Visual Spatial Tuning

Capturing spatial relationships from visual inputs is a cornerstone of human-like general intelligence. Several previous studies have tried to enhance the spatial awareness of Vision-Language Models (VLMs) by adding extra expert encoders, which brings extra overhead and usually harms general capabilities. To enhance the spatial ability in general architectures, we introduce Visual Spatial Tuning (VST), a comprehensive framework to cultivate VLMs with human-like visuospatial abilities, from spatial perception to reasoning. We first attempt to enhance spatial perception in VLMs by constructing a large-scale dataset termed VST-P, which comprises 4.1 million samples spanning 19 skills across single views, multiple images, and videos. Then, we present VST-R, a curated dataset with 135K samples that instruct models to reason in space. In particular, we adopt a progressive training pipeline: supervised fine-tuning to build foundational spatial knowledge, followed by reinforcement learning to further improve spatial reasoning abilities. Without the side-effect to general capabilities, the proposed VST consistently achieves state-of-the-art results on several spatial benchmarks, including $34.8\%$ on MMSI-Bench and $61.2\%$ on VSIBench. It turns out that the Vision-Language-Action models can be significantly enhanced with the proposed spatial tuning paradigm, paving the way for more physically grounded AI.

Visual Backdoor Attacks on MLLM Embodied Decision Making via Contrastive Trigger Learning

Multimodal large language models (MLLMs) have advanced embodied agents by enabling direct perception, reasoning, and planning task-oriented actions from visual inputs. However, such vision driven embodied agents open a new attack surface: visual backdoor attacks, where the agent behaves normally until a visual trigger appears in the scene, then persistently executes an attacker-specified multi-step policy. We introduce BEAT, the first framework to inject such visual backdoors into MLLM-based embodied agents using objects in the environments as triggers. Unlike textual triggers, object triggers exhibit wide variation across viewpoints and lighting, making them difficult to implant reliably. BEAT addresses this challenge by (1) constructing a training set that spans diverse scenes, tasks, and trigger placements to expose agents to trigger variability, and (2) introducing a two-stage training scheme that first applies supervised fine-tuning (SFT) and then our novel Contrastive Trigger Learning (CTL). CTL formulates trigger discrimination as preference learning between trigger-present and trigger-free inputs, explicitly sharpening the decision boundaries to ensure precise backdoor activation. Across various embodied agent benchmarks and MLLMs, BEAT achieves attack success rates up to 80%, while maintaining strong benign task performance, and generalizes reliably to out-of-distribution trigger placements. Notably, compared to naive SFT, CTL boosts backdoor activation accuracy up to 39% under limited backdoor data. These findings expose a critical yet unexplored security risk in MLLM-based embodied agents, underscoring the need for robust defenses before real-world deployment.

The FM Agent

Large language models (LLMs) are catalyzing the development of autonomous AI research agents for scientific and engineering discovery. We present FM Agent, a novel and general-purpose multi-agent framework that leverages a synergistic combination of LLM-based reasoning and large-scale evolutionary search to address complex real-world challenges. The core of FM Agent integrates several key innovations: 1) a cold-start initialization phase incorporating expert guidance, 2) a novel evolutionary sampling strategy for iterative optimization, 3) domain-specific evaluators that combine correctness, effectiveness, and LLM-supervised feedback, and 4) a distributed, asynchronous execution infrastructure built on Ray. Demonstrating broad applicability, our system has been evaluated across diverse domains, including operations research, machine learning, GPU kernel optimization, and classical mathematical problems. FM Agent reaches state-of-the-art results autonomously, without human interpretation or tuning -- 1976.3 on ALE-Bench (+5.2\%), 43.56\% on MLE-Bench (+4.0pp), up to 20x speedups on KernelBench, and establishes new state-of-the-art(SOTA) results on several classical mathematical problems. Beyond academic benchmarks, FM Agent shows considerable promise for both large-scale enterprise R\&D workflows and fundamental scientific research, where it can accelerate innovation, automate complex discovery processes, and deliver substantial engineering and scientific advances with broader societal impact.

Optical Computation-in-Communication enables low-latency, high-fidelity perception in telesurgery

Artificial intelligence (AI) holds significant promise for enhancing intraoperative perception and decision-making in telesurgery, where physical separation impairs sensory feedback and control. Despite advances in medical AI and surgical robotics, conventional electronic AI architectures remain fundamentally constrained by the compounded latency from serial processing of inference and communication. This limitation is especially critical in latency-sensitive procedures such as endovascular interventions, where delays over 200 ms can compromise real-time AI reliability and patient safety. Here, we introduce an Optical Computation-in-Communication (OCiC) framework that reduces end-to-end latency significantly by performing AI inference concurrently with optical communication. OCiC integrates Optical Remote Computing Units (ORCUs) directly into the optical communication pathway, with each ORCU experimentally achieving up to 69 tera-operations per second per channel through spectrally efficient two-dimensional photonic convolution. The system maintains ultrahigh inference fidelity within 0.1% of CPU/GPU baselines on classification and coronary angiography segmentation, while intrinsically mitigating cumulative error propagation, a longstanding barrier to deep optical network scalability. We validated the robustness of OCiC through outdoor dark fibre deployments, confirming consistent and stable performance across varying environmental conditions. When scaled globally, OCiC transforms long-haul fibre infrastructure into a distributed photonic AI fabric with exascale potential, enabling reliable, low-latency telesurgery across distances up to 10,000 km and opening a new optical frontier for distributed medical intelligence.

ERA: Transforming VLMs into Embodied Agents via Embodied Prior Learning and Online Reinforcement Learning

Recent advances in embodied AI highlight the potential of vision language models (VLMs) as agents capable of perception, reasoning, and interaction in complex environments. However, top-performing systems rely on large-scale models that are costly to deploy, while smaller VLMs lack the necessary knowledge and skills to succeed. To bridge this gap, we present \textit{Embodied Reasoning Agent (ERA)}, a two-stage framework that integrates prior knowledge learning and online reinforcement learning (RL). The first stage, \textit{Embodied Prior Learning}, distills foundational knowledge from three types of data: (1) Trajectory-Augmented Priors, which enrich existing trajectory data with structured reasoning generated by stronger models; (2) Environment-Anchored Priors, which provide in-environment knowledge and grounding supervision; and (3) External Knowledge Priors, which transfer general knowledge from out-of-environment datasets. In the second stage, we develop an online RL pipeline that builds on these priors to further enhance agent performance. To overcome the inherent challenges in agent RL, including long horizons, sparse rewards, and training instability, we introduce three key designs: self-summarization for context management, dense reward shaping, and turn-level policy optimization. Extensive experiments on both high-level planning (EB-ALFRED) and low-level control (EB-Manipulation) tasks demonstrate that ERA-3B surpasses both prompting-based large models and previous training-based baselines. Specifically, it achieves overall improvements of 8.4\% on EB-ALFRED and 19.4\% on EB-Manipulation over GPT-4o, and exhibits strong generalization to unseen tasks. Overall, ERA offers a practical path toward scalable embodied intelligence, providing methodological insights for future embodied AI systems.

HEAR: An EEG Foundation Model with Heterogeneous Electrode Adaptive Representation

Electroencephalography (EEG) is an essential technique for neuroscience research and brain-computer interface (BCI) applications. Recently, large-scale EEG foundation models have been developed, exhibiting robust generalization capabilities across diverse tasks and subjects. However, the heterogeneity of EEG devices not only hinders the widespread adoption of these models but also poses significant challenges to their further scaling and development. In this paper, we introduce HEAR, the first EEG foundation model explicitly designed to support heterogeneous EEG devices, accommodating varying electrode layouts and electrode counts. HEAR employs a learnable, coordinate-based spatial embedding to map electrodes with diverse layouts and varying counts into a unified representational space. This unified spatial representation is then processed by a novel spatially-guided transformer, which effectively captures spatiotemporal dependencies across electrodes. To support the development of HEAR, we construct a large-scale EEG dataset comprising 8,782 hours of data collected from over 150 distinct electrode layouts with up to 1,132 electrodes. Experimental results demonstrate that HEAR substantially outperforms existing EEG foundation models in supporting heterogeneous EEG devices and generalizing across diverse cognitive tasks and subjects.

Safe and Non-Conservative Contingency Planning for Autonomous Vehicles via Online Learning-Based Reachable Set Barriers

Autonomous vehicles must navigate dynamically uncertain environments while balancing the safety and driving efficiency. This challenge is exacerbated by the unpredictable nature of surrounding human-driven vehicles (HVs) and perception inaccuracies, which require planners to adapt to evolving uncertainties while maintaining safe trajectories. Overly conservative planners degrade driving efficiency, while deterministic approaches may encounter serious issues and risks of failure when faced with sudden and unexpected maneuvers. To address these issues, we propose a real-time contingency trajectory optimization framework in this paper. By employing event-triggered online learning of HV control-intent sets, our method dynamically quantifies multi-modal HV uncertainties and refines the forward reachable set (FRS) incrementally. Crucially, we enforce invariant safety through FRS-based barrier constraints that ensure safety without reliance on accurate trajectory prediction of HVs. These constraints are embedded in contingency trajectory optimization and solved efficiently through consensus alternative direction method of multipliers (ADMM). The system continuously adapts to the uncertainties in HV behaviors, preserving feasibility and safety without resorting to excessive conservatism. High-fidelity simulations on highway and urban scenarios, as well as a series of real-world experiments demonstrate significant improvements in driving efficiency and passenger comfort while maintaining safety under uncertainty. The project page is available at https://pathetiue.github.io/frscp.github.io/.

RLMR: Reinforcement Learning with Mixed Rewards for Creative Writing

Large language models are extensively utilized in creative writing applications. Creative writing requires a balance between subjective writing quality (e.g., literariness and emotional expression) and objective constraint following (e.g., format requirements and word limits). Existing methods find it difficult to balance these two aspects: single reward strategies fail to improve both abilities simultaneously, while fixed-weight mixed-reward methods lack the ability to adapt to different writing scenarios. To address this problem, we propose Reinforcement Learning with Mixed Rewards (RLMR), utilizing a dynamically mixed reward system from a writing reward model evaluating subjective writing quality and a constraint verification model assessing objective constraint following. The constraint following reward weight is adjusted dynamically according to the writing quality within sampled groups, ensuring that samples violating constraints get negative advantage in GRPO and thus penalized during training, which is the key innovation of this proposed method. We conduct automated and manual evaluations across diverse model families from 8B to 72B parameters. Additionally, we construct a real-world writing benchmark named WriteEval for comprehensive evaluation. Results illustrate that our method achieves consistent improvements in both instruction following (IFEval from 83.36% to 86.65%) and writing quality (72.75% win rate in manual expert pairwise evaluations on WriteEval). To the best of our knowledge, RLMR is the first work to combine subjective preferences with objective verification in online RL training, providing an effective solution for multi-dimensional creative writing optimization.

Robotic Manipulation via Imitation Learning: Taxonomy, Evolution, Benchmark, and Challenges

Robotic Manipulation (RM) is central to the advancement of autonomous robots, enabling them to interact with and manipulate objects in real-world environments. This survey focuses on RM methodologies that leverage imitation learning, a powerful technique that allows robots to learn complex manipulation skills by mimicking human demonstrations. We identify and analyze the most influential studies in this domain, selected based on community impact and intrinsic quality. For each paper, we provide a structured summary, covering the research purpose, technical implementation, hierarchical classification, input formats, key priors, strengths and limitations, and citation metrics. Additionally, we trace the chronological development of imitation learning techniques within RM policy (RMP), offering a timeline of key technological advancements. Where available, we report benchmark results and perform quantitative evaluations to compare existing methods. By synthesizing these insights, this review provides a comprehensive resource for researchers and practitioners, highlighting both the state of the art and the challenges that lie ahead in the field of robotic manipulation through imitation learning.

ADPro: a Test-time Adaptive Diffusion Policy for Robot Manipulation via Manifold and Initial Noise Constraints

Diffusion policies have recently emerged as a powerful class of visuomotor controllers for robot manipulation, offering stable training and expressive multi-modal action modeling. However, existing approaches typically treat action generation as an unconstrained denoising process, ignoring valuable a priori knowledge about geometry and control structure. In this work, we propose the Adaptive Diffusion Policy (ADP), a test-time adaptation method that introduces two key inductive biases into the diffusion. First, we embed a geometric manifold constraint that aligns denoising updates with task-relevant subspaces, leveraging the fact that the relative pose between the end-effector and target scene provides a natural gradient direction, and guiding denoising along the geodesic path of the manipulation manifold. Then, to reduce unnecessary exploration and accelerate convergence, we propose an analytically guided initialization: rather than sampling from an uninformative prior, we compute a rough registration between the gripper and target scenes to propose a structured initial noisy action. ADP is compatible with pre-trained diffusion policies and requires no retraining, enabling test-time adaptation that tailors the policy to specific tasks, thereby enhancing generalization across novel tasks and environments. Experiments on RLBench, CALVIN, and real-world dataset show that ADPro, an implementation of ADP, improves success rates, generalization, and sampling efficiency, achieving up to 25% faster execution and 9% points over strong diffusion baselines.