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Applications · Chemistry, Physics, and Earth Sciences

Peijia Lin, Zihan Zhang, zhangrui zhao, Shaohao Rui, Junyi An, Yun-Fei Shi, Fenglei Cao, Weijie Ma, Yutong Lu

Generating periodic data---such as fractional atomic coordinates in crystal structures and phase patterns in compressive light-field (CLF) displays---is challenging because wrap-around boundaries complicate probabilistic modeling and learning. While Bayesian Flow Networks (BFNs) offer a powerful generative framework with strictly additive accuracy in Euclidean space, existing periodic adaptations typically sacrifice additivity and become sensitive to schedule heuristics. We introduce \emph{PeriodicBFN}, which embeds each periodic scalar into a two-dimensional unit-circle representation and performs Gaussian Bayesian updates in the resulting Cartesian space, thereby restoring strictly additive accuracy. To address invariance in periodic generative modeling, we further derive a Rao--Blackwellized objective that analytically marginalizes global periodic translations, producing a translation-invariant target with reduced gradient variance. Experiments on crystal structure prediction and multi-layer phase synthesis for CLF displays demonstrate improved training stability and strong performance. To our knowledge, this is the first work to extend periodic-data generative modeling to phase synthesis for modern glasses-free 3D display systems.

Hao Pan, Jian Dai, Yuan Sun, Zhenwen Ren, Xingfeng Li

Multimodal image fusion integrates complementary information from different modalities. However, large cross-modal discrepancies and local conflicts often introduce uncertainty into fusion decisions. This uncertainty can bias modality allocation in inconsistent regions, leading to information loss or the propagation of artifacts. Therefore, we address this problem by formally casting image fusion as an integrated probabilistic decision system that couples prior decision-making with posterior risk minimization. Based on this view, we propose a dIfference-aware Decision-lEArning muLtimodal image fusion paradigm (IDEAL). It treats cross-modal differences as decision triggers and learns contribution policies conditioned on local conditions. Specifically, we use a difference-attention module to generate multi-scale difference maps as spatial decision conditions. We also obtain spectral conditions by projecting features into the frequency domain, where power-spectrum energy, complementary spectra, and spectral-entropy reliability characterize modality discrepancy and reliability. We then employ a symmetric Beta prior to map these decision conditions to gating weights, yielding explicit and interpretable modality contribution policies. To improve robustness, we introduce an uncertainty modulation mechanism that reverts the policy to conservative mixing when conditions is insufficient. Extensive experiments demonstrate stable and competitive performance.

Deep Learning · Large Language Models

Junbo Zhao, Ting Zhang, Can Li, Wei He, Jingdong Wang, Hua Huang

Multi-step theorem prediction is a central challenge in automated reasoning. Existing neural–symbolic approaches rely heavily on supervised parametric models, which exhibit limited generalization to evolving theorem libraries. In this work, we explore training-free theorem prediction through the lens of in-context learning (ICL). We identify a critical scalability bottleneck, termed Structural Drift: as reasoning depth increases, the performance of vanilla ICL degrades sharply, often collapsing to near zero. We attribute this failure to the LLM’s inability to recover latent topological dependencies, leading to unstructured exploration. To address this issue, we propose Theorem Precedence Graphs, which encode temporal dependencies from historical solution traces as directed graphs, and impose explicit topological constraints that effectively prune the search space during inference. Coupled with retrieval-augmented graph construction and a stepwise symbolic executor, our approach enables LLMs to act as structured planners without any gradient-based optimization. Experiments on the FormalGeo7k benchmark show that our method achieves 89.29\% accuracy, substantially outperforming ICL baselines and matching state-of-the-art supervised models. These results indicate that explicit structural priors offer a promising direction for scaling LLM-based symbolic reasoning.

Minghao Guo, Wojciech Matusik

Neural architecture design lacks first principles: innovations are discovered empirically and justified post-hoc, with no systematic way to diagnose *why* an architecture fails or derive *what* repair will succeed. We introduce the *Axiomatic Atlas*, encoding requirements as composable axioms over graph connectivity, operator contracts, numerical stability, and information preservation. Given an operator library and wiring conventions, the Atlas constructs certificates lower-bounding output variation via min-cut analysis and diagnoses failures by locating axiom violations. Crucially, the framework is prescriptive: each violation implies a targeted repair, reducing architecture design to constraint satisfaction. We prove variation bounds under exact and finite-precision arithmetic, enabling modular verification across transformers, MoEs, SSMs, and GNNs. Four Atlas-derived interventions validate the approach: +46 percentage points on GNN bottlenecks, $3\times$ robustness to MoE quantization, 83\% gap closure with adaptive expert budgets, and 0\%$\to$100\% retrieval via orthogonal keys---each against matched negative controls.

Deep Learning · Large Language Models

Zhanhong Jiang

Preference-based reinforcement learning (RL) is a key paradigm for aligning policies with human judgments, yet its theoretical behavior in distributed settings where preference data are fragmented across heterogeneous users remains poorly understood. Direct Preference Optimization (DPO) avoids explicit reward modeling but lacks convergence guarantees under federated and decentralized training, where communication constraints and non-IID preferences fundamentally alter optimization dynamics. We provide the first convergence and time-complexity analysis of DPO in distributed environments. Modeling personalized offline RL with user-specific preference distributions, we characterize the induced global optimization landscape. For federated DPO, we derive convergence rates that quantify the impact of client drift, communication frequency, and preference heterogeneity; for decentralized DPO, we establish convergence over general communication graphs and show how spectral connectivity governs optimization speed and consensus. Our results lay a theoretical foundation for scalable and privacy-preserving distributed preference optimization.

Applications · Computer Vision

Bo Peng, Ke Xu, Yurui Pan

Texture recognition is often framed as matching an image to a static training-set dictionary or codebook. In practice, this assumption is brittle: label-preserving transformations (illumination, scale, compression, blur) can shift test features away from the fixed training dictionary, producing a training-set codebook misalignment that limits accuracy. We propose STD-Former (Simple Texture Dictionary Transformer), a lightweight framework for image-conditioned texture dictionary encoding. Instead of comparing against a static codebook, STD-Former extracts a compact set of Intrinsic Textons (dictionary atoms / codewords) from the input image itself, yielding self-aligned representations at inference. Our design is intentionally simple and uses a decoupled two-stage recipe. In Stage 1, a Texture Dictionary Extractor (TDE) is pre-trained with a self-supervised Texton Coverage Loss that encourages the learned textons to collectively cover the image patch feature manifold. In Stage 2, a classifier is trained on the encoded dictionary representation; optionally, we add a Sparse Topological Loss derived from 0D persistent homology, which is equivalent to supervising only the (B-1) edges of a minimum spanning tree (MST) in each batch, providing efficient structure regularization. Across six standard texture benchmarks, STD-Former and STD-Former+ achieve new state-of-the-art results.

Dapeng Zhang, Zhenlong Yuan, Zhangquan Chen, Chih-Ting Liao, Yinda Chen, Fei Shen, Qingguo Zhou, Tat-Seng Chua

Vision-Language-Action (VLA) models have recently shown strong decision-making capabilities in autonomous driving. However, existing VLAs often struggle with achieving efficient inference and generalizing to novel autonomous vehicle configurations and driving scenarios. In this paper, we propose Reasoning-VLA, a general and efficient action-generation VLA framework. The proposed model employs a set of learnable action queries, implicitly guided by predefined spatial representations to enhance spatial awareness. These learnable queries interact with reasoning-enhanced vision–language features to generate continuous action trajectories in parallel. To promote robust generalization, we consolidate eight publicly available autonomous driving datasets into a standardized, Chain-of-Thought reasoning–based, and easy-to-use data format for model training. Leveraging both supervised learning and reinforcement learning fine-tuning, extensive empirical evaluations across multiple benchmarks demonstrate that Reasoning-VLA achieves state-of-the-art performance, strong generalization capability, and the excellent inference speed with parallel decode.

Optimization · Large Scale, Parallel and Distributed

Yijiang Li, Emon Dey, Zilinghan Li, Krishnan Raghavan, Ravi Madduri, Kibaek Kim

Federated learning~(FL) across multiple HPC facilities faces stochastic \emph{admission delays} from batch schedulers that dominate wall-clock time. Synchronous FL suffers from severe stragglers, while asynchronous FL accumulates stale updates when queues spike. We propose \fedqueue{}, a queue-aware FL protocol that incorporates scheduler delays directly into training and aggregation, which (i) predicts per-facility queue delays online to budget local work, (ii) applies cutoff-based admission that buffers late arrivals to bound staleness, and (iii) performs staleness-aware aggregation to stabilize heterogeneous local workloads. We prove the convergence for non-convex objectives at rate $\mathcal{O}(1/\sqrt{R})$ under bounded staleness, and show that the admission controls yield bounded staleness with high probability under queue-prediction error. Real-world cross-facility deployment of \fedqueue{} shows 20.5\% improvement over baseline algorithms. Controlled queue simulations demonstrate robust improvement over the baselines; in particular, about 34\% reduction in time to reach a target accuracy level under high queue variance and non-IID partitions.

Deep Learning · Algorithms

Ruoran Xu, Borong She, Xiaobo Jin, Qiufeng Wang

Deep learning optimization relies heavily on the assumption of smooth loss landscapes, a condition systematically violated by modern architectures due to non-smooth components like ReLU activations and quantization operators. In such non-smooth regimes, adaptive optimizers such as Adam suffer from gradient chattering—violent oscillations caused by conflicting signals within the Clarke subdifferential—leading to poor convergence and suboptimal generalization. To address this, we introduce Singularity-aware Adam (S-Adam), a novel optimizer that stabilizes training by dynamically modulating step sizes based on local geometric instability. Our key contribution is the Local Geometric Instability (LGI) metric, a computationally efficient estimator of the Clarke subdifferential diameter derived from the variance of randomized directional derivatives. S-Adam incorporates an adaptive damping mechanism $\exp(-\lambda \rho_t)$ that decelerates updates in high-instability regions while preserving fast convergence in smooth basins. We provide a rigorous convergence analysis using differential inclusions, proving that S-Adam converges almost surely to $(\delta, \epsilon)$-Clarke stationary points at the optimal $\mathcal{O}(1/\sqrt{T})$ rate. Empirical evaluations on Quantization-Aware Training (QAT) and high-volatility transfer learning demonstrate that S-Adam consistently outperforms AdamW and Prox-SGD, achieving accuracy gains of up to +6\% on CIFAR-100 and +3\% on TinyImageNet while effectively mitigating gradient oscillations.

Yuntong Hu, Sha Li, Liang Zhao, Naren Ramakrishnan

Retrieval-augmented generation (RAG) systems commonly improve robustness via query-time adaptations such as query expansion and iterative retrieval. While effective, these approaches are inherently stateless: adaptations are recomputed for each query and discarded thereafter, precluding cumulative learning and repeatedly incurring inference-time cost. Index-side approaches like key expansion introduce persistence but rely on offline preprocessing or heuristic updates that are weakly aligned with downstream task utility, leading to semantic drift and noise accumulation. We propose Evolving Retrieval Memory (ERM), a training-free framework that transforms transient query-time gains into persistent retrieval improvements. ERM updates the retrieval index through correctness-gated feedback, selectively attributes atomic expansion signals to the document keys they benefit, and progressively evolves keys via stable, norm-bounded updates. We show that query and key expansion are theoretically equivalent under standard similarity functions and prove convergence of ERM’s selective updates, amortizing optimal query expansion into a stable index with zero inference-time overhead. Experiments on BEIR and BRIGHT across 13 domains demonstrate consistent gains in retrieval and generation, particularly on reasoning-intensive tasks, at native retrieval speed.

Theory · Reinforcement Learning and Planning

Orin Levy, Yishay Mansour

We present the first high-probability optimal regret bound for a policy optimization technique applied to the problem of stochastic contextual multi-armed bandit (CMAB) with general offline function approximation. Our algorithm is both efficient and achieves an optimal regret bound of $\widetilde{O}(\sqrt{ K|\mathcal{A}|\log|\mathcal{F}|})$, where $K$ is the number of rounds, $\mathcal{A}$ is the set of arms, and $\mathcal{F}$ is the function class used to approximate the losses. Our results bridge the gap between theory and practice, demonstrating that the widely used policy optimization methods for the contextual bandit problem can achieve a rigorously-proved optimal regret bound. We support our theoretical results with an empirical evaluation of our algorithm.

Deep Learning · Large Language Models

Yuanteng Chen, Peisong Wang, Nanxin Zeng, Yuantian Shao, Shuang Qiu, Gang Li, Jing Liu, Jian Cheng

Test-time scaling improves LLM performance by generating multiple candidate solutions, yet token-level sampling requires temperature tuning that trades off diversity against stability. Fine-grained MoE, featuring hundreds of well-trained experts per layer and multi-expert activation per token, offers an unexplored alternative through its rich routing space. We empirically characterize fine-grained MoE routing and uncover an informative pattern: router scores exhibit a certain head of high-confidence experts followed by an uncertain tail of low-confidence candidates. While single-run greedy accuracy remains stable when fewer experts are activated, multi-sample pass@n degrades significantly—suggesting that the certain head governs core reasoning capability while the uncertain tail correlates with reasoning diversity. Motivated by these findings, we propose Expert-Sample, a training-free method that preserves high-confidence selections while injecting controlled stochasticity into the uncertain tail, enabling diverse generation without destabilizing outputs. Evaluated on multiple fine-grained MoE models across math, knowledge reasoning, and code tasks, Expert-Sample consistently improves pass@n and verification-based accuracy. On Qwen3-30B-A3B-Instruct evaluated on GPQA-Diamond with 32 parallel samples, pass@32 rises from 85.4% to 91.9%, and accuracy improves from 59.1% to 62.6% with Best-of-N verification.

Deep Learning · Attention Mechanisms

Zongye Hu, Weiqing Luo, Yanjie Fu, Yu Gan, Haofeng Zhang, Ziyi Huang

At the core of modern prompting techniques is contextual sensitivity, the ability of large language models to adapt their predictions based on inference-time context. Despite its central role, inference behavior under strong contextual influence remains poorly understood, particularly at the level of internal inference dynamics. To bridge this gap, we introduce a theoretical framework for analyzing contextual influence through inference dynamics, enabling quantitative characterization of inference behavior beyond output-level answer changes. Our analysis shows that inference dynamics do not exhibit unbounded drift under repeated contextual assertions. Instead, predictive representations converge to stable, query-dependent regimes that fundamentally constrain whether contextual signals can alter a model’s prediction. This leads to a surprising finding: Repeated contextual assertions do not act as accumulating evidence during inference and may therefore fail to alter a model’s prediction even under unbounded repetition, while in other cases a prediction change becomes inevitable. We empirically validate our theoretical predictions across diverse models and tasks, demonstrating strong alignment between theory and observed inference behavior. These contributions offer a principled pathway toward characterizing the limits of contextual influence during inference, and provide practical implications for designing and evaluating repetition-based prompting methods.

Zhangyang “Atlas” Wang, Kai Wang, Peihao Wang

Neural network checkpoints have quietly become a large-scale data resource: millions of trained weight vectors now exist, each encoding task-, domain-, and architecture-specific knowledge. This position paper argues that model checkpoints should be treated as a first-class data modality, and that generative modeling in weight space should be standardized as a core machine learning primitive. Recent advances demonstrate that neural weights can be synthesized on demand, often matching fine-tuning performance while reducing adaptation cost by orders of magnitude. We contend that these results reflect an underlying structural fact: high-performing models occupy low-dimensional, highly structured regions of weight space shaped by symmetry, flatness, modularity, and shared subspaces. Building on this view, we organize existing methods into a standardized five-stage pipeline for weight-space generation and survey applications where the approach is already practical, such as parameter-efficient adaptation, mid-scale model synthesis, and on-device learning. We then confront alternative views, clarify current limits, and issue a concrete call to action. Our goal is to shift the community’s default mindset from optimizing models per task to sampling models from learned weight distributions, accelerating toward an era in which AI systems routinely generate other AI systems.

Deep Learning · Algorithms

Hao Chen, Diwei Su, Zhuo Wang, Zuwang He, Menglu Chen, Xiuxing Li, Xia Wu

Prompt learning has recently emerged as a novel, parameter-efficient paradigm to tackle the missing modalities challenge. However, existing prompting methods often overlook the internal structural information within prompt vectors, limiting their effectiveness in guiding frozen backbone models under diverse missing modality scenarios. To address this limitation, we propose a Structured Prompt Refining (SPR) network that refines the internal structure of prompt vectors across multiple dimensions: (1) a Global Interaction Fusion Module captures bidirectional interactions across prompt layers, thereby mitigating sub-optimal adaptation from inconsistent guidance under missing modalities, (2) a Local Feature Refinement Module structures adjacent prompt vectors into coherent semantic units, leveraging local contextual relationships to maintain semantic integrity during modality absence, and (3) a Channel Feature Selection Module uses point-wise gating to adaptively suppress noise and enhance critical channels based on the specific missing modality. Using only 0.8% trainable parameters, SPR achieves significant improvements on three mainstream multimodal classification datasets. Notably, it surpasses state-of-the-art by 3.8% in F1-Macro on the MM-IMDB dataset, even at a 90% modality missing rate. Extensive experiments and in-depth ablations validate SPR's effectiveness and robustness under various missing conditions.

Applications · Robotics

Yuchen Wang, Jiangtao Kong, Sizhe Wei, Xiaochang Li, Haohong Lin, Hongjue Zhao, Tianyi Zhou, Lu Gan, Huajie Shao

Trajectory world models play a crucial role in robotic dynamics learning, planning, and control. While recent works have explored trajectory world models for diverse robotic systems, they struggle to scale to a large number of distinct system dynamics and overlook domain knowledge of physical structures. To address these limitations, we introduce *WestWorld*, a kno**W**ledge-**E**ncoded **S**calable **T**rajectory **World** model for diverse robotic systems. To tackle the scalability challenge, we propose a novel system-aware Mixture-of-Experts (Sys-MoE) that dynamically combines and routes specialized experts for different robotic systems via a learnable system embedding. To further enhance zero-shot generalization, we incorporate domain knowledge of robot physical structures by introducing a structural embedding that aligns trajectory representations with morphological information. After pretraining on 89 complex environments spanning diverse morphologies across both simulation and real-world settings, *WestWorld* achieves significant improvements over competitive baselines in zero- and few-shot trajectory prediction. Additionally, it shows strong scalability across a wide range of robotic environments and significantly improves performance on downstream model-based control for different robots. Finally, we deploy our model on a real-world Unitree Go1, where it demonstrates stable locomotion performance ([see our demo on the website](https://westworldrobot.github.io/)).

Deep Learning · Attention Mechanisms

Shangwen Sun, Alfredo Canziani, Yann LeCun, Jiachen Zhu

We study two recurring phenomena in Transformer language models. First, \emph{massive activations}, where a small number of hidden channels attain extremely large values for a few tokens. Second, \emph{attention sinks}, where certain tokens attract a disproportionate share of attention across many heads and layers. We present a unified inference-time mechanism explaining how massive activations emerge and propagate through layers, and how normalization transforms these tokens into sparse, nearly fixed vectors that reshape the attention space and induce sink or no-sink behavior. We further conduct ablations on models trained from scratch to disentangle architectural and training factors governing both phenomena. We find that attention sinks persist across architectures and can arise even without massive activations. The normalization strategy primarily determines the emergence of massive activations, while head dimension and context length modulate the frequency of attention sink formation.

Applications · Robotics

Zhanyi Sun, shuran song

We introduce Distribution Contractive Reinforcement Learning (DICE-RL), a framework that uses reinforcement learning (RL) as a “distribution contractor” to refine pretrained generative robot policies. DICE-RL turns a pretrained behavior prior into a high-performing “pro” policy by amplifying high-success behaviors from online feedback. We pretrain a diffusion-based policy for broad behavioral coverage, then finetune it with a stable, sample-efficient residual off-policy RL framework that combines selective behavior regularization with value-guided action selection. Extensive experiments and analyses show that DICE-RL reliably improves performance with strong stability and sample efficiency, enabling mastery of complex long-horizon manipulation skills both in simulation and on a real robot. Project website: [dice-rl-anonymous.github.io](https://dice-rl-anonymous.github.io/).

Deep Learning · Algorithms

Valérie Castin, Kimia Nadjahi, Pierre Ablin, Gabriel Peyré

Low-Rank Adaptation (LoRA) is the most widely adopted method for fine-tuning large language models. Notably, LoRA is inherently overparameterized: multiple pairs of low-rank factors can yield the same adapted weight matrix. We show—both theoretically and empirically—that these pairs exhibit significantly different condition numbers. As a result, converging to different loss minimizers directly impacts the convergence rate of LoRA. Building on this observation, we introduce Balanced Low-Rank Adaptation (BaLoRA), a variant of LoRA that projects iterates onto a balanced manifold. This manifold improves the conditioning of the loss landscape while preserving the adapted matrix. The projection step is computationally lightweight and integrates seamlessly into existing fine-tuning pipelines. Empirically, BaLoRA converges faster than standard LoRA and achieves superior performance across a range of fine-tuning tasks.

General Machine Learning · Evaluation

Zi Yang, Haifeng Ding, Fei Liu, Yingying Cheng, Han Cheng, Zhilei Chai, Haojie Zhou

Large language models are beginning to introduce a new paradigm for compilation: instead of only assisting at the source level, they can operate directly on **intermediate representations (IRs)**, the compiler’s internal code representation, Early studies suggest that LLM-guided optimization can sometimes rival traditional compiler optimizations on selected programs, but evidence remains fragmented. Yet the community still lacks a rigorous IR-level benchmark that tests whether a model not only understands IR but can rewrite it under compiler-grade semantic constraints with meaningful performance impact. We present **CIRBench**, a benchmark of 800 curated IR instances spanning four compiler-oriented tracks: Analysis infers IR properties, Repair fixes invalid IR, Refactor applies a single semantics-preserving compiler optimization, and Transform performs performance-oriented rewrites, together mirroring core optimization responsibilities in modern compilers. CIRBench combines verifier, equivalence checking, and end-to-end performance measurement into a unified, layered correctness-aware evaluation of LLMs on IR. On six mainstream LLMs, CIRBench shows that current models fail on many IR analysis and rewriting instances and on median underperform the compiler baseline, but we also observe a maximum speedup of $4.96\times$ over -O3. These findings highlight both the opportunities and the remaining challenges of using LLMs inside optimizing compilers.