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Received — 15 September 2026 ⏭

ASTRIL-MPC: Autonomous Traversal Framework of Articulated Tracked Robots with Language-Guided Neural-Kinematic MPC

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhenfeng Gan · Yanbo Chen · Lirong Che · Junbo Tan · Xueqian Wang
14 September 2026 at 12:00
arXiv:2609.13083v1 Announce Type: cross Abstract: In urban search and rescue, articulated tracked robots (ATRs) must traverse structured but contact-rich environments such as stairwells and cluttered building interiors. Reliable autonomy remains challenging because robot-terrain interaction (RTI) is hybrid and discontinuous, and effective flipper-track coordination is difficult to model analytically. We present ASTRIL-MPC, a language-guided neural kinematics model predictive control (MPC) framework for autonomous traversal. A learned kinematics model predicts short-horizon task-state increments from a height sequence and recent trajectories; NMPC plans with multi-objective costs and strict feasibility constraints; and a large language model (LLM) proposes bounded updates to selected weights and bounds through a safety-checked interface with range clipping, rate limiting, and consistency checks. The compiled predictor enables a full control cycle within 100 ms. Across three traversal tasks and a multi-height generalization setting, ASTRIL-MPC improves an aggregate traversal-quality score by up to 71% over a non-adaptive NMPC and by 67% over a PPO baseline, while eliminating measurable collision impacts during descent. These results indicate that combining learned kinematics, optimization-based planning, and language-guided retuning yields data-efficient and robust autonomy for articulated tracked robots.
Received — 4 April 2026 ⏭

MemFactory: Unified Inference & Training Framework for Agent Memory

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ziliang Guo · Ziheng Li · Bo Tang · Feiyu Xiong · Zhiyu Li
3 April 2026 at 12:00
arXiv:2603.29493v3 Announce Type: replace-cross Abstract: Memory-augmented Large Language Models (LLMs) are essential for developing capable, long-term AI agents. Recently, applying Reinforcement Learning (RL) to optimize memory operations, such as extraction, updating, and retrieval, has emerged as a highly promising research direction. However, existing implementations remain highly fragmented and task-specific, lacking a unified infrastructure to streamline the integration, training, and evaluation of these complex pipelines. To address this gap, we present MemFactory, the first unified, highly modular training and inference framework specifically designed for memory-augmented agents. Inspired by the success of unified fine-tuning frameworks like LLaMA-Factory, MemFactory abstracts the memory lifecycle into atomic, plug-and-play components, enabling researchers to seamlessly construct custom memory agents via a "Lego-like" architecture. Furthermore, the framework natively integrates Group Relative Policy Optimization (GRPO) to fine-tune internal memory management policies driven by multi-dimensional environmental rewards. MemFactory provides out-of-the-box support for recent cutting-edge paradigms, including Memory-R1, RMM, and MemAgent. We empirically validate MemFactory on the open-source MemAgent architecture using its publicly available training and evaluation data. Across the evaluation sets, MemFactory improves performance over the corresponding base models on average, with relative gains of up to 14.8%. By providing a standardized, extensible, and easy-to-use infrastructure, MemFactory significantly lowers the barrier to entry, paving the way for future innovations in memory-driven AI agents.
Received — 2 April 2026 ⏭

Dunhuang Daxiefei Decoction ameliorates acute lung injury via the HIF-1alpha/glycolysis/H3K18la axis

✇Omics In Lung
By: Jiayun Wang · Liangbo Jiao · Zhiqiang Yan · Shun Wang · Suwan Wu · Tao Guo · Yuetong Yang · Xiaoyingzi Su · Bo Tan · Aidong Yang
28 March 2026 at 18:00

J Ethnopharmacol. 2026 Mar 26;365:121591. doi: 10.1016/j.jep.2026.121591. Online ahead of print.

ABSTRACT

ETHNOPHARMACOLOGICAL RELEVANCE: Acute lung injury (ALI) lacks effective therapies. HIF-1α-driven glycolysis can promote histone lactylation and sustain pro-inflammatory (M1) macrophage responses. Daxiefei Decoction (DXFD), a classic traditional Chinese medicine formula, is used for pulmonary inflammatory diseases, but its immunometabolic mechanism remains unclear.

AIM OF THE STUDY: To evaluate the protective efficacy of DXFD against lipopolysaccharide (LPS)-induced ALI and to determine whether it acts through the HIF-1α/glycolysis/histone H3K18 lactylation (H3K18la) axis to regulate macrophage polarization.

MATERIALS & METHODS: DXFD constituents were characterized by UPLC-LTQ-Orbitrap-MS/MS, followed by network pharmacology, molecular docking, and molecular dynamics (MD) simulations. Lung transcriptomics and metabolomics were performed in ALI mice. Efficacy and mechanisms were assessed in LPS-challenged mice and RAW264.7 macrophages using histopathology, ELISA, qRT-PCR, Western blotting, and immunofluorescence. HIF-1α overexpression was used for validation.

RESULTS: DXFD dose-dependently alleviated lung injury and reduced pro-inflammatory cytokines in vivo, and suppressed M1 polarization in vivo and in LPS-stimulated macrophages. Multi-omics indicated activation of HIF-1α-associated inflammatory and glycolytic programs in ALI, which were normalized by DXFD. DXFD decreased glycolytic enzyme expression and reduced histone H3K18 lactylation (H3K18la); these effects were partially reversed by HIF-1α overexpression. Molecular docking and dynamics suggested stable binding of baicalin to HIF-1α.

CONCLUSIONS: DXFD mitigates ALI by dampening HIF-1α-dependent glycolysis and H3K18la, thereby restraining M1-driven inflammatory amplification.

PMID:41903585 | DOI:10.1016/j.jep.2026.121591

Received — 26 March 2026 ⏭

PERMA: Benchmarking Personalized Memory Agents via Event-Driven Preference and Realistic Task Environments

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shuochen Liu · Junyi Zhu · Long Shu · Junda Lin · Yuhao Chen · Haotian Zhang · Chao Zhang · Derong Xu · Jia Li · Bo Tang · Zhiyu Li · Feiyu Xiong · Enhong Chen · Tong Xu
25 March 2026 at 12:00
arXiv:2603.23231v1 Announce Type: new Abstract: Empowering large language models with long-term memory is crucial for building agents that adapt to users' evolving needs. However, prior evaluations typically interleave preference-related dialogues with irrelevant conversations, reducing the task to needle-in-a-haystack retrieval while ignoring relationships between events that drive the evolution of user preferences. Such settings overlook a fundamental characteristic of real-world personalization: preferences emerge gradually and accumulate across interactions within noisy contexts. To bridge this gap, we introduce PERMA, a benchmark designed to evaluate persona consistency over time beyond static preference recall. Additionally, we incorporate (1) text variability and (2) linguistic alignment to simulate erratic user inputs and individual idiolects in real-world data. PERMA consists of temporally ordered interaction events spanning multiple sessions and domains, with preference-related queries inserted over time. We design both multiple-choice and interactive tasks to probe the model's understanding of persona along the interaction timeline. Experiments demonstrate that by linking related interactions, advanced memory systems can extract more precise preferences and reduce token consumption, outperforming traditional semantic retrieval of raw dialogues. Nevertheless, they still struggle to maintain a coherent persona across temporal depth and cross-domain interference, highlighting the need for more robust personalized memory management in agents. Our code and data are open-sourced at https://github.com/PolarisLiu1/PERMA.

PhotoAgent: A Robotic Photographer with Spatial and Aesthetic Understanding

✇cs.AI, q-bio.NC updates on arXiv.org
By: Lirong Che · Zhenfeng Gan · Yanbo Chen · Junbo Tan · Xueqian Wang
25 March 2026 at 12:00
arXiv:2603.22796v1 Announce Type: cross Abstract: Embodied agents for creative tasks like photography must bridge the semantic gap between high-level language commands and geometric control. We introduce PhotoAgent, an agent that achieves this by integrating Large Multimodal Models (LMMs) reasoning with a novel control paradigm. PhotoAgent first translates subjective aesthetic goals into solvable geometric constraints via LMM-driven, chain-of-thought (CoT) reasoning, allowing an analytical solver to compute a high-quality initial viewpoint. This initial pose is then iteratively refined through visual reflection within a photorealistic internal world model built with 3D Gaussian Splatting (3DGS). This ``mental simulation'' replaces costly and slow physical trial-and-error, enabling rapid convergence to aesthetically superior results. Evaluations confirm that PhotoAgent excels in spatial reasoning and achieves superior final image quality.
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