松下电器产业株式会社

Vision–Language Models (VLMs) excel at reasoning in linguistic space but struggle with perceptual understanding that requires dense visual perception, e.g., spatial reasoning and geometric awareness. This limitation stems from the fact that current VLMs have limited mechanisms to capture dense visual information across spatial dimensions. We introduce Chain-of-Visual-Thought (CoVT), a framework that enables VLMs to reason not only with discrete text tokens but also through continuous visual tokens—compact latent representations that encode rich perceptual cues. With a small budget of roughly 20 tokens, CoVT distills knowledge from lightweight vision experts that capture complementary properties such as 2D appearance, 3D geometry, spatial layout, and edge structure. During training, the VLM with CoVT autoregressively predicts these visual tokens to reconstruct dense supervision signals (e.g., depth, segmentation, edges, and DINO features). At inference, the model reasons directly in the continuous visual latent space, preserving efficiency while optionally decoding dense predictions for interpretability. Evaluated across more than ten diverse benchmarks, including CV-Bench, MME-RealWorld, MMVP, RealWorldQA, MMStar, WorldMedQA, and HRBench, integrating CoVT into strong VLMs such as Qwen2.5-VL and LLaVA consistently improves performance by 3

The ability to react dynamically to tactile signals has long been considered crucial to agile human-level dexterity. Yet contemporary learning-based Vision-Language-Action (VLA) models for robotic manipulation generally either overlook the tactile modality or are limited to encoders with static cues, due in part to the scarcity of diverse training data and standardized evaluation, architectural constraints in current VLA models, and limitations of static tactile encoders. In this paper, we push the frontier of tactile-reactive manipulation by addressing all of these limitations. We propose a large-scale, 100-hour tactile-rich dataset collected via a novel, data-efficient recipe that prioritizes elementary motor primitives. To effectively exploit naturally high-frequency touch signals without sacrificing the existing capabilities of existing VLAs, we introduce a variable-rate Mixture-of-Transformers (MoT) architecture equipped with a novel temporal tactile VQ-VAE encoder. We demonstrate the effectiveness of tactile-reactive policies on 12 manipulation tasks requiring delicate force control and deformable object manipulation, achieving over 30

World models have shown great utility in improving the task performance of embodied agents. While prior work largely focuses on pixel-space world models, these approaches face practical limitations in mobile agent settings, where predicting complex visual GUI elements in future states is often difficult. In this work, we explore an alternative formulation of world modeling for mobile agents, where state transitions are described in natural language rather than predicting screenshot pixels. First, we introduce MobileWorldBench, a benchmark that evaluates the ability of vision-language models (VLMs) to function as world models for mobile agents. Second, we release MobileWorld, a large-scale dataset consisting of 1.4M samples, that improves the world modeling capabilities of VLMs. Finally, we propose a novel framework that integrates VLM world models into the planning framework of mobile agents, demonstrating that semantic world models can directly benefit mobile agents by improving task success rates.

This study evaluates, for Japan’s Kyushu area in 2030, the system-wide impacts of optimal demand-side control coordinated with hourly CO2 emission factors, using an integrated supply–demand simulation across multiple scenarios and carbon price levels. Hourly CO2 emission factors reflect the hourly generation mix, which is determined by power‑system operations and varies with electricity demand; therefore, these emission factors are related to consumers’ electricity consumption. The analysis focuses on the value of time-varying CO2 intensity as a control signal: because the emission associated with electricity consumption changes by hour, aligning flexible loads with low-intensity periods can materially reduce total emissions. Modeling approach in this study couples a supply-side operations model with a residential operation model that internalizes hourly CO2 emission factors to electricity price. Flexible demand-side control linked to hourly CO2 emission factors shifts electric-vehicle charging and heat-pump water-heater operation to low-CO2 hours, suppresses peaks in both purchased electricity and sold electricity. Compared with demand-side-only measures and supply-side-only measures, combining such demand-side control with supply-side carbon pricing achieves the largest reductions in both net demand-side and supply-side CO2 emissions. Moreover, the control method helps to reduce generation cost increase due to the carbon pricing. These findings imply that complementing supply-side carbon pricing like carbon levy with demand-side incentives indexed to hourly CO2 emission factors can achieve reduction of both CO2 emissions and generation costs, thereby easing social cost burden.

Dynamic runtime latency and memory constraints necessitate flexible large language model (LLM) deployment, where an LLM can be inferred with various quantization precisions based on available computational resources. Recent work on such any-precision quantization either relies on hardware-inefficient vector quantization or induces additional scaling factors when switching between bit-widths. Meanwhile, existing post-training quantization (PTQ) methods calibrated for a fixed low precision show poor generalizability under runtime precision change. In this work, we attribute the source of poor generalization across bit-widths to a precision-dependent outlier migration phenomenon where the distribution of PTQ-sensitive tokens changes across precisions. Motivated by this observation, we propose , a novel any-precision Mixture-of-Bits quantization framework that adjusts weight precision for flexible LLM inference based on token sensitivity. Specifically, we propose a many-in-one recursive residual quantization that can iteratively reconstruct higher-precision weights at runtime and mitigates outlier migration with a token-aware router to dynamically select the optimal inference precision of each token.Extensive experiments show that matches or surpasses frontier single-precision PTQ while exhibiting strong elasticity, achieving significant memory savings and throughput gains of up to 1.34× over state-of-the-art any-precision methods.