Shenzhen University

BLOOM frames transcriptomic virtual-embryo modeling as conditional generation: given an observed window of cell populations, a requested developmental stage and cell-state information, it generates the expression distribution at that stage. Pseudo-trajectories link independently sampled populations, while developmental age, cell-identity hierarchy, anatomical context, gene programmes and transcription-factor–target networks condition both a Gaussian base and a flow-matching generator. Across prenatal, visual-cortex and multi-organ endothelial mouse single-cell atlases, BLOOM ranks first for stage-averaged gene and cell correlations against 22 time-series forecasting baselines.

BLOOM frames transcriptomic virtual-embryo modeling as conditional generation: given an observed window of cell populations, a requested developmental stage and cell-state information, it generates the expression distribution at that stage. Pseudo-trajectories link independently sampled populations, while developmental age, cell-identity hierarchy, anatomical context, gene programmes and transcription-factor–target networks condition both a Gaussian base and a flow-matching generator. Across prenatal, visual-cortex and multi-organ endothelial mouse single-cell atlases, BLOOM ranks first for stage-averaged gene and cell correlations against 22 time-series forecasting baselines.

CovDiff-PaAno replaces cosine matching in patch-based time-series anomaly detection with regularized Mahalanobis scoring and adds raw, first-difference and second-difference views that share one frozen encoder while each keeps its own memory bank and covariance. In a ten-seed controlled comparison on TSB-AD-M, AUC-ROC rose from 0.7612 for reproduced PaAno to 0.7843 and Range-F1 from 0.4043 to 0.4384; on TSB-AD-U the corresponding scores rose from 0.8532 to 0.8626 and from 0.4822 to 0.4924. The encoder parameter count stays at about 0.397M, but inference time grows 3.47-fold and peak inference memory 10.01-fold.

CovDiff-PaAno replaces cosine matching in patch-based time-series anomaly detection with regularized Mahalanobis scoring and adds raw, first-difference and second-difference views that share one frozen encoder while each keeps its own memory bank and covariance. In a ten-seed controlled comparison on TSB-AD-M, AUC-ROC rose from 0.7612 for reproduced PaAno to 0.7843 and Range-F1 from 0.4043 to 0.4384; on TSB-AD-U the corresponding scores rose from 0.8532 to 0.8626 and from 0.4822 to 0.4924. The encoder parameter count stays at about 0.397M, but inference time grows 3.47-fold and peak inference memory 10.01-fold.

Image factuality assessment no longer stops at whether a picture is AI-generated: the EviScene multimodal agent refines its own questions during investigation and reaches 79.05% balanced accuracy and 52.26% evidence sufficiency on 1,527 test images.

Image factuality assessment no longer stops at whether a picture is AI-generated: the EviScene multimodal agent refines its own questions during investigation and reaches 79.05% balanced accuracy and 52.26% evidence sufficiency on 1,527 test images.

Phage therapy needs a matching phage for each clinical bacterial isolate, and conventional experimental screening is slow and labor-intensive. PhageHost-GNN encodes phage receptor-binding proteins and bacterial K-locus proteins as sequence, structure and genomic-context embeddings, feeds them to a graph neural network, and automatically selects the embedding model best suited to how novel a query strain is relative to the training set. On the Spain-derived Beamud2023 data, with both hosts and phages unseen, the routed model reached an AUPRC of 0.671, while fixed-embedding models stayed at or below 0.036.

Phage therapy needs a matching phage for each clinical bacterial isolate, and conventional experimental screening is slow and labor-intensive. PhageHost-GNN encodes phage receptor-binding proteins and bacterial K-locus proteins as sequence, structure and genomic-context embeddings, feeds them to a graph neural network, and automatically selects the embedding model best suited to how novel a query strain is relative to the training set. On the Spain-derived Beamud2023 data, with both hosts and phages unseen, the routed model reached an AUPRC of 0.671, while fixed-embedding models stayed at or below 0.036.

PhysForge treats material-conditioned dynamics from a video diffusion model as references for fitting the physical parameters of 3D assets, so a user-specified material actually changes how an object contacts and deforms. Previously such parameters were largely inferred from appearance, making it hard to assign a material to the same object or to estimate properties from complex interactions between objects and semantic parts. On PhysAssets and Google Scanned Objects it reaches the best CLIP and physical controllability scores for both common and uncommon materials, with average physical controllability 4.3 versus 3.8 for the strongest baseline.

PhysForge treats material-conditioned dynamics from a video diffusion model as references for fitting the physical parameters of 3D assets, so a user-specified material actually changes how an object contacts and deforms. Previously such parameters were largely inferred from appearance, making it hard to assign a material to the same object or to estimate properties from complex interactions between objects and semantic parts. On PhysAssets and Google Scanned Objects it reaches the best CLIP and physical controllability scores for both common and uncommon materials, with average physical controllability 4.3 versus 3.8 for the strongest baseline.

The work presents APEX, which shifts indirect prompt-injection defense from recognizing attack patterns to checking each proposed effect at the execution boundary: before untrusted execution it compiles the user's authorization into a contract, WRAP admits an effect only when the contract and runtime evidence reconstruct its authorization, and PLANT places probes on the contract's endorsed dependencies so unendorsed use of runtime information reveals itself before commitment; across six benchmarks and 13 baselines APEX reaches 0% attack success on five benchmarks and 0.56% on the sixth, and holds 0% under three adaptive attacks spanning all three capability-unit types.

The work presents APEX, which shifts indirect prompt-injection defense from recognizing attack patterns to checking each proposed effect at the execution boundary: before untrusted execution it compiles the user's authorization into a contract, WRAP admits an effect only when the contract and runtime evidence reconstruct its authorization, and PLANT places probes on the contract's endorsed dependencies so unendorsed use of runtime information reveals itself before commitment; across six benchmarks and 13 baselines APEX reaches 0% attack success on five benchmarks and 0.56% on the sixth, and holds 0% under three adaptive attacks spanning all three capability-unit types.

The authors present CrashSim, a human behavior-informed crash scenario generation framework that uses a vision-language model to interpret naturalistic driving scenes, hierarchically retrieves real-world crash priors, and converts their relative-motion patterns and behavior-stage timings into anchor guidance for a diffusion generator pretrained on nuScenes; results show CrashSim reproduces real-world pre-impact behavior, collision dynamics, collision geometry and crash-type distributions more closely than Strive and LD-scene, and it is used to build nuCrash, a dataset of over 4,000 crash and near-crash scenarios on which five planners show larger closed-loop performance differences than on nuScenes, with an LLM evaluation agent providing capability-level diagnoses and improvement guidance.

The authors present CrashSim, a human behavior-informed crash scenario generation framework that uses a vision-language model to interpret naturalistic driving scenes, hierarchically retrieves real-world crash priors, and converts their relative-motion patterns and behavior-stage timings into anchor guidance for a diffusion generator pretrained on nuScenes; results show CrashSim reproduces real-world pre-impact behavior, collision dynamics, collision geometry and crash-type distributions more closely than Strive and LD-scene, and it is used to build nuCrash, a dataset of over 4,000 crash and near-crash scenarios on which five planners show larger closed-loop performance differences than on nuScenes, with an LLM evaluation agent providing capability-level diagnoses and improvement guidance.