Vision-Language Models (VLMs) are increasingly applied to embodied task settings, yet it remains unclear whether their task-progress judgments are grounded in physical state comparison or in input-order shortcuts. We study a diagnostic two-frame setting: given two robot manipulation states and a task goal, the model must decide which state is closer to completion, or whether the two states are effectively equal. We observe a strong Forward-Backward Performance Gap: models often answer correctly in the natural forward order, but fail when the same two physical states are shown with their image slots swapped. This reversal is not intended to mimic the dominant deployment order of robot execution; rather, it is a counterfactual diagnostic for testing whether the selected physical state remains consistent under input-slot inversion. To reduce this order dependence, we introduce Equal-State supervision as a zero-point calibration signal, training models to output equal when no task-relevant progress difference is present instead of defaulting to a directional answer. From real manipulation videos, we construct the Embodied Causal-Temporal Reasoning (ECTR) dataset and propose Tri-Stage Weighted Alignment (TSWA), a staged fine-tuning procedure that strengthens spatial description, task-conditioned comparison, and answer calibration in sequence. On Qwen2.5-VL-7B, TSWA reduces GAP from 60.50% to 5.14% and raises overall accuracy from 44.66% to 80.19%. This comes with a small decrease in forward accuracy, but large gains in backward accuracy, equal-state recognition, and pairwise consistency, suggesting that TSWA replaces order-dependent correctness with more calibrated state comparison.

Multi-agent systems (MAS) are increasingly used for open-ended idea generation, driven by the expectation that collective interaction will broaden the exploration diversity. However, when and why such collaboration truly expands the solution space remains unclear. We present a systematic empirical study of diversity in MAS-based ideation across three bottom-up levels: model intelligence, agent cognition, and system dynamics. At the model level, we identify a compute efficiency paradox, where stronger, highly aligned models yield diminishing marginal diversity despite higher per-sample quality. At the cognition level, authority-driven dynamics suppress semantic diversity compared to junior-dominated groups. At the system level, group-size scaling yields diminishing returns and dense communication topologies accelerate premature convergence. We characterize these outcomes as collective failures emerging from structural coupling, a process where interaction inadvertently contracts agent exploration and triggers diversity collapse. Our analysis shows that this collapse arises primarily from the interaction structure rather than inherent model insufficiency, highlighting the importance of preserving independence and disagreement when designing MAS for creative tasks. Our code is available at https://github.com/Xtra-Computing/MAS_Diversity.

Object detection is a core challenge in computer vision. Traditional methods primarily rely on intermediate modalities such as text, speech, or visual cues to interpret user intent, leading to inefficient and potentially distorted expressions of intent. Brain signals, particularly fMRI signals, emerge as a novel modality that can directly reflect user intent, eliminating ambiguities introduced during modality conversion. However, brain signal-based object detection still faces challenges in accuracy and robustness. To address these challenges, we present BrainLoc, a lightweight object detection model guided by fMRI signals. First, we employ a multi-modal alignment strategy that enhances fMRI signal feature extraction by incorporating various modalities including images and text. Second, we propose a cross-domain fusion module that promotes interaction between fMRI features and category features, improving the representation of category information in fMRI signals. Extensive experiments demonstrate that BrainLoc achieves state-of-the-art performance in brain signal-based object detection tasks, showing significant advantages in both accuracy and convenience.