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Unmasking FCGR2B as a high-grade serous ovarian cancer specific marker of immune suppression and tumor progression through multi-omics mining

✇(Multiomics OR Omics) AND (Pancreatic)
By: Jialu Zhou · Tao Zeng · Mingxia Ye · Mingxia Li · Liang Wen · Xinyi Liu · Zhe Zhang · Yuanguang Meng

Transl Oncol. 2026 Apr 3;67:102748. doi: 10.1016/j.tranon.2026.102748. Online ahead of print.

ABSTRACT

BACKGROUND: Epithelial ovarian cancer (EOC) encompasses five major histological subtypes with marked genetic, immunological, and clinical heterogeneity. While genome-wide association studies (GWAS) have identified subtype-specific risk loci, a critical gap remains in understanding how plasma proteins influence immune-cell traits and contribute to EOC pathogenesis.

METHODS: We integrated subtype-stratified GWAS data from two EOC cohorts with plasma proteomics and immune-cell traits to construct protein-immune-EOC regulatory landscapes using a three-stage Mendelian randomization framework. Single-cell RNA-seq and multiplex immunofluorescence were employed to delineate the cellular distribution and spatial context of causal proteins. Subsequent analyses characterized immune infiltration, macrophage polarization, and clinicopathological associations. Drug-gene correlations were used to identify potential therapeutic targets, and transcriptomic analyses were applied to delineate the underlying transcriptional landscape.

RESULTS: We identified 20 subtype-specific protein-immune-EOC regulatory axes, with FCGR2B emerging as a causal plasma protein in immune regulation and high-grade serous ovarian cancer (HGSOC) progression. FCGR2B was highly expressed in tumor-associated macrophages and was associated with an M2-like polarization phenotype. Functional characterization revealed that FCGR2B was associated with shorter progression-free survival and an immunosuppressive tumor microenvironment. Transcriptomic analyses revealed altered NF-κB signaling upon FCGR2B knockdown, and drug-response data suggested a potential association between high FCGR2B expression and sensitivity to NF-κB inhibitors.

CONCLUSIONS: These findings delineate subtype-specific genetically informed protein-immune regulatory landscapes in EOC and identify FCGR2B as a key immunoregulatory and prognostic biomarker in HGSOC, suggesting FCGR2B as a potential therapeutic vulnerability that warrants further investigation.

PMID:41934917 | DOI:10.1016/j.tranon.2026.102748

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Deciphering functional intra-tumoral heterogeneity in BRAF<sup>V600E</sup>-driven mouse thyroid cancer reveals EMT trajectory and metabolic remodeling

✇Oncogene - Issue - nature.com science feeds
By: Jing Yu · Siyi Shen · Rongkui Luo · Tianyuan Qiu · Jingjing Chen · Wenqing Kong · Ruisen Ma · Ying Yan · Zhoumin Niu · Mei Ma · Zhuoyang Li · Yuting Wu · Yuying Li · Ling Zhang · Hao Ying

Oncogene, Published online: 04 April 2026; doi:10.1038/s41388-026-03742-8

Deciphering functional intra-tumoral heterogeneity in BRAFV600E-driven mouse thyroid cancer reveals EMT trajectory and metabolic remodeling
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Spliceosomal component SNRPE drives cell proliferation by regulating CTP synthase 1 mRNA splicing in ovarian cancer

✇Oncogene - Issue - nature.com science feeds
By: Yingying Pu · Zhongshao Chen · Qianqian Gao · Yanling Liu · Ning Yang · Yingwei Li · Beihua Kong

Oncogene, Published online: 04 April 2026; doi:10.1038/s41388-026-03764-2

Spliceosomal component SNRPE drives cell proliferation by regulating CTP synthase 1 mRNA splicing in ovarian cancer
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Single-Cell and Multi-Omics-Based Characterization of Gastric Cancer Identifies TPP1 as a Potential Target for Gastric Cancer Progression and Treatment

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Yingying Zhao · Jiakang Ma · Rujin Huang · Shuxian Pan

Oncol Res. 2026 Mar 23;34(4):27. doi: 10.32604/or.2026.070208. eCollection 2026.

ABSTRACT

BACKGROUND: Cancer-associated fibroblasts (CAFs) play critical roles in tumor progression and immunosuppression; however, their contribution to the functional classification and personalized treatment of gastric cancer remains poorly defined. This study aimed to identify effective therapeutic targets to facilitate individualized treatment strategies for patients with gastric cancer.

METHODS: Single-cell and bulk transcriptomic analyses were integrated to characterize gastric cancer fibroblasts. "Seurat", "Slingshot", and "CellChat" were used for dimensionality reduction, trajectory inference, and cell-cell communication analyses, respectively. Key metastasis-associated fibroblast modules were identified using High-dimensional weighted gene co-expression network analysis (hdWGCNA) to construct a prognostic model, which was further evaluated for immune infiltration, therapeutic response, and mutational features. The expression and function of the core gene tripeptidyl peptidase 1 (TPP1) were validated through immunoblotting, PCR, and functional assays.

RESULTS: Eight fibroblast subpopulations associated with gastric cancer metastasis exhibited distinct differentiation trajectories and transcriptional heterogeneity. Prognostic analysis indicated that metastasis-associated fibroblasts correlated with poor clinical outcomes. The high-risk subgroup showed marked immunosuppression, resistance to immunotherapy, and reduced mutational burden, with tumor progression-related pathways significantly enriched in this group. In vitro experiments further confirmed that TPP1 knockdown suppressed gastric cancer cell metastasis, invasion, and clonogenic capacity while inducing apoptosis.

CONCLUSION: This study characterized the heterogeneity of gastric cancer-associated fibroblasts using single-cell transcriptomic analysis and established a prognostic model based on metastasis-related fibroblast markers. The model demonstrated strong predictive performance for patient prognosis, immune landscape, and immunotherapy response. Furthermore, the findings highlighted the pivotal role of TPP1 in gastric cancer progression and its potential as a therapeutic target.

PMID:41930144 | PMC:PMC13040347 | DOI:10.32604/or.2026.070208

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ClawSafety: "Safe" LLMs, Unsafe Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Bowen Wei · Yunbei Zhang · Jinhao Pan · Kai Mei · Xiao Wang · Jihun Hamm · Ziwei Zhu · Yingqiang Ge
arXiv:2604.01438v1 Announce Type: new Abstract: Personal AI agents like OpenClaw run with elevated privileges on users' local machines, where a single successful prompt injection can leak credentials, redirect financial transactions, or destroy files. This threat goes well beyond conventional text-level jailbreaks, yet existing safety evaluations fall short: most test models in isolated chat settings, rely on synthetic environments, and do not account for how the agent framework itself shapes safety outcomes. We introduce CLAWSAFETY, a benchmark of 120 adversarial test scenarios organized along three dimensions (harm domain, attack vector, and harmful action type) and grounded in realistic, high-privilege professional workspaces spanning software engineering, finance, healthcare, law, and DevOps. Each test case embeds adversarial content in one of three channels the agent encounters during normal work: workspace skill files, emails from trusted senders, and web pages. We evaluate five frontier LLMs as agent backbones, running 2,520 sandboxed trials across all configurations. Attack success rates (ASR) range from 40\% to 75\% across models and vary sharply by injection vector, with skill instructions (highest trust) consistently more dangerous than email or web content. Action-trace analysis reveals that the strongest model maintains hard boundaries against credential forwarding and destructive actions, while weaker models permit both. Cross-scaffold experiments on three agent frameworks further demonstrate that safety is not determined by the backbone model alone but depends on the full deployment stack, calling for safety evaluation that treats model and framework as joint variables.
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A Self-Evolving Agentic Framework for Metasurface Inverse Design

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yi Huang · Bowen Zheng · Yunxi Dong · Hong Tang · Huan Zhao · S. M. Rakibul Hasan Shawon · Hualiang Zhang
arXiv:2604.01480v1 Announce Type: new Abstract: Metasurface inverse design has become central to realizing complex optical functionality, yet translating target responses into executable, solver-compatible workflows still demands specialized expertise in computational electromagnetics and solver-specific software engineering. Recent large language models (LLMs) offer a complementary route to reducing this workflow-construction burden, but existing language-driven systems remain largely session-bounded and do not preserve reusable workflow knowledge across inverse-design tasks. We present an agentic framework for metasurface inverse design that addresses this limitation through context-level skill evolution. The framework couples a coding agent, evolving skill artifacts, and a deterministic evaluator grounded in physical simulation so that solver-specific strategies can be iteratively refined across tasks without modifying model weights or the underlying physics solver. We evaluate the framework on a benchmark spanning multiple metasurface inverse-design task types, with separate training-aligned and held-out task families. Evolved skills raise in-distribution task success from 38% to 74%, increase criteria pass fraction from 0.510 to 0.870, and reduce average attempts from 4.10 to 2.30. On held-out task families, binary success changes only marginally, but improvements in best margin together with shifts in error composition and agent behavior indicate partial transfer of workflow knowledge. These results suggest that the main value of skill evolution lies in accumulating reusable solver-specific expertise around reliable computational engines, thereby offering a practical path toward more autonomous and accessible metasurface inverse-design workflows.
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CORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ao Qu · Han Zheng · Zijian Zhou · Yihao Yan · Yihong Tang · Shao Yong Ong · Fenglu Hong · Kaichen Zhou · Chonghe Jiang · Minwei Kong · Jiacheng Zhu · Xuan Jiang · Sirui Li · Cathy Wu · Bryan Kian Hsiang Low · Jinhua Zhao · Paul Pu Liang
arXiv:2604.01658v1 Announce Type: new Abstract: Large language model (LLM)-based evolution is a promising approach for open-ended discovery, where progress requires sustained search and knowledge accumulation. Existing methods still rely heavily on fixed heuristics and hard-coded exploration rules, which limit the autonomy of LLM agents. We present CORAL, the first framework for autonomous multi-agent evolution on open-ended problems. CORAL replaces rigid control with long-running agents that explore, reflect, and collaborate through shared persistent memory, asynchronous multi-agent execution, and heartbeat-based interventions. It also provides practical safeguards, including isolated workspaces, evaluator separation, resource management, and agent session and health management. Evaluated on diverse mathematical, algorithmic, and systems optimization tasks, CORAL sets new state-of-the-art results on 10 tasks, achieving 3-10 times higher improvement rates with far fewer evaluations than fixed evolutionary search baselines across tasks. On Anthropic's kernel engineering task, four co-evolving agents improve the best known score from 1363 to 1103 cycles. Mechanistic analyses further show how these gains arise from knowledge reuse and multi-agent exploration and communication. Together, these results suggest that greater agent autonomy and multi-agent evolution can substantially improve open-ended discovery. Code is available at https://github.com/Human-Agent-Society/CORAL.
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M3D-BFS: a Multi-stage Dynamic Fusion Strategy for Sample-Adaptive Multi-Modal Brain Network Analysis

✇cs.AI, q-bio.NC updates on arXiv.org
By: Rui Dong · Xiaotong Zhang · Jiaxing Li · Yueying Li · Jiayin Wei · Youyong Kong
arXiv:2604.01667v1 Announce Type: new Abstract: Multi-modal fusion is of great significance in neuroscience which integrates information from different modalities and can achieve better performance than uni-modal methods in downstream tasks. Current multi-modal fusion methods in brain networks, which mainly focus on structural connectivity (SC) and functional connectivity (FC) modalities, are static in nature. They feed different samples into the same model with identical computation, ignoring inherent difference between input samples. This lack of sample adaptation hinders model's further performance. To this end, we innovatively propose a multi-stage dynamic fusion strategy (M3D-BFS) for sample-adaptive multi-modal brain network analysis. Unlike other static fusion methods, we design different mixture-of-experts (MoEs) for uni- and multi-modal representations where modules can adaptively change as input sample changes during inference. To alleviate issue of MoE where training of experts may be collapsed, we divide our method into 3 stages. We first train uni-modal encoders respectively, then pretrain single experts of MoEs before finally finetuning the whole model. A multi-modal disentanglement loss is designed to enhance the final representations. To the best of our knowledge, this is the first work for dynamic fusion for multi-modal brain network analysis. Extensive experiments on different real-world datasets demonstrates the superiority of M3D-BFS.
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Hierarchical Memory Orchestration for Personalized Persistent Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Junming Liu · Yifei Sun · Weihua Cheng · Haodong Lei · Yuqi Li · Yirong Chen · Ding Wang
arXiv:2604.01670v1 Announce Type: new Abstract: While long-term memory is essential for intelligent agents to maintain consistent historical awareness, the accumulation of extensive interaction data often leads to performance bottlenecks. Naive storage expansion increases retrieval noise and computational latency, overwhelming the reasoning capacity of models deployed on constrained personal devices. To address this, we propose Hierarchical Memory Orchestration (HMO), a framework that organizes interaction history into a three-tiered directory driven by user-centric contextual relevance. Our system maintains a compact primary cache, coupling recent and pivotal memories with an evolving user profile to ensure agent reasoning remains aligned with individual behavioral traits. This primary cache is complemented by a high-priority secondary layer, both of which are managed within a global archive of the full interaction history. Crucially, the user persona dictates memory redistribution across this hierarchy, promoting records mapped to long-term patterns toward more active tiers while relegating less relevant information. This targeted orchestration surfaces historical knowledge precisely when needed while maintaining a lean and efficient active search space. Evaluations on multiple benchmarks achieve state-of-the-art performance. Real-world deployments in ecosystems like OpenClaw demonstrate that HMO significantly enhances agent fluidity and personalization.
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EvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hanrong Zhang · Shicheng Fan · Henry Peng Zou · Yankai Chen · Zhenting Wang · Jiayu Zhou · Chengze Li · Wei-Chieh Huang · Yifei Yao · Kening Zheng · Xue Liu · Xiaoxiao Li · Philip S. Yu
arXiv:2604.01687v1 Announce Type: new Abstract: Anthropic proposes the concept of skills for LLM agents to tackle multi-step professional tasks that simple tool invocations cannot address. A tool is a single, self-contained function, whereas a skill is a structured bundle of interdependent multi-file artifacts. Currently, skill generation is not only label-intensive due to manual authoring, but also may suffer from human--machine cognitive misalignment, which can lead to degraded agent performance, as evidenced by evaluations on SkillsBench. Therefore, we aim to enable agents to autonomously generate skills. However, existing self-evolving methods designed for tools cannot be directly applied to skills due to their increased complexity. To address these issues, we propose EvoSkills, a self-evolving skills framework that enables agents to autonomously construct complex, multi-file skill packages. Specifically, EvoSkills couples a Skill Generator that iteratively refines skills with a Surrogate Verifier that co-evolves to provide informative and actionable feedback without access to ground-truth test content. On SkillsBench, EvoSkills achieves the highest pass rate among five baselines on both Claude Code and Codex, and also exhibits strong generalization capabilities to six additional LLMs.
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Scale over Preference: The Impact of AI-Generated Content on Online Content Ecology

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tianhao Shi · Yang Zhang · Xiaoyan Zhao · Fengbin Zhu · Chenyi Lei · Han Li · Wenwu Ou · Yang Song · Yongdong Zhang · Fuli Feng
arXiv:2604.01690v1 Announce Type: new Abstract: The rapid proliferation of Artificial Intelligence-Generated Content (AIGC) is fundamentally restructuring online content ecologies, necessitating a rigorous examination of its behavioral and distributional implications. Leveraging a comprehensive longitudinal dataset comprising tens of millions of users from a leading Chinese video-sharing platform, this study elucidated the distinct creation and consumption behaviors characterizing AIGC versus Human-Generated Content (HGC). We identified a prevalent scale-over-preference dynamic, wherein AIGC creators achieve aggregate engagement comparable to HGC creators through high-volume production, despite a marked consumer preference for HGC. Deeper analysis uncovered the ability of the algorithmic content distribution mechanism in moderating these competing interests regarding AIGC. These findings advocated for the implementation of AIGC-sensitive distribution algorithms and precise governance frameworks to ensure the long-term health of the online content platforms.
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Domain-constrained knowledge representation: A modal framework

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chao Li · Yuru Wang · Chunyi Zhao
arXiv:2604.01770v1 Announce Type: new Abstract: Knowledge graphs store large numbers of relations efficiently, but they remain weak at representing a quieter difficulty: the meaning of a concept often shifts with the domain in which it is used. A triple such as Apple, instance-of, Company may be acceptable in one setting while being misleading or unusable in another. In most current systems, domain information is attached as metadata, qualifiers, or graph-level organization. These mechanisms help with filtering and provenance, but they usually do not alter the formal status of the assertion itself. This paper argues that domain should be treated as part of knowledge representation rather than as supplementary annotation. It introduces the Domain-Contextualized Concept Graph (DCG), a framework in which domain is written into the relation and interpreted as a modal world constraint. In the DCG form (C, R at D, C'), the marker at D identifies the world in which the relation holds. Formally, the relation is interpreted through a domain-indexed necessity operator, so that truth, inference, and conflict checking are all scoped to the relevant world. This move has three consequences: ambiguous concepts can be disambiguated at the point of representation; invalid assertions can be challenged against their domain; cross-domain relations can be connected through explicit predicates. The paper develops this claim through a Kripke-style semantics, a compact predicate system, a Prolog implementation, and mappings to RDF, OWL, and relational databases. The contribution is a representational reinterpretation of domain itself. The central claim is that many practical failures in knowledge systems begin when domain is treated as external to the assertion. DCG addresses that by giving domain a structural and computable role inside the representation.
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SenseMath: Do LLMs Have Number Sense? Evaluating Shortcut Use, Judgment, and Generation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haomin Zhuang · Xiangqi Wang · Yili Shen · Ying Cheng · Xiangliang Zhang
arXiv:2604.01988v1 Announce Type: new Abstract: Large language models often default to step-by-step computation even when efficient numerical shortcuts are available. This raises a basic question: do they exhibit number sense in a human-like behavioral sense, i.e., the ability to recognize numerical structure, apply shortcuts when appropriate, and avoid them when they are not? We introduce SenseMath, a controlled benchmark for evaluating structure-sensitive numerical reasoning in LLMs. SenseMath contains 4,800 items spanning eight shortcut categories and four digit scales, with matched strong-shortcut, weak-shortcut, and control variants. It supports three evaluation settings of increasing cognitive demand: Shortcut Use (whether models can apply shortcuts on shortcut-amenable problems); Applicability Judgment (whether they can recognize when a shortcut is appropriate or misleading); and Problem Generation (whether they can generate new problem items that correctly admit a given type of shortcut). Our evaluation across five LLMs, ranging from GPT-4o-mini to Llama-3.1-8B, shows a consistent pattern: when explicitly prompted, models readily adopt shortcut strategies and achieve substantial accuracy gains on shortcut-amenable items (up to 15%), yet under standard chain-of-thought prompting they spontaneously employ such strategies in fewer than 40% of cases, even when they demonstrably possess the requisite capability. Moreover, this competence is confined to the Use level; models systematically over-generalise shortcuts to problems where they do not apply, and fail to generate valid shortcut-bearing problems from scratch. Together, these results suggest that current LLMs exhibit procedural shortcut fluency without the structural understanding of when and why shortcuts work that underlies human number sense.
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ATBench: A Diverse and Realistic Trajectory Benchmark for Long-Horizon Agent Safety

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yu Li · Haoyu Luo · Yuejin Xie · Yuqian Fu · Zhonghao Yang · Shuai Shao · Qihan Ren · Wanying Qu · Yanwei Fu · Yujiu Yang · Jing Shao · Xia Hu · Dongrui Liu
arXiv:2604.02022v1 Announce Type: new Abstract: Evaluating the safety of LLM-based agents is increasingly important because risks in realistic deployments often emerge over multi-step interactions rather than isolated prompts or final responses. Existing trajectory-level benchmarks remain limited by insufficient interaction diversity, coarse observability of safety failures, and weak long-horizon realism. We introduce ATBench, a trajectory-level benchmark for structured, diverse, and realistic evaluation of agent safety. ATBench organizes agentic risk along three dimensions: risk source, failure mode, and real-world harm. Based on this taxonomy, we construct trajectories with heterogeneous tool pools and a long-context delayed-trigger protocol that captures realistic risk emergence across multiple stages. The benchmark contains 1,000 trajectories (503 safe and 497 unsafe), averaging 9.01 turns and 3.95k tokens, with 1,954 invoked tools drawn from pools spanning 2,084 available tools. Data quality is supported by rule-based and LLM-based filtering plus full human audit. Experiments on frontier LLMs, open-source models, and specialized guard systems show that ATBench is challenging even for strong evaluators, while enabling taxonomy-stratified analysis, cross-benchmark comparison, and diagnosis of long-horizon failure patterns.
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The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xinlei Yu · Zhangquan Chen · Yongbo He · Tianyu Fu · Cheng Yang · Chengming Xu · Yue Ma · Xiaobin Hu · Zhe Cao · Jie Xu · Guibin Zhang · Jiale Tao · Jiayi Zhang · Siyuan Ma · Kaituo Feng · Haojie Huang · Youxing Li · Ronghao Chen · Huacan Wang · Chenglin Wu · Zikun Su · Xiaogang Xu · Kelu Yao · Kun Wang · Chen Gao · Yue Liao · Ruqi Huang · Tao Jin · Cheng Tan · Jiangning Zhang · Wenqi Ren · Yanwei Fu · Yong Liu · Yu Wang · Xiangyu Yue · Yu-Gang Jiang · Shuicheng Yan
arXiv:2604.02029v1 Announce Type: new Abstract: Latent space is rapidly emerging as a native substrate for language-based models. While modern systems are still commonly understood through explicit token-level generation, an increasing body of work shows that many critical internal processes are more naturally carried out in continuous latent space than in human-readable verbal traces. This shift is driven by the structural limitations of explicit-space computation, including linguistic redundancy, discretization bottlenecks, sequential inefficiency, and semantic loss. This survey aims to provide a unified and up-to-date landscape of latent space in language-based models. We organize the survey into five sequential perspectives: Foundation, Evolution, Mechanism, Ability, and Outlook. We begin by delineating the scope of latent space, distinguishing it from explicit or verbal space and from the latent spaces commonly studied in generative visual models. We then trace the field's evolution from early exploratory efforts to the current large-scale expansion. To organize the technical landscape, we examine existing work through the complementary lenses of mechanism and ability. From the perspective of Mechanism, we identify four major lines of development: Architecture, Representation, Computation, and Optimization. From the perspective of Ability, we show how latent space supports a broad capability spectrum spanning Reasoning, Planning, Modeling, Perception, Memory, Collaboration, and Embodiment. Beyond consolidation, we discuss the key open challenges, and outline promising directions for future research. We hope this survey serves not only as a reference for existing work, but also as a foundation for understanding latent space as a general computational and systems paradigm for next-generation intelligence.
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Do Emotions in Prompts Matter? Effects of Emotional Framing on Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Minda Zhao · Yutong Yang · Chufei Peng · Rachel Gonsalves · Weiyue Li · Ruyi Yang · Zhixi Liu · Mengyu Wang
arXiv:2604.02236v1 Announce Type: new Abstract: Emotional tone is pervasive in human communication, yet its influence on large language model (LLM) behaviour remains unclear. Here, we examine how first-person emotional framing in user-side queries affect LLM performance across six benchmark domains, including mathematical reasoning, medical question answering, reading comprehension, commonsense reasoning and social inference. Across models and tasks, static emotional prefixes usually produce only small changes in accuracy, suggesting that affective phrasing is typically a mild perturbation rather than a reliable general-purpose intervention. This stability is not uniform: effects are more variable in socially grounded tasks, where emotional context more plausibly interacts with interpersonal reasoning. Additional analyses show that stronger emotional wording induces only modest extra change, and that human-written prefixes reproduce the same qualitative pattern as LLM-generated ones. We then introduce EmotionRL, an adaptive emotional prompting framework that selects emotional framing adaptively for each query. Although no single emotion is consistently beneficial, adaptive selection yields more reliable gains than fixed emotional prompting. Together, these findings show that emotional tone is neither a dominant driver of LLM performance nor irrelevant noise, but a weak and input-dependent signal that can be exploited through adaptive control.
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DySCo: Dynamic Semantic Compression for Effective Long-term Time Series Forecasting

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xiang Ao · Yinyu Tan · Mengru Chen
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.
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