vla

This paper analyzes how VLA policies should choose which backbone layers form the latent interface, finding that layer fusion often underperforms the best single layer and proposing InfoNCE as a compute-efficient proxy for layer selection that cuts GPU costs 9-33x while reducing selection regret.

WorldAuditBench is a benchmark of 213 anomaly-auditing tasks across 13 interactive 3D environments (Unreal Engine 5 / Three.js) that evaluates whether multimodal agents can couple action with visual reasoning. Frontier models achieve only 6.6–42.3% success versus 83.4% human performance, highlighting current limitations in evidence gathering and interpretation.

The paper introduces THAW-VLA, a method that distills world-model representations into Vision-Language-Action models for robotics, enhancing robustness and performance on simulation and real hardware without additional inference overhead.

VSArena v0.6.0 is a major update to a browser-based studio for running and evaluating Vision-Language-Action policies in 3D physics environments, emphasizing reliable and reproducible assessment without local simulators or physical robots.

A new open-source repository curates a list of egocentric (first-person vision) datasets, updated to include recent releases from 2023–2026 with sections on VLA papers and broken links removed.

Presents ForeWAM, a direct-policy World Action Model that provides predictive context to action generation via hidden future-slot KV states, avoiding explicit future video decoding while achieving high success rates on LIBERO benchmarks.

Xiaomi announces Robotics-1, a new robot foundation model with a VLA architecture trained on 100K+ hours of real-world robot data, featuring cross-embodiment generalization and fast adaptation to new tasks.

Introduces W2-VLA, a vision-language-action model for fine-grained robot manipulation that models task-conditioned future wrist observations, achieving improved manipulation performance on benchmarks and real-world tasks.

MIT researchers developed VLASH, a method enabling vision-language-action models to predict future robot states, doubling speed and reducing lag in tasks like pick-and-place and table tennis.

Anchor-Align augments behavioral cloning with vision-language anchoring to preserve pretrained representations and language-action alignment, improving real-robot success rates by over 20% on xArm7 and showing consistent gains in simulation benchmarks.

CLAP proposes a method to convert pretrained vision-language models (VLMs) into vision-language-action models (VLAs) by prepending natural-language action descriptions to action token sequences, preserving semantic capabilities without architectural changes. It achieves 90.8% on LIBERO and improves robustness.

This paper introduces robot-centric pointmaps, which encode 3D scene coordinates in the robot frame directly into image pixels, to resolve the frame mismatch between camera observations and robot action definitions in Vision-Language-Action models. The method improves Pi0.5 and SmolVLA on RoboCasa and generalizes better to unseen camera placements in real-robot experiments.

LingBot-VLA 2.0, an open-source embodied foundation model, has been released with 60,000 hours of pretraining data supporting 20 robot configurations across 17 brands, capable of sub-130ms inference on RTX 4090.

This paper presents Semantic Action RL, which uses reinforcement learning over Vision-Language-Action (VLA) prompts to enable robots to learn new tasks quickly in the real world.

Introduces Neuro-Symbolic Drive, a framework that uses rule-grounded reasoning traces from classical planners to fine-tune a driving VLA (Qwen3.5-4B), achieving significant reductions in average displacement error and miss rate compared to standard CoT reasoning.

This paper introduces PersonaDrive, a pipeline that conditions a vision-language-action (VLA) driving agent on retrieved demonstrations from a style-instructed human driving dataset, enabling style-diverse non-ego agents for closed-loop simulation and improving driving scores on Bench2Drive.

Robot world models and simulation platforms are experiencing open-source acceleration: NVIDIA launched Cosmos 3 and Isaac GR00T physical AI foundation models, AGIBOT released Genie Sim 3.0, a fully open-source simulation platform, VLA models become mainstream for manipulation policies, collectively lowering the entry barrier for the robotics field.

AffordanceVLA introduces a unified framework using structured affordance forecasting as an intermediate representation to improve perception-action mapping in robotic manipulation, leveraging vision-language models and a Mixture-of-Transformer architecture.

X Square Robot releases Wall-OSS-0.5, a 4B open-source VLA robot foundation model evaluated on a 17-task real-robot zero-shot suite without task-specific fine-tuning, aiming to directly measure pretraining capability.

Release of Wall-OSS-0.5, an open-weights vision-language-action model that achieves over 80% task progress on 4 of 17 real-robot tasks with zero fine-tuning, including on a deformable rope task not seen during pretraining. The model preserves general vision-language ability while improving embodied grounding.