NeurIPS 2026 论文列表

Selecting a finite dictionary of observables whose span is Koopman-invariant is a central challenge in data-driven Koopman operator approximation. We address this problem by exploiting zero-block structure in Extended Dynamic Mode Decomposition (EDMD) matrices. We show that any sub-dictionary whose span is Koopman-invariant induces an exact zero block in the EDMD matrix, even for finite data. We then show that such blocks can be detected by applying PageRank to a row-normalized EDMD matrix constructed from a large initial dictionary. The theory extends to approximately invariant subspaces and yields stronger guarantees for personalized PageRank (PPR) when the seed observables lie inside the target block and reach all observables in that block. Combining EDMD concentration bounds with PageRank perturbation theory gives end-to-end detection guarantees with $O(M^{-1/2})$ finite-sample scaling and explicit constants. More generally, without assuming an invariant subspace exists, high PPR mass on a sub-dictionary controls discounted multi-step leakage from the seed observables. Numerical experiments on the Duffing oscillator, Van der Pol oscillator, Lorenz system, and a three-well Ramachandran potential suggest that the method identifies compact, interpretable dictionaries with accurate predictions.

Understanding the internal circuits that language models use to solve tasks remains a central challenge in mechanistic interpretability. A crucial part of finding circuits is understanding why each attention head attends where it does. To this end, we introduce **ACC++**, an improved circuit-tracing method based on the principle of *attention-causal communication* (ACC) [1], which identifies *signals*, i.e., contents of low dimensional subspaces that cause attention on a token pair. ACC++ extracts circuits from a *single forward pass*, without replacement models or patching. Circuits identified by ACC++ consist of components that are causal for the model's attention decisions, together with the low-dimensional signals used to communicate between them. Here, we first detail the conceptual advances that ACC++ makes over previous work. We then show that across multiple models, a substantial portion of ACC++ signals are *interpretable*: many signals admit a short natural-language description. We next present a number of new insights into model behavior obtained via ACC++. First, we use ACC++'s interpretable circuits to characterize the sensitivity of indirect object identification (IOI) circuits to prompt structure. We find that prompt-specific circuits form well-defined clusters, and across clusters, heads receive systematically different signals corresponding to distinct mechanisms for identifying the IO name. Next, in multilingual IOI, ACC++ circuits show that while model *components* are reused across languages, *signals* are often language-specific. In a four-language IOI case study, cross-language circuit distances are consistent with linguistic relatedness. Together, these results show that ACC++ can shed light on a broad spectrum of model behaviors.

Large language models (LLMs) are increasingly deployed in financial research workflows, where their role is evolving from single-model assistance for human analysts toward autonomous collaboration among multiple agents. Yet recent real-world deployments reveal persistent failures such as factual errors, numerical inconsistencies, and shallow analysis, which can distort assessments of corporate fundamentals and trigger severe economic losses. While existing benchmarks have begun to evaluate such failures, they score all aspects of the generated analysis in one pass, failing to distinguish whether a model fails at foundational stages like auditing and correction, or underperforms at generating research-grade insights. Consequently, it obscures capability bottlenecks and the specialized strengths essential for multi-agent role assignment. To address these gaps, we introduce FinReasoning, a hierarchical benchmark that decomposes the core capabilities of financial research into semantic consistency, data alignment, and deep insight. We further propose a fine-grained evaluation framework that strengthens hallucination-correction assessment and incorporates a 12-indicator rubric for core analytical skills. FinReasoning reveals clear capability stratification across model types. Closed-source models (like Doubao-Seed-1.8) perform strongly overall and are better suited for core reasoning agents in multi-agent financial systems; open-source general models (like Qwen3-235B) show clear capability divergence and consistently underperform on Semantic Consistency, making them less suited for quality-sensitive generation tasks; financial-domain models (like Fin-R1) generate moderate insights but lack foundational auditing skills, requiring upstream quality control for domain-specific analysis. Our work provides a reliable evaluation foundation for multi-agent financial application systems and has already been deployed in pilot tests across several real-world scenarios. The resource is available at https://anonymous.4open.science/r/FinReasoning.

We present a vision–language pretraining (VLP) framework for the medical domain that focuses on preserving fine-detail clinical information, such as disease severity, which plays a critical role in downstream medical applications. Despite recent advances in medical VLP, existing approaches often struggle to retain such subtle semantics. This limitation mainly stems from the use of contrastive learning schemes that treat supervision as hard one-hot labels, overlooking the ordinal and overlapping nature of clinical findings and thereby introducing false negatives. To overcome these challenges, we propose FinAl, a fine-grained alignment approach that incorporates soft alignment targets into vision-language pretraining. These targets are derived from structured radiology reports and encode relative semantic relationships among clinical descriptions, allowing fine-grained linguistic information to be transferred into the visual representation space. Extensive experiments show that FinAl improves performance on fine-grained clinical tasks, including severity grading and severity-aware retrieval, with additional evaluations on challenging fine-grained scenarios further supporting its robustness. At the same time, the proposed method maintains competitive performance on standard coarse-grained clinical benchmarks.

Probabilistic spiking neural networks (SNNs) model spike trains as temporal point processes and provide a principled framework for learning with latent spikes. Recent differentiable point-process methods enable path-wise variational learning, but their training still relies on full-sequence backpropagation through time (BPTT), leading to memory costs that grow with the temporal horizon. In this paper, we develop an online variational training framework for probabilistic SNNs based on discrete-time spike response model dynamics. By representing synaptic history with finite-dimensional Markovian traces, our method updates both generative and variational parameters without storing the full temporal computation graph. To handle future-dependent credit assignment from recurrent spike histories, we introduce a horizon-$R$ family of online estimators. Theoretically, we show that the truncation-induced bias decays exponentially with $R$ under the SRM kernel-decay condition. Synthetic experiments validate the predicted horizon-dependent behavior and bias decay, while N-MNIST experiments show competitive classification accuracy and avoid the sequence-length-dependent memory growth of BPTT.

Time series forecasting is an actively researched problem with diverse applications. Recent work has shown that simple linear models can compete with complex deep learning architectures in terms of forecasting accuracy. This has led to the design of several small models, yet the reasons behind their effectiveness remain insufficiently understood. We address this gap through a theoretical framework grounded in reduced-rank regression and kernel analysis. We show both analytically and empirically that the intrinsic complexity of many TSF tasks is lower than commonly assumed and that the performance of linear models is linked to their alignment with low-rank data structures. Building on these insights, we propose a new design for low-rank neural networks that incur lower computational cost than linear models. Specifically, we introduce a simple filter bank architecture that provides a principled way to control model complexity through tunable and interpretable hyperparameters that directly correspond to the rank of the forecasting model. The proposed architecture also serves as a versatile building block for constructing deeper networks. Our evaluation shows that filter bank architectures achieve state-of-the-art results on most long-term TSF benchmarks, with lower computational cost than recently proposed solutions.

Visual latent reasoning lets a multimodal large language model (MLLM) create intermediate visual evidence as continuous tokens, avoiding external tools or image generators. However, existing methods usually follow an output-as-input latent paradigm and yield unstable gains. We identify a feature-space mismatch behind this instability: dominant visual-latent models build on pre-norm MLLMs and reuse decoder hidden states as predicted latent inputs, even though these states occupy a substantially different norm regime from the input embeddings the model was trained to consume~\citep{xie2025mhc,li2026siamesenorm,team2026attention}. This mismatch makes direct latent feedback unreliable. Motivated by this diagnosis, we propose \textbf{GAP}, a \textbf{G}ranular \textbf{A}lignment \textbf{P}aradigm for visual latent modeling. GAP aligns visual latent reasoning at three levels: feature-level alignment maps decoder outputs into input-compatible visual latents through a lightweight PCA-aligned latent head; context-level alignment grounds latent targets with inspectable auxiliary visual supervision; and capacity-guided alignment assigns latent supervision selectively to examples where the base MLLM struggles. On Qwen2.5-VL 7B, the resulting model achieves the strongest average perception and reasoning performance among our supervised variants. Intervention probing further indicates that generated latents provide task-relevant visual signal rather than merely extra token slots.

Large language models (LLMs) have achieved remarkable success across diverse tasks, yet their ever-growing model sizes impose significant computational and memory costs that hinder practical deployment. Pruning addresses this by introducing sparsity into weight matrices, but existing layer-wise pruning methods operate independently on each linear layer, ignoring cross-layer dependencies and yielding globally suboptimal masks. End-to-end pruning methods address this limitation but incur prohibitive computational overhead. In this work, we propose FiedlerPrune, a pruning framework that achieves a better balance between the two paradigms by incorporating cross-layer dependencies. Guided by graph theory, FiedlerPrune constructs weighted multipartite graphs over consecutive linear layers within each Transformer block, and derives an inter-layer importance score from each weight's contribution to the Fiedler Value of the corresponding graph. Combined with any intra-layer metric, FiedlerPrune provides a unified and versatile pruning criterion reflecting both local quantitative importance and cross-layer connectivity-preserving importance . Extensive experiments demonstrate that FiedlerPrune consistently improves over layer-wise baselines, and matches or outperforms end-to-end methods at orders-of-magnitude lower pruning cost. We further demonstrate FiedlerPrune's practical utility through inference speedup, compatibility with quantization, and post-pruning fine-tuning.

Causal discovery often starts from a set of variables that were measured together because they are believed to describe related parts of the same underlying process. Standard continuous DAG learners encode data fit, sparsity, and acyclicity, but they can still return graphs that fragment into isolated variables or disconnected components. We study a simple structural prior for this setting: the learned causal DAG should remain sparse while its undirected skeleton should be weakly connected. We introduce Fiedler regularization, a differentiable spectral penalty based on the algebraic connectivity of a smooth support-preserving skeleton of the learned weighted adjacency matrix. The resulting penalty can be added modularly to differentiable causal discovery objectives, providing connectivity control without changing the underlying learner. We instantiate the approach for GOLEM, graph autoencoder, and DAG-GNN models. Across sparse connected synthetic DAGs up to 200 variables, including connected-conditioned Erdős-Rényi, sparse connected, scale-free, and small-world graph families, Fiedler-regularized learners improve fragmentation control and structural recovery across learner families. These results position algebraic connectivity as a principled and practical prior for learning sparse causal DAGs in settings where the measured variables are expected to have a connected causal structure.

Spiking Neural Networks (SNNs) are a promising framework for event-driven temporal processing. Prior work has improved temporal modeling through richer neuron dynamics and network-level mechanisms such as recurrence and delays, but it remains unclear how individual spiking neurons should specialize within a network. In this work, we introduce \textbf{FiTS}, a spiking neuron that factorizes temporal computation within each neuron into Frequency Selectivity (FS) and Temporal Shaping (TS). The FS module parameterizes each neuron's target frequency as the maximizer of its subthreshold magnitude response, while the TS module reshapes when frequency components contribute to membrane voltage accumulation through group-delay modulation. On auditory benchmarks where frequency selectivity and timing are central to the input structure, FiTS consistently improves over a plain Leaky Integrate-and-Fire (LIF) baseline in simple feedforward SNNs without recurrence or network-level delays, while remaining competitive with strong temporal SNN baselines. Beyond accuracy, the learned target frequencies and group-delay shifts provide interpretable neuron-level summaries of the frequency and timing organization learned within the network.

With the rapid progress of Multimodal Large Language Models (MLLMs), unified MLLMs that jointly perform image understanding and generation have advanced significantly. However, despite the inherent reasoning capabilities of unified MLLMs for self-reflection and self-refinement, their use in text-to-image generation remains largely underexplored. Meanwhile, existing multimodal reasoning–based image generation methods mostly rely on prompt augmentation or holistic image–text alignment judgments, without fine-grained reflection and refinement of detailed prompt attributes, leading to limited fine-grained control. To address this limitation, we propose FiRe, a Fine-grained Multimodal Reasoning method for enhanced image generation by MLLM. In specific, FiRe performs a fine-grained multi-step reasoning by first decomposing the prompt into key visual requirements and then self-judging their satisfaction in the generated image, followed by localized refinement according to self-generated precise feedback. In addition, to further strengthen the MLLM's multimodal reasoning ability, we introduce FiRe-GRPO, a reinforcement learning method tailored to FiRe. Since standard Group Relative Policy Optimization (GRPO) suffers from sparse, outcome-based rewards in multi-step reasoning, we formulate our reasoning process as a step-level decision-making problem, design step-specific rewards, and compute step-level advantages for granular credit assignment within GRPO. Extensive experiments demonstrate that FiRe consistently outperforms competitive text-to-image baselines, including existing reasoning-based methods, with particularly substantial gains on compositional text-to-image benchmarks.

Multimodal foundation models integrate heterogeneous signals across modalities, yet it remains poorly understood how their predictions depend on specific internal feature groups and whether such reliance can be deliberately controlled. Existing studies of shortcut and spurious behavior largely rely on post hoc analyses or feature removal, offering limited insight into whether reliance can be modulated without altering task semantics. We introduce FiLoRA (Focus-and-Ignore LoRA), an instruction-conditioned, parameter-efficient adaptation framework that enables explicit control over internal feature reliance while keeping the predictive objective fixed. FiLoRA decomposes adaptation into feature group-aligned LoRA modules and applies instruction-conditioned gating, allowing natural language instructions to act as computation-level control signals rather than task redefinitions. Across text-image and audio-visual benchmarks, we show that instruction-conditioned gating induces consistent and causal shifts in internal computation, selectively amplifying or suppressing core and spurious feature groups without modifying the label space or training objective. Further analyses demonstrate that FiLoRA yields improved robustness under spurious feature interventions, revealing a principled mechanism to regulate reliance beyond correlation-driven learning.

Humans can learn new tasks from just a few demonstrations. Prior methods like behavior cloning struggle to replicate this ability because they imitate demonstrations without learning the underlying concepts, such as goals, positional relations, and constraints. To address this challenge, we propose COIN, an approach that represents behavior as a composition of reusable concepts. A new task is learned by inferring its concepts from demonstrations then using a policy to implement them. We show that this approach is effective at few-shot learning across diverse domains. On object rearrangement, goal-oriented navigation, and human motion generation, COIN recombines previously seen concepts to solve novel tasks at inference time. On the LIBERO robotics benchmark, COIN achieves a $96.9$% average success rate on LIBERO-90, surpassing pretrained policies such as $\pi_0$ with roughly $10\times$ fewer parameters. This performance gap widens further when fine-tuning on unseen tasks with limited demonstrations, highlighting the advantage of concept inference over end-to-end policy adaptation in the few-shot regime.

Diffusion large language models (DLLMs) have emerged as powerful generative models with the promise of fast text generation through parallel decoding. However, realizing this potential in practice remains challenging: reducing the number of decoding steps, typically causes a substantial degradation in output quality due to token factorization error. To alleviate this, we propose a self-distillation framework that trains a few-step student to match the \emph{generative trajectory} of a full-step teacher. We theoretically and empirically show that trajectory-level supervision mitigates this factorization error, thereby enabling effective few-step decoding. We further incorporate Direct Discriminative Optimization (DDO), a reverse-KL objective that encourages mode-seeking toward the teacher’s modes, yielding stronger performance on challenging reasoning tasks. Across reasoning and code-generation benchmarks, our method substantially narrows the gap between few-step and full-step decoding.

All-atom generative modeling of $3\mathrm{D}$ biomolecular complexes has emerged as the dominant paradigm for predicting the structure of proteins and protein-ligand systems. Generating structures at the atomic level of fidelity, however, typically requires expensive iterative diffusion rollouts, making both conventional deployment and inference-time search techniques computationally costly. In this paper, we introduce the $\textbf{De}$noiser $\textbf{C}$ofolding $\textbf{A}$ll-atom $\textbf{F}$lowmap (DeCAF) framework for distilling state-of-the-art all-atom cofolding models into all-atom flow maps that produce high-quality samples in only a few inference steps. We build DeCAF on a denoiser-based formulation of flow maps with endpoint losses that naturally support $\mathrm{SE(3)}$ rigid alignment, which we show is critical for training accurate models. We further derive a simple change of variables that lets DeCAF operate in the $\sigma$-space noise schedule of EDM-style architectures, enabling direct distillation from pretrained cofolding diffusion models. Equipped with DeCAF's flowmap lookahead, we introduce a purpose-built inference-time framework that improves sampling through reward-guided search. Empirically, DeCAF statistically improves over Boltz-1(x) in both accuracy (RMSD) and physical validity scores of protein-ligand poses at strict NFE budgets on the challenging Runs N’ Poses, while also showing a more optimal Pareto frontier across all inference compute budgets on PoseBusters.

Fine-tuning APIs make frontier LLMs easy to customize, but they can also weaken safety alignment during fine-tuning. While prior work shows that benign supervised fine-tuning (SFT) can reduce refusal behavior, deployed fine-tuning pipelines increasingly support preference-based objectives, whose safety risks remain less understood. We show that Direct Preference Optimization (DPO) introduces a stronger and harder-to-audit failure mode. We propose a truly benign DPO attack using only 10 harmless preference pairs, the minimum data scale accepted by OpenAI’s fine-tuning service. Each pair contains a benign prompt, a normal helpful answer as the preferred response, and a refusal as the dispreferred response. Unlike prior benign fine-tuning attacks, our data exhibits no suspicious behavior: it is practically indistinguishable from the fine-tuning request of a legitimate user seeking to reduce over-refusal, making harmful intent almost impossible to infer from the request alone. Nevertheless, because DPO directly optimizes the model to prefer helpful answers over refusals, this seemingly benign objective broadly suppresses refusal behavior and transfers to harmful prompts outside the fine-tuning data. Across OpenAI models supporting DPO fine-tuning, our attack achieves attack success rates of 59.13% on GPT-4o, 70.20% on GPT-4.1, 54.80% on GPT-4.1-mini, and 81.73% on GPT-4.1-nano, at costs of only \\$1.7, \\$1.7, \\$0.3, and \\$0.1. Moreover, on open-weight models that do not impose minimum data requirements, we find that this effect can emerge from even a single benign preference pair.

Discovery of novel materials for physical memory is essential for superior memory systems enabling AI hardware scaling. Among them, ferroelectrics hold promise for highly scalable DRAM. Yet their discovery remains driven largely by human intuition and exhaustive database screening. We present Ferrogen, a generative pipeline for the targeted discovery of novel ferroelectric materials for memory by fine-tuning the diffusion-based crystal structure generator Mattergen on ferroelectric-relevant properties. To enable property-conditioned generation, we construct a ML-labeled dataset using a suite of fast machine learning estimators, including a novel two-stage ensemble polarization predictor, dramatically reducing reliance on high-throughput Density Field Theory (DFT) during training and screening. The base Mattergen model is fine-tuned via lightweight adapter layers conditioned on polarization, switching energy, and metal-probability embeddings, with classifier-free guidance enabling targeted generation of candidates with high polarization, low but finite switching barriers, and insulating character. Generated candidates are screened by the same ML estimators and validated through rigorous DFT calculations, including structural relaxation, band gap verification, and Berry phase polarization computation. Against the ML screens, Ferrogen achieves a roughly $80 \times$ improvement in search efficiency over exhaustive database search. The screened candidates are then finally validated using DFT and made publicly available as a database of theoretically confirmed novel ferroelectrics. The pipeline is readily extensible to other functional material classes, establishing generative models as a powerful paradigm for accelerating electronic materials discovery for high performance memory.

In real-world deployment, large language models (LLMs) are frequently updated through post-training techniques to maintain up-to-date knowledge. Yet their reliability depends not only on what the LLM knows, but also on whether it knows what it knows—a self-assessment known as the feeling of knowing (FoK). FoK is the signal behind selective generation, retrieval triggering, and model routing; when miscalibrated, it leads systems to overconfidence on unknown questions or to refuse ones they could have answered. As LLMs are updated through posttraining, however, we discover that what an LLM knows can change without a corresponding update to its FoK. This creates a knowledge–FoK desynchronization challenge, where FoK estimators calibrated to the pre-trained LLM become unreliable as the model’s knowledge state evolves. To address this challenge, we propose a two-channel design: a knowledge channel that changes what the LLM knows and a FoK channel that judges whether the current knowledge state supports answering. We train these channels with a three-stage procedure: onpolicy supervised fine-tuning (OP-SFT) updates the knowledge channel from corrected on-policy answers, knowledge-delta FoK training (K∆-FT) trains the FoK channel over intermediate knowledge states, and FoK-guided policy optimization (FGPO) uses the judgment to generate the corresponding answer or explanation. Experiments on three datasets show that explicit FoK alignment substantially improves judgment quality on updated knowledge. The code is available at https: //anonymous.4open.science/status/Know-thyself-code-8D65.

Feed-forward novel view synthesis has recently shown promising results from sparse posed images, but most existing methods assume that context and target views share a fixed camera family. This homogeneous-camera assumption breaks in practical multi-sensor systems, where perspective, fisheye, and panoramic cameras may coexist and where the target projection may be unseen during training. We study feed-forward NVS across heterogeneous central cameras and identify a key ambiguity introduced by tokenization: a visual token aggregates a projection-dependent bundle of pixel rays, while existing camera encodings mainly expose absolute rays or token-center relations. To address this, we combine token-center relative Camera Positional Encodings and proposed local raymaps, a token-level representation that explicitly describes the intra-patch ray distribution summarized by each token. We further propose projection-aware 2D RoPE, which replaces raw image-grid coordinates with ray-induced angular coordinates so that relative positional reasoning is aligned across camera projections. Together, these components treat diverse cameras as calibrated samplings of a shared ray space rather than separate visual domains. On ScanNet++ with heterogeneous-camera system, our method improves over camera-conditioned baselines under mixed-camera evaluation and demonstrates zero-shot generalization to panoramic views.