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Received — 14 March 2026 ⏭

Integrated Network Toxicology and Metabolomics Elucidate Mechanisms of Carbosulfan-Induced Respiratory Toxicity in Rats

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Xian Ju · Di Liang · Hongyu Su · Yachun Zhang · Zhenyu Liang · Yiheng Liu · Wenqi Zhao · Dan Zhang · Zhe Chen · Keming Yun
14 March 2026 at 18:00

Int J Mol Sci. 2026 Feb 25;27(5):2170. doi: 10.3390/ijms27052170.

ABSTRACT

Carbosulfan is a widely used carbamate insecticide, yet its mechanisms of respiratory toxicity remain poorly understood. This study integrated network toxicology, untargeted metabolomics, and molecular docking to systematically investigate the potential mechanisms of carbosulfan-induced respiratory toxicity in male Sprague Dawley rats. Rats were administered a single oral dose of carbosulfan (125 or 250 mg/kg) and assessed after 12 h. Exposure resulted in significant pathological lung damage, characterized by disrupted alveolar architecture, inflammatory cell infiltration, and increased serum levels of the pro-inflammatory cytokines IL-6, IL-1β, and TNF-α. Network toxicology analysis identified 51 potential targets associated with respiratory toxicity, with core targets including SRC, EGFR, PTGS2, CXCL8, CYP3A4, and NR3C1. Enriched pathways were primarily related to neuroactive ligand-receptor interaction, VEGF signaling, and arachidonic acid metabolism. Untargeted metabolomics revealed significant metabolic perturbations in pathways central to antioxidant defense and energy homeostasis, including glutathione metabolism, the tricarboxylic acid cycle, and arginine biosynthesis. Molecular docking confirmed stable in silico binding affinities between carbosulfan and the predicted core targets. Integrative analysis suggests that carbosulfan exposure is associated with respiratory damage, potentially through interconnected mechanisms involving oxidative stress, inflammation, and disruption of cell signaling and metabolic enzyme systems. However, given the acute high-dose nature of the model and the interpretative integration of multi-omics data, these findings should be considered hypothesis-generating. This study provides a novel system-level perspective on carbosulfan-induced respiratory toxicity and highlights key pathways and targets for future validation in chronic exposure models.

PMID:41828400 | PMC:PMC12984169 | DOI:10.3390/ijms27052170

Integrated Network Toxicology and Metabolomics Elucidate Mechanisms of Carbosulfan-Induced Respiratory Toxicity in Rats

✇Omics In Lung
By: Xian Ju · Di Liang · Hongyu Su · Yachun Zhang · Zhenyu Liang · Yiheng Liu · Wenqi Zhao · Dan Zhang · Zhe Chen · Keming Yun
14 March 2026 at 18:00

Int J Mol Sci. 2026 Feb 25;27(5):2170. doi: 10.3390/ijms27052170.

ABSTRACT

Carbosulfan is a widely used carbamate insecticide, yet its mechanisms of respiratory toxicity remain poorly understood. This study integrated network toxicology, untargeted metabolomics, and molecular docking to systematically investigate the potential mechanisms of carbosulfan-induced respiratory toxicity in male Sprague Dawley rats. Rats were administered a single oral dose of carbosulfan (125 or 250 mg/kg) and assessed after 12 h. Exposure resulted in significant pathological lung damage, characterized by disrupted alveolar architecture, inflammatory cell infiltration, and increased serum levels of the pro-inflammatory cytokines IL-6, IL-1β, and TNF-α. Network toxicology analysis identified 51 potential targets associated with respiratory toxicity, with core targets including SRC, EGFR, PTGS2, CXCL8, CYP3A4, and NR3C1. Enriched pathways were primarily related to neuroactive ligand-receptor interaction, VEGF signaling, and arachidonic acid metabolism. Untargeted metabolomics revealed significant metabolic perturbations in pathways central to antioxidant defense and energy homeostasis, including glutathione metabolism, the tricarboxylic acid cycle, and arginine biosynthesis. Molecular docking confirmed stable in silico binding affinities between carbosulfan and the predicted core targets. Integrative analysis suggests that carbosulfan exposure is associated with respiratory damage, potentially through interconnected mechanisms involving oxidative stress, inflammation, and disruption of cell signaling and metabolic enzyme systems. However, given the acute high-dose nature of the model and the interpretative integration of multi-omics data, these findings should be considered hypothesis-generating. This study provides a novel system-level perspective on carbosulfan-induced respiratory toxicity and highlights key pathways and targets for future validation in chronic exposure models.

PMID:41828400 | PMC:PMC12984169 | DOI:10.3390/ijms27052170

MAPK14/SLC7A11/GPX4 axis dysregulation drives podocyte ferroptosis via mediating glycerophospholipid metabolism

✇Cell Death Discovery nature.com science feeds
By: Shi Qiu · Dandan Xie · Sifan Guo · Zhibo Wang · Ying Cai · Xian Wang · Zhencai Hu · Shiwei Wang · Chunsheng Lin · Hong Yao · Qiang Yang · Yu Guan · Qiqi Zhao · Songqi Tang · Wenjie Sun · Yiqiang Xie · Aihua Zhang
11 March 2026 at 08:00

Cell Death Discovery, Published online: 11 March 2026; doi:10.1038/s41420-026-02990-7

MAPK14/SLC7A11/GPX4 axis dysregulation drives podocyte ferroptosis via mediating glycerophospholipid metabolism

SynPlanResearch-R1: Encouraging Tool Exploration for Deep Research with Synthetic Plans

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hansi Zeng · Zoey Li · Yifan Gao · Chenwei Zhang · Xiaoman Pan · Tao Yang · Fengran Mo · Jiacheng Lin · Xian Li · Jingbo Shang
10 March 2026 at 12:00
arXiv:2603.07853v1 Announce Type: new Abstract: Research Agents enable models to gather information from the web using tools to answer user queries, requiring them to dynamically interleave internal reasoning with tool use. While such capabilities can in principle be learned via reinforcement learning with verifiable rewards (RLVR), we observe that agents often exhibit poor exploration behaviors, including premature termination and biased tool usage. As a result, RLVR alone yields limited improvements. We propose SynPlanResearch-R1, a framework that synthesizes tool-use trajectories that encourage deeper exploration to shape exploration during cold-start supervised fine-tuning, providing a strong initialization for subsequent RL. Across seven multi-hop and open-web benchmarks, \framework improves performance by up to 6.0% on Qwen3-8B and 5.8% on Qwen3-4B backbones respectively compared to SOTA baselines. Further analyses of tool-use patterns and training dynamics compared to baselines shed light on the factors underlying these gains. Our code is publicly available at https://github.com/HansiZeng/syn-plan-research.

PIRA-Bench: A Transition from Reactive GUI Agents to GUI-based Proactive Intent Recommendation Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuxiang Chai · Shunye Tang · Han Xiao · Rui Liu · Hongsheng Li
10 March 2026 at 12:00
arXiv:2603.08013v1 Announce Type: new Abstract: Current Graphical User Interface (GUI) agents operate primarily under a reactive paradigm: a user must provide an explicit instruction for the agent to execute a task. However, an intelligent AI assistant should be proactive, which is capable of anticipating user intentions directly from continuous visual inputs, such as mobile or desktop screenshots, and offering timely recommendations without explicit user prompting. Transitioning to this proactive paradigm presents significant challenges. Real-world screen activity is rarely linear; it consists of long-horizon trajectories fraught with noisy browsing, meaningless actions, and multithreaded task-switching. To address this gap, we introduce PIRA-Bench (Proactive Intent Recommendation Agent Benchmark), a novel benchmark for evaluating multimodal large language models (MLLMs) on continuous, weakly-supervised visual inputs. Unlike reactive datasets, PIRA-Bench features complex trajectories with multiple interleaved intents and noisy segments with various user profile contexts, challenging agents to detect actionable events while fitting to user preferences. Furthermore, we propose the PIRF baseline, a memory-aware, state-tracking framework that empowers general MLLMs to manage multiple task threads and handle misleading visual inputs. PIRA-Bench serves as an initial step toward robust and proactive GUI-based personal assistants.
Received — 11 March 2026 ⏭

Thinking with Gaze: Sequential Eye-Tracking as Visual Reasoning Supervision for Medical VLMs

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yiwei Li · Zihao Wu · Yanjun Lv · Hanqi Jiang · Weihang You · Zhengliang Liu · Dajiang Zhu · Xiang Li · Quanzheng Li · Tianming Liu · Lin Zhao
10 March 2026 at 12:00
arXiv:2603.06697v1 Announce Type: cross Abstract: Vision--language models (VLMs) process images as visual tokens, yet their intermediate reasoning is often carried out in text, which can be suboptimal for visually grounded radiology tasks. Radiologists instead diagnose via sequential visual search; eye-tracking captures this process as time-ordered gaze trajectories that reveal how evidence is acquired over time. We use eye-gaze as supervision to guide VLM reasoning by introducing a small set of dedicated gaze tokens. These tokens are trained to predict gaze-selected image patch indices in temporal order, encouraging the model to follow human-like evidence acquisition and integration. Experiments on MIMIC-EYE and multiple external zero-shot benchmarks show consistent gains over baselines, achieving state-of-the-art in-domain performance and improved out-of-domain robustness. These results highlight temporally ordered gaze as an effective supervision signal for learning visually grounded medical reasoning.

CCR-Bench: A Comprehensive Benchmark for Evaluating LLMs on Complex Constraints, Control Flows, and Real-World Cases

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xiaona Xue · Yiqiao Huang · Jiacheng Li · Yuanhang Zheng · Huiqi Miao · Yunfei Ma · Rui Liu · Xinbao Sun · Minglu Liu · Fanyu Meng · Chao Deng · Junlan Feng
10 March 2026 at 12:00
arXiv:2603.07886v1 Announce Type: cross Abstract: Enhancing the ability of large language models (LLMs) to follow complex instructions is critical for their deployment in real-world applications. However, existing evaluation methods often oversimplify instruction complexity as a mere additive combination of atomic constraints, failing to adequately capture the high-dimensional complexity arising from the intricate interplay of content and format, logical workflow control, and real-world applications. This leads to a significant gap between current evaluation practices and practical demands. To bridge this gap, we introduce CCR-Bench, a novel benchmark designed to assess LLMs' adherence to complex instructions. CCR-Bench is characterized by: (1) deep entanglement of content and formatting requirements in task specifications; (2) instructions that involve intricate task decomposition, conditional reasoning, and procedural planning; and (3) evaluation samples derived entirely from real-world industrial scenarios. Extensive experiments on CCR-Bench demonstrate that even state-of-the-art models exhibit substantial performance deficiencies, clearly quantifying the gap between current LLM capabilities and the demands of realworld instruction understanding. We believe that CCR-Bench offers a more rigorous and realistic evaluation framework, advancing the development of LLMs toward the next generation of models capable of understanding and executing complex tasks in industrial applications.

VORL-EXPLORE: A Hybrid Learning Planning Approach to Multi-Robot Exploration in Dynamic Environments

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ning Liu · Sen Shen · Zheng Li · Sheng Liu · Dongkun Han · Shangke Lyu · Thomas Braunl
10 March 2026 at 12:00
arXiv:2603.07973v1 Announce Type: cross Abstract: Hierarchical multi-robot exploration commonly decouples frontier allocation from local navigation, which can make the system brittle in dense and dynamic environments. Because the allocator lacks direct awareness of execution difficulty, robots may cluster at bottlenecks, trigger oscillatory replanning, and generate redundant coverage. We propose VORL-EXPLORE, a hybrid learning and planning framework that addresses this limitation through execution fidelity, a shared estimate of local navigability that couples task allocation with motion execution. This fidelity signal is incorporated into a fidelity-coupled Voronoi objective with inter-robot repulsion to reduce contention before it emerges. It also drives a risk-aware adaptive arbitration mechanism between global A* guidance and a reactive reinforcement learning policy, balancing long-range efficiency with safe interaction in confined spaces. The framework further supports online self-supervised recalibration of the fidelity model using pseudo-labels derived from recent progress and safety outcomes, enabling adaptation to non-stationary obstacles without manual risk tuning. We evaluate this capability separately in a dedicated severe-traffic ablation. Extensive experiments in randomized grids and a Gazebo factory scenario show high success rates, shorter path length, lower overlap, and robust collision avoidance. The source code will be made publicly available upon acceptance.

Adaptation of Agentic AI: A Survey of Post-Training, Memory, and Skills

✇cs.AI, q-bio.NC updates on arXiv.org
By: Pengcheng Jiang · Jiacheng Lin · Zhiyi Shi · Zifeng Wang · Luxi He · Yichen Wu · Ming Zhong · Peiyang Song · Qizheng Zhang · Heng Wang · Xueqiang Xu · Hanwen Xu · Pengrui Han · Dylan Zhang · Jiashuo Sun · Chaoqi Yang · Kun Qian · Tian Wang · Changran Hu · Manling Li · Quanzheng Li · Hao Peng · Sheng Wang · Jingbo Shang · Chao Zhang · Jiaxuan You · Liyuan Liu · Pan Lu · Yu Zhang · Heng Ji · Yejin Choi · Dawn Song · Jimeng Sun · Jiawei Han
10 March 2026 at 12:00
arXiv:2512.16301v3 Announce Type: replace Abstract: Large language model (LLM) agents are moving beyond prompting alone. ChatGPT marked the rise of general-purpose LLM assistants, DeepSeek showed that on-policy reinforcement learning with verifiable rewards can improve reasoning and tool use, and OpenClaw highlights a newer direction in which agents accumulate persistent memory and reusable skills. Yet the research landscape remains fragmented across post-training, retrieval, memory, and skill systems. This survey studies these developments under a single notion of \emph{adaptation}: improving an agent, its tools, or their interaction after pretraining. We organize the field with a four-paradigm framework spanning agent adaptation and tool adaptation. On the agent side, A1 (tool-execution-signaled) and A2 (agent-output-signaled) improve the agent itself through supervised fine-tuning, preference optimization, and reinforcement learning with verifiable rewards. On the tool side, T1 (agent-agnostic) provides reusable pre-trained modules any agent can call, while T2 (agent-supervised) uses the agent's outputs to train memory systems, skill libraries, or lightweight subagents. Using this framework, we review post-training methods, adaptive memory architectures, and agent skills; compare their trade-offs in cost, flexibility, and generalization; and summarize evaluation practices across deep research, software development, computer use, and drug discovery. We conclude by outlining open problems in agent-tool co-adaptation, continual learning, safety, and efficient deployment.

To Mix or To Merge: Toward Multi-Domain Reinforcement Learning for Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haoqing Wang · Xiang Long · Ziheng Li · Yilong Xu · Tingguang Li · Yehui Tang
10 March 2026 at 12:00
arXiv:2602.12566v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) plays a key role in stimulating the explicit reasoning capability of Large Language Models (LLMs). We can achieve expert-level performance in some specific domains via RLVR, such as coding or math. When a general multi-domain expert-level model is required, we need to carefully consider the collaboration of RLVR across different domains. The current state-of-the-art models mainly employ two different training paradigms for multi-domain RLVR: mixed multi-task RLVR and separate RLVR followed by model merging. However, most of the works did not provide a detailed comparison and analysis about these paradigms. To this end, we choose multiple commonly used high-level tasks (e.g., math, coding, science, instruction following, and agent) as our target domains and design extensive qualitative and quantitative experiments using open-source datasets. We find the RLVR across domains exhibits few mutual interferences, and reasoning-intensive domains demonstrate mutually synergistic effects. Furthermore, we analyze the internal mechanisms of mutual gains from the perspectives of weight space geometry, model prediction behavior, information constraints and self-verification. This project is named as M2RL that means Mixed multi-task training or separate training followed by model Merging for Reinforcement Learning, and the homepage is at https://github.com/mosAI25/M2RL.
  • ✇cs.AI, q-bio.NC updates on arXiv.org
  • Agentified Assessment of Logical Reasoning Agents Zhiyu Ni · Yifeng Xiao · Zheng Liang
    arXiv:2603.02788v2 Announce Type: replace Abstract: We present a framework for evaluating and benchmarking logical reasoning agents when assessment itself must be reproducible, auditable, and robust to execution failures. Building on agentified assessment, we use an assessor agent to issue tasks, enforce execution budgets, parse outputs, and record structured failure types, while the agent under test only needs to expose a standardized agent-to-agent interface. As a case study, we benchmark an
     
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Agentified Assessment of Logical Reasoning Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhiyu Ni · Yifeng Xiao · Zheng Liang
10 March 2026 at 12:00
arXiv:2603.02788v2 Announce Type: replace Abstract: We present a framework for evaluating and benchmarking logical reasoning agents when assessment itself must be reproducible, auditable, and robust to execution failures. Building on agentified assessment, we use an assessor agent to issue tasks, enforce execution budgets, parse outputs, and record structured failure types, while the agent under test only needs to expose a standardized agent-to-agent interface. As a case study, we benchmark an auto-formalization agent for first-order logic (FOL) reasoning on a solver-verified and repaired split of FOLIO. The agent translates natural language premises and conclusions into executable Z3Py programs and employs satisfiability modulo theories (SMT) solving to determine logical entailment. On the cleaned FOLIO validation set, the auto-formalization agent achieves 86.70% accuracy under the assessor protocol, outperforming a chain-of-thought baseline (73.89%).

DrivingGen: A Comprehensive Benchmark for Generative Video World Models in Autonomous Driving

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yang Zhou · Hao Shao · Letian Wang · Zhuofan Zong · Hongsheng Li · Steven L. Waslander
10 March 2026 at 12:00
arXiv:2601.01528v2 Announce Type: replace-cross Abstract: Video generation models, as one form of world models, have emerged as one of the most exciting frontiers in AI, promising agents the ability to imagine the future by modeling the temporal evolution of complex scenes. In autonomous driving, this vision gives rise to driving world models: generative simulators that imagine ego and agent futures, enabling scalable simulation, safe testing of corner cases, and rich synthetic data generation. Yet, despite fast-growing research activity, the field lacks a rigorous benchmark to measure progress and guide priorities. Existing evaluations remain limited: generic video metrics overlook safety-critical imaging factors; trajectory plausibility is rarely quantified; temporal and agent-level consistency is neglected; and controllability with respect to ego conditioning is ignored. Moreover, current datasets fail to cover the diversity of conditions required for real-world deployment. To address these gaps, we present DrivingGen, the first comprehensive benchmark for generative driving world models. DrivingGen combines a diverse evaluation dataset curated from both driving datasets and internet-scale video sources, spanning varied weather, time of day, geographic regions, and complex maneuvers, with a suite of new metrics that jointly assess visual realism, trajectory plausibility, temporal coherence, and controllability. Benchmarking 14 state-of-the-art models reveals clear trade-offs: general models look better but break physics, while driving-specific ones capture motion realistically but lag in visual quality. DrivingGen offers a unified evaluation framework to foster reliable, controllable, and deployable driving world models, enabling scalable simulation, planning, and data-driven decision-making.

Mean Flow Policy with Instantaneous Velocity Constraint for One-step Action Generation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Guojian Zhan · Letian Tao · Pengcheng Wang · Yixiao Wang · Yiheng Li · Yuxin Chen · Hongyang Li · Masayoshi Tomizuka · Shengbo Eben Li
10 March 2026 at 12:00
arXiv:2602.13810v2 Announce Type: replace-cross Abstract: Learning expressive and efficient policy functions is a promising direction in reinforcement learning (RL). While flow-based policies have recently proven effective in modeling complex action distributions with a fast deterministic sampling process, they still face a trade-off between expressiveness and computational burden, which is typically controlled by the number of flow steps. In this work, we propose mean velocity policy (MVP), a new generative policy function that models the mean velocity field to achieve the fastest one-step action generation. To ensure its high expressiveness, an instantaneous velocity constraint (IVC) is introduced on the mean velocity field during training. We theoretically prove that this design explicitly serves as a crucial boundary condition, thereby improving learning accuracy and enhancing policy expressiveness. Empirically, our MVP achieves state-of-the-art success rates across several challenging robotic manipulation tasks from Robomimic and OGBench. It also delivers substantial improvements in training and inference speed over existing flow-based policy baselines.
Received — 5 March 2026 ⏭

Role-Aware Conditional Inference for Spatiotemporal Ecosystem Carbon Flux Prediction

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yiming Sun · Runlong Yu · Rongchao Dong · Shuo Chen · Licheng Liu · Youmi Oh · Qianlai Zhuang · Yiqun Xie · Xiaowei Jia
5 March 2026 at 13:00
arXiv:2603.03531v1 Announce Type: cross Abstract: Accurate prediction of terrestrial ecosystem carbon fluxes (e.g., CO$_2$, GPP, and CH$_4$) is essential for understanding the global carbon cycle and managing its impacts. However, prediction remains challenging due to strong spatiotemporal heterogeneity: ecosystem flux responses are constrained by slowly varying regime conditions, while short-term fluctuations are driven by high-frequency dynamic forcings. Most existing learning-based approaches treat environmental covariates as a homogeneous input space, implicitly assuming a global response function, which leads to brittle generalization across heterogeneous ecosystems. In this work, we propose Role-Aware Conditional Inference (RACI), a process-informed learning framework that formulates ecosystem flux prediction as a conditional inference problem. RACI employs hierarchical temporal encoding to disentangle slow regime conditioners from fast dynamic drivers, and incorporates role-aware spatial retrieval that supplies functionally similar and geographically local context for each role. By explicitly modeling these distinct functional roles, RACI enables a model to adapt its predictions across diverse environmental regimes without training separate local models or relying on fixed spatial structures. We evaluate RACI across multiple ecosystem types (wetlands and agricultural systems), carbon fluxes (CO$_2$, GPP, CH$_4$), and data sources, including both process-based simulations and observational measurements. Across all settings, RACI consistently outperforms competitive spatiotemporal baselines, demonstrating improved accuracy and spatial generalization under pronounced environmental heterogeneity.

Graph Negative Feedback Bias Correction Framework for Adaptive Heterophily Modeling

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiaqi Lv · Qingfeng Du · Yu Zhang · Yongqi Han · Sheng Li
5 March 2026 at 13:00
arXiv:2603.03662v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) have emerged as a powerful framework for processing graph-structured data. However, conventional GNNs and their variants are inherently limited by the homophily assumption, leading to degradation in performance on heterophilic graphs. Although substantial efforts have been made to mitigate this issue, they remain constrained by the message-passing paradigm, which is inherently rooted in homophily. In this paper, a detailed analysis of how the underlying label autocorrelation of the homophily assumption introduces bias into GNNs is presented. We innovatively leverage a negative feedback mechanism to correct the bias and propose Graph Negative Feedback Bias Correction (GNFBC), a simple yet effective framework that is independent of any specific aggregation strategy. Specifically, we introduce a negative feedback loss that penalizes the sensitivity of predictions to label autocorrelation. Furthermore, we incorporate the output of graph-agnostic models as a feedback term, leveraging independent node feature information to counteract correlation-induced bias guided by Dirichlet energy. GNFBC can be seamlessly integrated into existing GNN architectures, improving overall performance with comparable computational and memory overhead.

Dual-Modality Multi-Stage Adversarial Safety Training: Robustifying Multimodal Web Agents Against Cross-Modal Attacks

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haoyu Liu · Dingcheng Li · Lukas Rutishauser · Zeyu Zheng
5 March 2026 at 13:00
arXiv:2603.04364v1 Announce Type: cross Abstract: Multimodal web agents that process both screenshots and accessibility trees are increasingly deployed to interact with web interfaces, yet their dual-stream architecture opens an underexplored attack surface: an adversary who injects content into the webpage DOM simultaneously corrupts both observation channels with a consistent deceptive narrative. Our vulnerability analysis on MiniWob++ reveals that attacks including a visual component far outperform text-only injections, exposing critical gaps in text-centric VLM safety training. Motivated by this finding, we propose Dual-Modality Multi-Stage Adversarial Safety Training (DMAST), a framework that formalizes the agent-attacker interaction as a two-player zero-sum Markov game and co-trains both players through a three-stage pipeline: (1) imitation learning from a strong teacher model, (2) oracle-guided supervised fine-tuning that uses a novel zero-acknowledgment strategy to instill task-focused reasoning under adversarial noise, and (3) adversarial reinforcement learning via Group Relative Policy Optimization (GRPO) self-play. On out-of-distribution tasks, DMAST substantially mitigates adversarial risks while simultaneously doubling task completion efficiency. Our approach significantly outperforms established training-based and prompt-based defenses, demonstrating genuine co-evolutionary progress and robust generalization to complex, unseen environments.

MEBM-Phoneme: Multi-scale Enhanced BrainMagic for End-to-End MEG Phoneme Classification

✇cs.AI, q-bio.NC updates on arXiv.org
By: Liang Jinghua · Zhang Zifeng · Li Songyi · Zheng Linze
4 March 2026 at 13:00
arXiv:2603.02254v1 Announce Type: cross Abstract: We propose MEBM-Phoneme, a multi-scale enhanced neural decoder for phoneme classification from non-invasive magnetoencephalography (MEG) signals. Built upon the BrainMagic backbone, MEBM-Phoneme integrates a short-term multi-scale convolutional module to augment the native mid-term encoder, with fused representations via depthwise separable convolution for efficient cross-scale integration. A convolutional attention layer dynamically weights temporal dependencies to refine feature aggregation. To address class imbalance and session-specific distributional shifts, we introduce a stacking-based local validation set alongside weighted cross-entropy loss and random temporal augmentation. Comprehensive evaluations on LibriBrain Competition 2025 Track2 demonstrate robust generalization, achieving competitive phoneme decoding accuracy on the validation and official test leaderboard. These results underscore the value of hierarchical temporal modeling and training stabilization for advancing MEG-based speech perception analysis.

MEBM-Speech: Multi-scale Enhanced BrainMagic for Robust MEG Speech Detection

✇cs.AI, q-bio.NC updates on arXiv.org
By: Li Songyi · Zheng Linze · Liang Jinghua · Zhang Zifeng
4 March 2026 at 13:00
arXiv:2603.02255v1 Announce Type: cross Abstract: We propose MEBM-Speech, a multi-scale enhanced neural decoder for speech activity detection from non-invasive magnetoencephalography (MEG) signals. Built upon the BrainMagic backbone, MEBM-Speech integrates three complementary temporal modeling mechanisms: a multi-scale convolutional module for short-term pattern extraction, a bidirectional LSTM (BiLSTM) for long-range context modeling, and a depthwise separable convolutional layer for efficient cross-scale feature fusion. A lightweight temporal jittering strategy and average pooling further improve onset robustness and boundary stability. The model performs continuous probabilistic decoding of MEG signals, enabling fine-grained detection of speech versus silence states - an ability crucial for both cognitive neuroscience and clinical applications. Comprehensive evaluations on the LibriBrain Competition 2025 Track1 benchmark demonstrate strong performance, achieving an average F1 macro of 89.3% on the validation set and comparable results on the official test leaderboard. These findings highlight the effectiveness of multi-scale temporal representation learning for robust MEG-based speech decoding.

Improving Diffusion Planners by Self-Supervised Action Gating with Energies

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuan Lu · Dongqi Han · Yansen Wang · Dongsheng Li
4 March 2026 at 13:00
arXiv:2603.02650v1 Announce Type: cross Abstract: Diffusion planners are a strong approach for offline reinforcement learning, but they can fail when value-guided selection favours trajectories that score well yet are locally inconsistent with the environment dynamics, resulting in brittle execution. We propose Self-supervised Action Gating with Energies (SAGE), an inference-time re-ranking method that penalises dynamically inconsistent plans using a latent consistency signal. SAGE trains a Joint-Embedding Predictive Architecture (JEPA) encoder on offline state sequences and an action-conditioned latent predictor for short horizon transitions. At test time, SAGE assigns each sampled candidate an energy given by its latent prediction error and combines this feasibility score with value estimates to select actions. SAGE can integrate into existing diffusion planning pipelines that can sample trajectories and select actions via value scoring; it requires no environment rollouts and no policy re-training. Across locomotion, navigation, and manipulation benchmarks, SAGE improves the performance and robustness of diffusion planners.
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