ICLR 2026 Papers

Molecular representations that fully capture geometric parameters such as bond angles and torsion angles are crucial for accurately predicting important molecular properties including enzyme catalytic activity, drug bioactivity, and molecular spectral characteristics, as demonstrated by extensive studies. However, current molecular graph representation learning approaches represent molecular geometric parameters only indirectly through combinations of atoms and bonds, neglecting the spatial relationships and interactions between these higher-order geometric structures. In this paper, we propose \textbf{TetraGT} (\textbf{Tetra}hedral \textbf{G}eometry-Driven Explicit \textbf{T}oken Interactions with Graph Transformer), a novel architecture that directly models molecular geometric parameters. Based on the spatial solid geometry theory of face angle and dihedral angle inequality, TetraGT explicitly represents bond angles and torsion angles as structured tokens for the first time, directly reflecting their intrinsic role in determining the molecular conformational stability and properties. Through our designed spatial tetrahedral attention mechanism, TetraGT achieves highly selective direct communication between structural tokens. Experimental results demonstrate that TetraGT achieves superior performance on the PCQM4Mv2 and OC20 IS2RE benchmarks. We also apply our pre-trained TetraGT model to downstream tasks including QM9, PDBBind, Peptides and LIT-PCBA, demonstrating that TetraGT delivers excellent results in transfer learning scenarios and shows scalability with increasing molecular size.

Linear sequence modeling methods, such as linear attention, state space modeling, and linear RNNs, offer significant efficiency improvements by reducing the complexity of training and inference. However, these methods typically compress the entire input sequence into a single fixed-size memory state, which leads to suboptimal performance on recall-intensive tasks. To address this limitation, we introduce a novel architecture called Mixture-of-Memories (MoM). MoM utilizes multiple independent memory states, with a router network directing input tokens to specific memory states. This approach greatly enhances the overall memory capacity while minimizing memory interference. MoM serves as a general framework that can be seamlessly combined with diverse memory update mechanisms across linear models. As a result, MoM performs exceptionally well on recall-intensive tasks, surpassing existing linear sequence modeling techniques. Despite incorporating multiple memory states, the computation of each memory state remains linear in complexity, allowing MoM to retain the linear-complexity advantage during training, while constant-complexity during inference. Our experimental results show that MoM outperforms current linear sequence models on downstream language tasks, particularly recall-intensive tasks, and even achieves performance comparable to Transformer models.

Diffusion-based generative models have reformed generative AI, and have enabled new capabilities in the science domain, for example, generating 3D structures of molecules. Due to the intrinsic problem structure of certain tasks, there is often a symmetry in the system, which identifies objects that can be converted by a group action as equivalent, hence the target distribution is essentially defined on the quotient space with respect to the group. In this work, we establish a formal framework for diffusion modeling on a general quotient space, and apply it to molecular structure generation which follows the special Euclidean group SE(3) symmetry. The framework reduces the necessity of learning the component corresponding to the group action, hence simplifies learning difficulty over conventional group-equivariant diffusion models, and the sampler guarantees recovering the target distribution, while heuristic alignment strategies lack proper samplers. The arguments are empirically validated on structure generation for small molecules and proteins, indicating that the principled quotient-space diffusion model provides a new framework that outperforms previous symmetry treatments.

Chess has played a uniquely important historical role as a testbed domain for artificial intelligence. Applying new architectures to improve absolute chess performance, and more recently to predict human moves at specified skill levels, has therefore garnered attention in the machine learning literature. Current approaches to these problems employ transformer models with widely varying architectural designs, and use unintuitive tokenization schemes that are not amenable to interpretability techniques, which hinders their applicability for teaching and human-AI interaction. We introduce Chessformer, a novel chess transformer model design that consists of an encoder-only model which processes chessboard squares as input tokens, instead of moves or the entire position, a dynamic positional encoding scheme that allows the model to flexibly adapt to the unique geometries present in chess, and an attention-based policy output design. We show that Chessformer advances the state of the art in all three major chess modeling goals: it significantly improves the chess-playing performance of a state-of-the-art chess engine, it surpasses the previous best human move-matching prediction performance with a much smaller model, and it enables substantial interpretability benefits. Our unified approach constitutes a broad advance across several important tasks in chess AI, and also demonstrates the benefits of carefully adapting transformers' tokenization systems, output systems, and positional encodings to reflect the structure of a domain of interest.

Ensuring synthesizability in generative small molecule design remains a major challenge. While recent developments in synthesizable molecule generation have demonstrated promising results, these efforts have been largely confined to 2D molecular graph representations, limiting the ability to perform geometry-based conditional generation. In this work, we present SYNCOGEN (Synthesizable Co-Generation), a single framework that combines simultaneous masked graph diffusion and flow matching for synthesizable 3D molecule generation. SYNCOGEN samples from the joint distribution of molecular building blocks, chemical reactions, and atomic coordinates. To train the model, we curated SYNSPACE, a dataset containing over 600K synthesis-aware building block graphs and 3.3M conformers. SYNCOGEN achieves state-of-the-art performance in unconditional small molecule graph and conformer generation, and the model delivers competitive performance in zero-shot molecular linker design and pharmacophore conditioning for protein ligand generation in drug discovery. Overall, this multimodal formulation represents a foundation for future applications enabled by non-autoregressive molecular generation, including analog expansion, lead optimization, and direct structure conditioning.

The field of 4D world modeling—aiming to jointly capture spatial geometry and temporal dynamics—has witnessed remarkable progress in recent years, driven by advances in large-scale generative models and multimodal learning. However, the development of truly general 4D world models remains fundamentally constrained by the availability of high-quality data. Existing datasets and benchmarks often lack the dynamic complexity, multi-domain diversity, and spatial-temporal annotations required to support key tasks such as 4D geometric reconstruction, future prediction, and camera-controlled video generation. To address this gap, we introduce OmniWorld, a large-scale, multi-domain, multi-modal dataset specifically designed for 4D world modeling. OmniWorld consists of a newly collected OmniWorld-Game dataset and several curated public datasets spanning diverse domains. Compared with existing synthetic datasets, OmniWorld-Game provides richer modality coverage, larger scale, and more realistic dynamic interactions. Based on this dataset, we establish a challenging benchmark that exposes the limitations of current state-of-the-art (SOTA) approaches in modeling complex 4D environments. Moreover, fine-tuning existing SOTA methods on OmniWorld leads to significant performance gains across 4D reconstruction and video generation tasks, strongly validating OmniWorld as a powerful resource for training and evaluation. We envision OmniWorld as a catalyst for accelerating the development of general-purpose 4D world models, ultimately advancing machines’ holistic understanding of the physical world.

Diffusion-based samplers learn to sample complex, high-dimensional distributions using energies or log densities alone, without training data. Yet, they remain impractical for molecular sampling because they are often slower than molecular dynamics and miss thermodynamically relevant modes. Inspired by enhanced sampling, we encourage exploration by introducing a sequential bias along bespoke, information-rich, low-dimensional projections of atomic coordinates known as collective variables (CVs). We introduce a repulsive potential centered on the CVs from recent samples, which pushes future samples towards novel CV regions and effectively increases the temperature in the projected space. Our resulting method improves efficiency, mode discovery, enables the estimation of free energy differences, and retains independent sampling from the approximate Boltzmann distribution via reweighting by the bias. On standard peptide conformational sampling benchmarks, the method recovers diverse conformational states and accurate free energy profiles. We are the first to demonstrate reactive sampling using a diffusion-based sampler, capturing bond breaking and formation with universal interatomic potentials at near-first-principles accuracy. The approach resolves reactive energy landscapes at a fraction of the wall-clock time of standard sampling methods, advancing diffusion-based sampling towards practical use in molecular sciences.

Molecular dynamics is crucial for understanding molecular systems but its applicability is often limited by the vast timescales of rare events like protein folding. Enhanced sampling techniques overcome this by accelerating the simulation along key reaction pathways, which are defined by collective variables (CVs). However, identifying effective CVs that capture the slow, macroscopic dynamics of a system remains a major bottleneck. This work proposes a novel framework coined BioEmu-CV that learns these essential CVs automatically from BioEmu, a recently proposed foundation model for generating protein equilibrium samples. In particular, we re-purpose BioEmu to learn time-lagged generation conditioned on the learned CV, i.e., predict the distribution of molecular states after a certain amount of time. This training process promotes the CV to encode only the slow, long-term information while disregarding fast, random fluctuations. We validate our learned CV on fast-folding proteins with two key applications: (1) estimating free energy differences using on-the-fly probability enhanced sampling and (2) sampling transition paths with steered molecular dynamics. Our empirical study also serves as a new systematic and comprehensive benchmark for MLCVs on fast-folding proteins larger than Alanine Dipeptide.

Benchmarking the hundreds of functional connectivity (FC) modeling methods on large-scale fMRI datasets is critical for reproducible neuroscience. However, the combinatorial explosion of model–data pairings makes exhaustive evaluation computationally prohibitive, preventing such assessments from becoming a routine pre-analysis step. To break this bottleneck, we reframe the challenge of FC benchmarking by selecting a small, representative *core-set* whose sole purpose is to preserve the relative performance ranking of FC operators. We formalize this as a ranking-preserving subset selection problem and propose **S**tructure-aware **C**ontrastive **L**earning for **C**ore-set **S**election (**SCLCS**), a self-supervised framework to select these core-sets. **SCLCS** first uses an adaptive Transformer to learn each sample's unique FC structure. It then introduces a novel **S**tructural **P**erturbation **S**core (**SPS**) to quantify the stability of these learned structures during training, identifying samples that represent foundational connectivity archetypes. Finally, while **SCLCS** identifies stable samples via a top-$k$ ranking, we further introduce a **density-balanced sampling strategy** as a necessary correction to promote diversity, ensuring the final core-set is both structurally robust and distributionally representative. On the large-scale REST-meta-MDD dataset, **SCLCS** preserves the ground-truth model ranking with just 10% of the data, outperforming state-of-the-art (SOTA) core-set selection methods by up to 23.2% in ranking consistency (nDCG@k). To our knowledge, this is the first work to formalize core-set selection for FC operator benchmarking, thereby making large-scale operators comparisons a feasible and integral part of computational neuroscience. Code is publicly available on: [https://github.com/lzhan94swu/SCLCS](https://github.com/lzhan94swu/SCLCS)