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CREST: Constraint-Release Execution for Multi-Robot Warehouse Shelf Rearrangement

arXiv:2603.28803v1 Announce Type: cross Abstract: Double-Deck Multi-Agent Pickup and Delivery (DD-MAPD) models the multi-robot shelf rearrangement problem in automated warehouses. MAPF-DECOMP is a recent framework that first computes collision-free shelf trajectories with a MAPF solver and then assigns agents to execute them. While efficient, it enforces strict trajectory dependencies, often leading to poor execution quality due to idle agents and unnecessary shelf switching. We introduce CREST, a new execution framework that achieves more continuous shelf carrying by proactively releasing trajectory constraints during execution. Experiments on diverse warehouse layouts show that CREST consistently outperforms MAPF-DECOMP, reducing metrics related to agent travel, makespan, and shelf switching by up to 40.5\%, 33.3\%, and 44.4\%, respectively, with even greater benefits under lift/place overhead. These results underscore the importance of execution-aware constraint release for scalable warehouse rearrangement. Code and data are available at https://github.com/ChristinaTan0704/CREST.

SecureVibeBench: Evaluating Secure Coding Capabilities of Code Agents with Realistic Vulnerability Scenarios

arXiv:2509.22097v2 Announce Type: replace-cross Abstract: Large language model-powered code agents are rapidly transforming software engineering, yet the security risks of their generated code have become a critical concern. Existing benchmarks have provided valuable insights, but they fail to capture scenarios in which vulnerabilities are actually introduced by human developers, making fair comparisons between humans and agents infeasible. We therefore introduce SecureVibeBench, a benchmark of 105 C/C++ secure coding tasks sourced from 41 projects in OSS-Fuzz for code agents. SecureVibeBench has the following features: (i) realistic task settings that require multi-file edits in large repositories, (ii)~aligned contexts based on real-world open-source vulnerabilities with precisely identified vulnerability introduction points, and (iii) comprehensive evaluation that combines functionality testing and security checking with both static and dynamic oracles. We evaluate 5 popular code agents like OpenHands, supported by 5 LLMs (e.g., Claude sonnet 4.5) on SecureVibeBench. Results show that current agents struggle to produce both correct and secure code, as even the best-performing one, produces merely 23.8\% correct and secure solutions on SecureVibeBench.

InCoder-32B: Code Foundation Model for Industrial Scenarios

arXiv:2603.16790v3 Announce Type: replace-cross Abstract: Recent code large language models have achieved remarkable progress on general programming tasks. Nevertheless, their performance degrades significantly in industrial scenarios that require reasoning about hardware semantics, specialized language constructs, and strict resource constraints. To address these challenges, we introduce InCoder-32B (Industrial-Coder-32B), the first 32B-parameter code foundation model unifying code intelligence across chip design, GPU kernel optimization, embedded systems, compiler optimization, and 3D modeling. By adopting an efficient architecture, we train InCoder-32B from scratch with general code pre-training, curated industrial code annealing, mid-training that progressively extends context from 8K to 128K tokens with synthetic industrial reasoning data, and post-training with execution-grounded verification. We conduct extensive evaluation on 14 mainstream general code benchmarks and 9 industrial benchmarks spanning 4 specialized domains. Results show InCoder-32B achieves highly competitive performance on general tasks while establishing strong open-source baselines across industrial domains.

The 1000 Chinese Pangenome empowers medical and population genetics

Nature, Published online: 01 April 2026; doi:10.1038/s41586-026-10315-y

Development of the pangenome-informed genome assembly (PIGA) workflow enabled the generation of 1,116 diploid genome assemblies (55 de novo and 1,061 pangenome-informed), representing an extensive resource of medically relevant genic variations.

Surgery-centered integrated strategies for personalized hepatocellular carcinoma care

31 March 2026 at 18:00

Cancer Biol Med. 2026 Mar 30:j.issn.2095-3941.2026.0045. doi: 10.20892/j.issn.2095-3941.2026.0045. Online ahead of print.

ABSTRACT

Hepatocellular carcinoma (HCC) remains a major global health burden characterized by late-stage diagnosis and high postoperative recurrence rates. This review presents a surgery-centered precision management framework integrating 3 synergistic components: early detection, precision surgery, and recurrence prevention. Early detection strategies incorporate multiparameter risk models including the gender, age, AFP-L3, AFP, and DCP (GALAD) as well as age, sex, AFP, and PIVKA-II (ASAP) scores, alongside circulating tumor DNA methylation-based liquid biopsy, thus enabling tumor identification at stages amenable to curative resection. Precision surgery optimizes patient selection through refined staging systems including the Chinese liver cancer staging (CNLC), and functional assessments including the albumin-bilirubin (ALBI) grade, whereas conversion therapy and minimally invasive approaches extend surgical eligibility to selected patients with intermediate-stage disease. To mitigate the risk of postoperative recurrence, distinguishing between early and late recurrence patterns and monitoring minimal residual disease are critical strategies. Perioperative systemic therapies, particularly immune checkpoint inhibitor-based combinations, show promise for eradicating micrometastatic disease. This integrated framework provides a cohesive, evidence-based approach to personalized HCC management aimed at maximizing curative potential and long-term survival.

PMID:41913379 | DOI:10.20892/j.issn.2095-3941.2026.0045

Dynamic Targetable Extracellular Vesicle Surface Proteins Monitor Depth of Response to CAR T Therapy

Res Sq [Preprint]. 2026 Mar 18:rs.3.rs-8913641. doi: 10.21203/rs.3.rs-8913641/v1.

ABSTRACT

Extracellular vesicles (EVs) represent a promising liquid biopsy platform in multiple myeloma (MM). We developed an MM EV Surface Protein Assay to quantify and dynamically monitor four MM EV subpopulations defined by targetable MM surface proteins (BCMA, CD38, GPRC5D, and CD319) across 336 serial blood samples from 45 relapsed/refractory MM (RRMM) patients treated with anti-BCMA chimeric antigen receptor (CAR) T-cell therapy. All four MM EV subpopulations significantly decreased in 43 patients with initial response, while BCMA+, GPRC5D+, and CD319+ MM EVs increased in 19 patients with progression, and antigen escape was detected by BCMA+ MM EVs. MM EV subpopulations differentiated minimal residual disease (MRD) status and complemented MRD for detecting early relapse before clinical progression. Notably, CD319+ MM EVs were early predictors of progression-free and overall survival in MRD-negative patients. This assay enables noninvasive monitoring of deep response, progression, and antigen escape, and stratifies survival in MRD-negative patients with RRMM.

PMID:41890853 | PMC:PMC13015583 | DOI:10.21203/rs.3.rs-8913641/v1

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