World Models & Planning

MaP-WAM is introduced, a Memory-as-Plans framework that decomposes memory-dependent world-action modeling into memory-grounded planning and plan-conditioned execution, and uses long-term multimodal episodic context as planning-time evidence rather than repeatedly conditioning the executor on the full history.

This work proposes a fully analog readout architecture based on voltage-to-time conversion of the VMM output based on sensing the column voltage, which avoids current-mode summing and scaling circuitry, improving area and energy efficiency.

Native unified modelling is position as a promising path towards systems that perceive, reason and create within a fully end-to-end framework through SenseNova-U1.5, an 8B-MoT native unified multimodal model that understands, reasons about, and generates visual content within an encoder-free and VAE-free architecture.

This work shows that for every fixed rational discount factor ($\gamma\in(0,1)$), exact planning remains NP-hard, and introduces a randomized polynomial-time approximation scheme for every fixed look-ahead depth.

The results show that inconsistency under uncertainty can be effectively learned, analyzed, and repaired through response-surface modeling, providing a scalable foundation for uncertainty-aware consistency management in CPS development.

A biologically grounded framework in which noisy neural and synaptic dynamics perform inference and learning via stochastic sampling from an internal energy function, capturing uncertainty over latent states and model parameters through neural and synaptic variability, respectively is discussed.

These results demonstrate that compact, domain-informed features derived from sensor streams outperform raw meteorological observations, offering bandwidth-efficient representations suitable for edge deployment over current and emerging wireless infrastructures.

This paper extends CONES to allow for loss functions $f_t'$s to also change over time, and shows that any online algorithm with sublinear {\it anytime} regret has a movement cost of $\Omega\left(\log T\right)$.

The spectral residual is characterized as a useful but domain-sensitive inductive bias for structural connectivity screening, andarse scaling extends to 20,000 nodes and separates one-time spectral setup from amortized screening cost.

It is proved that an unbounded gap between the update maps can coexist with vanishing predictive KL for every fixed finite $K\ge2$ in a stationary symmetric Gaussian HMM, and isolates two missing links between internal update gaps and predictive cost.

This work studies a route-conditioned order of unavoidable stages that every successful executor must traverse, recoverable from offline trajectories and belonging to none of them: a route-conditioned order of unavoidable stages that every successful executor must traverse.

This paper introduces Agent-Integrated Software (AIS) as a software pattern combining a conventional core, direct interaction, and a built-in agent, and Intent-Level Interaction Abstraction (IIA) as the task semantics through which users inspect and control delegated work.

An annual 1km maps of the probability of 2G, 3G and 4G coverage for 214 countries and territories for the years 1999 to 2030, which supports mapping the global digital divide, linking connectivity to household-survey outcomes, and humanitarian and infrastructure planning.

A single classical oracle is constructed relative to which one-way puzzles do not exist, while an EFI pair survives every distinguisher that queries the oracle classically throughout and holds advice about it, making its one superposition query at the end.

The results show that spectral adapters can efficiently adapt SAM for CRLM segmentation while retaining strong accuracy with limited trainable parameters.

EgoGenEval introduces EgoGenEval, a geometry-grounded, pose-free benchmark designed to evaluate the physical consistency of visual generators under ego-motion, and shows that pairwise supervision does not reliably improve camera-motion grounding and scene-state preservation together.

Frozen predictive world-model states as reusable features for causal, transferable, and low-overhead execution monitoring for reliable robot deployment are supported.

A reproducible pipeline that converts multi-dataset SMPLX motion into executable loco-manipulation behavior for the Galaxea R1 Pro wheeled humanoid and provides a complete bridge from human motion data to physically trackable wheeled-humanoid loco-manipulation rather than a visualization-only retargeter is presented.

The results demonstrate that robust simultaneous five-DoF end-effector pose and contact-force tracking is achievable on a standard underactuated quadrotor with a simple, rigid, single-link arm, without requiring a fully actuated platform, a complex arm, or dedicated force/torque sensing.

By decoupling haptic control loops from network bottlenecks, CARLAverse enables scalable, cross-institutional HITL experiments without compromising physical immersion.

Dist-GPRL is presented, a distance-aware and safety-guided reinforcement learning framework for structured robot skill adaptation that sequentially adapts overlapping local windows of sparse trajectory via-points rather than modifying the complete skill at every policy step.

SwarmNxt, an open-source software platform built on the open-source OmniNxt drone hardware, is presented, providing an open research infrastructure for physical swarm experimentation and validated through two real-world experiments.

This work introduces a probabilistic framework for evaluating VAD onset time under noisy temporal labels, and introduces S4VAD, the first state-space-model-based (SSM-based) VAD architecture that achieves the lowest latency while maintaining a competitive AUROC.

World in World is presented, a training-free inference-time interface that converts heterogeneous control evidence into camera- and time-labelled clean visual states, which are read through the native self attention of a frozen causal video model.

Recursive Code World Models (RCWM) is introduced, a framework for reconstructing complex 3D worlds in code from a single reference image that provides a recursive construction principle for building complex executable worlds from visual evidence.

GenQAS is introduced, a tensor network-guided RL framework that combines a fixed matrix product state warm-start with prioritized generative replay and can mitigate sample starvation in quantum architecture search and support more resource efficient circuit discovery.

This work introduces AmazonSWE, a dataset for training and evaluating large-scale spatiotemporal graph imputation methods that integrates processed satellite altimetry measurements from a range of sources, including the recent wide-swath SWOT sensor.

A novel framework for model-based reinforcement learning which introduces approximate inverse process models within the training of reinforcement policies, and proposes a lightweight feedforward architecture for approximate inverse models and integrate them within the policy network of standard RL algorithms.

The problem of maximizing the weighted average of performance certificates in the presence of uncertainty about the true system state is shown to be equivalent to an optimization over nested prediction sets, connecting to the literature on conformal prediction and extending prior art on single-level risk-averse decision making.

RadioDecomp is proposed, which treats a prior-guided predictor as a correctable base and uses deterministic residual refinement to learn its remaining predictable discrepancy, and is instantiate as RadioLSR (LoS-Shadow-Residual).

The model incorporates three core components: spatiotemporal embedding module, spatiotemporal fusion module, and LLM backbone, which adopts a differentiated parameter adaptation strategy to balance training efficiency and traffic data adaptability.

The XAI-assisted Recurrent neural network Attribution for Channel Estimation (X-RACE) framework is proposed, and novel temporal XAI metrics: Saturation Time, Importance Drift, and Relevance Contrast are proposed to characterize the LSTM's learning dynamics and memory convergence.

This work introduces MAPLE (Memory-Augmented Planning with Language and Evolution), an agent for maintaining optimization problems through successive natural-language requests and introduces NLDO, a benchmark of 15 trajectories and 180 updates spanning selection, scheduling, rostering, routing, and cloud-resource placement.

These findings position stage-aware human-AI collaboration as a practical approach for producing inspectable, revisable, and verifiable analyses while retaining domain-expert involvement in consequential decisions.

CAP is proposed, a single-stage humanoid locomotion policy that recovers this signal with a perceptive world-model encoder trained as a learned denoiser to reconstruct clean depth from a corrupted input, together with a co-active proprioceptive variational encoder that supplies depth-free body-state information.

GeoTrussRover combines an electrically actuated VGT, a wheeled base, and contact-semantic morphology planning and control, which stores task coordination in a hyper-redundant, load-bearing morphology and reuses it during locomotion.

Experimental results demonstrate that the KuaiRP models not only match the current state-of-the-art proprietary models in role-playing fidelity within the authors' target domains, but also successfully recover general agent capabilities, maintaining extremely low deployment costs.

This paper proposes a model-centric DevOps architecture for deploying DEVS-based digital twin simulations as managed services, treating simulation models as first-class DevOps artefacts defined in a declarative YAML language with a formal mapping to multiPDEVS.

A novel modification of the CSR format, Hierarchical CSR (HCSR), is proposed, which enhances SpMV performance on RISC-V processors and achieves the shortest execution time among all considered formats across a broad class of sparse matrices.

This research presents an embodied control approach based on real-time task Jacobian estimation of the combined hand and object system on the physical robot and showcases an alternative to compute- and data-heavy approaches such as RL and IL for achieving dexterous manipulation through computationally simple and data-efficient algorithms.

ShellVis serves as a case study of how sandboxing can bring live-programming techniques into the many real-world programming contexts where side effects are important, and is applied in the challenging context of shell scripting.

This research proposes a novel practical hybrid control scheme - learning agent-based MPC (LAMPC), combining the model-based AMPC approach and data-based learning methods to improve the holistic vehicle performance for multi-agent systems.

The overall conclusion is that formal verification is a viable complement to simulation, and could be adopted as a part of verification and validation for automated driving.