NeurIPS 2026 Papers

Mapping software issue descriptions to relevant code segments, a task known as code localization, is a primary bottleneck in automated software engineering. While recent attempts have automated it using code graphs that capture syntax-level dependencies among code elements, including functions and modules, their code-centric approaches often overlook the design rationales and prior fix patterns present in a codebase's development history, such as pull requests and commits. We propose Co-Locator, a collaborative multi-agent code localization framework that integrates code graphs reflecting functional dependencies and knowledge graphs constructed from triplets capturing design rationales and past code modifications scattered across the development history. During localization, a Code Graph (CG) agent explores functional hierarchies and consults a Knowledge Graph (KG) agent to gather insights into developer intents and prior bug fixes. Combined with a Retriever agent that assists CG and KG agents, Co-Locator leverages a holistic view of the code's functional and historical context, considering what the code implements and how it was changed, to identify locations to fix. Experiments show that Co-Locator significantly outperforms 12 state-of-the-art baseline methods.

Reliable uncertainty estimation is essential for deploying large language models in high-stakes reasoning tasks, yet most existing approaches rely on expensive sampling strategies or shallow token-level signals that ignore the internal logical structure of model outputs. We introduce Argument Graph Uncertainty, a post-hoc framework for multiple choice question tasks, that estimates model confidence directly from the logical structure of a single chain-of-thought reasoning trace. Our method uses a lightweight instruction-tuned model to segment the reasoning chain into propositional units, then applies a simple NLI model to infer relational edges and construct two complementary argument graphs over these segments. The resulting edge scores are aggregated into a Dirichlet posterior over answer options, whose concentration yields calibrated uncertainty estimates reflecting both local step-to-step coherence and global logical consistency across the reasoning trace. Evaluated on GPQA, a challenging graduate-level multiple-choice benchmark, our method consistently surpasses token-based and consistency-based uncertainty baselines across multiple reasoning models. We further demonstrate through extensive combination analyses that argument graph uncertainty captures an orthogonal signal to existing approaches, making it a complementary component in uncertainty ensembles. Crucially, our framework requires no additional calls to the evaluated model and relies entirely on small, efficient auxiliary models, producing fully interpretable, per-node uncertainty attributions at low computational cost.

With the growing number of fine-tuned variants derived from pre-trained models, model merging emerges as a key challenge: effectively integrating task-specific adaptations while preserving performance. However, naive weight averaging often fails when parameter updates are misaligned, revealing the need for a structured space in which merging becomes principled. In this work, we hypothesize that pre-trained models possess an intrinsic low-rank structure. Specifically, we posit that the transformation from input to output is mediated by a latent bottleneck of low intrinsic dimensionality; we leverage this low-dimensional space to construct a model embedding space. This space ensures that fine-tuned updates, i.e., model embeddings, of all adaptations remain confined within the intrinsic structure of the pre-trained model. As model embeddings lie in the same low-dimensional space, model merging via direct averaging becomes more effective. Moreover, we further introduce an aligned subspace of these model embeddings to improve the efficacy of model merging. Experiments across vision and multimodal tasks, including ViT and multimodal LLMs, demonstrate that our approach enables effective model merging and provides a unified geometric view of adaptation and combination in pre-trained models.

Engineering LLMs to accelerate life sciences research requires a robust alignment with biomedical knowledge. We observe that biomedical text exhibits a fundamentally different uncertainty structure from general text: dense low-confidence runs encode epistemic knowledge gaps (dense causal chains, rare entities) rather than the sparse aleatoric stylistic variation typical of general text. Based on this discovery, we propose Balanced Fine-Tuning (BFT), a dual-scale post-training method that combines group-normalized token reweighting with sequence-level reallocation toward knowledge-dense samples exhibiting dense epistemic uncertainty. Across medical evaluation, biological reasoning, sparse-reward RL, and biological representation tasks, BFT provides more consistent gains than SFT and DFT under a shared training setup. When replacing the default closed-source backbones in GeneAgent (GPT-4o) and VCWorld (Gemini-2.5-Flash), the BFT-aligned 70B model delivers stronger performance across biological process reasoning and chemical perturbation prediction. Critically, all BFT variants further improve after subsequent GRPO with sparse rewards, while SFT and DFT degrade, suggesting that epistemic-aware post-training provides a more robust policy initialization. Beyond text generation, BFT-aligned LLMs produce more accurate and professional biomedical profile texts; after encoding these profiles with a text embedding model, the resulting representations support gene-level, cell-level, and perturbation-response tasks, suggesting that BFT-enhanced generation can facilitate biological representation and, in turn, broader biomedical downstream tasks.

Causal Bayesian optimization (CBO) typically relies on the unrealistic assumption of a perfectly known causal graph. Recent methods relax this constraint by coupling optimization with structure learning, but they often waste sample budgets on structural uncertainties that are irrelevant to the optimizer. In this paper, we formalize \emph{routing-equivalence} to shift the objective from full causal-graph recovery to causal belief routing, where structural differences only matter when they alter optimizer-facing objects such as admissible exploration sets and \emph{do}-priors. Furthermore, we propose \agentcbo{}, a graph-belief routing framework that incorporates LLM causal priors through guarded proposal and commit mechanisms. Specifically, a \textsc{Language-Prior Agent} combines LLM beliefs with constraint-based discovery to extract a protected causal backbone alongside residual uncertain edges. A \textsc{Calibration Agent} and a \textsc{Residual Repair Agent} then refine only these uncertain structures through score-gated bounded local edits. Finally, a deterministic commit gate evaluates candidate updates using routing-proxy checks before allowing them to influence intervention selection. Across ten benchmarks, \agentcbo{} performs comparably to known-graph CBO, demonstrating that guarded LLM priors can improve fixed-budget CBO performance.

Node classification in multimodal graphs plays an important role in many real-world applications, where nodes are described by multiple modalities such as text and images, and the graph structure captures relations among entities. However, most existing Graph Neural Networks (GNNs) still focus on pairwise connections, overlooking higher-order relational patterns. Recent studies have explored simplicial complexes to capture higher-order interactions and integrated them into GNN frameworks. Motivated by this line of work, we propose Adaptive Gated Simplicial Propagation (AGSP), an end-to-end framework for node classification in multimodal graphs. Specifically, AGSP first introduces a simplicial propagation layer to capture higher-order relations beyond pairwise connections; then it applies an adaptive gating mechanism to balance multi-order topological relations, enhancing the discriminative ability of node classification. Extensive experiments validate that AGSP generally outperforms the state-of-the-art baselines, highlighting the effectiveness of combining multimodal information with higher-order graph topology through adaptive gated fusion.

Graph-structured optimization with linear constraints is fundamental to critical infrastructure but faces scalability limits due to massive strict hard constraints and high dimensionality. While recent projection-based methods such as Trainable Sampling Kaczmarz-Motzkin Net (T-SKM-Net) guarantee feasibility, they face high computational costs in dynamic environments by processing the entire constraint set and requiring expensive matrix factorizations. To bridge this gap, we propose the Accelerated Trainable-SKM (AT-SKM) Net framework. To concentrate computation on the active constraints and eliminate redundant calculations, we introduce a hybrid sampling strategy guided by a topology-aware heterogeneous GNN model. To efficiently handle topological shifts in graph-based constraints, we employ a Cholesky Update mechanism that theoretically reduces the equality projection complexity from $\mathcal{O}(N^3)$ to $\mathcal{O}(N^2)$ under low-rank perturbations. Experiments on random geometric graphs, N-1 Security-Constrained DC-OPF, and minimum-cost gas transport problem demonstrate that AT-SKM reduces iteration counts by up to 85% and achieves 2.95$\times$-7.29$\times$ SKM layer speedups, while maintaining zero constraint violations. Our code is available at [https://anonymous.4open.science/status/Anonymous_Submission-CFC7](https://anonymous.4open.science/status/Anonymous_Submission-CFC7)

Existing multimodal retrieval benchmarks largely emphasize semantic matching on daily-life images and offer limited diagnostics of professional knowledge and complex reasoning. To address this gap, we introduce ARK, a benchmark designed to analyze multimodal retrieval from two complementary perspectives: (i) knowledge domains (five domains with 17 subtypes), which characterize the content and expertise retrieval relies on, and (ii) reasoning skills (six categories), which characterize the type of inference over multimodal evidence required to identify the correct candidate. Specifically, ARK evaluates retrieval with both unimodal and multimodal queries and candidates, covering 16 heterogeneous visual data types. To avoid shortcut matching during evaluation, most queries are paired with targeted hard negatives that require multi-step reasoning. We evaluate 26 representative text-based and multimodal retrievers and observe a pronounced gap between knowledge- and reasoning-intensive retrieval, with fine-grained visual and spatial reasoning as persistent bottlenecks. We further show that enhancements such as re-ranking, rewriting, and agentic retrieval yield consistent gains, but substantial headroom remains. The dataset and evaluation pipeline will be released soon.

Predicting protein–ligand complex structures is a central challenge in drug discovery. While recent co-folding models such as AlphaFold-3 achieve accurate structure prediction, they fail to generalize to underexplored binding interfaces -- systematically misplacing ligands, particularly for allosteric or structurally novel targets. To address this gap, we present \textbf{ACER} (\textbf{A}daptive \textbf{C}o-folding via pocket \textbf{E}xploration and pose \textbf{R}anking), a training-free framework that (a) enables co-folding models to systematically explore alternative binding pockets, and (b) leverages the discovered pockets to increase pose accuracy. Our method enables the efficient discovery of non-prevalent pockets without prior expert knowledge. ACER improves pocket discovery and pose accuracy on allosteric targets and structurally novel complexes, successfully modeling binding interfaces that are under-represented or absent from the training set. Our results demonstrate how improved sampling dynamics enhance the generalisability of co-folding models without retraining.

Learning-rate selection is one of the most consequential and repeatedly tuned decisions in modern deep learning, and the ability to transfer it quickly across widths, depths, and architectures can substantially reduce the cost of scaling new models. Existing transfer laws explain width through maximal-update parameterization and depth through depth-based scaling laws, but many modern architectures are better described as neural computation graphs with heterogeneous merge rules rather than by depth alone. We develop a unified merge calculus for learning-rate scaling on such graphs. Its central object is a graph-aware complexity coefficient that combines local merge semantics with global path structure and induces a $-1/2$ power law for the target learning-rate scale. We further show that depth alone is generally insufficient once merge semantics vary across architecture families. We validate the framework on controlled graph families and lightweight representative architectures covering uniform sum, residual-aware sum, concat, weighted fusion, and hybrid designs. Across these settings, the resulting graph coefficients organize optimal learning rates more effectively than depth alone and support practical learning-rate transfer.

Low-rank plus sparse decompositions, such as robust PCA, typically model the low-rank component as dense and the sparse component as corruption. This viewpoint is poorly suited to graph adjacency matrices, where sparsity is structural and dense low-rank factors destroy the combinatorial and computational properties of the graph. We propose signed biclique decomposition (SBD), a sparse low-rank decomposition for integer-weighted graphs that represents the structured component as a sum of sparse signed binary rank-one factors, together with a sparse signed residual. The resulting representation remains discrete, interpretable, sparse, and compatible with standard linear algebra. We formulate fixed-rank and greedy SBD objectives and derive a gain-based greedy algorithm with theoretical guarantees. Empirically, SBD recovers block structure in noisy graphs, while its factorized representation enables efficient sparse matrix-matrix multiplication (SpMM). On real graphs with strong shared-neighborhood structure, SBD achieves compression factors above $10$ and SpMM speedups up to $6\times$ over cuSPARSE, suggesting a practical alternative to dense low-rank decompositions for graphs.

Dynamic link prediction requires determining whether the historical interactions of a source node provide reliable structural support for a specific target. Existing target-conditioned methods capture such support through structural heuristics such as repeated interactions or co-neighbor overlap, but typically instantiate them as identity-based encodings and inject them as passive token-level features before aggregation. This design is brittle when exact structural encodings are sparse, and the resulting structural signals can be diluted after being concatenated with high-dimensional time and edge features, while their dynamics remain unmodeled. In this paper, we revisit target-conditioned dynamic link prediction from a structural dynamics modeling perspective and propose TCSD, which formulates structural signals as target-conditioned states, evolves them over recent histories, and uses them to guide aggregation. Concretely, we construct hybrid structural states from exact indicators that preserve time-aware repeat and co-neighbor matches, and relaxed indicators that recover latent support beyond exact identity matching. TCSD further models the dynamics of these states to capture temporal consistency within indicators and complementarity across indicators. Instead of treating structural states as passive features, TCSD uses the learned dynamics as aggregation controllers to amplify target-relevant interactions and suppress irrelevant ones. Experiments on sixteen dynamic graphs show that TCSD outperforms ten baselines, achieving up to a 14.27% relative improvement in MRR.