NeurIPS 2026 论文列表

Causal discovery from nonlinear multivariate time series is challenging in high-dimensional systems, where the number of candidate directed relations grows quadratically with the number of variables. Existing methods are often limited by target-wise model fitting, repeated conditional testing, or expensive graph-search procedures. We propose SCDM, a shared-parameter meta-learning framework for scalable temporal causal discovery. SCDM treats each target variable as a task while learning a shared temporal predictor and a shared causal-strength matrix across tasks. Each meta-episode updates only a subset of target-variable tasks, allowing the model to accumulate structural evidence across the full system without exhaustive target-wise optimization. After training, a post-training graph readout converts the learned predictor into continuous directed causal scores for threshold-independent evaluation. We also provide a finite-error recovery principle showing that, under fully observed delayed-system assumptions and a positive risk-gap condition, thresholding SCDM scores recovers the graph when statistical, meta-optimization, and readout errors are below the population margin. Experiments on controlled synthetic, neuroimaging-inspired, high-dimensional, and real-world temporal benchmarks demonstrate competitive causal recovery and improved scalability in large temporal systems.

Genetic Programming (GP) is a search paradigm inspired from natural evolution for the automated discovery of expressions, functions, and computer programs. Cartesian GP (CGP) is a flavor of GP that uses a graph-based model to encode candidate programs, as contrasted to the conventional Tree-based GP (TGP). Since its inception two decades ago, CGP has predominantly been analyzed empirically, thus little is known regarding its theoretical performance guarantees. This paper analyzes CGP for the MAX problem, which has been only rigorously studied for TGP with a proven expected runtime superlinear in the optimal program length $n$. We prove that the expected time for the (1+1) CGP employing common mutation operators to evolve an equivalent optimal of the same output is polylogarithmic in n, specifically at most $O(\log^9{n})$. This is remarkable, as it shows an exponential speedup by switching to CGP. Our results shed light on the benefit and the compactness of the graph-based representation for programs, and on how CGP can navigate its search and fitness spaces efficiently. This is a first proven performance guarantee for CGP on an established benchmark problem. Experiments complement our theoretical findings.

Learning directed causal graphs from time-series data poses significant challenges, especially in fMRI where slow sampling rate obscures fast neural interactions. This temporal mismatch leads to undersampling, which can make multiple graphs equally plausible. We address this problem by explicitly modeling undersampling effects when recovering causal graphs. Our approach employs answer set programming (ASP) to enforce domain-specific constraints and optimize soft observational constraints, thereby identifying a Markov equivalence class for the resulting graph solutions. By customizing an ASP solver to collect multiple near-optimal solutions, we obtain not only the single best-fitting graph but an equivalence class of high-scoring graphs for expert consideration. This method, called Real-world noisy RASL (RnR), can also act as a meta-solver: it refines the output of other causal discovery algorithms by accounting for undersampling biases. In synthetic data and empirical brain network data, RnR produces more accurate causal graphs than state-of-the-art methods. When applied as a meta-solver (refining outputs of existing algorithms), it improves F1 scores by an average of 46\% over baseline methods; on standalone synthetic data benchmarks, it achieves a 64\% improvement. We demonstrate that RnR is robust to varying undersampling rates, maintaining high precision and recall even as sampling becomes more sparse, whereas baseline methods degrade significantly. Finally, we test RnR on real-world settings where ground truth connectivity is unknown such as human brain fMRI data, showing that incorporating undersampling-aware constraints via ASP yields more reliable and interpretable brain connectivity estimates from fMRI time series, bridging the gap between neural dynamics and observational data.

Constraint-based causal discovery requires a large number of conditional independence (CI) tests. In nonlinear and high-dimensional settings, generative CI methods offer strong expressive power, but often suffer from high per test cost and limited reusability across the many query dependent conditional resampling tasks arising in graph search. We propose a generative nonlinear CI testing and graph search framework for PC-style causal discovery. First, we develop the Flow Matching based Conditional Independence Test (FMCIT), which combines joint flow matching modeling with conditional imputation to reformulate generative CI testing from query specific conditional modeling into a conditional resampling process that can be reused across queries. This allows the same generative model to repeatedly generate the randomized copies required by CI testing throughout the entire graph search procedure. We then introduce GPC‑FMCIT, which embeds FMCIT into a guided PC pipeline with screening, guidance, refinement, and orientation. By using low-cost screening to shrink the search space, constructing edge-specific candidate conditioning sets from local graph structure, and invoking FMCIT only in a budget-controlled refinement stage, GPC-FMCIT reduces both the deployment cost of individual nonlinear CI queries and the overall query complexity. Experiments on synthetic datasets and real-data analyses show that the proposed method achieves a strong accuracy--efficiency trade-off under nonlinear and high-dimensional structures. Overall, our results suggest that, through the joint design of reusable conditional resampling and controlled graph search, generative nonlinear CI testing can become a practical system level module for high‑dimensional PC‑style causal discovery.

Atomic properties such as partial charges or multipoles encode chemically meaningful information that can inform downstream molecular property prediction, but their evaluation as machine learning targets has been complicated by the absence of a principled out-of-distribution evaluation protocol at the atomic level. In this work, we propose a held-out evaluation protocol that clusters atomic environments by SOAP descriptors and computes metrics accounting only for cluster labels unseen during training. Following this procedure, we use 5×5 cross-validation and Tukey's HSD to run a statistically rigorous comparison of E(3)-equivariant against non-equivariant, rotationally augmented models for predicting electron populations and multipoles of H, C, N, and O atoms. Building on our results, we introduce the Quantum Topological Neural Network (QT-Net), a rotationally augmented, non-equivariant graph neural network. We show that QT-Net can be used to infer properties of atoms in molecules from QM9 outside our training set, and that these inferred properties can yield improvement when used as input features for downstream molecular property prediction. To further validate the framework, molecular dipole moments computed from QT-Net's per-atom outputs recover the ground-truth values reported in QM9. We release all code and data, including a JAX implementation of QT-Net, to support the broader use of learned QTA properties as inductive biases for atomic-scale molecular machine learning.

Biomolecular generators are often adapted with reward feedback to improve task-specific utility, but pushing utility alone can concentrate generation on a narrow family of candidates. Maintaining diversity is difficult because sample diversity is a set-level property. We introduce Supergroup Relative Policy Optimization (SGRPO), a flexible GRPO-style framework that directly constructs rewards from set-level diversity. For each condition, SGRPO samples a supergroup of candidate sets, compares their diversity under the same condition, and redistributes the group diversity reward to individual rollouts through leave-one-out diversity contributions before combining it with rollout-level utility. This design decouples SGRPO from a particular generator, utility reward, or diversity metric, and allows instantiation with different GRPO-style approaches. We evaluate SGRPO on de novo small-molecule design, pocket-based small-molecule design, and de novo protein design, instantiating it with both GRPO and Coupled-GRPO across autoregressive and discrete diffusion generators. Across decoding sweeps, SGRPO expands the utility-diversity Pareto frontier and achieves the best frontier-level metrics relative to pretrained generators, GRPO, and memory-assisted GRPO when applicable. Our analyses further show that direct set-level diversity rewards remain effective with small groups and help preserve broader generation-distribution coverage during post-training. The code is available at https://anonymous.4open.science/r/SGRPO/README.md.

Designing proteins with desired functions or properties represents a core goal in synthetic biology, therapeutic development, and drug discovery. Recent advances in protein language models (PLMs) have enabled the generation of highly designable protein sequences, while preference alignment provides a promising way to steer designs toward desired functions and properties. Nevertheless, they often trigger catastrophic forgetting of pretrained knowledge, degrading basic designability and failing to balance multiple competing objectives. To address these issues, we draw inspiration from On-Policy Distillation (OPD), an advanced post-training method renowned for mitigating catastrophic forgetting through its mode-seeking nature. In this work, we propose ProteinOPD, a multi-objective preference alignment framework that can effectively balance multiple preference objectives while maintaining the inherent designability of PLMs. ProteinOPD adapts a pretrained PLM into preference-specific teachers and distills their knowledge into a shared student via token-level OPD on the student’s own trajectories. During this process, the student is aligned to a unique normalized geometric consensus of weighted teachers while ensuring bounded optimization under conflicts. This bridges the gap for OPD in multi-objective/teacher alignment. Extensive experiments show that ProteinOPD achieves substantial gains on target preference objectives without compromising the designability, with a 8× training speedup over RL-based alignment competitors.

Performing accurate protein-glycan docking is critical to understanding protein-glycan interactions which govern many biological processes. However, this important task is hampered by the lack of tailored datasets, benchmarks and models for protein-glycan docking. To fill this blank, we first curate a standard benchmark **ProtGlycanDock** for training and evaluating protein-glycan docking models under both protein and glycan generalization settings. On this benchmark, we evaluate ten models in three categories, physics-based docking methods, machine-learning-based docking models and molecular structure prediction models. To obtain more precise binding poses, we further propose two finetuning techniques to inject the knowledge of glycan conformations into a general-purpose molecular structure prediction model. These two techniques respectively augment model inputs with glycan topological features and refine glycan binding pose with a glycan energy loss. Benchmark results show the superiority of AlphaFold~3 among existing methods, and the two proposed finetuning techniques can be well combined to achieve a state-of-the-art model for protein-glycan docking. Also, we perform stratified benchmark analysis and case studies to better understand the strengths and weaknesses of current models, providing insights for future model improvements. We maintain datasets at https://huggingface.co/datasets/ProtGlycanDock/ProtGlycanDock and source codes at https://github.com/kasawa1234/ProtGlycanDock.

Structure-based drug design increasingly employs LLM agents to iteratively refine ligands against a target pocket, yet a viable ligand must satisfy two often-conflicting objectives---binding affinity and druggability---which single optimization steps rarely improve together. To quantify this difficulty, we introduce two diagnostic metrics: the first measures how often a single edit improves both objectives, and the second measures how often a gain on one objective comes with a loss on the other. Applying these diagnostics to current LLM-agent pipelines exposes a consistent failure mode: the agent performs molecular editing without knowing how the pocket-ligand complex responds to local modifications, thus rarely achieving joint improvement. Inspired by medicinal chemists, who probe the pocket-ligand complex with controlled analog edits before choosing an optimization direction, we propose PROBE, an optimization framework built around edit--response probing. PROBE first decomposes the ligand into editable sites and builds a pocket-specific site map that flags where joint gains are plausible, where the two objectives are likely in tension, and where liability substructures should be changed; it then performs controlled probe edits whose responses are distilled into an EditManual. Guided by the site map and EditManual, PROBE runs an iterative multi-agent loop in which an affinity agent, a druggability agent, and a co-optimization agent jointly produce edits. On the CrossDocked2020 benchmark, PROBE achieves state-of-the-art performance and substantially mitigates the failure modes exposed by our diagnostics metrics.

Predicting spatial gene expression from histology images could scale spatial transcriptomics (ST) to image-only cohorts, but conventional histology-based ST prediction is trained and evaluated mainly by per-gene spatial-profile reconstruction. This objective is misaligned with a key downstream use of ST: differentially expressed gene (DEG) discovery, where genes are ranked for a biological or morphology-defined contrast by evidence of between-group expression differences. We formulate image-based differential expression ranking (IDER), which asks whether predicted expression profiles preserve the contrast-specific ranked gene list obtained from measured profiles. IDER compares gene rankings induced by differential-expression statistics, rather than raw expression magnitudes or per-gene spatial correlations. We further introduce a differentiable IDER objective that aligns these statistics across genes and can be trained with morphology-derived proxy contrasts without predefined biological group labels. Experiments on public ST datasets show improved DEG-ranking agreement and pathway-enrichment overlap over conventional reconstruction objectives, including morphology-derived and pathologist-annotated tissue-region evaluations.

Understanding reaction mechanisms is central to advances in chemistry, materials science, and drug discovery, where transition states govern reactivity and kinetics. This position paper argues that current ML approaches for transition state (TS) prediction are fundamentally limited by inadequate datasets, weakly grounded methods, and flawed evaluation protocols, leading to overstated progress and questionable chemical reliability. While recent advances have improved performance, these gains are largely superficial when assessed against the core requirements of a valid TS. First, widely used datasets suffer from issues of chemical implausibility, lack of diversity, and insufficient validation, introducing noise and bias into model training. Second, existing methods often neglect essential physical constraints and rely on assumptions like fixed atom mappings, resulting in brittle, non-generalizable models. Third, standard evaluation metrics fail to capture chemical validity, often rewarding plausible but physically incorrect predictions. Empirical analyses, including quantum chemical validation and robustness studies, demonstrate that many reported transition states do not correspond to intended reactions and that models exhibit poor generalization and strong bias toward equilibrium configurations. We contend that meaningful progress in TS prediction requires a principled rethinking of dataset construction, incorporation of physical constraints into model design, and the development of chemically faithful evaluation frameworks.

Protein--nucleic-acid recognition underlies aptamer discovery and nucleic-acid therapeutics, yet computational models often trade mechanistic interpretability for predictive flexibility. Hybrid interpretable--latent architectures risk fallback takeover, where the latent branch silently becomes the true predictor while the explicit pathway remains decorative. We introduce PIGRAM, a patch--motif grammar model that decomposes proteins into local residue patches and nucleic-acid partners into secondary-structure motifs, learning a sign-separated grammar over explicit physicochemical and geometric attributes to preserve rare favorable interactions. Heterogeneous rule families are selectively refined into a coarse-to-fine atlas, and a frozen-grammar residual Transformer provides sparse, bounded corrections without displacing the grammar as the primary predictor. On a strict sequence-pair-disjoint benchmark, PIGRAM achieves a Pearson correlation of 0.494, approaching the unconstrained Transformer (0.511) while exposing signed rules, fine subtypes, and route-gated corrections for each prediction. The learned rules recover bidirectional patch--motif mechanisms, and residual corrections concentrate selectively on grammar-hard regions. In fluorescence ELISA experiments, suppressive-rule relief improved GFP binding, whereas favorable-rule disruption weakened NELF binding, providing direct experimental support for grammar-guided design. Together, these results show that interpretable grammar can serve as both a predictive scaffold and an experimentally actionable design principle for aptamer optimization.

The standard constraint-based paradigm for causal discovery with incomplete data---impute first, test second---is frequently miscalibrated: any consistent conditional independence (CI) test rejects a true null with probability approaching 1 when imputation error induces spurious conditional dependence. We introduce *PAIR-CI*, a nonparametric CI test that restores calibration by integrating multiple imputation directly into the inferential procedure via a paired permutation design. PAIR-CI compares cross-validated models that include and exclude the candidate variable while receiving the same imputed conditioning set, forcing imputation error to cancel in their loss difference rather than contaminate the test statistic. A provably consistent variance estimator jointly accounts for uncertainty arising from cross-validation and multiple imputation---to our knowledge, the first formal unification of these two inferential frameworks. In simulations, existing imputation-based CI tests exhibit false positive rates of 28--45\% when data are missing not at random (MNAR), whereas PAIR-CI averages below the nominal 5\% level across data-generating processes and missingness mechanisms. These gains are largest in nonlinear settings and grow with causal graph size: when integrated into the PC algorithm, PAIR-CI reduces structural Hamming distance by 8\% on 10-variable nonlinear graphs, 15\% on 30-variable equivalents, and up to 44\% on the 56-variable HAILFINDER network, with stable performance in all settings.

We demonstrate that AI agents can autonomously improve mathematical proofs of quantitative results. As a key step towards this frontier, we achieve strict improvements on Grothendieck's and Korenblum's constants, whose precise values have remained surprisingly elusive despite their recurrence in both the mathematical and physical sciences. By encoding relevant literature as a partially formalized Lean blueprint, we structure the agent's search space to localized proof refinements that automatically propagate to a verified final bound. Concretely, Grothendieck's constant ($\mathcal{K}_G$) is shown to be: $$ \mathcal{K}_G \leq \tfrac{\pi}{2 \log (1 + \sqrt{2})} - 10^{-17}, $$ which is the first explicit numerical improvement since Krivine's landmark 1979 result. For Korenblum's constant ($\mathcal{K}_K$), the lower bound is tightened to: $$ \mathcal{K}_K \geq 0.3554 + 0.001165, $$ a third-digit improvement over Wang's recent 2025 result. In summary, we establish that AI agents can autonomously refine formal proofs of open problems and provide a generalizable framework for future mathematical discovery.

Scientific discovery is fundamentally open-ended, characterized by epistemic uncertainty, and requiring endogenous goal-setting and recursive knowledge accumulation. While current agentic systems have demonstrated success in addressing goal-based discovery tasks, they struggle to sustain discovery in open-ended settings. In this work, we posit that open-ended discovery is an emergent network-level property of a socially driven system. We introduce $\texttt{ASCollab}$, which translates four key algorithmic conditions: (1) population heterogeneity in research behaviors, (2) endogenous interaction networks where collaborations and attention routing emerge organically, and (3) socially driven peer evaluations, which are enabled by (4) a global, shared memory that reflects evolving research states. Specifically, social memory is implemented to capture time-varying expertise and reputational signals, and shifting attention paid to historical artifacts produced by the system. Through experiments on large-scale discovery problems in genomics, cell biology, and epidemiology, we demonstrate that $\texttt{ASCollab}$ produces discoveries judged by domain experts to be both sound and significant. Crucially, the system independently recovers real-world scientific findings. Furthermore, systematic investigations show that social dynamics fostered by each of the four conditions are vital, where heterogeneous agents operating within self-organizing networks significantly outperform both fixed-workflow baselines and homogeneous networks.

Designing small-molecule drugs that selectively bind to a target protein while avoiding unintended interactions with other proteins is essential for reducing side effects that contribute substantially to clinical attrition. Although structure-based drug design (SBDD) has advanced rapidly with deep learning, most generative models still optimize binding to a single target and disregard selectivity due to limited training data and unknown off-target interactions. Motivated by this gap, we introduce SelectBench, a comprehensive benchmark assessing the selectivity of generative SBDD models. SelectBench provides three curated evaluation tasks reflecting different real-world drug discovery scenarios: literature-based case studies, safety screening panels used in pharmaceutical development, and large-scale structure-based evaluation sets. We further introduce a suite of metrics to quantify selectivity and conduct a systematic evaluation of nine representative SBDD models, including methods explicitly trained for selectivity and widely-used inference-time guidance techniques. Our results show that even selectivity-trained models achieve only modest off-target discrimination and inference-time guidance provides limited improvements, underscoring the need for method improvement. SelectBench provides both a characterization of where current generative SBDD stands on selectivity and an open, extensible platform to accelerate progress.

Discovering explanatory subgraphs is essential for interpreting Graph Neural Networks (GNNs). Many existing and popular explainers optimize by maximizing mutual information (MMI) between selected subgraphs and predicted labels; however, the resulting optimization landscape is often non-smooth and poorly conditioned, leading to suboptimal local optima. To address this issue, we develop a norm-theoretic framework for subgraph analysis. We show that subgraphs containing model-utilized features tend to induce graph embeddings with larger $\ell_2$ norms, as they align more strongly with the supportive subspace shaped by the GNN’s weight matrices, a phenomenon consistently observed across multiple datasets. Based on this insight, we propose NORMA, a parameterized explainer that uses subgraph-embedding $\ell_2$ norms as a stable optimization signal to guide subgraph selection. Experiments on fsix benchmark datasets demonstrate that NORMA effectively alleviates the optimization difficulties of MMI-based methods and achieves superior explanation quality.

Discovering object-centric representations from images can significantly enhance the robustness, sample efficiency and generalizability of vision models. Works on images with multi-part objects typically follow an implicit object representation approach, which fail to recognize these learned objects in occluded or out-of-distribution contexts. This is due to the assumption that object part-whole relations are implicitly encoded into the representations through indirect training objectives. We address this limitation by proposing a novel method that uses explicit graph representations for parts and introduce a new co-part algorithm for multi-part object discovery. The part representations are refined with a part-centric regularization objective. We then introduce three benchmarks to evaluate the robustness of object-centric methods in recognizing multi-part objects within occluded and out-of-distribution settings. Experimental results on simulated, realistic, and real-world images show marked improvements in the quality of discovered objects compared to state-of-the-art methods, as well as the accurate recognition of multi-part objects in occluded and out-of-distribution contexts. We also show that the discovered object-centric representations can more accurately predict key multi-part object properties in a downstream task, highlighting the potential of our method to advance the field of object-centric representations.

Computational RNA design methods routinely evaluate designed sequences by refolding them with the same structure-prediction model used during optimization, measuring agreement with the design target. While scalable, this practice carries a fundamental risk: when optimization and evaluation share the same underlying model, high scores can reflect model-specific idiosyncrasies rather than genuine structural quality. To characterize the severity and mechanism of this problem, we audited nine RNA design methods spanning four algorithmic paradigms, evaluating approximately 11,000 sequences against predictors from two independent model families: physics-based thermodynamic models and machine-learning models, alongside five tertiary-structure predictors and large-scale SHAPE chemical-mapping data. We find that 78.1% of designs produced by single-objective optimization pass one predictor yet fail another, while ensemble-based methods reduce this disagreement 30-fold. A controlled experiment identifies optimization objective breadth, not search strategy, as the primary driver of this fragility. Cross-predictor disagreement carries a calibrated experimental signal: lower disagreement predicts higher SHAPE accuracy across 40,000 sequences, yet remains null-to-anti-predictive for biological function, establishing a clear scope boundary. We release **ACCORD** (**A**cross-oracle **C**alibration for **CO**mputational **R**NA **D**esign), a benchmarking workflow that reports per-predictor scores, experimentally calibrated uncertainty tiers, and explicit warnings for invalid use cases, providing the community with a principled foundation for RNA design evaluation.