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Applications · Health / Medicine

Ana Sanchez Fernandez, Thomas Pinetz, Werner Zellinger, Günter Klambauer

Biomedical imaging data presents enormous potential for deep learning models to predict invaluable properties, such as diseases and drug effects. However, unavoidable alterations of the technical conditions cause *batch effects*: variations between groups of samples that are not due to any biological signal of interest. Batch effects greatly hinder the generalization abilities of deep learning models, preventing their practical use in the real world. Unsupervised Domain Adaptation (UDA) methods have been proposed to mitigate batch effects, but they usually assume that the data is comprised of only one source domain and one target domain, whereas biological datasets are comprised of multiple domains, both at training and at inference time. While Batch Normalization–based test-time and meta-learning adaptation methods offer a promising mechanism for domain alignment, we show that existing approaches exhibit degraded performance under the usual inference scenarios of small target batch sizes and label shift. We address these limitations by leveraging negative control samples, which are consistently present in every experimental batch in biological datasets, as stable context for adaptation. We propose CS-ARM-BN, a meta-learning BN adaptation method that uses controls both during training and inference to stabilize domain statistics. We perform a suite of experiments of Mechanism-Of-Action (MoA) classification, a crucial task for drug discovery, on the large JUMP-CP imaging dataset. Our experiments show that CS-ARM-BN substantially improves robustness to batch size and class distribution shifts, enabling practical use of deep learning models for biomedical images.

Deep Learning · Large Language Models

Yu Li, Lehui Li, Lin Chen, Qingmin Liao, Fengli Xu, Yong Li

In modern AI research, baseline and dataset selection is a high-stakes decision in experimental design. It operationalizes a research idea into a concrete evaluation protocol and largely determines the validity and comparability of empirical conclusions. However, making appropriate choices is increasingly difficult as baselines and datasets proliferate, while suitability is inherently context-dependent and rarely captured by baseline and dataset metadata. To address these challenges, we present \textbf{AgentExpt}, a comprehensive framework for baseline and dataset recommendation. We first curate a large-scale, high-quality knowledge base that links 108{,}825 accepted papers to their used baselines and datasets. Based on this resource, we design a \textit{collective perception-enhanced retriever} that represents each baseline or dataset by integrating first-person self-descriptions with third-person citation contexts, thereby effectively positioning them within the scholarly network. We further design a \textit{reasoning-augmented reranker} that encodes baseline-dataset interaction chains as a reasoning prior to fine-tune an LLM, producing refined rankings with interpretable justifications. Experiments show that our framework outperforms the strongest baseline, with average gains of +5.85\% in Recall@20 and +7.90\% in HitRate@10, and ablation studies confirm the effectiveness of our designed components. Overall, AgentExpt advances the efficient and reliable automation of experimental design. Our code is available at \url{https://anonymous.4open.science/r/Agentexpt-DD3E}.

Reinforcement Learning · Online

Shutong Ding, Zejia Zhong, Zhongyi Wang, Ke Hu, Bikang Pan, Jingya Wang, Ye Shi

Recent advances in reinforcement learning (RL) have achieved great successes by leveraging the multimodality and exploration capability of diffusion policies. Among these approaches, one representative branch focuses on the weighted-based policy optimization. This design enables better exploration capability of diffusion model, particularly at the beginning of training, but suffer from the low exploitation in Q-value information, resulting in a slow policy convergence. Another branch pay attention to gradient-based policy optimization, which sufficiently exploit the gradient of Q function yet tend to collapse into a unimodal policy with low diversity. To address this issue, we propose CGPO, \textbf{C}ritic-\textbf{G}uided diffusion \textbf{P}olicy \textbf{O}ptimization, which effectively balances exploration and exploitation with the training-free guidance technique integrated into the denoising process of diffusion policy. Concretely, CGPO steers action generation toward high-value regions defined by the critic network and uses the guided actions as regression objectives. In this manner, CGPO reduces the time required to obtain high-quality actions and improves final performance with better balance between exploration-exploitation tradeoff. We validate the effectiveness of CGPO on 5 MuJoCo locomotion tasks, and CGPO achieves state-of-the-art performance compared with existing diffusion-based RL methods. Notably, CGPO is the first success to incorporate diffusion policy into real-world RL, with its superior performance on Franka robot arm grasping tasks.

Lehui Li, Yuyao Wang, Jisheng Yan, Wei Zhang, Jinliang Deng, Haoliang Sun, Zhongyi Han, Yongshun Gong

Incorporating textual information into time-series forecasting holds promise for addressing event-driven non-stationarity; however, a fundamental modality gap hinders effective fusion: textual descriptions express temporal impacts implicitly and qualitatively, whereas forecasting models rely on explicit and quantitative signals. Through controlled semi-synthetic experiments, we show that existing methods over-attend to redundant tokens and struggle to reliably translate textual semantics into usable numerical cues. To bridge this gap, we propose \method{}, which introduces a Temporal Evolution Semantic Space as an intermediate bottleneck between modalities. This space consists of interpretable, numerically grounded temporal primitives—mean shift, volatility, shape, and lag—extracted from text by an LLM via structured prompting and filtered through confidence-aware gating. Experiments on four real-world datasets demonstrate up to a 29\% reduction in forecasting error compared to state-of-the-art uni-modal and multimodal baselines. The code is available at https://anonymous.4open.science/r/MMTSF.

Deep Learning · Large Language Models

Dharshan Kumaran, Arthur Conmy, Federico Barbero, Simon Osindero, Viorica Patraucean, Petar Veličković

Verbal confidence—prompting LLMs to state their confidence as a number or category—is widely used to extract uncertainty estimates from black-box models. However, how LLMs internally generate such scores remains unknown. We address two questions: first, when confidence is computed -- just-in-time when requested, or automatically during answer generation and cached for later retrieval; and second, what verbal confidence represents -- token log-probabilities, or a richer evaluation of answer quality? Focusing on Gemma 3 27B and Qwen 2.5 7B, we provide convergent evidence for cached retrieval. Activation steering, patching, noising, and swap experiments reveal that confidence representations emerge at answer-adjacent positions before appearing at the verbalization site. Attention blocking pinpoints the information flow: confidence is gathered from answer tokens, cached at the first post-answer position, then retrieved for output. Critically, linear probing and variance partitioning reveal that these cached representations explain substantial variance in verbal confidence beyond token log-probabilities, suggesting a richer answer-quality evaluation rather than a simple fluency readout. These findings demonstrate that verbal confidence reflects automatic, sophisticated self-evaluation—not post-hoc reconstruction—with implications for understanding metacognition in LLMs and improving calibration.

General Machine Learning · Representation Learning

Daniil Karzanov, Marcin Detyniecki

Deep neural networks for image classification often exhibit overconfidence on out-of-distribution (OOD) samples. To address this, we introduce Geometrically Constrained Outlier Synthesis (GCOS), a training-time regularization framework aimed at improving OOD robustness during inference. GCOS addresses a limitation of prior synthesis methods by generating virtual outliers in the hidden feature space that respect the learned manifold structure of in-distribution (ID) data. The synthesis proceeds in two stages: (i) a dominant-variance subspace extracted from the training features identifies geometrically informed, off-manifold directions; (ii) a conformally-inspired shell, defined by the empirical quantiles of a nonconformity score from a calibration set, adaptively controls the synthesis magnitude to produce boundary samples. The shell ensures that generated outliers are neither trivially detectable nor indistinguishable from in-distribution data, facilitating smoother learning of robust features. This is combined with a contrastive regularization objective that promotes separability of ID and OOD samples in a chosen score space, such as Mahalanobis or energy-based. Experiments demonstrate that GCOS outperforms state-of-the-art methods using standard energy-based inference on near-OOD benchmarks, defined as tasks where outliers share the same semantic domain as in-distribution data. As an exploratory extension, the framework naturally transitions to conformal OOD inference, which translates uncertainty scores into statistically valid p-values and enables thresholds with formal error guarantees, providing a pathway toward more predictable and reliable OOD detection.

Deep Learning · Large Language Models

Suizhi Huang, Mei Li, Han Yu, Xiaoxiao Li

Textual Gradient-style optimizers (TextGrad) enable gradient-like feedback propagation through compound AI systems. However, they do not work well for deep chains. The root cause of this limitation stems from the *Semantic Entanglement* problem in these extended workflows. In standard textual backpropagation, feedback signals mix local critiques with upstream contexts, leading to *Attribution Ambiguity*. To address this challenge, we propose TextResNet, a framework that reformulates the optimization process to achieve precise signal routing via four key innovations. Firstly, in the forward pass, it enforces Additive Semantic Deltas to preserve an Identity Highway for gradient flow. Secondly, in the backward pass, it introduces Semantic Gradient Decomposition via a Semantic Projector to disentangle feedback into causally independent subspaces. Thirdly, it implements Causal Routing, which routes projected signals to their specific components. Finally, it performs Density-Aware Optimization Scheduling to leverage the disentangled signals to dynamically allocate resources to key system bottlenecks. Our results show that TextResNet not only achieves superior performance compared to TextGrad, but also exhibits remarkable stability for agentic tasks in compound AI systems where baselines collapse. The code will be made public after the review.

Xiaoda Yang, Majun Zhang, Changhao Pan, Nick Huang, Yang Yuguang, fan zhuo, Pengfei Zhou, Jin Zhou, Sizhe Shan, Shan Yang 等

Unified audio--visual generation is rapidly gaining industrial and creative relevance, enabling applications in virtual production and interactive media. However, when moving from general audio--video synthesis to music–dance co-generation, the task becomes substantially harder: musical rhythm, phrasing, and accents must drive choreographic motion at fine temporal resolution, and such rhythmic coupling is not captured by unimodal metrics or generic audiovisual consistency scores used in current evaluation practice. We introduce TMD-Bench, a benchmark for text-driven music–dance co-generation that assesses systems across unimodal generation quality, instruction adherence, and cross-modal rhythmic alignment. The benchmark integrates computable physical metrics with perceptual multimodal judgments, and is supported by a curated rhythm-aligned music–dance dataset and a fine-grained Music Captioner for structured music semantics. TMD-Bench further reveals that (i) modern commercial audio--visual models (e.g., Veo 3, Sora 2) produce high-quality music and video, while rhythmic coupling remains less consistently optimized and leaves room for improvement, and (ii) our unified baseline RhyJAM trained on rhythm-aligned data achieves competitive beat-level synchronization while maintaining competitive unimodal fidelity. This presents prospects for building next-generation music–dance models that explicitly optimize rhythmic and kinetic coherence.

Applications · Computer Vision

Xiaoda Yang, Yuxiang Liu, Shenzhou Gao, Can Wang, Jingyang Xue, Lixin Yang, Yao Mu, Tao Jin, Shuicheng YAN, Zhimeng Zhang 等

Modern vision-language models achieve strong performance in static perception, but remain limited in the complex spatiotemporal reasoning required for embodied, egocentric tasks. A major source of failure is their reliance on temporal priors learned from passive video data, which often leads to spatiotemporal hallucinations and poor generalization in dynamic environments. To address this, we present EgoTSR, a curriculum-based framework for learning task-oriented spatiotemporal reasoning. EgoTSR is built on the premise that embodied reasoning should evolve from explicit spatial understanding to internalized task-state assessment and finally to long-horizon planning. To support this paradigm, we construct EgoTSR-Data, a large-scale dataset comprising 46 million samples organized into three stages: Chain-of-Thought supervision for explicit reasoning, weakly supervised tagging for internalizing reasoning, and long-horizon sequences composed of orthogonal atomic subtasks. We further introduce a Reasoning-Enhanced Task Decomposition mechanism that explicitly models causal dependencies between actions and objects, enabling controllable planning from high-level task descriptions. To rigorously validate our approach, we establish a Dual-Level Evaluation Framework that covers both atomic spatial perception and logical planning. Extensive experiments demonstrate that EgoTSR effectively eliminates chronological biases, achieving 92.4% accuracy on long-horizon logical reasoning tasks while maintaining 88.2% fine-grained perceptual precision, significantly outperforming existing open-source and closed-source state-of-the-art models.

Applications · Chemistry, Physics, and Earth Sciences

Hongfei Wu, Lijun Wu, Guoqing Liu, Zhirong Liu, Bin Shao, Zun Wang

In this paper, we develop SE3Set, an SE(3) equivariant hypergraph neural network architecture tailored for advanced molecular representation learning. Hypergraphs are not merely an extension of traditional graphs; they are pivotal for modeling high-order relationships, a capability that conventional equivariant graph-based methods lack due to their inherent limitations in representing intricate many-body interactions. To achieve this, we first construct hypergraphs by proposing a new fragmentation method that considers both chemical and three-dimensional spatial information of the molecular system. We then design SE3Set, which incorporates equivariance into the hypergraph neural network. This ensures that the learned molecular representations are invariant to spatial transformations, thereby providing robustness essential for the accurate prediction of molecular properties. SE3Set has shown performance on par with state-of-the-art (SOTA) models for small molecule datasets like QM9 and MD17. It demonstrates outstanding performance on the MD22 dataset, achieving a remarkable ~20\% improvement in accuracy across all molecules. Furthermore, on the OE62 dataset, SE3Set outperforms all short-range models. We also conducted a detailed analysis of OE62, highlighting the prevalence of complex many-body interactions in large molecules. This exceptional performance of SE3Set across diverse molecular structures underscores its transformative potential in computational chemistry, offering a route to more accurate and physically nuanced modeling. The code of this work is available at https://github.com/Navantock/SE3Set.

Applications · Health / Medicine

Ivan Svatko, Maxime Sanchez, Ihab Bendidi, Gilles Cottrell, Auguste Genovesio

Representation learning has driven major advances in natural image analysis by enabling models to acquire high-level semantic features. In microscopy imaging, however, it remains unclear what current representation learning methods actually learn. In this work, we conduct a systematic study of representation learning for the two most widely used and broadly available microscopy data types, representing critical scales in biology: cell culture and tissue imaging. To this end, we introduce a set of simple yet revealing baselines on curated benchmarks, including untrained models and simple structural representations of cellular tissue. Our results show that, surprisingly, state-of-the-art methods perform comparably to these baselines. We further show that, in contrast to natural images, existing models fail to consistently acquire high-level, biologically meaningful features. Moreover, we demonstrate that commonly used benchmark metrics are insufficient to assess representation quality and often mask this limitation. In addition, we investigate how detailed comparisons with these benchmarks provide ways to interpret the strengths and weaknesses of models for further improvements. Together, our results suggest that progress in microscopy image representation learning requires not only stronger models, but also more diagnostic benchmarks that measure what is actually learned.

Deep Learning · Large Language Models

Ming Zhong, Xiang Zhou, Ting-Yun Chang, Qingze Wang, Nan Xu, Xiance Si, Dan Garrette, Shyam Upadhyay, Jeremiah Zhe Liu, Jiawei Han 等

Large Language Models (LLMs) have catalyzed vibe coding, where users leverage LLMs to generate and iteratively refine code through natural language interactions until it passes their *vibe check*. *Vibe check* reflects human preference and goes beyond functionality: the solution should feel right, read cleanly, preserve intent, and remain correct. However, current code evaluation remains anchored to pass@k and captures only functional correctness, overlooking non-functional instructions that users routinely apply. In this paper, we hypothesize that instruction following is the missing piece underlying *vibe check* besides functional correctness. To quantify models' code instruction-following capabilities with measurable signals, we present **VeriCode**, a taxonomy of 30 verifiable code instructions together with deterministic verifiers. We use the taxonomy to augment established evaluation suites, resulting in **Vibe Checker**, a testbed to assess both instruction following and functional correctness. Evaluating 31 LLMs, we show that even the strongest models struggle to comply with multiple instructions and exhibit functional regression. Most importantly, *a composite score of functional correctness and instruction following correlates best with human preference*, with instruction following emerging as the primary differentiator among LLMs.

General Machine Learning · Methodology

Terry Tong, Yu Feng, Surbhi Goel, Dan Roth

Large language models can solve algorithmic problems either through direct natural language (NL) reasoning or by generating executable code delegated to an external solver. However, little progress has been made on **understanding why**. Comparing NL reasoning and solver-based pipelines directly is ill-posed: they differ simultaneously in representation space and execution mechanism. We introduce a three-route framework that makes this comparison tractable by introducing an intermediary step---code generation with LLM-based execution. This enables our empirical analysis, which shows a statistically significant gap supporting code $>$ NL by +28.9\% across 48 different algorithmic tasks and 6 models. A statistical analysis indicates that natural-language reasoning does not provide additional decision-relevant information beyond what is already captured by code representations. Consequently, replacing NL traces with code traces incurs minimal performance loss while enabling deterministic execution. A systematic comparison of LLM-based reasoning and external execution further shows that execution, rather than trace representation, is the primary performance bottleneck.

Deep Learning · Theory

Jingwen Liu, Ezra Edelman, Surbhi Goel, Bingbin Liu

As data scaling approaches its natural limits, recent work has explored the potential of data reuse, whose impact on optimization remains poorly understood. This work investigates how data repetition affects learning efficiency in terms of compute. We investigate an inverse-scaling effect that using less data can lead to faster convergence, which is observed across algorithmic tasks, architectures and optimizers and cannot be explained using prior theory. Instead, we argue that the speedup comes from appropriate layer-wise norm growth, which can be achieved faster when the dataset size is smaller. We provide theoretical justification by analyzing the benefits of sampling biases induced by small datasets, and we present extensive empirical evidence supporting this hypothesis. Together, our results highlight the potential of unlocking more efficiency gain by jointly considering different scaling axes.

Probabilistic Methods · Bayesian Models and Methods

Weifeng Zhang, Wenyuan Zhao, Amir Hossein Rahmati, Yucheng Wang, Zhiyuan Wang, Chao Tian, Xiaoning Qian

Principled uncertainty quantification (UQ) is increasingly recognized as essential for trustworthy artificial general intelligence (AGI). Bayesian Low-Rank Adaptation (LoRA) provides a principled mechanism for uncertainty-aware fine-tuning of large language models (LLMs). However, existing techniques either face scalability constraints, e.g. Laplace-LoRA, or rely on approximate inference schemes that lead to poorly calibrated posterior uncertainty, often manifesting as overconfident predictions under distribution shift. To address this challenge, we propose GPan-LoRA, the first scalable Gaussian Process (GP)-based framework for Bayesian LoRA, which integrates neural network-based sparse GP approximations with amortized variational inference. By preserving the Bayesian function prior and posterior semantics intrinsic to GPs, GPan-LoRA achieves a faithful balance between computational scalability and principled UQ. Empirically, GPan-LoRA produces well-calibrated uncertainty that remains reliable under distribution shift, mitigating overconfident failures while preserving competitive task performance.

Deep Learning · Large Language Models

Wenbo An, Lianwei Wu, Zehao Wang

Watermarking has emerged as a critical solution for the detection and provenance tracing of content generated by large language models. However, existing methods still suffer from significant limitations, including difficulties in achieving personalized attribution, substantial degradation of generation quality, and weak robustness against attacks. To address these challenges, we propose IPMark, the first IP-inspired hierarchical personalized watermarking framework. Specifically, to enable personalization and efficient detection, IPMark employs a hierarchical addressing framework to structurally organize model and user identities. Subsequently, addressing the inherent semantic distortion caused by token-level watermarking, we design a semantic-syntactic dual-stream embedding strategy. Centered on sentence-level candidate selection and reinforced by dual signals from syntactic and semantic features, this approach optimizes the injection process, thereby significantly enhancing generation quality while ensuring strong robustness. Experimental results demonstrate that IPMark achieves the lowest perplexity among baselines, ensuring superior generation quality while maintaining strong robustness and significantly reducing detection latency through hierarchical retrieval.

Social Aspects · Safety

Xikang Yang, Biyu Zhou, Xuehai Tang, Jizhong Han, Songlin Hu

Large language models (LLMs) are increasingly deployed in high-stakes settings, yet they remain vulnerable to sequential jailbreaks that exploit multi-turn interaction to circumvent safety mechanisms. Current safety evaluations are largely outcome-based, offering little insight into the latent decision processes that lead to unsafe compliance. We propose an interpretable cognitive modeling framework that couples a controlled elicitation paradigm, the Contextual Iowa Gambling Task (C-IGT), with a Generalized Rescorla--Wagner (GRW) architecture to decompose behavior into measurable mechanisms. Across a diverse set of mainstream LLMs, we find that sequential vulnerability is not explained by scale alone but emerges from interactions among cognitive factors, including optimism-biased learning, perceptual reward amplification, and choice inertia. Moreover, counterfactual feedback and psychologically framed rewards (e.g., regret, authority, threat) substantially accelerate the transition from refusal to compliance. These results yield principled cognitive profiles of LLM ``irrationality'' and provide insights for interdisciplinary research on LLM agents at the intersection of machine learning and human behavioral science.

Deep Learning · Large Language Models

Luke Huang, Zhuoyang Zhang, Qinghao Hu, Shang Yang, Song Han

Reinforcement learning (RL) is widely used to improve large language models (LLMs) on reasoning tasks, and asynchronous RL training is attractive because it increases end-to-end throughput. However, for widely adopted critic-free policy-gradient methods such as REINFORCE and GRPO, high asynchrony makes the policy-gradient estimator **markedly higher variance**: stale off-policy rollouts induce heavy-tailed importance ratios, causing a small fraction of samples to dominate each update. This amplification makes gradients noisy and learning unstable relative to matched on-policy training. Across math and reasoning benchmarks, we find collapse is preceded by sharp drops in effective sample size (ESS) and unstable gradient norms. Motivated by this diagnosis, we propose **V**ariance **C**ontrolled **P**olicy **O**ptimization (VCPO), a drop-in stabilization method for REINFORCE/GRPO-style algorithms that (i) **rescales learning rate according to effective sample size** to dampen unreliable updates, and (ii) applies a **closed-form minimum-variance baseline** for the off-policy setting, avoiding an auxiliary value model and adding minimal overhead. Empirically, VCPO substantially improves robustness in highly asynchronous regimes across models sizes and tasks, reducing long-context, multi-turn training compute by 1.96×. Overall, our results demonstrate explicitly controlling policy-gradient variance is key to making asynchronous RL reliable at scale.

Deep Learning · Generative Models and Autoencoders

Harold Haodong Chen, Xinxiang Yin, Wenjie Shu, Hongfei (Faye) Zhang, Zixin Zhang, Chenfei Liao, Litao Guo, Qifeng Chen, YINGCONG CHEN

Text-to-image (T2I) generation has achieved remarkable progress, yet existing methods often lack the ability to dynamically reason and refine during generation--a hallmark of human creativity. Current reasoning-augmented paradigms mostly rely on explicit thought processes, where intermediate reasoning is decoded into discrete text at fixed steps with frequent image decoding and re-encoding, leading to inefficiencies, information loss, and cognitive mismatches. To bridge this gap, we introduce **LatentMorph**, a novel framework that seamlessly integrates implicit latent reasoning into the T2I generation process. At its core, LatentMorph introduces four lightweight components: (***i***) a **condenser** for summarizing intermediate generation states into compact visual memory, (***ii***) a **translator** for converting latent thoughts into actionable guidance, (***iii***) a **shaper** for dynamically steering next image token predictions, and (***iv***) an RL-trained **invoker** for adaptively determining when to invoke reasoning. By performing reasoning entirely in continuous latent spaces, LatentMorph avoids the bottlenecks of explicit reasoning and enables more adaptive self-refinement. Extensive experiments demonstrate that LatentMorph **(I)** enhances the base model Janus-Pro by 16% on GenEval and 25% on T2I-CompBench; **(II)** outperforms explicit paradigms (*e.g.*, TwiG) by 15% and 11% on abstract reasoning tasks like WISE and IPV-Txt, **(III)** while reducing inference time by 44% and token consumption by 51%; and **(IV)** exhibits 71% cognitive alignment with human intuition on reasoning invocation.

Deep Learning · Everything Else

Antonios Tragoudaras, Daniil Cherniavskii, Chenyu Zhang, Antonis Vozikis, Thijmen Nijdam, Derck Prinzhorn, Márk Bodrácska, Nicu Sebe, Andrii Zadaianchuk, Efstratios Gavves

Recent advances in image and video generation raise hopes that these models possess world modeling capabilities—the ability to generate realistic, physically plausible videos. This could revolutionize applications in robotics, autonomous driving, and scientific simulation. However, before treating these models as world models, we must ask: Do they adhere to physical laws? Current evaluation methods rely on subjective judgments or trajectory matching, limiting their usage for physical reasoning estimation, where many generations could be physically plausible. Thus, we introduce **Morpheus**, one of the first physics-informed evaluation frameworks for measuring the ability of video generation models to comprehend Newtonian dynamics. **Morpheus** features 130 real-world videos capturing physical phenomena, guided by conservation laws. Using those as conditioning for video generation, we assess physical plausibility leveraging interpretable metrics evaluated with respect to infallible conservation laws known per physical setting, leveraging advances in physics-informed neural networks and vision-language foundation models. Importantly, **Morpheus** targets controlled Newtonian rigid-body settings to enable quantitative checks. Our findings reveal that even with advanced prompting and video conditioning, contemporary models struggle to encode physical principles despite generating aesthetically pleasing videos.