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A Passive-Oxygenation Silicone Platform for Biomass Production: Maximizing Labor Productivity and Process Efficiency in Cellular Agriculture Development
The commercial production of cell-based food is currently hindered by existing bioreactor technologies, which require substantial capital investment, specialized operating skills, and complex processing setups. To democratize cell-based food production, we developed the "oxy-thru cultivator", a simple, autoclavable, closed-bag bioreactor fabricated from polydimethylsiloxane (PDMS). By leveraging the high oxygen-permeability of PDMS, this platform enables passive oxygenation across the entire...
Zero-Poisson Ratio Elastomeric Substrates for Distortion-Free Stretchable Displays
arXiv:2606.03000v1 Announce Type: new Abstract: Stretchable displays are critical for emerging wearable electronics, soft sensors, and next-generation AR/VR interfaces. Although recent advances have enabled foldable, twistable, and rollable displays, intrinsically stretchable substrates often exhibit significant lateral contraction under tensile strain due to their high Poisson ratio, leading to unintended wrapping, distortion, and shrinkage. Here, we report a transparent...
D$^3$-MoE:Dual Disentangled Diffusion Mixture-of-Experts for Style-Controllable End-to-End Autonomous Driving
Announce Type: new Abstract: Traditional end-to-end autonomous driving frameworks frequently suffer from the "style-averaging" dilemma when trained on high-variance human demonstrations, yielding homogenized, style-uncontrollable, and even kinematically unsafe policies. To overcome this limitation, we present D$^3$-MoE (Dual Disentangled Diffusion Mixture-of-Experts), which disentangles trajectory modeling along two complementary axes. On the behavioral axis, generation is decoupled from...
CLEAR: Cognition and Latent Evaluation for Adaptive Routing in End-to-End Autonomous Driving
arXiv:2606.06219v1 Announce Type: new Abstract: End-to-end autonomous driving models often struggle to balance multi-modal maneuver generation with real-time inference constraints. While diffusion models successfully capture diverse driving behaviors, their iterative denoising process incurs unacceptable latency for safety-critical deployment. To address this, we propose CLEAR (Cognition and Latent Evaluation for Adaptive Routing), a framework that combines ultra-fast generative planning...
Inherited input and local transformations shape the spatiotemporal organization of pathway specific striatal signals for motivated behavior
Adaptive behavior requires neural circuits to link sensory events with their location, predictive value, and temporal relationship to outcomes. Although striatal circuits are implicated in this process, it remains unclear how motivationally relevant signals are organized across striatal regions and direct- and indirect-pathway spiny projection neurons, and which components reflect afferent input versus local transformations. Using striatum-wide calcium recordings during visual conditioning...
NTR: Neural Token Reconstruction for Scene Token Bottleneck in End-to-End Driving
Announce Type: new Abstract: Recent perception-free end-to-end (E2E) autonomous driving methods bypass explicit perception outputs by compressing dense image patch tokens into compact scene tokens for downstream trajectory generation and scoring. While these scene tokens form a compact visual bottleneck for the planner, they receive supervision solely from the planning objective, providing limited constraints on the encoded visual information.
Test-Time Trajectory Optimization for Autonomous Driving
arXiv:2606.07170v1 Announce Type: new Abstract: End-to-end planners for autonomous driving typically generate a set of candidate trajectories, score each one, and return the highest-scoring candidate. However, the scorer is applied only after the proposals are generated and cannot influence the set of trajectories: a weak set of candidates limits planning performance regardless of the scorer's quality. We instead treat the scorer as a learned trajectory-level reward function and search for...
LiAuto-GeoX: Efficient Grounded Driving Transformer
Announce Type: new Abstract: Dense 3D reconstruction has demonstrated immense potential for spatial understanding, yet its viability as a real-time, onboard representation for autonomous driving remains an open challenge. Existing large-scale visual geometry models typically require substantial computational resources and lack the long-range geometric fidelity, surround-view consistency, and real-time efficiency demanded by dynamic driving environments. To bridge this gap, we present...
PLAN-S: Bridging Planning with Latent Style Dynamics for Autonomous Driving World Models
Announce Type: new Abstract: Latent world models (LWMs) have strengthened end-to-end autonomous driving by forecasting compact scene dynamics for downstream planning. However, existing LWM-based planners usually generate trajectories directly from entangled latent representations. This compact latent-to-planner pathway lacks explicit modeling of risk, drivability, and diverse style preferences, making driving-style dynamics difficult to supervise, inspect, or modulate before a final...