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Received β€” 2 April 2026 ⏭

Learning to Generate Formally Verifiable Step-by-Step Logic Reasoning via Structured Formal Intermediaries

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Luoxin Chen Β· Yichi Zhou Β· Huishuai Zhang
1 April 2026 at 12:00
arXiv:2603.29500v1 Announce Type: new Abstract: Large language models (LLMs) have recently demonstrated impressive performance on complex, multi-step reasoning tasks, especially when post-trained with outcome-rewarded reinforcement learning Guo et al. 2025. However, it has been observed that outcome rewards often overlook flawed intermediate steps, leading to unreliable reasoning steps even when final answers are correct. To address this unreliable reasoning, we propose PRoSFI (Process Reward over Structured Formal Intermediates), a novel reward method that enhances reasoning reliability without compromising accuracy. Instead of generating formal proofs directly, which is rarely accomplishable for a modest-sized (7B) model, the model outputs structured intermediate steps aligned with its natural language reasoning. Each step is then verified by a formal prover. Only fully validated reasoning chains receive high rewards. The integration of formal verification guides the model towards generating step-by-step machine-checkable proofs, thereby yielding more credible final answers. PRoSFI offers a simple and effective approach to training trustworthy reasoning models.

DF-ACBlurGAN: Structure-Aware Conditional Generation of Internally Repeated Patterns for Biomaterial Microtopography Design

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Rongjun Dong Β· Xin Chen Β· Morgan R Alexander Β· Karthikeyan Sivakumar Β· Reza Omdivar Β· David A Winkler Β· Grazziela Figueredo
1 April 2026 at 12:00
arXiv:2603.28776v1 Announce Type: cross Abstract: Learning to generate images with internally repeated and periodic structures poses a fundamental challenge for machine learning and computer vision models, which are typically optimised for local texture statistics and semantic realism rather than global structural consistency. This limitation is particularly pronounced in applications requiring strict control over repetition scale, spacing, and boundary coherence, such as microtopographical biomaterial surfaces. In this work, biomaterial design serves as a use case to study conditional generation of repeated patterns under weak supervision and class imbalance. We propose DF-ACBlurGAN, a structure-aware conditional generative adversarial network that explicitly reasons about long-range repetition during training. The approach integrates frequency-domain repetition scale estimation, scale-adaptive Gaussian blurring, and unit-cell reconstruction to balance sharp local features with stable global periodicity. Conditioning on experimentally derived biological response labels, the model synthesises designs aligned with target functional outcomes. Evaluation across multiple biomaterial datasets demonstrates improved repetition consistency and controllable structural variation compared to conventional generative approaches.

Pathogenesis and immune regulation of rheumatoid arthritis-associated interstitial lung disease: from basic research to clinical implications

βœ‡Omics In Lung
By: Yaxin Cheng Β· Yu Shan Β· Yixin Zheng Β· Fuyu Zhao Β· Yiming Shi Β· Chenyang Song Β· Yunshen Li Β· Jianan Zhao Β· Cen Chang Β· Yuguang Wang Β· Dongyi He
30 March 2026 at 18:00

Front Immunol. 2026 Mar 13;17:1770348. doi: 10.3389/fimmu.2026.1770348. eCollection 2026.

ABSTRACT

Interstitial lung disease (ILD) is one of the most common extra-articular manifestations of rheumatoid arthritis (RA). Some patients with RA-ILD may develop progressive pulmonary fibrosis, leading to severe impairment of lung function and respiratory failure, which impacts quality of life and can even be life-threatening. This review identified genetic susceptibility, environmental factors, and immune dysregulation as key contributors to the etiology and pathogenesis of RA-ILD. We highlight that autoantibodies, adaptive immune abnormalities, and tertiary lymphoid organ formation significantly drive pulmonary inflammation and fibrosis, while pro-inflammatory cytokines and epithelial-mesenchymal transition (EMT) further contribute to lung tissue injury. Current treatment options, including glucocorticoids, immunosuppressants, and antifibrotic agents such as nintedanib and pirfenidone, are often limited by substantial side effects. Additionally, emerging therapies like JAK inhibitors, CAR-T cells, and the upcoming phosphodiesterase-4B inhibitor, nerandomilast, show promise, but no curative treatment exists to date. Future research could focus on multi-omics technologies and conducting multicenter clinical trials to establish therapeutic targets and advance precision medicine for RA-ILD.

PMID:41909710 | PMC:PMC13021622 | DOI:10.3389/fimmu.2026.1770348

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