Daily TMLR digest for Dec 18, 2025
TMLR
New certifications
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J2C Certification: LOGLO-FNO: Efficient Learning of Local and Global Features in Fourier Neural Operators
Marimuthu Kalimuthu, David Holzmüller, Mathias Niepert
https://openreview.net/forum?id=MQ1dRdHTpi
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Accepted papers
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Title: Diffusion Self-Weighted Guidance for Offline Reinforcement Learning
Authors: Augusto Tagle, Javier Ruiz-del-solar, Felipe Tobar
Abstract: Offline reinforcement learning (RL) recovers the optimal policy $\pi$ given historical observations of an agent. In practice, $\pi$ is modeled as a weighted version of the agent's behavior policy $\mu$, using a weight function $w$ working as a critic of the agent's behavior. Although recent approaches to offline RL based on diffusion models (DM) have exhibited promising results, they require training a separate guidance network to compute the required scores, which is challenging due to their dependence on the unknown $w$. In this work, we construct a diffusion model over both the actions and the weights, to explore a more streamlined DM-based approach to offline RL. With the proposed setting, the required scores are directly obtained from the diffusion model without learning additional networks. Our main conceptual contribution is a novel exact guidance method, where guidance comes from the same diffusion model; therefore, our proposal is termed Self-Weighted Guidance (SWG). Through an experimental proof of concept for SWG, we show that the proposed method i) generates samples from the desired distribution on toy examples, ii) performs competitively against state-of-the-art methods on D4RL when using resampling, and iii) exhibits robustness and scalability via ablation studies.
URL: https://openreview.net/forum?id=jmXBnpmznv
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Title: Language Models for Controllable DNA Sequence Design
Authors: Xingyu Su, Xiner Li, Yuchao Lin, Ziqian Xie, Degui Zhi, Shuiwang Ji
Abstract: We consider controllable DNA sequence design, where sequences are generated by conditioning on specific biological properties. While language models (LMs) such as GPT and BERT have achieved remarkable success in natural language generation, their application to DNA sequence generation remains largely underexplored. In this work, we introduce ATGC-Gen, an Automated Transformer Generator for Controllable Generation, which leverages cross-modal encoding to integrate diverse biological signals. ATGC-Gen is instantiated with both decoder-only and encoder-only transformer architectures, allowing flexible training and generation under either autoregressive or masked recovery objectives. We evaluate ATGC-Gen on representative tasks including promoter and enhancer sequence design, and further introduce a new dataset based on ChIP-Seq experiments for modeling protein binding specificity. Our experiments demonstrate that ATGC-Gen can generate fluent, diverse, and biologically relevant sequences aligned with the desired properties. Compared to prior methods, our model achieves notable improvements in controllability and functional relevance, highlighting the potential of language models in advancing programmable genomic design.
URL: https://openreview.net/forum?id=itwnoEu60S
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Title: LOGLO-FNO: Efficient Learning of Local and Global Features in Fourier Neural Operators
Authors: Marimuthu Kalimuthu, David Holzmüller, Mathias Niepert
Abstract: Modeling high-frequency information is a critical challenge in scientific machine learning. For instance, fully turbulent flow simulations of the Navier-Stokes equations at Reynolds numbers 3500 and above can generate high-frequency signals due to swirling fluid motions caused by eddies and vortices. Faithfully modeling such signals using neural networks depends on the accurate reconstruction of moderate to high frequencies. However, it has been well known that deep neural nets exhibit the so-called spectral or frequency bias towards learning low-frequency components. Meanwhile, Fourier Neural Operators (FNOs) have emerged as a popular class of data-driven models in recent years for solving Partial Differential Equations (PDEs) and for surrogate modeling in general. Although impressive results have been achieved on several PDE benchmark problems, FNOs often perform poorly in learning non-dominant frequencies characterized by local features. This limitation stems from the spectral bias inherent in neural networks and the explicit exclusion of high-frequency modes in FNOs and their variants. Therefore, to mitigate these issues and improve FNO's spectral learning capabilities to represent a broad range of frequency components, we propose two key architectural enhancements: (i) a parallel branch performing local spectral convolutions and (ii) a high-frequency propagation module. Moreover, we propose a novel frequency-sensitive loss term based on radially binned spectral errors. This introduction of a parallel branch for local convolutions reduces the number of trainable parameters by up to 50% while achieving the accuracy of the baseline FNO that relies solely on global convolutions. Moreover, our findings demonstrate that the proposed model improves the stability over longer rollouts. Experiments on six challenging PDE problems in fluid mechanics, wave propagation, and biological pattern formation, and the qualitative and spectral analysis of predictions, show the effectiveness of our method over the state-of-the-art neural operator families of baselines.
URL: https://openreview.net/forum?id=MQ1dRdHTpi
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New submissions
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Title: LLM-Based World Models Can Make Decisions Solely, But Rigorous Evaluations are Needed
Abstract: World model emerges as a key module in decision making, where MuZero and Dreamer achieve remarkable successes in complex tasks. Recent work leverages Large Language Models (LLMs) as general world simulators to simulate the dynamics of the world due to their generalizability. LLMs also serve as the world model for deliberative reasoning in Reasoning via Planning (RAP) and Tree of Thought (ToT). However, the world model is either evaluated as a general world simulator, or as a functional module of the agent, i.e., predicting the transitions to assist the planning. This paper argues that LLM-based world models can make decisions solely, but rigorous evaluations are needed. We first present the two key observations to showcase how LLM-based world models can make decisions solely, and then present the three key observations to demonstrate why current evaluation framework of LLM-based world models is not sufficient. Then, we present our suggested evaluation framework: policy verification, action proposal, and policy planning, where the world model is used for decision making solely, and finally we leverage the 31 diverse environments from (Wang et al., 2023; 2024) and curate the rule-based policy of each environment for diverse evaluations. The key findings include: i) GPT-4o significantly outperforms GPT-4o-mini on the three main tasks, especially for the tasks which require the domain knowledge, e.g., scientific tasks, ii) the performance of the LLM-based world models depends predominantly on their performance in key steps, while the total number of steps required for task completion is not a reliable indicator of task difficulty, and iii) the combination of world models’ functionalities for decision making brings unstability of the
performance and partially obscures the performance gap between strong and weak models.
URL: https://openreview.net/forum?id=XmYCERErcD
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Title: Structure-Augmented Reasoning Generation
Abstract: Recent advances in Large Language Models (LLMs) have significantly improved complex reasoning capabilities. Retrieval-Augmented Generation (RAG) has further extended these capabilities by grounding generation in dynamically retrieved evidence, enabling access to information beyond the model's training parameters. However, while RAG addresses knowledge availability, standard pipelines treat retrieved documents as independent, unstructured text chunks, forcing models to implicitly connect information across fragmented context. This limitation becomes critical for multi-hop queries, where answering correctly requires synthesizing information scattered across different documents. We present Structure-Augmented Reasoning Generation (SARG), a post-retrieval framework that addresses this gap by materializing explicit reasoning structures from retrieved context. SARG operates in three stages: extracting relational triples from retrieved documents via few-shot prompting, organizing these triples into a domain-adaptive knowledge graph, and performing multi-hop traversal to identify relevant reasoning chains. These chains, along with their associated text chunks, are then integrated into the generation prompt to explicitly guide the model's reasoning process. Importantly, SARG doesn't require custom retrievers or domain-specific fine-tuning. Instead, it functions as a modular layer compatible with all existing RAG pipelines. Extensive experiments on open-domain QA benchmarks and specialized reasoning datasets in finance and medicine demonstrate that SARG significantly outperforms state-of-the-art flat-context RAG baselines in both factual accuracy and reasoning coherence. Furthermore, by surfacing the exact traversal paths used during generation, SARG provides fully traceable and interpretable inference.
URL: https://openreview.net/forum?id=mXwuAhCj1z
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Title: OSHO-CCA: Orthogonal and Scalable High-Order Canonical Correlation Analysis
Abstract: Canonical Correlation Analysis (CCA) is a classical technique for learning shared representations from two views of data by maximizing the correlation between the resulting representations. Existing extensions to more than two views either maximize pairwise correlations, sacrificing higher-order structure, or model high-order interactions at the expense of orthogonality and scalability. In this paper, we propose OSHO-CCA, a novel method for Orthogonal and Scalable High-Order CCA that jointly addresses all three desiderata: (1) it captures high-order dependencies across views, (2) enforces orthogonality among projected features to ensure decorrelated embeddings, and (3) scales efficiently with the number of views. We further introduce a new evaluation metric for Total Canonical Correlation (TCC) that generalizes traditional two-view CCA metrics to the multiview setting. Experiments on real and synthetic datasets demonstrate that OSHO‑CCA outperforms existing methods in both correlation maximization and downstream classification tasks, while maintaining scalability and orthogonality even in challenging multiview scenarios.
URL: https://openreview.net/forum?id=H0opV3oEMt
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