DySCo: Dynamic Semantic Compression for Effective Long-term Time Series Forecasting

3 April 2026 at 12:00

arXiv:2604.01261v1 Announce Type: cross Abstract: Time series forecasting (TSF) is critical across domains such as finance, meteorology, and energy. While extending the lookback window theoretically provides richer historical context, in practice, it often introduces irrelevant noise and computational redundancy, preventing models from effectively capturing complex long-term dependencies. To address these challenges, we propose a Dynamic Semantic Compression (DySCo) framework. Unlike traditional methods that rely on fixed heuristics, DySCo introduces an Entropy-Guided Dynamic Sampling (EGDS) mechanism to autonomously identify and retain high-entropy segments while compressing redundant trends. Furthermore, we incorporate a Hierarchical Frequency-Enhanced Decomposition (HFED) strategy to separate high-frequency anomalies from low-frequency patterns, ensuring that critical details are preserved during sparse sampling. Finally, a Cross-Scale Interaction Mixer(CSIM) is designed to dynamically fuse global contexts with local representations, replacing simple linear aggregation. Experimental results demonstrate that DySCo serves as a universal plug-and-play module, significantly enhancing the ability of mainstream models to capture long-term correlations with reduced computational cost.

Integrated transcriptomic and proteomic analyses elucidate the stress tolerance network of <em>Saccharomyces boulardii</em> under gastrointestinal challenge

Omics in Gastric

By: Dingkang Wang · Lerong Liu · Xiaoru Chen · Yiting Kang · Dan Li · Bin Ma · Yuexi Zhu · Yanqin Rong · Hongfei Yu · Yongjie Zhang

31 March 2026 at 18:00

Food Funct. 2026 Mar 31. doi: 10.1039/d5fo04958j. Online ahead of print.

ABSTRACT

The probiotic yeast Saccharomyces boulardii is renowned for its clinical efficacy, which is intrinsically linked to its exceptional ability to survive the harsh gastrointestinal (GI) environment. However, a comprehensive understanding of the molecular mechanisms and regulatory pathways underlying the stress tolerance of S. boulardii remains limited. This study employed an integrated transcriptomic and proteomic approach to systematically map the dynamic responses of S. boulardii to simulated GI transit. Our analysis revealed that the intestinal phase posed a significantly greater challenge than the gastric phase, triggering extensive molecular reprogramming. A core adaptive strategy was the marked upregulation of the central carbon metabolism, particularly glycolysis, as evidenced by the concerted overexpression of key enzymes at both transcriptional and translational levels, indicating a heightened demand for energy to fuel stress defence mechanisms. Furthermore, significant enrichment was observed in the pathways related to nitrogen and fatty acid metabolism. Integration of the multi-omics datasets highlighted the complexity of the regulatory response, with frequent discordance between mRNA and protein abundance underscoring the importance of post-transcriptional regulation. This study provides a detailed molecular profile of the stress tolerance network in S. boulardii, elucidating the strategic metabolic rewiring and multi-layered regulation that underpin its probiotic resilience. The findings offer valuable insights and a foundational resource for the future development of enhanced probiotic therapies.

PMID:41914832 | DOI:10.1039/d5fo04958j

Integrated transcriptomic and proteomic analyses elucidate the stress tolerance network of <em>Saccharomyces boulardii</em> under gastrointestinal challenge

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)

By: Dingkang Wang · Lerong Liu · Xiaoru Chen · Yiting Kang · Dan Li · Bin Ma · Yuexi Zhu · Yanqin Rong · Hongfei Yu · Yongjie Zhang

31 March 2026 at 18:00

Food Funct. 2026 Mar 31. doi: 10.1039/d5fo04958j. Online ahead of print.

ABSTRACT

The probiotic yeast Saccharomyces boulardii is renowned for its clinical efficacy, which is intrinsically linked to its exceptional ability to survive the harsh gastrointestinal (GI) environment. However, a comprehensive understanding of the molecular mechanisms and regulatory pathways underlying the stress tolerance of S. boulardii remains limited. This study employed an integrated transcriptomic and proteomic approach to systematically map the dynamic responses of S. boulardii to simulated GI transit. Our analysis revealed that the intestinal phase posed a significantly greater challenge than the gastric phase, triggering extensive molecular reprogramming. A core adaptive strategy was the marked upregulation of the central carbon metabolism, particularly glycolysis, as evidenced by the concerted overexpression of key enzymes at both transcriptional and translational levels, indicating a heightened demand for energy to fuel stress defence mechanisms. Furthermore, significant enrichment was observed in the pathways related to nitrogen and fatty acid metabolism. Integration of the multi-omics datasets highlighted the complexity of the regulatory response, with frequent discordance between mRNA and protein abundance underscoring the importance of post-transcriptional regulation. This study provides a detailed molecular profile of the stress tolerance network in S. boulardii, elucidating the strategic metabolic rewiring and multi-layered regulation that underpin its probiotic resilience. The findings offer valuable insights and a foundational resource for the future development of enhanced probiotic therapies.

PMID:41914832 | DOI:10.1039/d5fo04958j

AWPD: Frequency Shield Network for Agnostic Watermark Presence Detection

cs.AI, q-bio.NC updates on arXiv.org

By: Xiang Ao · Yiling Du · Zidan Wang · Mengru Chen · Siyang Lu

16 March 2026 at 12:00

arXiv:2603.06723v2 Announce Type: replace-cross Abstract: Invisible watermarks, as an essential technology for image copyright protection, have been widely deployed with the rapid development of social media and AIGC. However, existing invisible watermark detection heavily relies on prior knowledge of specific algorithms, leading to limited detection capabilities for ``unknown watermarks'' in open environments. To this end, we propose a novel task named Agnostic Watermark Presence Detection (AWPD), which aims to identify whether an image carries a copyright mark without requiring decoding information. We construct the UniFreq-100K dataset, comprising large-scale samples across various invisible watermark embedding algorithms. Furthermore, we propose the Frequency Shield Network (FSNet). This model deploys an Adaptive Spectral Perception Module (ASPM) in the shallow layers, utilizing learnable frequency gating to dynamically amplify high-frequency watermark signals while suppressing low-frequency semantics. In the deep layers, the network introduces Dynamic Multi-Spectral Attention (DMSA) combined with tri-stream extremum pooling to deeply mine watermark energy anomalies, forcing the model to precisely focus on sensitive frequency bands. Extensive experiments demonstrate that FSNet exhibits superior zero-shot detection capabilities on the AWPD task, outperforming existing baseline models. Code and datasets will be released upon acceptance.

UWPD: A General Paradigm for Invisible Watermark Detection Agnostic to Embedding Algorithms

cs.AI, q-bio.NC updates on arXiv.org

By: Xiang Ao · Yiling Du · Zidan Wang · Mengru Chen

10 March 2026 at 12:00

arXiv:2603.06723v1 Announce Type: cross Abstract: Invisible watermarks, as an essential technology for image copyright protection, have been widely deployed with the rapid development of social media and AIGC. However, existing invisible watermark detection heavily relies on prior knowledge of specific algorithms, leading to limited detection capabilities for "unknown watermarks" in open environments. To this end, we propose a novel task named Universal Watermark Presence Detection (UWPD), which aims to identify whether an image carries a copyright mark without requiring decoding information. We construct the UniFreq-100K dataset, comprising large-scale samples across various invisible watermark embedding algorithms. Furthermore, we propose the Frequency Shield Network (FSNet). This model deploys an Adaptive Spectral Perception Module (ASPM) in the shallow layers, utilizing learnable frequency gating to dynamically amplify high-frequency watermark signals while suppressing low-frequency semantics. In the deep layers, the network introduces Dynamic Multi-Spectral Attention (DMSA) combined with tri-stream extremum pooling to deeply mine watermark energy anomalies, forcing the model to precisely focus on sensitive frequency bands. Extensive experiments demonstrate that FSNet exhibits superior zero-shot detection capabilities on the UWPD task, outperforming existing baseline models. Code and datasets will be released upon acceptance.

Agent Data Protocol: Unifying Datasets for Diverse, Effective Fine-tuning of LLM Agents

cs.AI, q-bio.NC updates on arXiv.org

By: Yueqi Song · Ketan Ramaneti · Zaid Sheikh · Ziru Chen · Boyu Gou · Tianbao Xie · Yiheng Xu · Danyang Zhang · Apurva Gandhi · Fan Yang · Joseph Liu · Tianyue Ou · Zhihao Yuan · Frank Xu · Shuyan Zhou · Xingyao Wang · Xiang Yue · Tao Yu · Huan Sun · Yu Su · Graham Neubig

5 March 2026 at 13:00

arXiv:2510.24702v2 Announce Type: replace-cross Abstract: Public research results on large-scale supervised finetuning of AI agents remain relatively rare, since the collection of agent training data presents unique challenges. In this work, we argue that the bottleneck is not a lack of underlying data sources, but that a large variety of data is fragmented across heterogeneous formats, tools, and interfaces. To this end, we introduce the agent data protocol (ADP), a light-weight representation language that serves as an "interlingua" between agent datasets in diverse formats and unified agent training pipelines downstream. The design of ADP is expressive enough to capture a large variety of tasks, including API/tool use, browsing, coding, software engineering, and general agentic workflows, while remaining simple to parse and train on without engineering at a per-dataset level. In experiments, we unified a broad collection of 13 existing agent training datasets into ADP format, and converted the standardized ADP data into training-ready formats for multiple agent frameworks. We performed SFT on these data, and demonstrated an average performance gain of ~20% over corresponding base models, and delivers state-of-the-art or near-SOTA performance on standard coding, browsing, tool use, and research benchmarks, without domain-specific tuning. All code and data are released publicly, in the hope that ADP could help lower the barrier to standardized, scalable, and reproducible agent training.

From What to How: Bridging User Requirements with Software Development Using Large Language Models

cs.AI, q-bio.NC updates on arXiv.org

By: Xiao He · Ru Chen · Jialun Cao

17 February 2026 at 13:00

arXiv:2602.13611v1 Announce Type: cross Abstract: Recently, large language models (LLMs) are extensively utilized to enhance development efficiency, leading to numerous benchmarks for evaluating their performance. However, these benchmarks predominantly focus on implementation, overlooking the equally critical aspect of software design. This gap raises two pivotal questions: (1) Can LLMs handle software design? (2) Can LLMs write code following the specific designs? To investigate these questions, this paper proposes DesBench, a design-aware benchmark for evaluating LLMs on three software design-related tasks: design-aware code generation, object-oriented modeling, and the design of acceptance test cases. DesBench comprises 30 manually crafted Java projects that include requirement documents, design models, implementations, and acceptance tests, amounting to a total of 30 design models, 194 Java classes, and 737 test cases. We evaluated seven state-of-the-art LLMs, including three DeepSeek R1, two Qwen2.5, and two GPT models, using DesBench. The results reveal that LLMs remain significantly challenged by the intricacies of software design: (1) For code generation, LLMs struggle to produce correct implementations when provided with only high-level or no designs. (2) In object-oriented modeling, while LLMs can accurately identify objects and classes, they face challenges in defining operations and inter-class relationships. (3) Acceptance test cases generated by LLMs from functional requirements achieve code coverage quality comparable to those written by humans. Our research highlights the current limitations of LLMs in managing software design and calls for further investigation into new design methodologies and languages suitable for LLM-based development.

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