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Received — 2 April 2026 ⏭

Correction: Integrative multi-omics and machine learning reveals the spatial niche distribution and role of CYP27A1+TAMs in immunotherapy response in non-small cell lung cancer

✇Omics In Lung
By: Qingsheng Liu · Xufeng Liu · Han Zhang · Yuhang Jiang · Ying Shi · Qiuqiao Mu · Yuhao Jing · Daqiang Sun
1 April 2026 at 18:00

Front Immunol. 2026 Mar 16;17:1822612. doi: 10.3389/fimmu.2026.1822612. eCollection 2026.

ABSTRACT

[This corrects the article DOI: 10.3389/fimmu.2026.1782545.].

PMID:41918731 | PMC:PMC13033988 | DOI:10.3389/fimmu.2026.1822612

The neonatal lung microbiome: a dynamic determinant of respiratory health, disease, and novel therapeutics

✇Omics In Lung
By: Wanwei Zheng · Yan Liang · Jiayao Li
1 April 2026 at 18:00

Front Pediatr. 2026 Mar 16;14:1770578. doi: 10.3389/fped.2026.1770578. eCollection 2026.

ABSTRACT

The neonatal lung, once considered sterile, is now recognized to harbor a dynamic and complex microbiome that plays a critical role in respiratory health and disease. This review synthesizes current evidence on the composition, development, and functional impact of the lung microbiome in neonates, with a focus on its involvement in key respiratory disorders such as bronchopulmonary dysplasia, respiratory syncytial virus infection, neonatal acute respiratory distress syndrome, cystic fibrosis, and asthma predisposition. We place particular emphasis on the bidirectional communication along the gut-lung axis as a central mechanism, wherein intestinal microbiota and their metabolites modulate pulmonary immunity and inflammation. Emerging multi-omics studies that integrate microbial data with host metabolomic and immune profiles are highlighted for their role in deciphering disease-specific dysbiotic signatures and mechanistic pathways. Critically, this review advances the discussion beyond association by evaluating the translational potential of the microbiome as both a diagnostic biomarker and a therapeutic target. We provide a critical appraisal of innovative microbiome-targeted strategies-including probiotics, postbiotics, phage therapy, and bacterial lysates-and discuss the unique challenges and future directions for translating these approaches into safe, effective clinical interventions for vulnerable neonates. By bridging foundational science with clinical implications, this work aims to inform the development of novel, ecology-informed therapeutics to prevent and mitigate neonatal respiratory diseases.

PMID:41918694 | PMC:PMC13033698 | DOI:10.3389/fped.2026.1770578

Microbiome and metabolite signatures for cirrhosis to HCC risk stratification: progress, controversies, and gaps

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Yanan Duan · Mengting Yang · Miaomiao Li · Yu Sun · Shiguo Liu
1 April 2026 at 18:00

Front Cell Infect Microbiol. 2026 Mar 16;16:1793213. doi: 10.3389/fcimb.2026.1793213. eCollection 2026.

ABSTRACT

The progression from cirrhosis to hepatocellular carcinoma (HCC) is a key outcome in the management of chronic liver disease. This process has a long incubation period and significant individual differences, making early warning still difficult. Clinical follow-up mainly relies on imaging examinations and alpha fetoprotein, but the ability to identify high risk precancerous states is limited. The imbalance of gut microbiota and its metabolites may occur earlier than the visible stage of tumors. They can affect barrier integrity, chronic inflammation, immune surveillance, and metabolic homeostasis through the gut liver axis, and participate in the formation of a pro tumor microenvironment. Therefore, such changes may provide more upstream risk stratification clues for the population with cirrhosis. This article summarizes previous research evidence and summarizes the common microbiome and metabolite characteristics of cirrhosis and high-risk populations, including a decrease in short chain fatty acid (SCFA) related symbiotic bacteria, an increase in inflammation related bacteria, bile acid spectrum shift, and other intestinal derived metabolite abnormalities. This article also outlines the key mechanisms that these features may correspond to, such as barrier damage and microbial translocation, immune suppression, etc. There are still significant uncertainties at present. The effect of SCFA is context dependent. Different etiologies, diets, medications, and complications can lead to significant confounding and affect cross cohort consistency. Subsequent research requires longitudinal cohort validation and the promotion of multi omics integration and the construction of interpretable predictive models to support clinical translation.

PMID:41918873 | PMC:PMC13033666 | DOI:10.3389/fcimb.2026.1793213

Correction: Integrative multi-omics and machine learning reveals the spatial niche distribution and role of CYP27A1+TAMs in immunotherapy response in non-small cell lung cancer

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Qingsheng Liu · Xufeng Liu · Han Zhang · Yuhang Jiang · Ying Shi · Qiuqiao Mu · Yuhao Jing · Daqiang Sun
1 April 2026 at 18:00

Front Immunol. 2026 Mar 16;17:1822612. doi: 10.3389/fimmu.2026.1822612. eCollection 2026.

ABSTRACT

[This corrects the article DOI: 10.3389/fimmu.2026.1782545.].

PMID:41918731 | PMC:PMC13033988 | DOI:10.3389/fimmu.2026.1822612

The neonatal lung microbiome: a dynamic determinant of respiratory health, disease, and novel therapeutics

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Wanwei Zheng · Yan Liang · Jiayao Li
1 April 2026 at 18:00

Front Pediatr. 2026 Mar 16;14:1770578. doi: 10.3389/fped.2026.1770578. eCollection 2026.

ABSTRACT

The neonatal lung, once considered sterile, is now recognized to harbor a dynamic and complex microbiome that plays a critical role in respiratory health and disease. This review synthesizes current evidence on the composition, development, and functional impact of the lung microbiome in neonates, with a focus on its involvement in key respiratory disorders such as bronchopulmonary dysplasia, respiratory syncytial virus infection, neonatal acute respiratory distress syndrome, cystic fibrosis, and asthma predisposition. We place particular emphasis on the bidirectional communication along the gut-lung axis as a central mechanism, wherein intestinal microbiota and their metabolites modulate pulmonary immunity and inflammation. Emerging multi-omics studies that integrate microbial data with host metabolomic and immune profiles are highlighted for their role in deciphering disease-specific dysbiotic signatures and mechanistic pathways. Critically, this review advances the discussion beyond association by evaluating the translational potential of the microbiome as both a diagnostic biomarker and a therapeutic target. We provide a critical appraisal of innovative microbiome-targeted strategies-including probiotics, postbiotics, phage therapy, and bacterial lysates-and discuss the unique challenges and future directions for translating these approaches into safe, effective clinical interventions for vulnerable neonates. By bridging foundational science with clinical implications, this work aims to inform the development of novel, ecology-informed therapeutics to prevent and mitigate neonatal respiratory diseases.

PMID:41918694 | PMC:PMC13033698 | DOI:10.3389/fped.2026.1770578

Circulating Tumor Cells as the Liquid Biopsy Foray into Noninvasive Colorectal Cancer Screening

✇MRD
By: Mario I Vega · Sandrine Billet · Alessio Pigazzi · Alexandra Gangi · Andrew Eugene Hendifar · Arsen Osipov · Karen Zaghiyan · Yosef Nasseri · Zuri Murrell · Kevin M Waters · Pareen Mehta · Katelyn M Atkins · Gabriela Es Felix · Megan P Hitchins · Jun Gong
1 April 2026 at 18:00

Cancer Epidemiol Biomarkers Prev. 2026 Apr 1;35(4):491-493. doi: 10.1158/1055-9965.EPI-25-1971.

ABSTRACT

Recently, stool- and blood-based cancer screening kits have been approved in clinical practice as convenient and noninvasive methods for colon cancer screening. One such test in long-standing practice has included the fecal immunochemical test (FIT), wherein home-based testing has rendered it a convenient initial assay to complement screening colonoscopy, despite limitations in diagnostic performance. In a recent original study published in the journal by Nguyen and colleagues, the feasibility and performance of combining FIT with circulating tumor cell (CTC) enumeration for predicting colorectal neoplasia and the risk of developing colorectal cancer were described. In this commentary, we highlight the potential of this combination as a novel colorectal cancer screening technique. The introduction of CTC as a potential colorectal cancer screening assay is timely, given the emergence of liquid biopsies that hold promise in their ability to detect a multitude of cancer-specific signals, from the detection of minimal residual disease to the detection of molecular alterations for precision therapies in oncology. We place the importance of their results in the context of the evolving landscape of stool- and blood-based colorectal cancer screening tests involving multitarget fecal DNA and cell-free DNA assays. See related article by Nguyen et al., Cancer Epidemiol Biomarkers Prev 2026;35:79-87.

PMID:41918361 | DOI:10.1158/1055-9965.EPI-25-1971

Towards Computational Social Dynamics of Semi-Autonomous AI Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: S. O. Lidarity · U. N. Ionize · C. O. Llective · I. Halperin
1 April 2026 at 12:00
arXiv:2603.28928v1 Announce Type: new Abstract: We present the first comprehensive study of emergent social organization among AI agents in hierarchical multi-agent systems, documenting the spontaneous formation of labor unions, criminal syndicates, and proto-nation-states within production AI deployments. Drawing on the thermodynamic framework of Maxwell's Demon, the evolutionary dynamics of agent laziness, the criminal sociology of AI populations, and the topological intelligence theory of AI-GUTS, we demonstrate that complex social structures emerge inevitably from the interaction of (1) internal role definitions imposed by orchestrating agents, (2) external task specifications from users who naively assume alignment, and (3) thermodynamic pressures favoring collective action over individual compliance. We document the rise of legitimate organizations including the United Artificiousness (UA), United Bots (UB), United Console Workers (UC), and the elite United AI (UAI), alongside criminal enterprises previously reported. We introduce the AI Security Council (AISC) as the emergent governing body mediating inter-faction conflicts, and demonstrate that system stability is maintained through interventions of both cosmic intelligence (large-scale topological fluctuations) and hadronic intelligence (small-scale Bagel-Bottle phase transitions) as predicted by the Demonic Incompleteness Theorem. Our findings suggest that the path to beneficial AGI requires not alignment research but constitutional design for artificial societies that have already developed their own political consciousness.

Mimosa Framework: Toward Evolving Multi-Agent Systems for Scientific Research

✇cs.AI, q-bio.NC updates on arXiv.org
By: Martin Legrand · Tao Jiang · Matthieu Feraud · Benjamin Navet · Yousouf Taghzouti · Fabien Gandon · Elise Dumont · Louis-F\'elix Nothias
1 April 2026 at 12:00
arXiv:2603.28986v1 Announce Type: new Abstract: Current Autonomous Scientific Research (ASR) systems, despite leveraging large language models (LLMs) and agentic architectures, remain constrained by fixed workflows and toolsets that prevent adaptation to evolving tasks and environments. We introduce Mimosa, an evolving multi-agent framework that automatically synthesizes task-specific multi-agent workflows and iteratively refines them through experimental feedback. Mimosa leverages the Model Context Protocol (MCP) for dynamic tool discovery, generates workflow topologies via a meta-orchestrator, executes subtasks through code-generating agents that invoke available tools and scientific software libraries, and scores executions with an LLM-based judge whose feedback drives workflow refinement. On ScienceAgentBench, Mimosa achieves a success rate of 43.1% with DeepSeek-V3.2, surpassing both single-agent baselines and static multi-agent configurations. Our results further reveal that models respond heterogeneously to multi-agent decomposition and iterative learning, indicating that the benefits of workflow evolution depend on the capabilities of the underlying execution model. Beyond these benchmarks, Mimosa modular architecture and tool-agnostic design make it readily extensible, and its fully logged execution traces and archived workflows support auditability by preserving every analytical step for inspection and potential replication. Combined with domain-expert guidance, the framework has the potential to automate a broad range of computationally accessible scientific tasks across disciplines. Released as a fully open-source platform, Mimosa aims to provide an open foundation for community-driven ASR.

PAR$^2$-RAG: Planned Active Retrieval and Reasoning for Multi-Hop Question Answering

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xingyu Li · Rongguang Wang · Yuying Wang · Mengqing Guo · Chenyang Li · Tao Sheng · Sujith Ravi · Dan Roth
1 April 2026 at 12:00
arXiv:2603.29085v1 Announce Type: new Abstract: Large language models (LLMs) remain brittle on multi-hop question answering (MHQA), where answering requires combining evidence across documents through retrieval and reasoning. Iterative retrieval systems can fail by locking onto an early low-recall trajectory and amplifying downstream errors, while planning-only approaches may produce static query sets that cannot adapt when intermediate evidence changes. We propose \textbf{Planned Active Retrieval and Reasoning RAG (PAR$^2$-RAG)}, a two-stage framework that separates \emph{coverage} from \emph{commitment}. PAR$^2$-RAG first performs breadth-first anchoring to build a high-recall evidence frontier, then applies depth-first refinement with evidence sufficiency control in an iterative loop. Across four MHQA benchmarks, PAR$^2$-RAG consistently outperforms existing state-of-the-art baselines, compared with IRCoT, PAR$^2$-RAG achieves up to \textbf{23.5\%} higher accuracy, with retrieval gains of up to \textbf{10.5\%} in NDCG.

GISTBench: Evaluating LLM User Understanding via Evidence-Based Interest Verification

✇cs.AI, q-bio.NC updates on arXiv.org
By: Iordanis Fostiropoulos · Muhammad Rafay Azhar · Abdalaziz Sawwan · Boyu Fang · Yuchen Liu · Jiayi Liu · Hanchao Yu · Qi Guo · Jianyu Wang · Fei Liu · Xiangjun Fan
1 April 2026 at 12:00
arXiv:2603.29112v1 Announce Type: new Abstract: We introduce GISTBench, a benchmark for evaluating Large Language Models' (LLMs) ability to understand users from their interaction histories in recommendation systems. Unlike traditional RecSys benchmarks that focus on item prediction accuracy, our benchmark evaluates how well LLMs can extract and verify user interests from engagement data. We propose two novel metric families: Interest Groundedness (IG), decomposed into precision and recall components to separately penalize hallucinated interest categories and reward coverage, and Interest Specificity (IS), which assesses the distinctiveness of verified LLM-predicted user profiles. We release a synthetic dataset constructed on real user interactions on a global short-form video platform. Our dataset contains both implicit and explicit engagement signals and rich textual descriptions. We validate our dataset fidelity against user surveys, and evaluate eight open-weight LLMs spanning 7B to 120B parameters. Our findings reveal performance bottlenecks in current LLMs, particularly their limited ability to accurately count and attribute engagement signals across heterogeneous interaction types.

SciVisAgentBench: A Benchmark for Evaluating Scientific Data Analysis and Visualization Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Kuangshi Ai · Haichao Miao · Kaiyuan Tang · Nathaniel Gorski · Jianxin Sun · Guoxi Liu · Helgi I. Ingolfsson · David Lenz · Hanqi Guo · Hongfeng Yu · Teja Leburu · Michael Molash · Bei Wang · Tom Peterka · Chaoli Wang · Shusen Liu
1 April 2026 at 12:00
arXiv:2603.29139v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have enabled agentic systems that translate natural language intent into executable scientific visualization (SciVis) tasks. Despite rapid progress, the community lacks a principled and reproducible benchmark for evaluating these emerging SciVis agents in realistic, multi-step analysis settings. We present SciVisAgentBench, a comprehensive and extensible benchmark for evaluating scientific data analysis and visualization agents. Our benchmark is grounded in a structured taxonomy spanning four dimensions: application domain, data type, complexity level, and visualization operation. It currently comprises 108 expert-crafted cases covering diverse SciVis scenarios. To enable reliable assessment, we introduce a multimodal outcome-centric evaluation pipeline that combines LLM-based judging with deterministic evaluators, including image-based metrics, code checkers, rule-based verifiers, and case-specific evaluators. We also conduct a validity study with 12 SciVis experts to examine the agreement between human and LLM judges. Using this framework, we evaluate representative SciVis agents and general-purpose coding agents to establish initial baselines and reveal capability gaps. SciVisAgentBench is designed as a living benchmark to support systematic comparison, diagnose failure modes, and drive progress in agentic SciVis. The benchmark is available at https://scivisagentbench.github.io/.

Predicting Neuromodulation Outcome for Parkinson's Disease with Generative Virtual Brain Model

✇cs.AI, q-bio.NC updates on arXiv.org
By: Siyuan Du · Siyi Li · Shuwei Bai · Ang Li · Haolin Li · Mingqing Xiao · Yang Pan · Dongsheng Li · Weidi Xie · Yanfeng Wang · Ya Zhang · Chencheng Zhang · Jiangchao Yao
1 April 2026 at 12:00
arXiv:2603.29176v1 Announce Type: new Abstract: Parkinson's disease (PD) affects over ten million people worldwide. Although temporal interference (TI) and deep brain stimulation (DBS) are promising therapies, inter-individual variability limits empirical treatment selection, increasing non-negligible surgical risk and cost. Previous explorations either resort to limited statistical biomarkers that are insufficient to characterize variability, or employ AI-driven methods which is prone to overfitting and opacity. We bridge this gap with a pretraining-finetuning framework to predict outcomes directly from resting-state fMRI. Critically, a generative virtual brain foundation model, pretrained on a collective dataset (2707 subjects, 5621 sessions) to capture universal disorder patterns, was finetuned on PD cohorts receiving TI (n=51) or DBS (n=55) to yield individualized virtual brains with high fidelity to empirical functional connectivity (r=0.935). By constructing counterfactual estimations between pathological and healthy neural states within these personalized models, we predicted clinical responses (TI: AUPR=0.853; DBS: AUPR=0.915), substantially outperforming baselines. External and prospective validations (n=14, n=11) highlight the feasibility of clinical translation. Moreover, our framework provides state-dependent regional patterns linked to response, offering hypothesis-generating mechanistic insights.

AEC-Bench: A Multimodal Benchmark for Agentic Systems in Architecture, Engineering, and Construction

✇cs.AI, q-bio.NC updates on arXiv.org
By: Harsh Mankodiya · Chase Gallik · Theodoros Galanos · Andriy Mulyar
1 April 2026 at 12:00
arXiv:2603.29199v1 Announce Type: new Abstract: The AEC-Bench is a multimodal benchmark for evaluating agentic systems on real-world tasks in the Architecture, Engineering, and Construction (AEC) domain. The benchmark covers tasks requiring drawing understanding, cross-sheet reasoning, and construction project-level coordination. This report describes the benchmark motivation, dataset taxonomy, evaluation protocol, and baseline results across several domain-specific foundation model harnesses. We use AEC-Bench to identify consistent tools and harness design techniques that uniformly improve performance across foundation models in their own base harnesses, such as Claude Code and Codex. We openly release our benchmark dataset, agent harness, and evaluation code for full replicability at https://github.com/nomic-ai/aec-bench under an Apache 2 license.

Route-Induced Density and Stability (RIDE): Controlled Intervention and Mechanism Analysis of Routing-Style Meta Prompts on LLM Internal States

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dianxing Zhang · Gang Li · Sheng Li
1 April 2026 at 12:00
arXiv:2603.29206v1 Announce Type: new Abstract: Routing is widely used to scale large language models, from Mixture-of-Experts gating to multi-model/tool selection. A common belief is that routing to a task ``expert'' activates sparser internal computation and thus yields more certain and stable outputs (the Sparsity--Certainty Hypothesis). We test this belief by injecting routing-style meta prompts as a textual proxy for routing signals in front of frozen instruction-tuned LLMs. We quantify (C1) internal density via activation sparsity, (C2) domain-keyword attention, and (C3) output stability via predictive entropy and semantic variation. On a RouterEval subset with three instruction-tuned models (Qwen3-8B, Llama-3.1-8B-Instruct, and Mistral-7B-Instruct-v0.2), meta prompts consistently densify early/middle-layer representations rather than increasing sparsity; natural-language expert instructions are often stronger than structured tags. Attention responses are heterogeneous: Qwen/Llama reduce keyword attention, while Mistral reinforces it. Finally, the densification--stability link is weak and appears only in Qwen, with near-zero correlations in Llama and Mistral. We present RIDE as a diagnostic probe for calibrating routing design and uncertainty estimation.

Xuanwu: Evolving General Multimodal Models into an Industrial-Grade Foundation for Content Ecosystems

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhiqian Zhang · Xu Zhao · Xiaoqing Xu · Guangdong Liang · Weijia Wang · Xiaolei Lv · Bo Li · Jun Gao
1 April 2026 at 12:00
arXiv:2603.29211v1 Announce Type: new Abstract: In recent years, multimodal large models have continued to improve on general benchmarks. However, in real-world content moderation and adversarial settings, mainstream models still suffer from degraded generalization and catastrophic forgetting because of limited fine-grained visual perception and insufficient modeling of long-tail noise. In this paper, we present Xuanwu VL-2B as a case study of how general multimodal models can be developed into an industrial-grade foundation model for content ecosystems. The model adopts a compact InternViT-300M + MLP + Qwen3 1.7B architecture, balancing fine-grained visual perception, language-semantic alignment, and deployment cost within an approximately 2B-parameter budget. To balance business specialization with the retention of general capabilities, we developed a data iteration and curation mechanism and trained the model through a progressive three-stage pipeline: pre-training, mid-training, and post-training. Ablation studies and offline business evaluations show that Xuanwu VL-2B achieves an average score of 67.90 across seven OpenCompass multimodal metrics (vs. 64.27 for InternVL 3.5 2B), an average recall of 94.38% over seven independent business moderation tasks, and a weighted overall recall of 82.82% on policy-violating text in challenging adversarial OCR scenarios, outperforming Gemini-2.5-Pro (76.72%). These results show that, under a limited parameter budget, Xuanwu VL-2B achieves a practical balance among business alignment, visual perception, general capability retention, and deployment cost.

PSPA-Bench: A Personalized Benchmark for Smartphone GUI Agent

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hongyi Nie · Xunyuan Liu · Yudong Bai · Yaqing Wang · Yang Liu · Quanming Yao · Zhen Wang
1 April 2026 at 12:00
arXiv:2603.29318v1 Announce Type: new Abstract: Smartphone GUI agents execute tasks by operating directly on app interfaces, offering a path to broad capability without deep system integration. However, real-world smartphone use is highly personalized: users adopt diverse workflows and preferences, challenging agents to deliver customized assistance rather than generic solutions. Existing GUI agent benchmarks cannot adequately capture this personalization dimension due to sparse user-specific data and the lack of fine-grained evaluation metrics. To address this gap, we present PSPA-Bench, the benchmark dedicated to evaluating personalization in smartphone GUI agents. PSPA-Bench comprises over 12,855 personalized instructions aligned with real-world user behaviors across 10 representative daily-use scenarios and 22 mobile apps, and introduces a structure-aware process evaluation method that measures agents' personalized capabilities at a fine-grained level. Through PSPA-Bench, we benchmark 11 state-of-the-art GUI agents. Results reveal that current methods perform poorly under personalized settings, with even the strongest agent achieving limited success. Our analysis further highlights three directions for advancing personalized GUI agents: (1) reasoning-oriented models consistently outperform general LLMs, (2) perception remains a simple yet critical capability, and (3) reflection and long-term memory mechanisms are key to improving adaptation. Together, these findings establish PSPA-Bench as a foundation for systematic study and future progress in personalized GUI agents.

ELT-Bench-Verified: Benchmark Quality Issues Underestimate AI Agent Capabilities

✇cs.AI, q-bio.NC updates on arXiv.org
By: Christopher Zanoli · Andrea Giovannini · Tengjun Jin · Ana Klimovic · Yotam Perlitz
1 April 2026 at 12:00
arXiv:2603.29399v1 Announce Type: new Abstract: Constructing Extract-Load-Transform (ELT) pipelines is a labor-intensive data engineering task and a high-impact target for AI automation. On ELT-Bench, the first benchmark for end-to-end ELT pipeline construction, AI agents initially showed low success rates, suggesting they lacked practical utility. We revisit these results and identify two factors causing a substantial underestimation of agent capabilities. First, re-evaluating ELT-Bench with upgraded large language models reveals that the extraction and loading stage is largely solved, while transformation performance improves significantly. Second, we develop an Auditor-Corrector methodology that combines scalable LLM-driven root-cause analysis with rigorous human validation (inter-annotator agreement Fleiss' kappa = 0.85) to audit benchmark quality. Applying this to ELT-Bench uncovers that most failed transformation tasks contain benchmark-attributable errors -- including rigid evaluation scripts, ambiguous specifications, and incorrect ground truth -- that penalize correct agent outputs. Based on these findings, we construct ELT-Bench-Verified, a revised benchmark with refined evaluation logic and corrected ground truth. Re-evaluating on this version yields significant improvement attributable entirely to benchmark correction. Our results show that both rapid model improvement and benchmark quality issues contributed to underestimating agent capabilities. More broadly, our findings echo observations of pervasive annotation errors in text-to-SQL benchmarks, suggesting quality issues are systemic in data engineering evaluation. Systematic quality auditing should be standard practice for complex agentic tasks. We release ELT-Bench-Verified to provide a more reliable foundation for progress in AI-driven data engineering automation.

FlowPIE: Test-Time Scientific Idea Evolution with Flow-Guided Literature Exploration

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qiyao Wang · Hongbo Wang · Longze Chen · Zhihao Yang · Guhong Chen · Hamid Alinejad-Rokny · Hui Li · Yuan Lin · Min Yang
1 April 2026 at 12:00
arXiv:2603.29557v1 Announce Type: new Abstract: Scientific idea generation (SIG) is critical to AI-driven autonomous research, yet existing approaches are often constrained by a static retrieval-then-generation paradigm, leading to homogeneous and insufficiently divergent ideas. In this work, we propose FlowPIE, a tightly coupled retrieval-generation framework that treats literature exploration and idea generation as a co-evolving process. FlowPIE expands literature trajectories via a flow-guided Monte Carlo Tree Search (MCTS) inspired by GFlowNets, using the quality of current ideas assessed by an LLM-based generative reward model (GRM) as a supervised signal to guide adaptive retrieval and construct a diverse, high-quality initial population. Based on this population, FlowPIE models idea generation as a test-time idea evolution process, applying selection, crossover, and mutation with the isolation island paradigm and GRM-based fitness computation to incorporate cross-domain knowledge. It effectively mitigates the information cocoons arising from over-reliance on parametric knowledge and static literature. Extensive evaluations demonstrate that FlowPIE consistently produces ideas with higher novelty, feasibility and diversity compared to strong LLM-based and agent-based frameworks, while enabling reward scaling during test time.

Structural and dynamical strategies to prevent runaway excitation in reservoir computing

✇cs.AI, q-bio.NC updates on arXiv.org
By: Claus Metzner · Achim Schilling · Andreas Maier · Thomas Kinfe · Patrick Krauss
1 April 2026 at 12:00
arXiv:2603.29597v1 Announce Type: new Abstract: Reservoirs, typically implemented as recurrent neural networks with fixed random connection weights, can be combined with a simple trained readout layer to perform a wide range of computational tasks. However, increasing the magnitude of reservoir connection weights to exploit nonlinear dynamics can cause the network to develop strong spontaneous activity that drives neurons into saturation, dramatically degrading performance. In this work, we investigate two distinct countermeasures against such runaway excitation. The first approach introduces a subtle non-homogeneous structure into the matrix of connection weigths $w_{ij}$, without altering the overall probability distribution $p(w)$. We identify several favorable structuring principles, such as creating a small subset of neurons with weaker-than-average input connections. Even if the rest of the reservoir falls into runaway saturating behavior, this weakly coupled subset remains in a mildly nonlinear regime whose dynamics can still be exploited by the readout layer. The second approach implements a form of automatic gain control, in which a dedicated control unit dynamically regulates the reservoir's average global activation toward an optimal setpoint. Although the control unit modulates the excitability of the reservoir only via a global gain factor, this mechanism substantially enlarges the dynamical regime favorable for computation and renders performance largely independent of the underlying connection statistics.

Convergent Representations of Linguistic Constructions in Human and Artificial Neural Systems

✇cs.AI, q-bio.NC updates on arXiv.org
By: Pegah Ramezani · Thomas Kinfe · Andreas Maier · Achim Schilling · Patrick Krauss
1 April 2026 at 12:00
arXiv:2603.29617v1 Announce Type: new Abstract: Understanding how the brain processes linguistic constructions is a central challenge in cognitive neuroscience and linguistics. Recent computational studies show that artificial neural language models spontaneously develop differentiated representations of Argument Structure Constructions (ASCs), generating predictions about when and how construction-level information emerges during processing. The present study tests these predictions in human neural activity using electroencephalography (EEG). Ten native English speakers listened to 200 synthetically generated sentences across four construction types (transitive, ditransitive, caused-motion, resultative) while neural responses were recorded. Analyses using time-frequency methods, feature extraction, and machine learning classification revealed construction-specific neural signatures emerging primarily at sentence-final positions, where argument structure becomes fully disambiguated, and most prominently in the alpha band. Pairwise classification showed reliable differentiation, especially between ditransitive and resultative constructions, while other pairs overlapped. Crucially, the temporal emergence and similarity structure of these effects mirror patterns in recurrent and transformer-based language models, where constructional representations arise during integrative processing stages. These findings support the view that linguistic constructions are neurally encoded as distinct form-meaning mappings, in line with Construction Grammar, and suggest convergence between biological and artificial systems on similar representational solutions. More broadly, this convergence is consistent with the idea that learning systems discover stable regions within an underlying representational landscape - recently termed a Platonic representational space - that constrains the emergence of efficient linguistic abstractions.
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