Engineering Sciences

PanoVLN performs vision-and-language navigation in continuous environments with a 4B vision-language model and RGB-only panoramic input, combining longer action-sequence supervision, confidence-guided execution, decision-centric training data, and fused panoramic geometric features to reach 77.3% and 78.0% success rate on R2R-CE and RxR-CE Val-Unseen, exceeding the previous best by 11.9 and 8.7 percentage points, and enabling real-world quadruped navigation with fewer pauses.

WorldLine introduces an action-driven visual simulator that decouples transferable robot-object dynamics learning from heterogeneous action grounding: it pretrains on more than 10,000 hours of action-free robot videos, grounds them with over 2,000 hours of action trajectories across more than ten embodiments through image-space action maps, adds multi-view, failure-enriched and relational-regularized training, and distills a robot-focused few-step causal rollout; across held-out and out-of-domain settings it maintains visual quality and robot-motion agreement, improves robot-mask IoU by 0.1626 over the strongest baseline on failed trajectories, predicts trajectory success with 74% mean accuracy on RoboTwin and AgiBot, and improves task success by up to 21.

Real2Gym is an agentic Real2Sim2Real framework that reconstructs human and robot demonstration videos into visually aligned, natively physics-validated Blender and MuJoCo interactive environments, where an agent generates executable code and distills successes and failures into reusable skills, reaching 87.5% task success with roughly 75% fewer policy-execution tokens than GPT-6 Astra across 24 reconstructed DROID and EgoDex environments and turning a zero-shot real-robot failure on narrow-clearance plate placement into success after simulation-based evolution on a Franka arm.

StructRL is an online reinforcement learning framework that automatically decomposes each long-horizon instruction into subtasks verifiable by binary environment-state criteria and organizes them into ordered dependency groups, granting intermediate rewards only after all prerequisites of a subtask are complete and scaling each reward by completion pace; across RoboCasa365 and LIBERO-Long with the GR00T-N1.5 and π0.5 VLA backbones it consistently outperforms the evaluated online RL baselines (e.g., on GR00T-N1.5, RoboCasa365 rises from 41.5% to 49.1% and LIBERO-Long from 92.4% to 96.6%).

The authors built EngiWorld, a benchmark structured around the complete engineering design loop, with 1,301 expert-curated tasks across 6 domains (CAD, CAE, CAM, BIM, EDA, and 3D visualization) and 26 professional software platforms, evaluated through a unified domain-verifier suite that checks geometric validity, physical feasibility, and rule compliance of final and intermediate artifacts; across seven frontier models the strongest reaches an EngiScore of only 44.3, and just 3.6% of multi-software attempts succeed.

CrossBFM freezes the backward map of a Behavior Foundation Model pretrained on the Unitree G1 and, using the frame-level cross-embodiment correspondence supplied by retargeted data, distills that latent space onto three humanoids (Inhouse M3, Booster T1, Fourier N1) with a unified encoder that has no robot-specific parameters, training the encoder in under one GPU-hour and each tracker in about 10 more GPU-hours; all three prompting modes transfer (motion tracking, goal reaching between poses, reward optimization), the latent-conditioned policy loses only 0.

The authors introduce LibraryDesignBench, a two-phase benchmark in which a designer agent implements a full-featured library from a specification that lists required capabilities without prescribing interfaces, and three user agents from different model families then solve problems with it, scored by pass rate squared times simplicity relative to reference solutions written with the real production library; across 15 library-design tasks, 242 expert-validated problems and four languages, Opus 5.5 scores 48.9, 2.3 points above the production library, designers reproduce production-library abstractions on 11 of 15 tasks, 64% of audited excess code traces to rigid or hard-to-use interfaces rather than missing capabilities, and adding prescriptive agent-first guidance raises the score to 46.

The work proposes EVO-WAM, a framework that enables complete autoregressive rollouts without external execution feedback via state prediction and anchored multi-frame context, then selects task-completing prefixes with a vision-language model and verifies video-action consistency with an inverse dynamics model for iterative self-training; on seven unseen RoboTwin 2.0 tasks it raises average success from 26.9% to 68.0% for Cosmos3 and from 28.5% to 46.4% for DreamZero, and on three real-world long-horizon composite tasks it raises Cosmos3 from 20.0% to 76.7%.

Under matched backbone, data, and training budget, the study compares explicit and latent world action models and finds that latent models match explicit ones in distribution (96.85 vs 97.75) yet fall behind on all three generalization axes—environmental perturbation, data efficiency, and task generalization—while leaving future video tokens at pure noise and running a single forward pass recovers most of the gap, motivating Simple-WAM, which leads explicit models in generalization at latent-level inference speed.

The work introduces AnyStep World Action Model, a framework that performs budget-aligned flow-map distillation from frozen-teacher trajectory intervals and trains a lightweight risk-benefit scheduler to predict teacher-trajectory difficulty and budget-specific student fidelity from a single one-step preview, selecting the smallest denoising budget that meets a fidelity requirement; on RoboTwin 2.0 it reduces average denoising steps by 60.2%, 49.8%, and 85.28% on Motus, FastWAM, and LingBotVA while keeping average success within 0.24 percentage points of full-budget baselines, raises one-step success by 7.07, 12.08, and 8.94 percentage points respectively, and achieves 1.67-6.14x per-call speedups on six real-world manipulation tasks.