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Received — 27 May 2026 ⏭

Geo-Expert: Towards Expert-Level Geological Reasoning via Parameter-Efficient Fine-Tuning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chenyou Guo · Zongqi Liu · Yizhou Zhang · Zhaorui Jiang · Ze Liu
26 May 2026 at 12:00
arXiv:2605.24844v1 Announce Type: new Abstract: While general-purpose Large Language Models (LLMs) applied to Geology often hallucinate when reasoning about subsurface structures and deep-time evolution, current AI in Earth sciences predominantly targets surface remote sensing and GIS. To bridge this gap, we introduce Geo-Expert, a family of parameter-efficient geological LLMs fine-tuned on a custom-curated, high-quality instruction dataset processed using our custom instruction synthesis pipeline. We investigate the impact of model scaling and architecture by fine-tuning three base models: Qwen3-8B, Qwen3-32B, and Gemma-3-27B, with Low-Rank Adaptation (LoRA) method. Our extensive evaluation on a novel domain-specific benchmark, Geo-Eval, reveals that a domain-aligned 8B model can outperform open-weight 70B generalists and proprietary GPT-4o on specialized geological reasoning, while a 32B variant approaches frontier reasoning models. The optimized 8B model further offers a competitive cost-performance ratio for deployment. This work provides a reproducible recipe for democratizing scientific LLMs and establishes a baseline for geological artificial intelligence.

Distributionally Robust Transfer Learning with Structurally Missing Covariates, with Application to Cross-National Cardiac Arrest Prediction

✇cs.AI, q-bio.NC updates on arXiv.org
By: Siqi Li · Chuan Hong · Ziye Tian · Benjamin Sieu-Hon Leong · Koshi Nakagawa · Hideharu Tanaka · Sang Do Shin · Khuong Quoc Dai · Do Ngoc Son · Marcus Eng Hock Ong · Nan Liu · Molei Liu
26 May 2026 at 12:00
arXiv:2605.24212v1 Announce Type: cross Abstract: Deploying clinical prediction models across healthcare systems often fails when key training covariates are unavailable at deployment and labeled outcomes are limited in the target domain. For example, high-performing models for out-of-hospital cardiac arrest (OHCA) rely on detailed prehospital measurements routinely collected in high-resource settings but unavailable in many international registries. Existing methods either discard missing covariates, sacrificing predictive information, or rely on untestable assumptions about their target distribution. We propose DRUM (\underline{D}istributionally \underline{R}obust \underline{U}nsupervised transfer learning with structurally \underline{M}issing covariates), a framework that transfers prediction models to target populations where certain covariates are structurally absent and outcome labels are unavailable. DRUM partitions covariates into shared components ($X$), observed across all settings, and missing components ($A$), observed only in the source. Rather than imputing missing covariates, DRUM optimizes worst-case predictive performance over the unknown target distribution of $A \mid X$ using a neural network generator, with a robustness parameter controlling allowable deviation from the source conditional. We further develop a bias correction procedure that reduces sensitivity to nuisance estimation error. Simulations show substantial improvements in both mean and worst-case prediction error under distribution shift. Applied to cross-national OHCA prediction, transferring models from a US registry to multiple Asian registries where prehospital variables are unrecorded, DRUM yields better-calibrated predictions and improved clinical classification performance across sites.

Cross-Domain Energy-Guided Diffusion Generation for Off-Dynamics Reinforcement Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yu Yang · Yihong Guo · Anqi Liu · Pan Xu
26 May 2026 at 12:00
arXiv:2605.24810v1 Announce Type: cross Abstract: Off-dynamics offline reinforcement learning seeks to learn a target-domain policy from a large source dataset and a limited target dataset under mismatched transition dynamics. Existing approaches such as reward augmentation and data filtering are constrained to the source dataset and cannot synthesize new target behavior to improve coverage beyond the collected source trajectories. While recent model-based methods attempt to address this by learning target-aware dynamics, the generated experience is constructed only at the transition level, which leads to accumulated errors over long horizons. These limitations necessitate a shift toward trajectory-level generation for off-dynamics offline RL. We propose CEDGE, a Cross-domain Energy-guided Diffusion GEneration framework. CEDGE trains a trajectory diffusion model on source-domain trajectories and adapts the generated samples to the target domain through energy guidance. This guidance is derived by minimizing the distribution mismatch between the source and desired target-domain trajectories and is decomposed into return, domain, and behavior energy components. The resulting energy-guided trajectories are useful both for direct planning and as synthetic data for policy learning. Since target adaptation is achieved via energy guidance rather than retraining the diffusion model, CEDGE can be efficiently adapted to new target dynamics compared to previous methods. Experiments on the ODRL benchmark demonstrate that trajectory-level energy-guided generation improves diffusion planning under dynamics shifts and produces synthetic data that improves downstream target policy learning.

DRScaffold: Boosting Dense-Scene Reasoning in Lightweight Vision Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xinrui Shi · Kai Liu · Ziqing Zhang · Jianze Li · Anqi Li · Yulun Zhang
26 May 2026 at 12:00
arXiv:2605.26038v1 Announce Type: cross Abstract: Lightweight vision-language models perform competitively on standard benchmarks yet fail systematically in dense-scene reasoning, where multiple objects, attributes, and relations must be jointly grounded and resolved through multi-step inference. Such capability is critical for real-world applications where models must reliably interpret cluttered environments. Yet existing training signals provide no explicit grounding between reasoning steps and the underlying visual entities and relations, leaving lightweight models free to generate fluent but visually unanchored reasoning chains. To address this gap, we first introduce DRBench, a benchmark of 14,573 questions across 2,943 images, organized into five task categories spanning three progressive reasoning layers. Building on DRBench, we propose DRScaffold, a supervised fine-tuning framework that decomposes the supervision target into four causally ordered stages, enforcing grounded reasoning without architectural modification. Experiments on three lightweight VLMs demonstrate substantial gains on DRBench while preserving or improving performance on general-purpose benchmarks. Notably, Qwen2.5-VL-3B trained with DRScaffold surpasses the frozen Qwen2.5-VL-32B on DRBench, demonstrating that structured supervision can substitute for a significant portion of model scale in dense-scene reasoning. Our code and models are available at https://github.com/irene-shi/DRScaffold .

AutoResearchClaw: Self-Reinforcing Autonomous Research with Human-AI Collaboration

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiaqi Liu · Shi Qiu · Mairui Li · Bingzhou Li · Haonian Ji · Siwei Han · Xinyu Ye · Peng Xia · Zihan Dong · Meng Chen · Congyu Zhang · Letian Zhang · Guiming Chen · Haoqin Tu · Xinyu Yang · Lu Feng · Xujiang Zhao · Haifeng Chen · Jiawei Zhou · Xiao Wang · Weitong Zhang · Hongtu Zhu · Yun Li · Jieru Mei · Hongliang Fei · Jiaheng Zhang · Linjie Li · Linjun Zhang · Yuyin Zhou · Sheng Wang · Caiming Xiong · James Zou · Zeyu Zheng · Cihang Xie · Mingyu Ding · Huaxiu Yao
26 May 2026 at 12:00
arXiv:2605.20025v2 Announce Type: replace Abstract: Automating scientific discovery requires more than generating papers from ideas. Real research is iterative: hypotheses are challenged from multiple perspectives, experiments fail and inform the next attempt, and lessons accumulate across cycles. Existing autonomous research systems often model this process as a linear pipeline: they rely on single-agent reasoning, stop when execution fails, and do not carry experience across runs. We present AutoResearchClaw, a multi-agent autonomous research pipeline built on five mechanisms: structured multi-agent debate for hypothesis generation and result analysis, a self-healing executor with a \textsc{Pivot}/\textsc{Refine} decision loop that transforms failures into information, verifiable result reporting that prevents fabricated numbers and hallucinated citations, human-in-the-loop collaboration with seven intervention modes spanning full autonomy to step-by-step oversight, and cross-run evolution that converts past mistakes into future safeguards. On ARC-Bench, a 25-topic experiment-stage benchmark, AutoResearchClaw outperforms AI Scientist v2 by 54.7%. A human-in-the-loop ablation across seven intervention modes reveals that precise, targeted collaboration at high-leverage decision points consistently outperforms both full autonomy and exhaustive step-by-step oversight. We position AutoResearchClaw as a research amplifier that augments rather than replaces human scientific judgment. Code is available at https://github.com/aiming-lab/AutoResearchClaw.

SoK: A Comprehensive Security Analysis of Jailbreak Resilience in GPT and DeepSeek Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xiaodong Wu · Xiangman Li · Qi Li · Lingshuang Liu · Jianbing Ni
26 May 2026 at 12:00
arXiv:2506.18543v2 Announce Type: replace-cross Abstract: The rapid proliferation of Large Language Models (LLMs) has heightened concerns regarding their exposure to jailbreak attacks, which craft adversarial inputs designed to elicit unsafe content. Although proprietary models such as GPT-4 have been extensively evaluated, the robustness of emerging open-source systems like DeepSeek remains insufficiently examined, despite their growing use in LLM applications. In this paper, we conduct the first comprehensive jailbreak analysis of the DeepSeek model family, comparing it with GPT-3.5 and GPT-4 through the HarmBench benchmark. We investigate seven representative attack methods across 510 harmful behaviors, organized along both functional and semantic dimensions. Findings indicate that DeepSeek provides partial resilience against optimization-driven attacks such as TAP-T, but also results in greater susceptibility to prompt-based and manually engineered adversarial inputs. In contrast, GPT-4 Turbo demonstrates more robust and consistent safety alignment across a wide range of behaviors, likely due to stronger safety optimization and reinforcement learning from human feedback. In addition, fine-grained behavioral analysis and case studies reveal that DeepSeek often fails to consistently apply safety constraints to adversarial prompts, leading to uneven refusal behaviors. Overall, our results highlight an inherent trade-off between model efficiency and alignment generalization, underscoring the importance of targeted safety tuning and robust alignment strategies to ensure secure deployment of open-source LLMs.

STAPO: Stabilizing Reinforcement Learning for LLMs by Silencing Rare Spurious Tokens

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shiqi Liu · Zeyu He · Guojian Zhan · Letian Tao · Zhilong Zheng · Jiang Wu · Yinuo Wang · Yang Guan · Kehua Sheng · Bo Zhang · Keqiang Li · Jingliang Duan · Shengbo Eben Li
26 May 2026 at 12:00
arXiv:2602.15620v5 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) has significantly improved large language model reasoning, but existing RL fine-tuning methods rely heavily on heuristic techniques such as entropy regularization and reweighting to maintain stability. In practice, they often suffer from late-stage performance collapse, leading to degraded reasoning quality and unstable training. We identify a key factor behind this instability: a small fraction of tokens, termed spurious tokens (around 0.01%), which contribute little to the reasoning outcome but receive disproportionately amplified gradient updates due to inheriting the full sequence-level reward. We present a unified framework for evaluating token-level optimization impacts across spurious risk, gradient norms, and entropy changes. Building on the analysis of token characteristics that severely disrupt optimization, we propose the Silencing Spurious Tokens (S2T) mechanism to efficiently suppress their gradient perturbations. Incorporating this mechanism into a group-based objective, we propose Spurious-Token-Aware Policy Optimization (STAPO), which promotes stable and effective large-scale model refinement. Across six mathematical reasoning benchmarks using Qwen 1.7B, 8B, and 14B base models, STAPO consistently demonstrates superior entropy stability and achieves an average performance improvement of 11.49% ($\rho_{\mathrm{T}}$=1.0, top-p=1.0) and 3.73% ($\rho_{\mathrm{T}}$=0.7, top-p=0.9) over GRPO, 20-Entropy, and JustRL.

Proteomic and lipidomic analyses reveal molecular subtypes and potential targets in early-stage lung adenocarcinoma among non-smokers

✇Pulmonary nodule
By: Yaohui Sun · Jian Liu · Xin Xu · Qiqi Liu · Hua Cheng · Wenwen Huo · Ronghao Wang · Qingdong Cao
29 April 2026 at 18:00

Cell Rep. 2026 May 26;45(5):117215. doi: 10.1016/j.celrep.2026.117215. Epub 2026 Apr 28.

ABSTRACT

Early-stage lung adenocarcinoma (LUAD) in never smokers exhibits distinct biological features, yet the metabolic programs driving early invasion remain unclear. We integrate proteomic and lipidomic profiling of primary LUAD tumors from never smokers, matched normal adjacent tissues (NATs), and benign pulmonary nodules (BPNs). Integrated multi-omics analysis reveals coordinated dysregulation of lipid metabolism and immune signaling in early LUAD. Proteome-based network fusion stratifies invasive LUAD into immune-metabolic synergistic (IMS) and metabolic-stress-driven (MSD) subtypes. IMS tumors retain apolipoprotein-associated lipid modules and favorable immune features, whereas MSD tumors exhibit stress-response programs. Mechanistically, APOA1 and APOC1 emerge as key nodes linking lipid homeostasis to invasion, and their depletion promotes LUAD cell migration and invasion. We establish a two-protein, four-lipid diagnostic panel demonstrating robust performance across tissue and plasma cohorts. These findings provide a molecular basis for early detection and risk stratification in never smokers.

PMID:42054209 | DOI:10.1016/j.celrep.2026.117215

Received — 13 April 2026 ⏭

Establishment and characterization of an immortalized porcine gastric epithelial cell line and identification of NPC1 as a key mediator of aflatoxin B1 toxicity

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Liqi Liu · JianLin Yuan · Jinyi Bai · Yuhan Ma · Kaitao Kong · Xiaofei He · Shuangshuang Chen · Jin Chai · Keren Long · Long Jin · Mingzhou Li · Juan Deng
12 April 2026 at 18:00

Gene. 2026 Apr 9:150160. doi: 10.1016/j.gene.2026.150160. Online ahead of print.

ABSTRACT

Porcine gastric epithelial cells (PGECs) serve as a valuable model for studying the molecular and pathogenic mechanisms of the stomach. However, PGECs face limitations such as isolation challenges, short lifespan, and restricted proliferation. To address this, we established an immortalized PGECs (i-PGECs) to enable in vitro investigation of pathogen infection mechanisms. Primary PGECs were isolated from the acid-secreting glands using stepwise digestion with multiple enzymes (dispase II/collagenase I/hyaluronidase). Immortalization was achieved via lentiviral vectors expressing simian virus 40 large T antigen (SV40T) and human telomerase reverse transcriptase (hTERT), with successful expression confirmed by qRT-PCR (P < 0.05). Epithelial identity of i-PGECs was confirmed by stable expression of CK18, EpCAM, and E-cadherin, as shown by qRT-PCR and immunofluorescence. i-PGECs retained the morphological and ultrastructural features of PGECs and exhibited enhanced proliferation, as demonstrated by WST-8 assays, apoptosis and cell cycle analysis, karyotyping, and transmission electron microscopy (TEM). Telomere length analysis and scratch wound assays demonstrated stable telomere maintenance and consistent migration capacity unaffected by passaging. RNA-sequencing and differential expressed genes (DEGs) analysis revealed significantly upregulating of genes involved in cell proliferation pathways (P < 0.01). Following aflatoxin B1 (AFB1) exposure, i-PGECs significantly upregulated immune-related factors, such as NPC1 and PLAUR (P < 0.01). CRISPR/Cas9-mediated knockout of NPC1 in i-PGECs conferred increased resistance to AFB1-induced cytotoxicity, as shown by WST-8 assay. The i-PGECs remained stable after more than 50 passages, supporting their use as a reliable for in vitro model investigating the mechanisms of toxicity infection in the porcine gastric epithelium.

PMID:41966285 | DOI:10.1016/j.gene.2026.150160

Epigenome-wide Mendelian randomization with multi-omics validation identifies epigenetic drivers of idiopathic pulmonary fibrosis

✇Omics In Lung
By: Zhaoqi Li · Luna Hong · Xingyu Lv · Shuangyu Yang · Fengzhan Ye · Linjie Huang · Shanping Jiang · Huiying Zhao
11 April 2026 at 18:00

Commun Biol. 2026 Apr 11. doi: 10.1038/s42003-026-10033-1. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a complex disease without clear etiology or effective therapy. While DNA methylation has been implicated in IPF pathogenesis, the tissue-specific causal effects of the epigenetic factors on IPF remain undetermined. Here, we perform epigenome-wide Mendelian randomization using blood-based methylation quantitative trait loci of 420,509 CpG sites and genome-wide association study for IPF to elucidate the causal effects of the CpG sites on IPF. Totally, 452 CpG sites has shown putative causal effects on IPF risk after Bonferroni correction. Among them, 13 CpG sites have shown strong colocalization evidence with genetic factors associated with IPF. Specifically, DNA methylation at CpG sites within MAN2A2 and TRIM27 shows significant differences between IPF lungs and controls, correlating with altered mRNA expressions of these genes in lung tissues. The CpG site in MAN2A2 is a binding site of ZNF384 according to transcription factor databases. RNA sequencing in the TGFβ1-induced alveolar epithelia confirms significantly reduced expression of MAN2A2 and ZNF384 comparing to the controls. Collectively, our study suggests a putative causal link between DNA methylation within MAN2A2 and IPF risk, wherein lung-specific DNA methylation in MAN2A2 may perturb the interaction between ZNF384 and MAN2A2, revealing novel roles for these genes in IPF pathogenesis.

PMID:41965819 | DOI:10.1038/s42003-026-10033-1

Epigenome-wide Mendelian randomization with multi-omics validation identifies epigenetic drivers of idiopathic pulmonary fibrosis

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Zhaoqi Li · Luna Hong · Xingyu Lv · Shuangyu Yang · Fengzhan Ye · Linjie Huang · Shanping Jiang · Huiying Zhao
11 April 2026 at 18:00

Commun Biol. 2026 Apr 11. doi: 10.1038/s42003-026-10033-1. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a complex disease without clear etiology or effective therapy. While DNA methylation has been implicated in IPF pathogenesis, the tissue-specific causal effects of the epigenetic factors on IPF remain undetermined. Here, we perform epigenome-wide Mendelian randomization using blood-based methylation quantitative trait loci of 420,509 CpG sites and genome-wide association study for IPF to elucidate the causal effects of the CpG sites on IPF. Totally, 452 CpG sites has shown putative causal effects on IPF risk after Bonferroni correction. Among them, 13 CpG sites have shown strong colocalization evidence with genetic factors associated with IPF. Specifically, DNA methylation at CpG sites within MAN2A2 and TRIM27 shows significant differences between IPF lungs and controls, correlating with altered mRNA expressions of these genes in lung tissues. The CpG site in MAN2A2 is a binding site of ZNF384 according to transcription factor databases. RNA sequencing in the TGFβ1-induced alveolar epithelia confirms significantly reduced expression of MAN2A2 and ZNF384 comparing to the controls. Collectively, our study suggests a putative causal link between DNA methylation within MAN2A2 and IPF risk, wherein lung-specific DNA methylation in MAN2A2 may perturb the interaction between ZNF384 and MAN2A2, revealing novel roles for these genes in IPF pathogenesis.

PMID:41965819 | DOI:10.1038/s42003-026-10033-1

Received — 8 April 2026 ⏭

ClawArena: Benchmarking AI Agents in Evolving Information Environments

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haonian Ji · Kaiwen Xiong · Siwei Han · Peng Xia · Shi Qiu · Yiyang Zhou · Jiaqi Liu · Jinlong Li · Bingzhou Li · Zeyu Zheng · Cihang Xie · Huaxiu Yao
7 April 2026 at 12:00
arXiv:2604.04202v1 Announce Type: cross Abstract: AI agents deployed as persistent assistants must maintain correct beliefs as their information environment evolves. In practice, evidence is scattered across heterogeneous sources that often contradict one another, new information can invalidate earlier conclusions, and user preferences surface through corrections rather than explicit instructions. Existing benchmarks largely assume static, single-authority settings and do not evaluate whether agents can keep up with this complexity. We introduce ClawArena, a benchmark for evaluating AI agents in evolving information environments. Each scenario maintains a complete hidden ground truth while exposing the agent only to noisy, partial, and sometimes contradictory traces across multi-channel sessions, workspace files, and staged updates. Evaluation is organized around three coupled challenges: multi-source conflict reasoning, dynamic belief revision, and implicit personalization, whose interactions yield a 14-category question taxonomy. Two question formats, multi-choice (set-selection) and shell-based executable checks, test both reasoning and workspace grounding. The current release contains 64 scenarios across 8 professional domains, totaling 1{,}879 evaluation rounds and 365 dynamic updates. Experiments on five agent frameworks and five language models show that both model capability (15.4% range) and framework design (9.2%) substantially affect performance, that self-evolving skill frameworks can partially close model-capability gaps, and that belief revision difficulty is determined by update design strategy rather than the mere presence of updates. Code is available at https://github.com/aiming-lab/ClawArena.

Representation learning to advance multi-institutional studies with electronic health record data from US and France

✇cs.AI, q-bio.NC updates on arXiv.org
By: Doudou Zhou · Han Tong · Linshanshan Wang · Suqi Liu · Xin Xiong · Ziming Gan · Romain Griffier · Boris Hejblum · Yun-Chung Liu · Chuan Hong · Clara-Lea Bonzel · Tianrun Cai · Kevin Pan · Yuk-Lam Ho · Lauren Costa · Vidul A. Panickan · J. Michael Gaziano · Kenneth Mandl · Vianney Jouhet · Rodolphe Thiebaut · Zongqi Xia · Kelly Cho · Katherine Liao · Tianxi Cai
7 April 2026 at 12:00
arXiv:2502.08547v2 Announce Type: replace Abstract: The widespread adoption of electronic health records has created new opportunities for translational clinical research, yet this promise remains constrained by fragmented data across privacy-siloed institutions and substantial heterogeneity in local coding practices. While privacy-preserving collaborative learning allows institutions to work together without sharing patient-level data, it does not address inconsistencies in how clinical concepts are represented across sites. We introduce a graph-based framework that addresses this gap by treating data harmonization as a scalable representation learning problem. Rather than relying on fixed standards or manual mappings, the framework integrates institution-specific summary statistics from health records, curated biomedical knowledge graphs, and semantic information derived from large language models to learn a shared semantic space. This joint learning approach aligns diverse, site-specific vocabularies while preserving patient privacy. Evaluated across seven institutions and two languages, the framework provides a robust, data-centric foundation for training and deploying clinical models across heterogeneous healthcare systems.

A Model Can Help Itself: Reward-Free Self-Training for LLM Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Mengqi Li · Lei Zhao · Anthony Man-Cho So · Ruoyu Sun · Xiao Li
7 April 2026 at 12:00
arXiv:2510.18814v2 Announce Type: replace-cross Abstract: Can language models improve their reasoning performance without external rewards, using only their own sampled responses for training? We show that they can. We propose Self-evolving Post-Training (SePT), a simple post-training method that alternates between self-generation and training on self-generated responses. It repeatedly samples questions, uses the model itself to generate low-temperature responses, and then finetunes the model on the self-generated data. In this self-training loop, we use an online data refresh mechanism, where each new batch is generated by the most recently updated model. Across six math reasoning benchmarks, SePT improves a strong no-training baseline, defined as the untuned base model evaluated at its best swept decoding temperature, on several tested models. In some settings, SePT can even approach the performance of Reinforcement Learning with Verifiable Rewards (RLVR). Additional ablations demonstrate the importance of online data refresh and temperature decoupling. Overall, our results identify a practical regime in which reasoning can be improved using self-generated supervision alone. Our code is available at https://github.com/ElementQi/SePT.
Received — 4 April 2026 ⏭

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
3 April 2026 at 12:00
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.

DarwinNet: An Evolutionary Network Architecture for Agent-Driven Protocol Synthesis

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jinliang Xu · Bingqi Li
3 April 2026 at 12:00
arXiv:2604.01236v1 Announce Type: cross Abstract: Traditional network architectures suffer from severe protocol ossification and structural fragility due to their reliance on static, human-defined rules that fail to adapt to the emergent edge cases and probabilistic reasoning of modern autonomous agents. To address these limitations, this paper proposes DarwinNet, a bio-inspired, self-evolving network architecture that transitions communication protocols from a \textit{design-time} static paradigm to a \textit{runtime} growth paradigm. DarwinNet utilizes a tri-layered framework-comprising an immutable physical anchor (L0), a WebAssembly-based fluid cortex (L1), and an LLM-driven Darwin cortex (L2)-to synthesize high-level business intents into executable bytecode through a dual-loop \textit{Intent-to-Bytecode} (I2B) mechanism. We introduce the Protocol Solidification Index (PSI) to quantify the evolutionary maturity of the system as it collapses from high-latency intelligent reasoning (Slow Thinking) toward near-native execution (Fast Thinking). Validated through a reliability growth framework based on the Crow-AMSAA model, experimental results demonstrate that DarwinNet achieves anti-fragility by treating environmental anomalies as catalysts for autonomous evolution. Our findings confirm that DarwinNet can effectively converge toward physical performance limits while ensuring endogenous security through zero-trust sandboxing, providing a viable path for the next generation of intelligent, self-optimizing networks.

Omni-SimpleMem: Autoresearch-Guided Discovery of Lifelong Multimodal Agent Memory

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiaqi Liu · Zipeng Ling · Shi Qiu · Yanqing Liu · Siwei Han · Peng Xia · Haoqin Tu · Zeyu Zheng · Cihang Xie · Charles Fleming · Mingyu Ding · Huaxiu Yao
3 April 2026 at 12:00
arXiv:2604.01007v2 Announce Type: replace Abstract: AI agents increasingly operate over extended time horizons, yet their ability to retain, organize, and recall multimodal experiences remains a critical bottleneck. Building effective lifelong memory requires navigating a vast design space spanning architecture, retrieval strategies, prompt engineering, and data pipelines; this space is too large and interconnected for manual exploration or traditional AutoML to explore effectively. We deploy an autonomous research pipeline to discover Omni-SimpleMem, a unified multimodal memory framework for lifelong AI agents. Starting from a na\"ive baseline (F1=0.117 on LoCoMo), the pipeline autonomously executes ${\sim}50$ experiments across two benchmarks, diagnosing failure modes, proposing architectural modifications, and repairing data pipeline bugs, all without human intervention in the inner loop. The resulting system achieves state-of-the-art on both benchmarks, improving F1 by +411% on LoCoMo (0.117$\to$0.598) and +214% on Mem-Gallery (0.254$\to$0.797) relative to the initial configurations. Critically, the most impactful discoveries are not hyperparameter adjustments: bug fixes (+175%), architectural changes (+44%), and prompt engineering (+188% on specific categories) each individually exceed the cumulative contribution of all hyperparameter tuning, demonstrating capabilities fundamentally beyond the reach of traditional AutoML. We provide a taxonomy of six discovery types and identify four properties that make multimodal memory particularly suited for autoresearch, offering guidance for applying autonomous research pipelines to other AI system domains. Code is available at this https://github.com/aiming-lab/SimpleMem.

Well-Being and Cognitive Factors Influencing Health Care Workers’ Adherence to Internet-Based Stress Management: Mixed Methods Analysis of a Nonrandomized Controlled Study

✇Journal of Medical Internet Research
By: Miqi Li · Quan Wang · Danli Zheng · Danni Feng · Xiaorong Lang
2 April 2026 at 23:00
Background: High stress levels are common among health care workers (HCWs), threatening their health and workforce stability. Internet-based mobile stress management (MSM) is a promising intervention for reducing work-related stress; however, poor adherence limits effectiveness. Exploring factors influencing HCWs’ adherence may thus aid in developing optimal interventions. Objective: The research aimed to investigate (1) how HCWs’ well-being and cognitive factors influenced MSM treatment adherence and (2) what HCWs’ specific needs for MSM were. Methods: This study was a convergent mixed methods secondary analysis of a nonrandomized controlled trial. HCWs who were currently employed, had internet access, had no serious medical problems, and were willing to participate were recruited by convenience sampling through an MSM project in a large Chinese general hospital from August 11, 2021, to January 31, 2022. Those intending to leave the hospital or with insufficient medical condition for follow-up were excluded. Quantitative data were collected from 157 HCWs (n=135, 86% female participants; mean age of 33.7, SD 4.9 y) electronically via Research Electronic Data Capture (REDCap). Measures included sociodemographic characteristics, the Fatigue Assessment Scale, the 14-item Perceived Stress Scale, a user experience questionnaire, an attitudes scale (perceived usefulness, feasibility, and enjoyment), and self-reported practice frequency. Qualitative data were collected via an open-ended question answered by 96 participants. Quantitative data were analyzed using hierarchical regression and structural equation modeling. Qualitative data were analyzed using reflexive-thematic analysis in NVivo (QSR International). Results: In the quantitative study (n=157), hierarchical regression analyses showed that fatigue was a significant negative predictor of adherence (b=−0.050, 95% CI −0.086 to −0.015; =−2.859; 2-tailed =.005), while user experience (b=0.074, 95% CI 0.042-0.106; =4.569; 2-tailed

Efficient amyloid-β degradation in Alzheimer’s disease using SPYTACs

✇Cell
By: Fei Teng · Jing Liu · Tongtong Cui · Xiangtian Tan · Kailun Liu · Zongren Hou · Li Zhou · Yuanzhi Xie · Rongqi Li · Da Li · Bojin Li · Dongmei Wang · Qi Zhou · Baoyang Hu · Wei Li
4 March 2026 at 08:00
SPYTAC is a synthetic peptide-programmed targeted protein degradation platform harnessing LRP1 to drive lysosomal degradation of extracellular amyloid-β in the brain and periphery. In 5×FAD mice, SPYTAC treatment efficiently degrades amyloid-β, preserves neurons, and improves cognition with reduced neuroinflammation and microhemorrhage when compared with antibody therapy.

Restoring circadian rhythms in the hypothalamic paraventricular nucleus reverses aging biomarkers and extends lifespan in male mice

✇Cell
By: Haijiao Zhao · Meimei Liao · Ran Huo · Ting He · Hongni Tian · Zeqi Li · Chen Chen · Ziqing Yu · Juan Chai · Xiaocui Song · Ruichao Shao · Shuhua Ying · Wen Gao · Ling Liu · Di Sang · Qi Li · Haohong Li · Fengchao Wang · Dapeng Ju · Eric Erquan Zhang
4 March 2026 at 08:00
Enhancing circadian amplitude in mouse hypothalamic paraventricular nucleus neurons by 3′-deoxyadenosine treatment alleviates age-related pathologies and extends lifespan.
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