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Applications · Health / Medicine

heng rao, Jason Zhang, Yu Gu, Zhenghao Liu, Ge Yu, Jeffrey Su, Yang Cao, Fan Yang, Minghan Chen

Predicting long-horizon trajectories of biological dynamical systems remains challenging due to substantial system heterogeneity. Most existing machine learning approaches are system-specific, requiring retraining for each new system and exhibiting limited generalization across distinct biological regimes. To address this limitation, we create a large-scale benchmark of over 1,000 ODE-based systems biology models spanning diverse organisms, biological processes, and dynamical behaviors. Building on this benchmark, we propose a regime-aware trajectory prediction framework that enables cross-system generalization and uncertainty quantification for unseen systems. Our approach introduces structured initial states derived from biological regime priors, such as growth trends and oscillatory rhythms, into conditional flow matching, replacing the standard Gaussian source distribution. We provide theoretical justification for this initialization and empirically demonstrate state-of-the-art accuracy (31\% MAE reduction), well-calibrated uncertainty (17\% CRPS improvement), and efficient long-horizon inference across the benchmark.

Deep Learning · Generative Models and Autoencoders

Paul Caucheteux, Clément Bonet, Anna Korba

Many modern generative models can be viewed as minimizing divergences between probability distributions, yet they rely on different algorithmic and geometric principles. Wasserstein gradient flows provide a continuous-time formulation for optimizing over distributions, and can be approximated through their implicit discretization via the Jordan–Kinderlehrer–Otto (JKO) scheme. In this work, we present a unified theoretical framework for generative modeling based on Wasserstein gradient flows, which we refer to as Generative Wasserstein Flows. We show that a broad class of existing methods can be derived as instances of parametric JKO schemes for f-divergences objectives, and we establish equivalences between several recently proposed algorithms. We extend this framework beyond f-divergences to integral probability metrics, deriving new JKO-based generative algorithms for objectives such as Maximum Mean Discrepancy. We also clarify their connections with GANs. Finally, we analyze parametric Wasserstein flows, where the evolution is restricted to distributions generated by parameterized maps. We characterize the resulting dynamics as projected or preconditioned Wasserstein gradient flows, highlighting the role of the Wasserstein geometry in shaping the learning dynamics of generative models.

Reinforcement Learning · Batch/Offline

Ying Ba, Tianyu Zhang, Mohan Zhou, Yalong Bai, Wenyi Mo, Guiwei Zhang, Bing Su, Ji-Rong Wen

Text-to-image generation models have achieved remarkable progress in preference optimization, yet achieving robust alignment across diverse reward models remains a significant challenge. Existing multi-reward fusion approaches rely on weighted summation, which is costly to tune and insufficient for balancing conflicting objectives. More critically, optimization with reward models is highly susceptible to reward hacking, where reward scores increase while the perceived quality of generated images deteriorates. We demonstrate that optimizing against a unified global target under heterogeneous reward upper bounds can induce reward hacking, a risk further exacerbated by the inherent instability of weak reward models. To mitigate this, we propose a Pareto Frontier-Guided Optimal Transport framework. Our method constructs a prompt-specific Pareto frontier and maps dominated samples toward it via distribution-aware optimal transport. Furthermore, we develop both online and offline optimization strategies tailored to diverse reward signal characteristics. To provide a more rigorous assessment, we introduce the Joint Domination Rate (JDR) and Joint Collapse Rate (JCR) as principled metrics to quantify multi-reward synergy and reward hacking. Experimental results show that our approach outperforms strong baselines with an 11\% gain in JDR and achieves a near 80\% win rate in human evaluations.

Applications · Everything Else

Yaohui Han, Rongliang Fu, Yanming Liu, Shuo Ren, Shuai Dong, Yunpeng Wang, Tinghuan Chen, Bei Yu, Tsung-Yi Ho

In the field of integrated circuits, leaf cells are the basic units, serving as the fundamental building blocks (e.g., standard cells) that are widely reused in various VLSI designs, forming the basis for more complex circuits. Therefore, the design quality of leaf cell layouts significantly impacts the PPA (Power, Performance, and Area) of the final VLSI designs. To automatically design leaf cell layouts that are close to expert designs, we propose GenLeaf. GenLeaf first utilizes a supervised, performance-aware embedding model to represent layouts and automatically calculate their similarity scores. Since there are expert-designed layouts but no corresponding scripts, we implement Bayesian optimization to generate a layout-script dataset for LLM training. With subsequent supervised fine-tuning and further preference optimization, GenLeaf can generate leaf cell layouts through scripts whose performance closely resembles that designed by human engineers. Experiment results demonstrate that GenLeaf outperforms expert-designed golden layouts across key performance metrics.

Applications · Everything Else

Zan Gong, Biting Huang, Fei He

Program termination is a fundamental liveness property in software verification. Proving termination of a given program is a formidable challenge due to the undecidability of the problem. In this paper, we propose LIFT, a termination verification framework that leverages LLMs to generate loop bounds within a guess-and-check workflow. LIFT couples this generation with a sound formal validation procedure that both guarantees all reported terminations and refutes invalid loop bounds via violation analysis. Experiments on publicly accessible termination benchmarks show that LIFT significantly outperforms existing termination verification tools.

Applications · Chemistry, Physics, and Earth Sciences

Dongzhe Zheng, Tao Zhong, Christine Allen-Blanchette

In this paper, we study solution operators of physical field equations on geometric meshes from a function-space perspective. We reveal that Hodge orthogonality fundamentally resolves spectral interference by isolating unlearnable topological degrees of freedom from learnable geometric dynamics, enabling an additive approximation confined to structure-preserving subspaces. Building on Hodge theory and operator splitting, we derive a principled operator-level decomposition. The result is a Hybrid Eulerian-Lagrangian architecture with an algebraic-level inductive bias we call Hodge Spectral Duality (HSD). In our framework, we use discrete differential forms to capture topology-dominated components and an orthogonal auxiliary ambient space to represent complex local dynamics. Our method achieves superior accuracy and efficiency on geometric graphs with enhanced fidelity to physical invariants.

Deep Learning · Robustness

Aviv Adler, Jennifer Tang

A central insight in lossless data compression is the close connection between probabilistic next-symbol prediction and efficient sequence compression, whereby predictive models can be combined with classical coding techniques to achieve strong compression performance. Applying this approach with powerful modern learned models, such as LLMs, has been shown to achieve markedly better compression than traditional techniques across a wide range of domains. However, significant practical challenges remain, including model non-determinism, in which a model produces different predictions on different machines despite identical parameters and inputs; such mismatches between the encoder and decoder can lead to complete decoding failure. Probability Matching Interval Coding (PMATIC) was recently introduced as a drop-in framework for mismatch-robust coding and shown to enable reliable compression and decompression in the presence of bounded prediction mismatch (Adler & Tang, 2026). In this work, we present PMATIC+, a generalization of PMATIC that allows the incorporation of tight theoretical results into the design and more flexible parameter optimization, resulting in substantial improvements in compression efficiency and robustness.

Applications · Everything Else

Luankang Zhang, Hao Wang, Zhongzhou Liu, MINGJIA YIN, Yonghao Huang, Jiaqi Li, Wei Guo, Yong Liu, Huifeng Guo, Defu Lian 等

The scarcity of high-quality training data presents a fundamental bottleneck to scaling machine learning models. This challenge is particularly acute in recommendation systems, where extreme sparsity in user interactions leads to rugged optimization landscapes and poor generalization. We propose the Recursive Self-Improving Recommendation (RSIR) framework, a paradigm in which a model bootstraps its own performance without reliance on external data or teacher models. RSIR operates in a closed loop: the current model generates plausible user interaction sequences, a fidelity-based quality control mechanism filters them for consistency with user’s approximate preference manifold, and a successor model is augmented on the enriched dataset. Our theoretical analysis shows that RSIR acts as a data-driven implicit regularizer, smoothing the optimization landscape and guiding models toward more robust solutions. Empirically, RSIR yields consistent, cumulative gains across multiple benchmarks and architectures. Notably, even smaller models benefit, and weak models can generate effective training curricula for stronger ones. These results demonstrate that recursive self-improvement is a general, model-agnostic approach to overcoming data sparsity, suggesting a scalable path forward for recommender systems and beyond. Our anonymized code is available at https://anonymous.4open.science/r/RSIR-7C5B.

Deep Learning · Theory

Laura Ying Schulz, Daniel Mitropolsky, Tomaso A Poggio

While large models achieve impressive results, their learning dynamics are far from understood. Many domains of interest -- such as natural language syntax, coding languages, arithmetic problems -- are captured by context-free grammars (CFGs). In this work, we extend prior work on neural language modeling of CFGs in a novel direction: how language modeling behaves with respect to CFG substructure, namely "subgrammars". We first define subgrammars, and prove a set of fundamental theorems regarding language modeling and subgrammars. We show that language modeling loss (or equivalently the Kullback-Leibler divergence) recurses linearly over its top-level subgrammars; applied recursively, the loss decomposes into losses for "irreducible" subgrammars. We also prove that the constant in this linear recurrence is a function of the expected "recursion", a notion we introduce. We show that under additional assumptions, parametrized models learn subgrammars in parallel. Empirically, we confirm that small transformers learn subgrammars in parallel, unlike children, who first master simple substructures. We also briefly explore several other questions regarding subgrammars. We find that subgrammar pretraining can improve final performance, but only for tiny models relative to the grammar, while alignment analyses show that pretraining consistently lead to internal representations that better reflect the grammar’s substructure in all cases; we also observe persistent difficulty with deeper recursion, a limitation that appears even of large language models.

Haotian Chi, Zeyu Feng, Xingrui Yu, Linbo Luo, Yew Soon ONG, Ivor Tsang, Hechang Chen, Yi Chang, Haiyan Yin

Planning collaboration strategies for multi-agent embodied systems remains a core challenge for LLM-based planners, which often fail to capture the physical and coordination constraints of realworld environments. To address this, we present EvoCF, an agentic memory-driven evolutionary counterfactual planning framework for discovering improved multi-agent collaboration strategies through counterfactual plan generation and evaluation. First, we propose a symbolic constraint inductor that induces reusable symbolic constraints from failures, forming an evolving rule library. Then, we propose an evolutionary counterfactual plan generator that systematically explores semantically consistent plan variants through rule-conditioned mutations, enabling robust collaboration strategies beyond short-sighted one-shot LLM plans. Finally, we design an agentic memory-grounded evaluator that ranks candidate plans using retrieval-augmented evidence, producing interpretable, constraint-aware selections. Across multi-agent embodied simulation benchmarks, EvoCF consistently discovers more robust and executable plans compared to baseline approaches. Our results demonstrate that grounding multi-agent planning in agentic memory and counterfactual reasoning significantly enhances both effectiveness and robustness.

Social Aspects · Security

Wei Wang, Zihao Guan, Xing Zhou, Yan Ding, Yusong Tan, Jie Yu, Bao Li

Deploying large language models (LLMs) on untrusted hardware entails a risk of weight extraction, which can lead to unauthorized replication and misuse of the model. A practical approach is to leverage Trusted Execution Environments (TEEs) and protect model security by obfuscating model weights. However, existing obfuscation schemes struggle to simultaneously provide strong security guarantees and high performance: schemes with security guarantees incur substantial overhead due to frequent TEE interactions, whereas schemes that achieve efficient inference are insecure. We propose SLIM, a secure inference framework that exploits the iterative structure of LLMs to let transformed representations cascade through consecutive obfuscated layers, thereby minimizing interactions with the TEE. SLIM introduces a T-Way Mixing algorithm that performs consecutive inter-vector covering using carefully constructed block-diagonal Householder matrices and combines it with successive random permutations, providing thorough weight obfuscation while keeping TEE-side computation lightweight. Evaluations demonstrate that SLIM provides robust security guarantees and significantly outperforms prior state-of-the-art obfuscation schemes in terms of performance, delivering up to a $13.80\times$ speedup while preserving fidelity.

Applications · Everything Else

Ishaan Singh Chandok, Core Francisco Park

Scientific data annotation, such as tracking animals in video or proofreading neural reconstructions, remains bottlenecked by the “last mile” problem: even with strong automation, verification and correction consume substantial human effort. Standard approaches train models to directly predict annotations, discarding the rich supervision in how experts navigate, click, verify, and correct. We introduce a framework for studying behavioral cloning on scientific annotation: 9 synthetic tasks paired with synthetic annotations that simulate realistic human strategies including exploration, mistake correction, and strategic decision-making. Our experiments reveal several findings. First, skills emerge hierarchically: models learn GUI mechanics before task-critical decisions, and commit fewer mistakes than the training data while retaining the ability to correct errors when they occur. Second, scaling models on multi-task behavioral cloning shows that larger models are more data efficient, but exhibit worse decision-making despite similar placement accuracy. Third, multi-task pretraining enables efficient fine-tuning to new tasks, while training from scratch fails entirely. Fourth, linear probes reveal that models internally represent latent variables of the annotation process such as task phase and data position; interestingly, we find a shared mistake representation that generalizes across different annotation tasks. Overall, our framework establishes systematic benchmarks and identifies key bottlenecks, providing a foundation for scaling behavioral cloning to real-world scientific data annotation.

Deep Learning · Large Language Models

Haolong Qian, Xianliang Yang, Ma yinuo, Lirong Che, Feng Lu, Ye Guo, Lei Song, Jiang Bian, Chun Yuan

Knowledge distillation from powerful reasoning models underpins the development of Small Language Models (SLMs). A prevailing assumption in this paradigm is that training data with higher perceived quality, often defined by rigorous logic and superior reward scores, monotonically enhances downstream performance. In this paper, we identify a counter-intuitive \textbf{Quality-Utility Paradox} across diverse model families(Qwen2.5, LLaMA-3, DeepSeek): data refined by a superior Synthesis Oracle consistently underperforms the SLM's self-generated Rejection Sampling (RFT) data, despite achieving higher reward scores. We argue that Oracle models introduce an intrinsic representation bias that shifts training data into a distribution incompatible with the target SLM, where the SLM allocates limited computational capacity to stylistic imitation rather than logical reasoning. We utilize a \textbf{Style-Aligned Refinement} strategy to correct logical errors and strictly preserve the SLM's native syntax. Our experiments demonstrate that maintaining native syntax effectively mitigates syntactic adaptation costs, enabling distilled models to match or even surpass self-generated baselines. These findings underscore the necessity of syntactic alignment and advocate for model-aware reward designs that prioritize distributional compatibility alongside logical rigor. Our datasets and code will be publicly available.

Applications · Everything Else

Shiva Malay, Perampalli Shravan Nayak, Sagar Davasam, Srinivas Sunkara, Sai Rajeswar Mudumba

The rapid evolution of Large Language Models (LLMs) has shifted their role from passive information providers to active agents capable of executing complex workflows. However, the realization of a true "AI worker" is currently hindered by benchmarks that fail to capture the intricacy of professional environments, which demand long-horizon planning, complex tool usage, and adherence to strict access protocols. To bridge this gap, we introduce EnterpriseOps-Gym, a benchmark environment designed to evaluate agentic planning in realistic enterprise settings. EnterpriseOps-Gym provides: (i) 1,150 expert-curated tasks across eight interconnected domains (including HR, IT, Customer Service and productivity tools) that require managing persistent state and adhering to strict outcome-based verification logic; and (ii) a high-fidelity, containerized sandbox environment hosting 164 database tables and 512 functional tools. Our evaluation reveals critical limitations in state-of-the-art models: even the top-performing Claude Sonnet~4.5 achieves only 34.1\% success, struggling significantly with planning consistency, error recovery, and policy constraints. Furthermore, we observe that agents frequently fail to refuse infeasible tasks, leading to unintended and potentially harmful side effects on the system. These findings indicate that current agents are not yet ready for enterprise deployment. By releasing EnterpriseOps-Gym, we provide a concrete testbed to advance the reliability of autonomous agents in professional workflows.

Deep Learning · Attention Mechanisms

Xiaojie Yu, Haibo Zhang, Jeremiah D. Deng, Lizhi Peng

The maximal coding rate reduction ($\text{MCR}^2$) objective is proposed for learning low-dimensional subspace representations and for principled deep model design, where layer structures are derived by unrolling its optimization steps. However, existing methods motivated by this objective do not fully adhere to design principles implied by the $\text{MCR}^2$ gradient, which weakens the principled and interpretable foundations of the resulting models. In this work, we introduce PACEAttention, a novel principled attention mechanism inspired by the \textit{geometric insight }of $\text{MCR}^2$. From the geometric perspective, gradient-based updates of $\text{MCR}^2$ move features along directions shaped by the underlying low-dimensional feature structure. Our method captures this structure by leveraging randomization to guide feature updates. This principled construction enables the resulting PACENet to exhibit enhanced interpretability, with different heads attending to distinct image regions and capturing \textit{fine-grained} structures under simple supervised training. Besides, two learnable weights in PACEAttention enable explicit regulation of the feature update dynamics, reflecting the relative contributions of different components across layers. Experiments demonstrate that PACEAttention achieves superior performance and more stable scalability than previous principled modules while remaining low complexity.

Theory · Learning Theory

Dmitry Yarotsky, Eugene Golikov, Yaroslav Gusev

We develop a general mathematical framework to analyze scaling regimes and derive explicit analytic solutions for gradient flow (GF) in large learning problems. Our key innovation is a formal power series expansion of the loss evolution, with coefficients encoded by diagrams akin to Feynman diagrams. We show that this expansion has a well-defined large-size limit that can be used to reveal different learning phases and, in some cases, to obtain explicit solutions of the nonlinear GF. We focus on learning Canonical Polyadic (CP) decompositions of high-order tensors, and show that this model has several distinct extreme lazy and rich GF regimes such as free evolution, NTK and under- and over-parameterized mean-field. We show that these regimes depend on the parameter scaling, tensor order, and symmetry of the model in a specific and subtle way. Moreover, we propose a general approach to summing the formal loss expansion by reducing it to a PDE; in a wide range of scenarios, it turns out to be 1st order and solvable by the method of characteristics. We observe a very good agreement of our theoretical predictions with experiment.

Applications · Everything Else

Linbin Tang, Jingyan You, Zilin Kang, Hanzhang Liu, Sophia Zhang, Zenan Li, Chenrui Cao, Liangcheng Song, Jiaao Wu, Xian Zhang 等

Recent formal reasoning systems achieve IMO-level performance, but create a fragmented landscape: algebra and number theory use Lean, while geometry relies on domain-specific languages with limited formal guarantees. This fragmentation increases the trusted computing base and hinders unified model development. Existing geometry-in-Lean efforts (LeanEuclid, LeanGeo) introduce custom axiom systems incompatible with standard \mathlib{}, and their small scale ($<$1K problems) prevents large-scale training. However, native \mathlib{} autoformalization of geometry poses unique challenges: explicating implicit diagrammatic assumptions (e.g., topological configuration and non-degeneracy)---unlike existing custom systems that defer validity checks to external solvers---and adapting to \mathlib{}'s small, rapidly-evolving geometric constructs. We present \method{}, a framework that addresses these challenges through a four-stage pipeline---constraint explication, configuration anchoring, formalization mapping, and iterative repair---to automatically formalize geometry in native \mathlib{}. We construct OMNI-Geometry (768 competition problems) and Numina-Geometry (177,597 problems), the largest geometry formalization dataset in Lean. Human evaluation shows 48.89\% TOP1 and 73.33\% TOP5 accuracy. Training Goedel v2 on our formalizations improves proof success from 13.6\% to 15.1\%, validating dataset quality for unified neural theorem proving.

Applications · Neuroscience, Cognitive Science

Michael Ibrahim, Hanqi Zhao, Eli Sennesh, Zhi Li, Anqi Wu, Jacob Yates, Chengrui Li, Hadi Vafaii

Poisson-distributed latent variable models are widely used in computational neuroscience, but differentiating through discrete stochastic samples remains challenging. Two approaches address this: *Exponential Arrival Time* (EAT) simulation and *Gumbel-SoftMax* (GSM) relaxation. We provide the first systematic comparison of these methods, along with practical guidance for practitioners. Our main technical contribution is a modification to the EAT method that theoretically guarantees an unbiased first moment (exactly matching the firing rate), and reduces second-moment bias. We evaluate these methods on their distributional fidelity, gradient quality, and performance on two tasks: (1) variational autoencoders with Poisson latents, and (2) partially observable generalized linear models, where latent neural connectivity must be inferred from observed spike trains. Across all metrics, our modified EAT method exhibits better overall performance (often comparable to exact gradients), and substantially higher robustness to hyperparameter choices. Together, our results clarify the trade-offs between these methods and offer concrete recommendations for practitioners working with Poisson latent variable models.

Reinforcement Learning · Deep RL

Kazuki Ota, Takayuki Osa, Motoki Omura, Tatsuya Harada

Two-player games such as board games have long been used as traditional benchmark for reinforcement learning. This work revisits a regularized policy optimization with reverse Kullback-Leibler divergence and entropy divergence and analyzes this combination on two-player zero-sum settings from theoretical and empirical perspectives. From a theoretical perspective, we investigate the stability of the policy update rule on two theoretical settings: game-theoretic normal-form games and finite-length games. We provide convergence guarantees and verify our theoretical results by numerical experiments on synthetic games. From an empirical perspective, we derive a practical model-free reinforcement learning algorithm based on the regularized policy optimization. We validate the efficiency in training of our algorithm through comprehensive experiments on five board games: Animal Shogi, Gardner Chess, Go, Hex, and Othello. The experimental results demonstrate that our agent achieves more efficient learning than existing methods across the environments.

Applications · Computer Vision

Bo Du, Xiaochen Ma, Xuekang Zhu, Zhe Yang, Chaoqun Niu, Jian liu, Ji-Zhe Zhou

Fake Image Detection (FID), aiming at unified detection across four image forensic subdomains, is critical in real-world forensic scenarios. Compared with ensemble approaches, monolithic FID models are theoretically more promising, but to date, consistently yield inferior performance in practice. In this work, by discovering the "heterogeneous phenomenon'', which is the intrinsic distinctness of artifacts across subdomains, we diagnose the cause of this underperformance for the first time: the collapse of the artifact feature space driven by such phenomenon. The core challenge for developing a practical monolithic FID model thus boils down to the "unified-yet-discriminative" reconstruction of the artifact feature space. To address this paradoxical challenge, we hypothesize that high-level semantics can serve as a structural prior for the reconstruction, and further propose Semantic-Induced Constrained Adaptation (SICA), the first monolithic FID paradigm. Extensive experiments on our $ \textit{OpenMMSec} $ dataset demonstrate that SICA outperforms 15 state-of-the-art methods and reconstructs the target unified-yet-discriminative artifact feature space in a near-orthogonal manner, thus firmly validating our hypothesis. The code and dataset will be made publicly available.