Vision-Language-Action (VLA) models are built on the premise that pre-trained Vision-Language Models (VLMs) can supply general semantic understanding ability for embodied tasks. However, we find that robotic fine-tuning can severely erode this capability: VQA benchmarks and layer-wise CKA analysis reveal rapid representational drift, and co-training with general vision-language data does not prevent the collapse. We propose TwinBrainVLA, an asymmetric dual-stream VLA that keeps a pre-trained "Left Brain" strictly frozen as a semantic generalist while training a "Right Brain" as an embodied control specialist. Its Asymmetric Mixture-of-Transformers (AsyMoT) mechanism lets the Right Brain query frozen Left-Brain key-value representations at every transformer layer, integrating intact semantic knowledge without corrupting it. Across SimplerEnv, RoboCasa, LIBERO, and real-robot experiments, TwinBrainVLA outperforms strong baselines and large-scale embodied models, achieving a 14.3% improvement on SimplerEnv and a 2.7× gain in physical-robot instruction generalization.

Test-time scaling (TTS) has emerged as a powerful paradigm for enhancing large language model (LLM) reasoning by allocating additional compute during inference. External TTS, particularly Best-of- selection, generates multiple reasoning trajectories and selects the optimal candidate. However, existing process reward models (PRMs) for Best-of-N selection face a critical limitation: they were not originally designed for this task and instead rely on human-designed evaluation criteria (e.g., readability, coherence, formatting) that often misalign with actual answer correctness, leading to unstable and oscillatory performance as N increases. We introduce TrajSelector a framework specifically designed for robust and effective Best-of-N selection. Our key insight is to leverage the sampler LLM’s intrinsic hidden states as self-reflective signals, which naturally align with correctness rather than human-imposed stylistic preferences. A lightweight verifier (0.6B–4B parameters) scores reasoning steps using these hidden states, trained via outcome supervision to eliminate reliance on step-level human annotations. Experiments across six mathematical reasoning benchmarks demonstrate that TrajSelector achieves robust monotonic improvements: in Best-of-32 settings, even our smallest 0.6B verifier outperforms majority voting by 4.61% and existing 7B PRMs by 4.31–12.21%, while our 4B verifier reaches 64.05% accuracy with stable scaling behavior as N increases from 1 to 64.

Vision-Language-Action (VLA) policies are commonly trained from dense robot demonstration trajectories, often collected through teleoperation, by sampling every recorded frame as if it provided equally useful supervision. We argue that this convention creates a temporal supervision imbalance: long low-change segments dominate the training stream, while manipulation-critical transitions such as alignment, contact, grasping, and release appear only sparsely. We introduce FrameSkip, a data-layer frame selection framework that scores trajectory frames using action variation, visual-action coherence, task-progress priors, and gripper-transition preservation, then remaps training samples toward high-importance frames under a target retention ratio. Because FrameSkip operates only in the dataloader, it leaves the VLA architecture, action head, training objective, and inference procedure unchanged. Across RoboCasa-GR1, SimplerEnv, and LIBERO, FrameSkip improves the success-retention trade-off over full-frame training and simpler frame selection variants, achieving a macro-average success rate of 76.15% across the three benchmarks compared with 66.50% for full-frame training while using a compressed trajectory view that retains 20% of unique frames in the main setting.

Reinforcement learning with verifiable rewards (RLVR) improves vision-language models (VLMs) by optimizing outcome rewards derived from final answers. However, such outcome-only rewards do not tell the model which image regions justify an answer. For questions that require visual grounding, these rewards cannot distinguish responses supported by relevant visual evidence from those produced by language-prior shortcuts or lucky guesses. We introduce **EASE** (Evidence-Anchored Spatial Attention), which augments multimodal RLVR with visual-evidence process supervision. EASE converts annotated evidence regions into a smoothed visual-token target and uses it to guide response-to-image attention during RL training, but only on high-reward trajectories. The annotations are used solely as privileged training labels, while inference requires only the original image and question. Across Qwen2.5-VL-7B, Qwen3-VL-4B, and Qwen3-VL-8B, EASE raises average scores over DAPO by 2.5 to 3.1 points on perception, hallucination, visual math, and multimodal reasoning benchmarks. Diagnostics and ablations show that EASE better aligns visual attention with annotated evidence regions. Code are available at https://github.com/Nrich-sunny/Attend-to-Evidence/.

Vision-language-action (VLA) models are built on the premise that semantic understanding from pretrained language or vision-language backbones should guide robot action prediction. Yet robot fine-tuning is optimized as imitation over task-specific action distributions, and many evaluations can be solved through visual or instruction-action shortcuts. We introduce **RoboSemanticBench (RSB)**, an embodied benchmark for diagnosing semantic grounding in action prediction: whether post-trained VLA models can use complex instruction semantics to select and manipulate the correct physical target. In each episode, a robot receives a multiple-choice math or general-knowledge question, observes candidate answer blocks, and must grasp the block corresponding to the correct answer. RSB covers controlled arithmetic, grade-school mathematical understanding, and commonsense or factual understanding under four-choice and ten-choice suites. Across representative VLA models, we find that many policies learn to grasp candidate blocks but select the semantically correct block at near-random or below-random rates after controlling for grasp success, revealing a persistent gap between backbone-level semantic competence and action prediction.

Large reasoning models (LRMs) show strong capabilities in complex reasoning, yet their marginal gains on evidence-dependent factual questions are limited. We find this limitation is partially attributable to a reasoning–answer hit gap, where the model identifies the correct facts during reasoning but fails to incorporate them into the final response, thereby reducing factual fidelity. To address this issue, we propose MR-ALIGN, a Meta-Reasoning informed alignment framework that enhances factuality without relying on external verifiers. MR-ALIGN quantifies state-transition probabilities along the model’s thinking process and constructs a transition-aware implicit reward that reinforces beneficial reasoning patterns while suppressing defective ones at the atomic thinking segments. This re-weighting reshapes token-level signals into probability-aware segment scores, encouraging coherent reasoning trajectories that are more conducive to factual correctness. Empirical evaluations across four factual QA datasets and one long-form factuality benchmark show that MR-ALIGN consistently improves accuracy and truthfulness while reducing misleading reasoning. These results highlight that aligning the reasoning process itself, rather than merely the outputs, is pivotal for advancing factuality in LRMs.

Robot imitation data are often multimodal: similar visual-language observations may be followed by different action chunks because human demonstrators act with different short-horizon intents, task phases, or recent context. Existing frame-conditioned VLA policies infer each chunk from the current observation and instruction alone, so under partial observability they may resample different intents across adjacent replanning steps, leading to inter-chunk conflict and unstable execution. We introduce IntentVLA, a history-conditioned VLA framework that encodes recent visual observations into a compact short-horizon intent representation and uses it to condition chunk generation. We further introduce AliasBench, a 12-task ambiguity-aware benchmark on RoboTwin2 with matched training data and evaluation environments that isolate short-horizon observation aliasing. Across AliasBench, SimplerEnv, LIBERO, and RoboCasa, IntentVLA improves rollout stability and outperforms strong VLA baselines.