The paper introduces LaPla, a Vision‑Language‑Action framework that uses a latent‑aligned planning approach to convert discrete semantic reasoning into continuous, physics‑constrained driving actions. It employs a residual VQ‑VAE to encode vehicle kinematics into a structured latent space, then projects multimodal inputs—images, past actions, and text—directly into this latent space, allowing a frozen decoder to generate physically plausible trajectories without quantization errors. Experiments on nuScenes and NVIDIA AlpaSim show LaPla reduces long‑horizon L2 error by 15.52% and improves closed‑loop success rates by 33.34 percentage points while cutting inference latency.
By Ruoyu Yao, Yusen Xie, Qingzhao Liu, Pei Liu, Zewei Yang, Yipeng Zhu, Xiaolong Wang, Jun Ma
The article discusses how current world models, while achieving high predictive likelihood and visual fidelity, often fail to preserve the evidence needed for safe decision-making in embodied systems. It identifies three structural mismatches—likelihood versus risk, prediction versus intervention, and finite-horizon prediction versus accumulated consequences—and proposes the Risk‑Informed World Model (RIWM) as a decision‑centric framework. RIWM emphasizes consequences, intervention, epistemic uncertainty, and recoverability, integrating decision‑relevant representation, counterfactual reasoning, safety‑critical episodic memory, and runtime safety assurance to better support safety‑critical embodied systems.
By Kailang Ma, Heye Huang, Inhi Kim, Kitae Jang
AnyBox is a zero‑shot framework that estimates the full 9DoF pose (6D pose plus 3D dimensions) of boxes from a single RGB‑D image, leveraging the geometric regularity of boxes. It alternates between pose and scale estimation, using a binary search guided by the discrepancy between a reprojected template and the observed mask, and employs a depth‑consistency filter and an early‑stopping rule to prune implausible hypotheses. On public benchmarks and a warehouse dataset, AnyBox improves detection AP by up to 36 points and boosts robotic box‑shelving success by 28%.
By Yintao Ma, Sajjad Pakdamansavoji, Charles Eret, Rui Heng Yang, Xuan Zhao, Yingxue Zhang, Tongtong Cao, Amir Rasouli
The paper introduces BRIDGE, an open‑source 88 cm tall humanoid robot designed through a data‑driven morphology‑control co‑design framework that optimizes the robot’s body shape for human‑like movement. A new metric combining kinematic retargeting fidelity and dynamic tracking performance is proposed to evaluate morphological fidelity, and the framework achieves state‑of‑the‑art results compared to existing humanoids such as Bumi, K1, and Toddlerbot. The resulting platform, released with its control policy and supporting materials, demonstrates superior fidelity in capturing human motion, robust balance, and highly dynamic maneuvers.
By Jianren Wang, Letian Qian, Zikai Wang, Weiwei Wu, Junjie Zong, Abhinav Gupta, Deepak Pathak
IRWOZ 2.0 is a refined dialogue dataset for industrial human‑robot interaction, expanding to 390 dialogues across four domains—Assembly, Delivery, Position, and Relocation. The dataset was improved using large language models (Mistral and Claude‑3.5) for generation and quality refinement, including manual corrections and automated typo removal. Benchmark tests show a substantial boost in dialogue state‑tracking performance, with GPT‑2’s BLEU‑4 score rising from 0.1651 to 0.5604 compared to the original IRWOZ.
By Chen Li, Dimitrios Chrysostomou
FWBC‑VLA is a force‑aware framework that links vision‑language‑action (VLA) models with whole‑body compensation control for wheeled‑legged robots. It introduces HSR‑Force, a sensorless residual‑torque estimator that infers contact strength and encodes this information as tokens for the VLA action decoder, allowing the policy to detect contact onset, loading, and release. The system fine‑tunes a pretrained VLA backbone on a large WL&Arm dataset, combines proprioceptive, Jacobian‑derived force, and contact estimates to generate corrective actions, and demonstrates effectiveness in real‑world tasks such as whiteboard wiping and door opening.
By Yutian Zhang, Siyuan Ma, Liwen Yang, Yang Li, Ce Hao, Haozhen Chi, Dong We, Qiaojun Yu, Dibo Hou
AdaRoboVLG is a Vision‑Language‑Grasp framework that separates a generalizable base grasp policy from task‑specific understanding. The base policy generates and evaluates physically feasible grasp candidates using kinematic mapping and force‑closure stability, while foundation‑model modules supply composable spatial, cognitive, and temporal priors that adapt grasp synthesis to different robotic hands and environments without retraining. Experiments show strong cross‑hand generalization, effective handling of diverse grasping challenges, and functional grasping in cluttered, dynamic settings.
By Sixu Yan, Shikang Wang, Binhua Huang, Xuanlai Tang, Guohua Fan, Fan Huang, Haoxuan Li, Yongkang Li, Yuhan Li, Bencheng Liao, Zeyu Zhang, Wenyu Liu, Hangxin Liu, Xinggang Wang
The paper introduces a low‑cost, open experimental platform for end‑to‑end autonomous driving on miniature Ackermann vehicles, combining a physical car, printed track, data collection tools, trajectory registration, and a Webots digital twin. It implements command‑conditioned behavior cloning, achieving a mean cross‑track error of 6.1 cm on the real vehicle and demonstrating the impact of camera field of view in simulation. Using synthetic data from the digital twin and a sim‑to‑real image translator, a higher‑capacity policy trained on both synthetic and real data completes all four track routes, outperforming the baseline trained only on real data.
By Gustavo Claudio Karl Couto, Eric Aislan Antonelo, Gabriel George Zipperer
The paper introduces GEO Defender, a two‑stage defense system designed to protect generative search engines from malicious Generative Engine Optimization (GEO) attacks that rewrite web documents to manipulate generated answers. GEO Defender comprises a Shield Reranker, which learns a defensive residual to demote GEO‑rewritten documents while maintaining relevance, and a Training‑Free Shield Generation component that creates a natural‑language library guiding the target LLM’s source usage during inference. Experiments on both closed‑source and open‑source large language models show that GEO Defender dramatically lowers attack success rates from 50.32% to 6.20%, preserves over 94% of benign evidence usage, and maintains answer quality while generalizing to unseen attacks.
By Haozhang Li, Yangguang Shao, Xinjie Lin, Zhong Guan, Mi Zhou, Junzheng Shi
The paper introduces Evidence‑Gated Regularization (EGR), a modality‑agnostic training objective that mitigates modality entanglement in Vision‑Language‑Action (VLA) policies. EGR uses per‑frame, per‑sensor task‑relevance signals to enforce invariance on low‑evidence sensors and single‑sensor sufficiency on high‑evidence ones, adding no inference‑time overhead. Evaluations on a BEHAVIOR‑1K benchmark and two real‑robot setups (bi‑manual Kinova arms with RGB cameras and a single‑arm MELFA ASSISTA with vision and GelSight tactile sensors) show significant improvements in success rates across various corruption and fallback scenarios.
By Yue Yang, Diego Romeres, Chiori Hori, Gedas Bertasius, Daniel Szafir, Siddarth Jain
The paper investigates the use of thermal infrared video to non‑invasively monitor cardiorespiratory and sudomotor activity in industrial human‑machine interfaces. It presents a signal‑processing pipeline that tracks facial regions, aggregates thermal signals, and separates slow sudomotor trends from faster heart‑rate and breathing‑rate components. Experiments on 31 driver‑monitoring sessions show that thermal imaging can estimate heart rate, breathing rate, and electrodermal activity with reasonable accuracy, while highlighting challenges such as ROI selection, polarity changes, latency, and subject variability.
By Constantino \'Alvarez Casado, Mohammad Rahman, Sasan Sharifipour, Nhi Nguyen, Manuel Lage Ca\~nellas, Xiaoting Wu, Miguel Bordallo L\'opez
The paper introduces set difference captioning for autonomous driving datasets, aiming to generate natural‑language descriptions of differences between two image subsets. It adapts a two‑stage approach to focus on object‑centric patches, allowing attribution of differences to specific objects or categories. A new benchmark, AD‑Diff Bench, is presented to evaluate these methods, especially for sparse, real‑world differences, with open‑weight models to ensure reproducibility.
By Julian Truetsch, Felix Hauser, Christoph Stiller, Frank Bieder
ProAct is a dual‑system framework for real‑time embodied social interaction that separates a low‑latency Behavioral System, which streams multimodal interaction and generates continuous non‑verbal motion, from a slower Cognitive System that performs long‑horizon social reasoning and produces proactive intentions. The Cognitive System uses an efficient memory mechanism and a user‑motivation prediction module to decide when to intervene, while the Behavioral System translates these intentions into fluid motion via an intention‑conditioned streaming flow‑matching generator with a disentangled ControlNet branch. The framework is deployed on a physical humanoid robot and validated through real‑world user studies, motion‑generation benchmarks, and a new ProActBench benchmark for proactive trigger detection and restraint.
By Zeyi Zhang, Zixi Kang, Ruijie Zhao, Yusen Feng, Biao Jiang, Hanyu Ji, Libin Liu
The paper proves that several decision and approximation problems for ReLU neural networks are computationally hard. For any number of layers λ≥2, deciding whether a network’s output is positive (and thus whether it is surjective) is W[ℓ−1]-hard when parameterized by the input dimension d. In particular, for two-layer networks, the related geometric problem of zonotope non‑containment is W[1]-hard in the ambient dimension, and computing or approximating the Lp‑Lipschitz constant is NP‑hard and W[ℓ−1]-hard with respect to d. The results also show that these problems remain hard when parameterized by the number of layers for constant d, implying that naive enumeration algorithms running in n^{(ℓ−1)d}·poly(N) time are essentially optimal under the Exponential Time Hypothesis.
By Vincent Froese, Moritz Grillo, Christoph Hertrich, Moritz Stargalla
Drive‑HWM introduces a hierarchical slow‑fast world modeling framework for autonomous driving. The slow model predicts multi‑step future representations, while the fast model jointly predicts the next frame and immediate action using a lightweight multimodal backbone and an autoregressive expert. Dynamic‑Aware Latents, learned through optical‑flow prediction, explicitly capture motion dynamics, and experiments on NAVSIM v1 and v2 show strong driving performance with validated ablation studies.
By Zhaoxin Fan, Tianbao Zhang, Wenjun Wu, Xiaofeng Wang, Yeying Jin, Jian Zhao, Zheng Zhu, Shuicheng Yan
EasySteer is a unified framework for high‑performance, extensible large‑language‑model steering built on vLLM. It offers a modular architecture with pluggable interfaces for analysis‑based and learning‑based methods, fine‑grained parameter control, pre‑computed steering vectors for eight application domains, and an interactive demo system. Integrated with vLLM’s optimized inference engine, EasySteer delivers a 10.8–22.3× speedup over existing frameworks and demonstrates effectiveness in overthinking mitigation, hallucination reduction, and other key applications.
By Haolei Xu, Xinyu Mei, Yuchen Yan, Rui Zhou, Wenqi Zhang, Weiming Lu, Yueting Zhuang, Yongliang Shen
The paper introduces a method to detect which web scrapers feed data to large language models (LLMs) by deploying dynamic websites that issue unique canary tokens to each scraper. By querying LLMs for information about these sites, the authors can identify when an LLM consistently outputs the unique tokens, indicating exposure to a specific scraper. Experiments on 22 production LLM systems show the technique reliably uncovers both known and undisclosed scrapers, offering a tool for third parties to monitor and control unwanted web scraping.
By Steven Seiden, Triss Ren, Caroline Zhang, Taein Kim, Enze Liu, Emily Wenger
The paper presents an end‑to‑end JEPA world model that enhances latent prediction with inverse dynamics and state alignment to improve goal‑conditioned robotic planning. By preventing latent collapse and grounding representations in physical configuration, the model achieves top success rates on tasks such as TwoRoom, PushT, and OGBench‑Cube, outperforming the baseline LeWorldModel. Ablation studies confirm that state alignment consistently boosts planning success over inverse dynamics alone across all four benchmark tasks.
By Muyuan Liu (GENISOM AI, Beijing, China), Yue Huang (GENISOM AI, Beijing, China), Zheng Liang (GENISOM AI, Beijing, China), Xiang Gao (GENISOM AI, Beijing, China)
FlexMap is a vectorized high‑definition map construction framework that works with flexible camera configurations without needing calibrated rigs or explicit 2D‑to‑BEV transformations. It replaces geometric projection with a geometry foundation model that encodes cross‑view 3D structure, and uses a spatial‑temporal enhancement module and a camera‑aware decoder to separate spatial reasoning from temporal aggregation. Experiments on nuScenes and Argoverse 2 show that FlexMap outperforms pose‑dependent baselines and remains accurate even when camera views are missing or pose estimates are inaccurate.
By Run Wang, Chaoyi Zhou, Amir Salarpour, Xi Liu, Zhi-Qi Cheng, Feng Luo, Mert D. Pes\'e, Siyu Huang
The paper presents a complete characterization of when two deep ReLU networks realize the same function, showing that this occurs iff one can be transformed into the other using a set of axioms from many‑valued logic. It introduces a symbolic calculus that maps networks to substitution graphs, proves a completeness theorem linking equivalent formulas, and provides an algorithm to reconstruct networks from these graphs. The framework yields a new compositional normal form for MV logic that preserves the algebraic structure of deep ReLU networks.
By Yani Zhang, Helmut B\"olcskei