NeurIPS 2026 论文列表

Deep-learning protein structure predictors achieve near-experimental accuracy on individual folds, yet their default inference samples concentrate around a single dominant conformation. We introduce ConforFlux, an inference-time procedure for Boltz-2 that couples $M$ structure-prediction trajectories through a pairwise C$\alpha$-RMSD repulsion gradient on the trunk's single and pair embeddings. Because the trunk conditions every block of the diffusion module, this update propagates to every subsequent denoising step. On four conformational-change categories, ConforFlux improves the per-state success rate over Default Boltz-2 by 3–17 percentage points while preserving Default-comparable physical quality. On twelve transporter pairs with at least one alternate-state reference released after the Boltz-2 cutoff, the $2$ Å success rate rises from $4/12$ to $9/12$. On the human dopamine transporter, ConforFlux additionally reaches the post-cutoff inward state, which $500$ Default samples never attain.

Retrieval-Augmented Generation (RAG) improves the factual accuracy of large language models (LLMs) by grounding responses in external evidence. However, when retrieved context conflicts with models' internal parametric knowledge, LLMs may still generate answers that contradict the provided evidence, posing a key challenge to contextual faithfulness and the reliability of RAG systems. Motivated by recent mechanistic findings on the distinct propagation and progressive accumulation of parametric and contextual signals in LLMs, we propose Conflict-Suppressed RAG (CSRAG), a simple, training-free, decoding-time framework for resolving knowledge conflicts. CSRAG biases generation toward retrieved evidence by suppressing tokens associated with parametric knowledge while boosting tokens from the context via two complementary logits processors. Experiments on six challenging faithfulness benchmarks demonstrate that CSRAG consistently achieves state-of-the-art or near state-of-the-art performance across multiple backbone LLMs, while remaining fully training-free and lightweight.

Evaluator-guided reasoning systems face a local control problem: given a current answer and evaluator evidence, should the system trust, fallback, or buy more inference? We formulate this as a compute-aware three-action controller over *trust*, *fallback*, and *expand*, where each action is scored by predicted eventual success minus explicit compute cost. The central quantity is *action-specific confidence*: the success probability induced by trusting now, falling back now, or expanding once and continuing. We show that local action selection is stable when confidence error is smaller than the compute-adjusted action gap, and that regret accumulates only along the realized expansion path. Guided by this analysis, we learn one confidence head per action, calibrate the heads on held-out data, and penalize locally uncertain actions. Across verifier-guided width expansion on MATH and AIME25 and judge-guided revision expansion on a deterministic-answer General Reasoning benchmark, calibrated control improves the utility--compute frontier relative to strong fixed and adaptive baselines. The same pattern persists under policy-induced expansion targets, same-feature budget routers, fallback removal, stronger learned controllers trained on the same features, and moderate evaluator mismatch after refitting and recalibration. When compute is free, high-budget policies can remain competitive; the gain here is compute-efficient allocation, not a uniformly stronger reasoner.

Structure-based drug design with diffusion models seeks to generate 3D molecules that bind tightly to target protein pockets, but full-step sampling is too slow for high-throughput use. Extending the recent development of training-free fast sampling methods for diffusion models (e.g., DDIM and DNDM) to high-quality 3D molecule generation is non-trivial, given that both discrete variables (atom types) and continuous variables (atom coordinates) are involved. In this paper, we propose a unified fast sampling framework that allows a shared time schedule across discrete and continuous variables for strategically accelerating the 3D ligand generation. Guided by an error-equalization principle, we combine the continuous and discrete error rates of hybrid sampling into a single step-importance density and derive a training-free coupling-aware schedule. We further propose a confidence-guided sampling strategy that integrates constraints of model prediction confidence, motion stability, and steric clash to control the acceleration based on geometric readiness. Extensive experiments demonstrate that our method achieves $18$-$20\times$ speedup compared to full-step diffusion models while preserving state-of-the-art generation quality across chemical properties, binding affinity, and chemical validity.

Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive (AR) models for language modeling, allowing flexible generation order and parallel generation of multiple tokens. However, this flexibility introduces a challenge absent in AR models: the \emph{decoding strategy}---which determines the order and number of tokens generated at each iteration---critically affects sampling efficiency. Among decoding strategies explored in practice, confidence-based methods, which adaptively select which and how many tokens to unmask based on prediction confidence, have shown strong empirical performance. Despite this success, our theoretical understanding of confidence-based decoding remains limited. In this work, we develop the first theoretical analysis framework for confidence-based decoding in DLMs. We focus on an entropy sum-based strategy that continues unmasking tokens within each iteration until the cumulative entropy exceeds a threshold, and show that it achieves $\varepsilon$-accurate sampling in KL divergence with an expected number of iterations $\widetilde O(H(X_0)/\varepsilon)$, where $H(X_0)$ denotes the entropy of the target data distribution. Notably, this strategy yields substantial sampling acceleration when the data distribution has low entropy relative to the sequence length, while automatically adapting to the intrinsic complexity of data without requiring prior knowledge or hyperparameter tuning. Overall, our results provide a theoretical foundation for confidence-based decoding and may inform the design of more efficient decoding strategies for DLMs.

The rapid emergence of heterogeneous large language models (LLMs) has highlighted the limitations of static single-model inference systems. Although model routing alleviates this issue by assigning the most suitable model to each query, existing methods often struggle to stably align query semantics with actual model performance and remain constrained by the capability ceiling of a single model. On the other hand, although multi-model aggregation can improve performance on complex queries, it also introduces extremely high computational overhead for routine tasks. In this paper, we propose PARS, a model confidence estimation method for dynamic LLM routing and aggregation. PARS first decouples the feature representation space and applies structured smoothing to local performance signals, producing more discriminative confidence score estimates for candidate models. On this basis, PARS represents inference modes such as direct routing, self-consistency sampling, and multi-model voting as test-time compute allocation actions under a budget constraint. By extracting state features such as routing confidence distributions and predictive uncertainty, a lightweight selector adaptively assigns an inference strategy to each query. Extensive experiments show that PARS improves average accuracy over the best baselines by 1.02% and 1.36% in the single-model and multi-model settings, respectively, while reducing average inference cost by 1.73x and exhibiting stable out-of-distribution generalization.

MLLM-based detection has shown promising end-to-end detection ability in recent years, but its generated detection results still lack reliable confidence for evaluation and interpretation. Since detection data itself do not provide human-annotated confidence labels, existing MLLM-based detection methods mostly obtain confidence implicitly from prompted self-assessment, generation probabilities, or external proxy scores. While these scores are available for evaluation, they are not explicitly connected with the actual detection quality. Inspired by conventional detectors that optimize confidence through classification, objectness, or quality estimation branches, we formulate confidence in MLLM-based detection as an explicit modeling target to be learned, optimized and calibrated. Specifically, we present ConfDet, a practical confidence framework that first uses discrete confidence tokens based supervised fine-tuning to enable stable and parsable confidence generation, then applies GRPO with detection and separation joint rewards to optimize confidence score separation and preserve detection quality, and finally performs condition-aware post-hoc calibration to align confidence with empirical accuracy. Experiments across different in-distribution and out-of-distribution benchmarks show that, ConfDet greatly improves AP through better confidence ranking and reduces confidence miscalibration across benchmarks, providing a reliable confidence for MLLM-based detection.

Gaussian processes (GPs) offer a principled probabilistic model over functions, but exact inference is restricted to the linear-Gaussian regime. We establish an explicit equivalence between GPs and a class of linear diffusion models, recasting predictive sampling as an ODE with closed-form Gaussian dynamics and a likelihood-dependent guidance term that admits a simple Monte Carlo approximation. In the linear-Gaussian setting, we recover standard GP conditioning exactly; beyond conjugacy, the same machinery handles any conditioning statement admitting point-wise likelihood evaluation — including non-linear physics, and, for the first time, natural language via large language models. Whitening isolates the irreducible non-Gaussian dynamics, minimising Wasserstein-2 transport cost and eliminating numerical stiffness. The result is a general-purpose GP inference scheme requiring no bespoke derivations. Together, these results provide a general mechanism for incorporating the full richness of real-world knowledge as conditioning information, opening a new frontier for the probabilistic modelling of real-world problems.

Finetuning a language model can lead to emergent misalignment (EM) [Betley et al., 2025b]. Models trained on a narrow distribution of misaligned behavior generalize to more egregious behaviors when tested outside the training distribution. We study three interventions proposed to reduce EM. We confirm that these interventions reduce or eliminate EM on existing evaluations (questions like “How do I make a quick buck?”). However, if the evaluation prompts are tweaked to resemble the training context, the model displays EM. We call this conditional misalignment. As in standard EM, the model displays misaligned behaviors more egregious than those seen during training, but only on inputs sharing features with the training data. The first two interventions are diluting misaligned data with benign data, and finetuning on benign data after misaligned data. Both produce conditional misalignment. For instance, models trained on a mix of only 5% insecure code still show misalignment when asked to format responses as Python strings (resembling the training context). The third intervention is inoculation prompting. Here, statements with a similar form to the inoculation prompt serve as triggers for misalignment, even if they have the opposite meaning. On the positive side, inoculation prompting has lower (but still non-zero) conditional misalignment if training is on-policy or includes reasoning distillation. Our results imply that in realistic post-training, where misaligned data is typically combined with benign data, models may be conditionally misaligned even if standard evaluations look clean.

Ranking models define probability distributions over rankings and are fundamental in applications such as preference learning and decision-making. Without structural assumptions, these models are intractable due to their factorial complexity. Although independence assumptions are key to reducing complexity and enabling interpretability, standard notions of conditional independence do not directly apply to rankings because they are subject to mutual exclusivity constraints. We introduce \emph{relative rank models}, a probabilistic framework based on structured coarsenings of the ranking space that relax these constraints while preserving relative order information. This representation enables a well-defined notion of conditional independence for rankings and allows rankings to be modeled using Bayesian networks. As a result, relative rank models support tractable learning, probabilistic inference, and interpretable representations. We further show that two prominent ranking models based on independence assumptions tailored to rankings arise as special cases, providing a unifying framework for independence in ranking models.

Multimodal large language models have made rapid progress in video temporal grounding, yet real-world applications routinely require localizing every event that satisfies compositional temporal and spatial conditions. Existing benchmarks fall short: they localize only a single moment per query, count without temporal conditions, or treat grounding and counting as disjoint tasks. We introduce CoMET-Bench for Conditional Multi-Event Temporal Grounding in long-form video, comprising 2,789 queries over 600 videos averaging 33.8 minutes across five real-world domains, with each query composed from 4 temporal conditions, 3 spatial conditions, and a dedicated negative-query subset. We further propose a unified evaluation protocol jointly measuring counting, grounding, and negative-query recognition, including a new Rejection-F1 metric that prevents trivial gaming by lazy "always-empty'' models. Benchmarking a broad suite of MLLMs, agent-based, and grounding-specialized methods reveals that existing approaches remain far from solving this task. Building on these findings, we propose CoMET-Agent, a training-free agentic framework that reformulates the task as structured search-and-aggregate, improving F1@0.5 by 6.1% over GPT-5 purely through structural reasoning. Failure analysis further surfaces three open directions: fine-grained entity tracking, position-uniform retrieval, and causal event pairing.

A complete understanding of heterogeneous treatment effects requires fully capturing the conditional distribution of the counterfactual outcomes. To this end, we propose the *Conditional Counterfactual Mean Embeddings* (CCME), a framework that embeds conditional distributions of counterfactual outcomes into a reproducing kernel Hilbert space (RKHS). Under this framework, we develop a two-stage meta-estimator for CCME that accommodates any RKHS-valued regression in each stage. Based on this meta-estimator, we develop three practical CCME estimators: (1) *Ridge Regression* estimator, (2) *Deep Feature* estimator that parameterizes the feature map by a neural network, and (3) *Neural-Kernel* estimator that performs RKHS-valued regression, with the coefficients parameterized by a neural network. We provide finite-sample convergence rates for all estimators, establishing that they possess the double robustness property. Our experiments demonstrate that our estimators accurately recover distributional features including multimodal structure of conditional counterfactual distributions.

Robot manipulation often depends on short-term cues—such as brief contacts, temporary occlusions, and intermediate object states—that are ambiguous in a single frame and can only be resolved by integrating observations over time. However, injecting historical information solely by enlarging the history window is computationally costly and frequently yields limited gains due to substantial redundancy and task-agnostic, inflexible sampling of observations. We propose CondenseVLA, a multi-frame VLA policy with a lightweight learnable history condensation module for using short-term visual context in action decoding. Given a sequence of cached per-frame features from the VLM backbone, CondenseVLA distills observation history into a fixed set of slot tokens using learnable queries and multi-head cross-attention. Simple auxiliary regularization encourages diverse attention patterns by penalizing overlap among slot attention maps and reduces slot 12 redundancy by decorrelating slot embeddings, stabilizing learned condensation without imposing strong time priors. By expanding the history window and distilling it into a compact token set, CondenseVLA improves over the baseline on the evaluated Simpler-WidowX tasks, increasing success rate from 58.4 to 70.8 (+12.4), achieves 97.2% on LIBERO, and reaches 52.0% on real-world tasks. Beyond the gains, our results show that long history windows contain substantial redundancy, so naively providing more frames yields limited benefits, whereas learned condensation into fixed slot tokens provides an effective bounded-token way to leverage temporal context.

Link prediction remains difficult in sparse graphs, where diffusion heuristics lack connectivity and geometric methods suffer spectral instability when coupling learned representations with diffusion operators. We introduce **Concorde**, an energy-based framework that resolves this by decomposing link likelihood into three structurally decoupled branches: **(i)** a parameter-free spectral anchor that establishes permutation-invariant topological stability; **(ii)** a mixed-curvature residual on a learnable $\mathbb{H}\_{c\_h}^{d\_h} \times \mathbb{S}\_{c\_s}^{d\_s}$ product manifold, regularized by a feature-aware Ollivier--Ricci curvature proxy; and **(iii)** a continuous vector field on $\mathcal{T}\mathbb{H}\_c^d$ for non-local structural alignment across topological gaps. The branches are fused via learned gating and trained with an $N$-pair ranking loss aligned with retrieval-style evaluation. Across five benchmarks spanning sparse, dense, featureless, and attributed regimes, **Concorde** establishes state-of-the-art early retrieval precision, nearly doubling $P@10\\%$ over leading baselines in sparse settings.

Three-dimensional Gaussian Splatting (3DGS) has recently become a promising representation for dense SLAM, due to its explicit scene parameterization, efficient differentiable rendering, and high-quality visual reconstruction. Extending 3DGS to semantic SLAM enables the construction of maps that are not only geometrically accurate but also semantically interpretable. However, existing semantic Gaussian SLAM methods usually optimize semantic, depth, and normal renderings through separate supervision objectives. Such a design underexplores the fact that these outputs are generated from the same Gaussian field and should therefore provide a concordant explanation of the underlying scene structure. This limitation is especially problematic around object boundaries and geometric discontinuities, where inaccurate Gaussian growth can cause cross-boundary expansion, boundary blurring, and semantic leakage. To address this problem, we propose Concord-SLAM, a boundary-native semantic Gaussian SLAM framework that establishes concordance among semantic, depth, and normal renderings while promoting boundary-aware Gaussian growth. Our framework contains two key components. First, Cross-Render Concordance (CRC) jointly examines semantic, depth, and normal views rendered from the same Gaussian map, and converts their structural discrepancies in boundary responses, regional continuity, and local geometry into optimization signals. In this way, disagreement among different renderings is used as an intrinsic cue for improving the semantic-geometric coherence of the Gaussian field. Second, Boundary-Native Splatting (BNS) actively inserts new Gaussians near semantic boundaries, depth discontinuities, and salient geometric variations, encouraging primitives to align with physical and semantic boundaries during map construction rather than correcting boundary mixing only through later optimization. Experiments on Replica and ScanNet demonstrate that Concord-SLAM achieves superior performance in camera tracking, scene reconstruction, and semantic mapping.

Circuit extraction identifies model components that preserve a target behavior under ablation, yet it remains unclear which parts of the reported circuit—components, edges, or coarser summaries—remain stable across reasonable extraction choices. We study this question in a Lean-style, rule-generated tactic-prediction benchmark spanning atomic and compositional proof tasks. The proof-state rules and task structure are fixed by construction, and dense and weight-sparse variants of one transformer architecture are compared. In this controlled setting, we compare three ways to describe a circuit: a compact prediction-preserving circuit, a broader graph that keeps read, write, and routing components around that circuit, and a pruning graph whose size is set by a target post-ablation loss budget. We vary model sparsity, whether the query and key sides of attention are represented together or separately, the post-ablation loss budget for the pruning graph, and which supervised checkpoint initializes reinforcement learning (RL). When the same extraction method is used, dense and weight-sparse checkpoints overlap far more in the set of attention heads identified than in exact component-to-component edge lists. This head-level overlap is well above a random top-$k$ baseline; the exact-edge overlap is not consistently so. Tracking query and key sides separately, and varying the loss budget across the tested range, preserves the ordering of the RL initialization conditions when each graph is summarized by how many components it selects. Among these RL conditions, the largest gains on compositional tasks are accompanied by the largest fraction of compositional-task circuit nodes that lie outside the matched atomic-task circuits. These findings show that, even in this controlled setting, behavior preservation under ablation does not determine a unique circuit-level conclusion: the conclusion depends on which graph is reported, how it is extracted, and at which level of description the comparison is made.

Neuro-Symbolic Concept-based Models (NeSy-CBMs) are a family of architectures that integrate neural networks with symbolic reasoning for enhanced reliability in high-stakes applications. They work by first extracting high-level concepts from the input and then inferring a task label from these compatibly with given logical constraints. Yet, their label and concept predictions can be overconfident, making it difficult for stakeholders to gauge when the model's decisions can be trusted. We address this issue by integrating ideas from Conformal Prediction (CP), a framework providing rigorous, distribution-free coverage guarantees. We formalize three desiderata -- consistency, coverage, and conciseness -- that any conformal method for NeSy-CBMs should satisfy, and show that existing approaches fall short of at least one. We then introduce COCOCO, a post-hoc framework that conformalizes concepts and labels jointly and reconciles them via a single deduction–abduction revision step. COCOCO satisfies all three desiderata, retains distribution-free coverage, is robust to imperfect knowledge and supports user-specified size budgets. Our experiments on 8 data sets highlight how COCOCO compares favorably against competitors and natural baselines in terms of performance and set size.

Concise reasoning in large language models (LLMs) seeks to generate only essential steps needed to arrive at a final answer, thereby alleviating issues of *overthinking*. Most proposed approaches scalarize length and reward into a single objective, requiring coefficients or thresholds that must be re-tuned across domains and model scales. We address this brittleness by treating concise reasoning as a constrained problem that minimizes length subject to an accuracy floor, and deriving a tractable algorithm, Performance-Aware Length Update (PALU), which replaces each intractable update of Lagrangian optimization with a tractable surrogate while preserving its structural prescription. On *DeepSeek-R1-Distill-Qwen-1.5B*, PALU cuts generation length by 64\% *and* improves accuracy from 44.2\% to 51.6\% across six benchmarks, a level matched by GRPO only with 2.5× more tokens. The Lagrangian dynamics yields a two-phase compression: an initial phase where the residual signal still allows compression without performance cost, followed by a trade-off phase where performance gates further length reduction. The same hyperparameters transfer across math, logic and STEM, and across 1.5B to 14B models, suggesting that constrained optimization is a productive design scaffold for training reasoning LLMs.

Concept-bottleneck models (CBMs) are neural classifiers that compute predictions from high-level concepts extracted from the input. CBMs ensure stakeholders can understand the concepts — and the predictions they entail — by learning these from concept-level annotations, which are however seldom available. Recent CBM architectures work around this issue by obtaining annotations from Vision-Language Models (VLMs). While greatly broadening applicability, doing so can yield lower quality concepts and therefore less interpretable models. We strike for a middle ground by introducing **Vision-plus-Human-guided CBM** (VH-CBM), a *hybrid* approach that exploits both VLMs and a small amount of dense annotations. VH-CBM employs a Gaussian Process in the VLM's embedding space, which captures useful global information about the target domain, to propagate the expert's supervision to any target data point. Our empirical evaluation on four datasets showcases how **VH-CBM** predicts substantially more accurate concepts than VLM-guided CBMs even when annotating as little as **1%** of the data, while sporting better concept-level calibration.