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Received — 8 April 2026 ⏭

Toward Full Autonomous Laboratory Instrumentation Control with Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yong Xie · Kexin He · Andres Castellanos-Gomez
7 April 2026 at 12:00
arXiv:2604.03286v1 Announce Type: new Abstract: The control of complex laboratory instrumentation often requires significant programming expertise, creating a barrier for researchers lacking computational skills. This work explores the potential of large language models (LLMs), such as ChatGPT, and LLM-based artificial intelligence (AI) agents to enable efficient programming and automation of scientific equipment. Through a case study involving the implementation of a setup that can be used as a single-pixel camera or a scanning photocurrent microscope, we demonstrate how ChatGPT can facilitate the creation of custom scripts for instrumentation control, significantly reducing the technical barrier for experimental customization. Building on this capability, we further illustrate how LLM-assisted tools can be extended into autonomous AI agents capable of independently operating laboratory instruments and iteratively refining control strategies. This approach underscores the transformative role of LLM-based tools and AI agents in democratizing laboratory automation and accelerating scientific progress.
Received — 4 April 2026 ⏭

ExpertFlow: Efficient Mixture-of-Experts Inference via Predictive Expert Caching and Token Scheduling

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xin He · Shunkang Zhang · Kaijie Tang · Shaohuai Shi · Yuxin Wang · Zihao Zeng · Zhenheng Tang · Xiaowen Chu · Haiyan Yin · Ivor W. Tsang · Yew Soon Ong
3 April 2026 at 12:00
arXiv:2410.17954v2 Announce Type: replace Abstract: Sparse Mixture-of-Experts (MoE) models can outperform dense large language models at similar computation by activating only a small set of experts per token. However, stacking many expert modules introduces substantial parameter memory, which makes MoE models difficult to deploy in memory-constrained environments such as single-GPU devices. Offloading alleviates this issue by storing inactive experts in CPU memory and loading them on demand, but existing methods remain limited: static caches disregard input-dependent routing, and methods that train separate models to predict expert usage ahead of time are often inaccurate or require significant training cost. We propose ExpertFlow, a lightweight MoE inference system that addresses this routing dependency through three coordinated components: 1) a transformer-based routing path predictor that estimates expert usage across all MoE layers in a single forward pass, 2) a token scheduler that groups tokens with similar predicted routes to improve expert utilization, and 3) a predictive expert cache that loads only the required experts while correcting mispredictions at runtime. Together, these components enable efficient expert loading and execution, reducing GPU memory usage by up to 93.72% and improving inference throughput by up to 10x over strong offloading baselines on a single GPU.
Received — 2 April 2026 ⏭

ContractSkill: Repairable Contract-Based Skills for Multimodal Web Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zijian Lu · Yiping Zuo · Yupeng Nie · Xin He · Weibei Fan · Lianyong Qi · Shi Jin
1 April 2026 at 12:00
arXiv:2603.20340v2 Announce Type: replace-cross Abstract: Self-generated skills for web agents are often unstable and can even hurt performance relative to direct acting. We argue that the key bottleneck is not only skill generation quality, but the fact that web skills remain implicit and therefore cannot be checked or locally repaired. To address this, we present ContractSkill, a framework that converts a draft skill into an executable artifact with explicit procedural structure, enabling deterministic verifica tion, fault localization, and minimal local repair. This turns skill refinement from full rewriting into localized editing of a single skill artifact. Experiments on VisualWebArena show that Contract Skill is effective in realistic web environments, while MiniWoB provides a controlled test of the mechanism behind the gain. Under matched transfer layers, repaired artifacts also remain reusable after removing the source model from the loop, providing evi dence of portability within the same benchmark family rather than full-benchmark generalization. These results suggest that the central challenge is not merely generating skills, but mak ing them explicit, executable, and repairable. Code is available at https://github.com/underfitting-lu/contractskill.git.
Received — 26 March 2026 ⏭

SIRT3 deacetylates STEAP4 to modulate cuproptosis sensitivity via mitochondrial metabolic reprogramming in HBV-related HCC

✇Omics in Hepatocellular
By: Ze-Bang Du · Xin-Mou Wu · Jia-Ming Lei · Yu-Xin Cai · Jie He · Bo Qian · Xin-Xin He · Wen-Hao Han · Yuan Lu · Xiao-Gang Xia · Han-Ying Zheng · Dong-Bei Guo · You-Liang Yao · Wen-Gang Li · Yu-Chun Lin · Zhong-Ning Lin
17 March 2026 at 18:00

Cell Death Differ. 2026 Mar 16. doi: 10.1038/s41418-026-01713-w. Online ahead of print.

ABSTRACT

Hepatitis B virus (HBV) infection remains a leading etiological driver of hepatocellular carcinoma (HCC). Cuproptosis is a recently defined copper-dependent form of regulated cell death that selectively eliminates mitochondria-dependent cells; whether HBV rewires this vulnerability remains unknown. Here we unveil a novel HBV X protein (HBx)-driven mechanism of cuproptosis evasion. Integrative analysis of clinical specimens, HBx-transgenic (HBx-Tg) mice, and multi-omics datasets revealed marked downregulation of STEAP4 (six-transmembrane epithelial antigen of prostate 4), a metalloreductase essential for cuproptosis sensitivity, in HBV-positive HCC. Mechanistically, HBx attenuates sirtuin 3 (SIRT3), impairing deacetylation of STEAP4 at lysine 404 and abolishing its mitochondrial targeting. Consequently, cells switch from the tricarboxylic acid (TCA) cycle respiration to glycolysis, reducing sensitivity to the copper ionophore elesclomol (ES). Restoring STEAP4 expression or pharmacological activation of SIRT3 with honokiol (HKL) re-instated mitochondrial STEAP4 localization and re-sensitized HBV-related HCC cells to cuproptosis; combination with ES produced synergistic tumor suppression in vitro and in orthotopic models. Collectively, our findings establish the SIRT3-STEAP4 axis as a novel regulator of cuproptosis resistance in HBV-related HCC. HBx-mediated repression of SIRT3 disrupts STEAP4 deacetylation and mitochondrial targeting, fostering metabolic reprogramming and evasion of copper-induced cell death. The results provide a pre-clinical rationale for copper-directed combination strategies in HBV-associated HCC.

PMID:41840161 | DOI:10.1038/s41418-026-01713-w

Received — 11 March 2026 ⏭

Symmetry-Driven Generation of Crystal Structures from Composition

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shi Yin · Jinming Mu · Xudong Zhu · Linxin He
10 March 2026 at 12:00
arXiv:2602.17176v3 Announce Type: replace-cross Abstract: Crystal structure prediction (CSP), which aims to predict the three-dimensional atomic arrangement of a crystal from its composition, is central to materials discovery and mechanistic understanding. However, given the composition in a unit cell, existing methods struggle with the NP-hard combinatorial challenge of rigorous symmetry enforcement or rely on retrieving known templates, which inherently limits both physical fidelity and the ability to discover genuinely new materials. To solve this, we propose a symmetry-driven generative framework. Our approach leverages large language models to encode chemical semantics and directly generate fine-grained Wyckoff patterns from atomic stoichiometry, effectively circumventing the limitations inherent to database lookups. Crucially, to overcome the exponentially complex problem of combinatorial site assignments, we incorporate domain knowledge through an efficient, linear-complexity heuristic beam search algorithm that rigorously enforces algebraic consistency between site multiplicities and atomic stoichiometry. By integrating this symmetry-consistent template into a diffusion backbone, our approach constrains the stochastic generative trajectory to a physically valid geometric manifold. This framework achieves state-of-the-art performance across stability, uniqueness, and novelty (SUN) benchmarks, alongside superior matching performance, thereby establishing a new paradigm for the rigorous exploration of targeted crystallographic space which can be previously uncharted, with no reliance on a priori structural knowledge.
Received — 5 March 2026 ⏭

Overcoming the Combinatorial Bottleneck in Symmetry-Driven Crystal Structure Prediction

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shi Yin · Jinming Mu · Xudong Zhu · Lixin He
5 March 2026 at 13:00
arXiv:2602.17176v2 Announce Type: replace-cross Abstract: Crystal structure prediction (CSP), which aims to predict the three-dimensional atomic arrangement of a crystal from its composition, is central to materials discovery and mechanistic understanding. However, given the composition and atomic counts in a unit cell, existing methods struggle with the NP-hard combinatorial challenge of rigorous symmetry enforcement or rely on retrieving known templates, which inherently limits both physical fidelity and the ability to discover genuinely new materials. To solve this, we propose a symmetry-driven generative framework. Our approach leverages large language models to encode chemical semantics and directly generate fine-grained Wyckoff patterns from atomic stoichiometry and counts, effectively circumventing the limitations inherent to database lookups. Crucially, to overcome the exponentially complex problem of combinatorial site assignments, we incorporate domain knowledge through an efficient, linear-complexity heuristic beam search algorithm that rigorously enforces algebraic consistency between site multiplicities and atomic stoichiometry and counts. By integrating this symmetry-consistent template into a diffusion backbone, our approach constrains the stochastic generative trajectory to a physically valid geometric manifold. This framework achieves state-of-the-art performance across stability, uniqueness, and novelty (SUN) benchmarks, alongside superior matching performance, thereby establishing a new paradigm for the rigorous exploration of targeted crystallographic space which can be previously uncharted, with no reliance on existing databases or a priori structural knowledge.
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