivariant Graph Metanetworks
Task Priors: Enhancing Model Evaluation by Considering the Entire Space of Downstream Tasks
TextIT: Inference-Time Representation Alignment for Improved Visual Text Generation in Diffusion Models
The Fragility of Polarity: A Perturbative Analysis of the sign Hypothesis in Sparse Networks
The Geometry and Topology of Modular Addition Representations
The Performance Cost of Representational Misalignment
Topological Alignment of Shared Vision-Language Embedding Space
Towards Interpretable Deep Neural Networks for Tabular Data
Towards Mitigating Systematics in Large-Scale Surveys via Few-Shot Optimal Transport-Based Feature Alignment
Transformers as Unrolled Inference in Probabilistic Laplacian Eigenmaps
Two-Scale Latent Dynamics for Recurrent-Depth Transformers
Understanding Task Transfer in Vision-Language Models
Unifying Vision-Language Latents for Zero-label Image Caption Enhancement
Universal Properties of Activation Sparsity in Modern Large Language Models
Universally Converging Representations of Matter Across Scientific Foundation Models
Unraveling the cognitive patterns of Large Language Models through module communities
VISGate: ROI-Conditioned Dual-Head Encoders that Align Visual Features and Brain Responses
VQ-Kernels: Unraveling Deep Learning of High-Dimensional Data Geometry
Weight Weaving: Parameter Pooling for Data-Free Model Merging
Where does an LLM begin computing an instruction?
Why and How Auxiliary Tasks Improve JEPA Representations
Windsock is Dancing: Adaptive Multimodal Retrieval-Augmented Generation