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Applications · Health / Medicine

Nikita Makarov, Maria Bordukova, Lena von Voithenberg, Estrella Pivel-Villanueva, Sabrina Mielke, Jonathan Wickes, Hanchen Wang, Mingyu Ma, Keunwoo Choi, Kyunghyun Cho 等

Precision oncology requires forecasting clinical events and trajectories, yet modeling sparse, multi-modal clinical time series remains a critical challenge. We introduce TwinWeaver, an open-source framework that serializes longitudinal patient histories into text, enabling unified event prediction as well as forecasting with large language models, and use it to build Genie Digital Twin (GDT) on 93,054 patients across 20 cancer types. In benchmarks, GDT significantly reduces forecasting errors, achieving a median Mean Absolute Scaled Error (MASE) of 0.87 compared to 0.97 for the strongest time-series baseline ($p<0.001$). Furthermore, GDT improves risk stratification, achieving an average concordance index (C-index) of 0.703 across survival, progression, and therapy switching tasks, surpassing the best baseline of 0.662. GDT also generalizes to out-of-distribution clinical trials, matching trained baselines at zero-shot and surpassing them with fine-tuning, achieving a median MASE of 0.75–0.88 and outperforming the strongest baseline in event prediction with an average C-index of 0.672 versus 0.648. Finally, TwinWeaver enables an interpretable clinical reasoning extension, providing a scalable and transparent foundation for longitudinal clinical modeling.

Applications · Health / Medicine

Ziyu Zhao, Yiyang Liu, Yajiao Wang, Xiaotao Wang, Yang Li, Yuyang Peng, Jiaheng Zhou, Jinqiao Wang, Yingying Chen, Ge Yang 等

Despite progress of Multimodal Large Language Models (MLLMs) in biomedical visual question answering (VQA), existing benchmarks provide limited assessment of their scientific reasoning capabilities. Most datasets adopt single-image question construction and outcome-oriented evaluation, where correctness is judged by answer plausibility rather than alignment with experimental evidence. Such formulations fail to capture the evidence-constrained, multi-step nature of biomedical reasoning, and obscure whether models can derive conclusions through causal interpretation of experimental observations. To address these critical gaps in reasoning evaluation, we propose a principled benchmark construction framework that reconstructs scientific reasoning paths directly from biomedical literature. By jointly modeling clusters of experimentally related images together with their captions and context, the framework generates tightly coupled question–reasoning–answer triples that require multi-image integration and explicit evidence-driven inference. Based on this framework, we introduce \textbf{SORBE} (\textbf{S}cientific \textbf{O}bservation \& \textbf{R}easoning for \textbf{B}iomedical \textbf{E}valuation), a large-scale multi-image biomedical VQA benchmark designed to evaluate evidence alignment and multi-step experimental reasoning. Under a process-oriented evaluation metric, state-of-the-art biomedical-specialized MLLMs exhibit substantial performance degradation, revealing systematic limitations in evidence grounding and causal reasoning that are not reflected by existing benchmarks.

Applications · Computer Vision

Guanyu Hu, Dimitrios Kollias, Xinyu Yang

Interpretable methods such as Vision–Language Models (VLMs) have advanced affect analysis by aligning images with textual descriptions. However, relying on text as an intermediate proxy faces critical limitations: linguistic templates are inherently discrete, making them fundamentally incompatible with continuous Valence–Arousal (VA) regression, while also acting as a bottleneck for fine-grained visual nuances. Cognitive psychology suggests that human affective perception is not mediated by linguistic translation, but is grounded in direct perceptual resemblance to internalized Visual Archetypes. Motivated by this, we propose AURA, a archetype framework that replaces brittle linguistic proxy with a self-organizing archetype manifold. By adaptively allocating representational density based on data complexity, AURA enables precise continuous regression and reshapes affective taxonomies, decomposing coarse labels into interpretable, geometrically coherent visual primitives. This paradigm offers a transparent, perceptually grounded decision trail, achieving state-of-the-art performance across discrete and continuous tasks.

Social Aspects · Accountability, Transparency, and Interpretability

Jialun Cao, Yuk-Kit Chan, Zixuan Ling, Wenxuan Wang, Shuqing Li, Mingwei Liu, Ruixi Qiao, Yuting Han, Chaozheng Wang, Boxi Yu 等

Code-related benchmarks play a critical role in evaluating large language models (LLMs), yet their quality fundamentally shapes how the com- munity interprets model capabilities. In the past few years, awareness of benchmark quality has grown. Yet, after a decade-scale (2014 - 2025) survey over 572 code benchmarks, we observed a lag between growing awareness and actual prac- tice. For example, in 2025 alone, the number of benchmarks that ignore code coverage when pro- viding test cases nearly matches the total count accumulated across the previous ten years. In response, we take a clear position: Code bench- marks must prioritize rigor in benchmark con- struction, reliability in evaluation, and repro- ducibility in release. To operationalize this po- sition, we introduce a code benchmark guideline HOW2BENCH with 55 checklists. Finally, our further human study also exposed that the current issues not only stem from the significant effort required, but also from a lack of awareness re- garding their importance.

Deep Learning · Theory

David Koplow, Tomaso A Poggio, Liu Ziyin

Hebbian and anti-Hebbian plasticity are widely observed in the brain and are classically modeled as mechanistic, local homosynaptic rules stabilized by homeostatic constraints. This raises an identifiability question: does observing Hebbian/anti-Hebbian structure in synaptic updates uniquely imply an underlying Hebbian computation? We identify an alternative, emergent route. We show that near stationarity, L2 weight decay generically drives the \emph{learning-signal} component of many update rules to align with a Hebbian direction, with alignment increasing monotonically with decay strength. This Hebbian-like signature is not specific to SGD and can arise even for non-learning or random update rules long before learning has ceased. We further show that stochastic perturbations can induce anti-Hebbian alignment, yielding a simple tradeoff with weight decay and a phase boundary in regression settings. These mechanisms do not replace standard Hebbian theory; they can coexist with genuine Hebbian plasticity and complicate the interpretation of synaptic measurements, motivating experiments that distinguish mechanistic Hebbian computation from emergent Hebbian signatures.

Applications · Computer Vision

Runhui Huang, Jie Wu, Rui Yang, Zhe Liu, Hengshuang Zhao

In this paper, we propose **AlphaGRPO**, a novel framework that applies Group Relative Policy Optimization (GRPO) to AR-Diffusion Unified Multimodal Models (UMMs) to enhance multimodal generation capabilities without relying on external knowledge injection. Our approach unlocks the model's intrinsic potential to perform advanced reasoning tasks: Reasoning Text-to-Image Generation, where the model actively infers implicit user intents, and Self-Reflective Refinement, where it autonomously diagnoses and corrects misalignments in generated outputs. To address the challenge of providing stable supervision for real-world multimodal generation, we introduce the Decompositional Verifiable Reward (**DVReward**). Unlike holistic scalar rewards, DVReward utilizes an LLM to decompose complex user requests into atomic, verifiable semantic and quality questions, which are then evaluated by a general MLLM to provide reliable and interpretable feedback. Extensive experiments demonstrate that AlphaGRPO yields robust improvements across multimodal generation benchmarks, including GenEval, TIIF-Bench, DPG-Bench and WISE, while also achieving significant gains in editing tasks on GEdit without training on editing tasks. These results validate that our self-reflective reinforcement approach effectively leverages inherent understanding to guide high-fidelity generation.

Deep Learning · Foundation Models

Yonggang Zhang, Zhiqin Yang, Wei Xue, Dong Fang, Bo Han, Yike Guo

Direct Preference Optimization (DPO) has emerged as a popular alternative to Reinforcement Learning from Human Feedback (RLHF), offering theoretical equivalence with simpler implementation. We prove this equivalence is _conditional_ rather than universal, depending on an implicit assumption frequently violated in practice: the RLHF-optimal policy must prefer human-preferred responses. When this assumption fails, DPO optimizes _relative advantage_ over the reference policy rather than _absolute alignment_ with human preferences, leading to pathological convergence where policies decrease DPO loss while preferring dispreferred responses. We characterize when this assumption is violated, show the existence of an undesirable solution space, and prove that DPO and RLHF optimize fundamentally different objectives in such cases. To address this, we introduce Constrained Preference Optimization (CPO), augmenting RLHF with constraints for provable alignment. We further provide a geometric interpretation through soft margin ranking, revealing DPO implements margin ranking with potentially negative targets. Our theoretical analysis establishes when DPOs' guarantees hold and provides solutions preserving simplicity with provable alignment. Comprehensive experiments on standard benchmarks demonstrate that CPO achieves state-of-the-art performance.

Probabilistic Methods · Bayesian Models and Methods

Luwei Wang, Dagmara Panas, Ke Wang, Sohan Seth, Bruce Guthrie

Constrained clustering incorporates prior knowledge in the form of pairwise constraints to guide data partitioning. While effective, existing Bayesian approaches are often limited in scalability to large datasets and provide weak interpretability due to the lack of explicit feature relevance modeling. We propose BASIL, a scalable Bayesian semi-supervised clustering framework that leverages stochastic variational inference to jointly infer cluster assignments and feature importance weights. This joint formulation enables the identification of discriminative features consistent with the imposed constraints. To robustly handle noisy or inconsistent supervision, BASIL introduces an adaptive constraint-weighting mechanism that down-weights unreliable constraints. Experiments on synthetic and real-world benchmarks demonstrate that our approach achieves competitive clustering performance while improving scalability and interpretability over existing baselines. We further demonstrate applicability to large-scale health data, including medical imaging and electronic health records.

Deep Learning · Attention Mechanisms

Francesco D&amp;#x27;Angelo, Oğuz Yüksel, Swathi Narashiman, Nicolas Flammarion

Induction heads are attention circuits believed to underlie in-context learning in transformers, yet a precise characterization of the estimators they implement remains elusive. We study transformers trained on order-$k$ Markov chains and prove that a two-layer disentangled transformer implements a soft context-matching estimator that aggregates contributions from all partial context matches, weighted exponentially by their degree of overlap. This mechanism admits two complementary smoothing interpretations. First, prepending a beginning-of-sequence (BOS) token induces additive pseudo-counts, recovering Dirichlet-style smoothing. Second, a finite attention temperature enables interpolation across context orders, analogous to Jelinek–Mercer smoothing but with data-dependent weights that adapt to each sequence's local structure. Experiments on trained transformers confirm that learned attention patterns match our theoretical construction and approach Bayes-optimal performance, where hard counting fails. Our results bridge mechanistic interpretability of induction heads with classical statistical smoothing, revealing that transformers learn to regularize in-context estimation rather than simply count.

Social Aspects · Accountability, Transparency, and Interpretability

Advaith Malladi, Shashank Srivastava

Natural-language explanations are widely used to interpret machine learning models, yet many prioritize human plausibility over accurately reflecting or predicting model behavior. Prior approaches often rely on human-written rationales, producing post-hoc explanations that neither align with the model’s decision function nor generalize. We introduce OPEX , a natural-language explanation model that directly optimizes for behavioral faithfulness: the ability of an explanation to reflect and predict a model’s observable input–output behavior. OPEX is trained using reinforcement learning with Group Relative Policy Optimization (GRPO), optimizing two complementary metrics: recoverability, which measures whether explanations recover model predictions on seen examples, and simulatability, which measures prediction of model behavior on unseen inputs. Across structured and text-based tasks, OPEX achieves high simulatability (∼0.85) and recoverability (∼0.99), outperforming GPT-4o, LLaMA-3.3-70B, and human-written explanations; despite having a 8B-parameter backbone. Human user studies show a 15% improvement in classification accuracy over competent baselines

Deep Learning · Attention Mechanisms

Timon Klein, Jonas Kusch, Sebastian Sager, Stefan Schnake, Steffen Schotthöfer

The pursuit of reducing the memory footprint of the self-attention mechanism in multi-headed self attention (MHA) spawned a rich portfolio of methods, e.g., group-query attention (GQA) and multi-head latent attention (MLA). The methods leverage specialized low-rank factorizations across embedding dimensions or attention heads. From the point of view of classical low-rank approximation, these methods are unconventional and raise questions of which objects they really approximate and how to interpret the low-rank behavior of the resulting representations. To answer these questions, this work proposes a generalized view on the weight objects in the self-attention layer and a factorization strategy, which allows us to construct a parameter efficient scheme, called Tucker Attention. Tucker Attention requires an order of magnitude fewer parameters for comparable validation metrics, compared to GQA and MLA, as evaluated in LLM and ViT test cases. Additionally, Tucker Attention~encompasses GQA, MLA, MHA as special cases and is fully compatible with flash-attention and rotary position embeddings (RoPE). This generalization strategy yields insights of the actual ranks achieved by MHA, GQA, and MLA, and further enables simplifications for MLA.

Social Aspects · Accountability, Transparency, and Interpretability

Charles Ye, Jasmine Cui, Dylan Hadfield-Menell

Language models remain vulnerable to prompt injection attacks despite extensive safety training. We trace this failure to role confusion: models assign roles based on how text sounds, not where it actually comes from. We design novel role probes to capture how models internally identify “who is speaking.” These reveal why prompt injection works: untrusted text that imitates a role inherits that role’s authority. We test this insight by injecting fabricated reasoning into user prompts and tool outputs, achieving average success rates of 60% on StrongREJECT and 61% on agent exfiltration, across multiple open- and closed-weight models with near-zero baselines. Strikingly, the degree of internal role confusion strongly predicts attack success before generation begins. Our findings reveal a fundamental gap: security is defined at the interface but authority is assigned in latent space. More broadly, we introduce a unifying, mechanistic framework for prompt injection, demonstrating that diverse prompt-injection attacks exploit the same underlying role-confusion mechanism. Code available at: https://anonymous.4open.science/r/role-science-B522.

Deep Learning · Theory

Konstantin Nikolaou, Jonas Scheunemann, Sven Krippendorf, Samuel Tovey, Christian Holm

Neural scaling laws describe predictable power-law relationships between model size, dataset size, compute cost, and performance. While these laws are applied to improve the performance of modern foundation models, the mechanisms underpinning them are less understood, in part due to the absence of scalable analysis tools. To this end, we introduce a framework for efficiently measuring the alignment between the empirical neural tangent kernel (eNTK) and loss residuals. Applying this framework to scaling experiments reveals a consistent pattern: larger and better-performing models exhibit *lower* kernel alignment throughout training. We interpret this *unalignment* through the lens of *spectral reach*: the capacity of a model to learn from progressively weaker spectral modes in its eNTK. This interpretation allows us to explain why larger models achieve lower losses: they sustain learning on weaker signals that smaller models cannot access. We further demonstrate that feature learning improves spectral reach and provide a mechanistic explanation of how this occurs, suggesting practical avenues for performance improvement.

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}.

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.

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.

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 · 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.

General Machine Learning · Supervised Learning

Zihan Yu, Guanren Wang, Jingtao Ding, Huandong Wang, Yong Li

Symbolic regression (SR) traditionally balances accuracy and complexity, implicitly assuming that simpler formulas are structurally more rational. We argue that this assumption is insufficient: existing algorithms often exploit this metric to discover accurate and compact but structurally irrational formulas that are numerically ill-conditioned and physically inexplicable. Inspired by the structural stability of real physical laws, we propose the Effective Information Criterion (EIC) to quantify formula rationality. EIC models formulas as information channels and measures the amplification of inherent rounding noise during recursive calculation, effectively distinguishing physically plausible structures from pathological ones without relying on ground truth. Our analysis reveals a stark structural stability gap between human-derived equations and SR-discovered results. By integrating EIC into SR workflows, we provide explicit structural guidance: for heuristic search, EIC steers algorithms toward stable regions to yield superior Pareto frontiers; for generative models, EIC-based filtering improves pre-training sample efficiency by 2–4 times and boosts generalization $R^2$ by 22.4\%. Finally, an extensive study with 108 human experts shows that EIC aligns with human preferences in 70\% of cases, validating structural stability as a critical prerequisite for human-perceived interpretability. We release our code at https://anonymous.4open.science/r/EIC-91B2.

Deep Learning · Attention Mechanisms

Yulong Huang, Xiang Liu, Hongxiang Huang, Xiaopeng LIN, Zunchang LIU, Xiaowen Chu, Zeke Xie, Bojun Cheng

Linear Attention (LA) offers a promising paradigm for scaling large language models (LLMs) to long sequences by avoiding the quadratic complexity of self-attention. Recent LA models such as Mamba2 and GDN interpret linear recurrences as closed-form online stochastic gradient descent (SGD), but naive SGD updates suffer from rapid information decay and suboptimal convergence in optimization. While momentum-based optimizers provide a natural remedy, they pose challenges in simultaneously achieving training efficiency and effectiveness. To address this, we develop a chunkwise parallel algorithm for LA with a stepwise momentum rule by geometrically reordering the update coefficients. Further, from a dynamical systems perspective, we analyze the momentum-based recurrence as a second-order system that introduces complex conjugate eigenvalues. This analysis guides the design of stable gating constraints. The resulting model, Momentum DeltaNet (MDN), leverages Triton kernels to achieve comparable training throughput with competitive linear models such as Mamba2 and KDA. Extensive experiments on the 400M and 1.3B parameter models demonstrate consistent performance improvements over strong baselines, including Transformers, Mamba2 and GDN, across diverse downstream evaluation benchmarks.