In online advertising, the inherent complexity and dynamic nature of advertising environments necessitate the use of auto-bidding services to assist advertisers in bid optimization. The complexity escalates in multi-channel scenarios, where effective allocation of budgets and constraints across channels with distinct behavioral patterns becomes critical for optimizing return on investment. Current approaches predominantly employ either optimization-based strategies or reinforcement learning (RL) techniques. However, optimization-based methods lack the flexibility to adapt to dynamic market conditions, while RL-based approaches struggle to capture essential historical dependencies and observational patterns within the constraints of Markov Decision Process (MDP) frameworks. To address these limitations, we propose AHBid, an Adaptable Hierarchical Bidding framework that integrates generative planning with real-time control. The framework employs a high-level generative planner utilizing diffusion models to dynamically allocate budgets and constraints through effective capture of historical context and temporal patterns. We introduce a constraint enforcement mechanism to ensure compliance with specified constraints, complemented by a trajectory refinement mechanism that enhances adaptability to environmental changes through historical data utilization. The system further incorporates a control-based bidding algorithm that synergistically combines historical knowledge with real-time information, significantly improving both adaptability and operational efficacy. Extensive experiments are conducted using both large-scale offline datasets and online A/B tests, demonstrating the effectiveness of AHBid by yielding a 13.57% increase in overall return compared to existing baselines.
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Web3 technologies and decentralised finance (DeFi) have not only revolutionised finance, but they have also given rise to completely different concepts in terms of how we handle our wealth and how we utilise the Internet, which is still an unreliable medium for the exchange of information. To overcome trust issues, cryptographic tools were introduced and these days we even put our digital assets on the blockchain. These new technologies enabled us to make most financial solutions decentralised. A widely popular financial product that is implemented on blockchains is a constant product market maker (CPMM). A CPMM can be used to swap digital assets between mutually untrusting parties by relying on prices calculated from its bonding curve. A blockchain-based automated market maker (AMM) quotes prices that usually lag behind external market prices, and this price difference can be exploited by automated bots, aka. arbitrageurs. The question we investigate in this work focuses on this issue. More specifically, we experiment with different approaches from financial mathematics to account for stochastic price changes, perform extensive measurements to quantify the characteristics of the DeFi market, focusing on mainnet Ethereum and develop stochastic grid optimisers to fit model parameters to actual AMM market data that we extract from the logs of the Ethereum virtual machine. We report on model performance that is more aligned with reality compared to previous works that have relied on naive two-parameter Black-Scholes (BS) models. Our results reveal that hidden Markov models can give better estimates for both the number of arbitrage trades and the arbitrage profits when combined with a jump diffusion model and a two-parameter BS model.
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Diffusion large language models (dLLMs) offer a theoretical advantage in parallel generation over standard autoregressive models. However, parallel generation alone does not guarantee practical speedups. Realizing this efficiency requires specialized inference mechanisms, such as diffusion-aware caching and reuse. Consequently, as inference efficiency becomes a prerequisite for practical deployment, recent research has actively explored acceleration techniques across algorithms, architectures, and systems. However, rigorous comparisons remain difficult, as end-to-end latency stems from intricate trade-offs between algorithmic, architectural, and system-level factors that are often conflated in existing benchmarks. In this survey, we introduce a unified latency decomposition framework for dLLMs to disentangle these factors and analyze their impact on inference speed in real deployments. Guided by this framework, we categorize acceleration techniques along three axes covering algorithmic innovations, architectural and system optimizations, and inference-time scaling. Finally, we provide guidelines for reproducible benchmarking and highlight open challenges for realizing the full potential of parallel generation.
Deep neural networks deliver strong performance but remain opaque, limiting their use in high-stakes domains that require transparency and human oversight. Concept Bottleneck Models (CBMs) address this gap by introducing a human-interpretable concept layer that mediates inputs and decisions, enabling semantic explanations and test-time intervention. This survey provides a unified review of CBMs organized along four dimensions: concept acquisition, concept-based decision making, concept intervention, and concept evaluation. We summarize the evolution of concept construction from manual annotation to lexicon-based mining, LLM/VLM-guided generation, and visually grounded discovery via prototypes and diffusion models; review emerging CBM architectures beyond strict bottlenecks; and consolidate evaluation and intervention protocols emphasizing faithfulness, sparsity, and intervenability, with particular relevance to high-stakes domains such as healthcare. We synthesize fragmented literature and outline key challenges and future directions for concept-based interpretable decision making.
Federated Learning (FL) facilitates collaborative training of a global model whose performance is boosted by private data owned by distributed clients, without compromising data privacy. Yet the wide applicability of FL is hindered by entanglement of data distributions across different clients. This paper demonstrates for the first time that by disentangling data distributions FL can in principle achieve efficiencies comparable to those of distributed systems, requiring only one round of communication. To this end, we propose a novel FedDistr algorithm, which employs stable diffusion models to decouple and recover data distributions. Empirical results on the CIFAR100 and DomainNet datasets show that FedDistr significantly enhances model utility and efficiency in both disentangled and near-disentangled scenarios while ensuring privacy, outperforming traditional federated learning methods.
Fragmented image recovery is of significant importance in computer vision, such as cultural relic and artwork restoration, archival document recovery, and digital forensics. The goal is to recover the original image topology from an unordered set of fragments and spatially align and stitch them together. The adjacency relationships among fragments are discrete, sparse, and highly structured, making it difficult for traditional methods to effectively handle global topological consistency. To address this challenge, we propose a fragment adjacency recovery method based on a conditional graph diffusion model. First, we perform discrete denoising pretraining with structural masking to learn structure-aware node representations from perturbed adjacency matrices, using graph neural networks for message passing. Building on this, we design a masked discrete diffusion process tailored for fragment reconstruction, which progressively restores the connectivity between fragments. Furthermore, to enhance the controllability of the generation process, we introduce a topology-guided mechanism that steers the generation of adjacency structures via a topological scoring function, ensuring that the reconstructed fragment graph satisfies global topological constraints. Experimental results demonstrate that our method achieves state-of-the-art performance on hand-torn calligraphy, painting replica datasets and document datasets, outperforming existing approaches in both accuracy and robustness.
Modern generative models for limit order books (LOBs) can reproduce realistic market dynamics, but they remain fundamentally passive: they either model what typically happens without accounting for hypothetical future market conditions, or they require interaction with another agent to explore alternative outcomes. This limits their usefulness for stress testing, scenario analysis, and decision-making. We propose DiffLOB, a regime-conditioned Diffusion model for controllable and counterfactual generation of LOB trajectories. DiffLOB explicitly conditions the generative process on future market regimes—including trend, volatility, liquidity, and order-flow imbalance, which enables the model to answer counterfactual queries of the form: “If the future market regime were X instead of Y, how would the limit order book evolve?” We introduce the first systematic evaluation framework for counterfactual LOB generation consisting of three criteria: (1) Realism, measuring how well generated trajectories can reproduce marginal distributions, temporal dependence structure and regime variables; (2) Counterfactual validity, testing whether interventions on future regimes induce consistent changes in the generated LOB dynamics; (3) Counterfactual usefulness, assessing whether synthetic counterfactual trajectories improve downstream prediction of future market regimes.
Macro placement is a pivotal stage in VLSI physical design, fundamentally determining the overall chip performance. Recent data-driven placement methods have demonstrated significant potential, yet they often struggle to handle sequential dependencies and to balance topological connectivity with physical constraints. To bridge this gap, we propose MacroDiff+, a physics-guided geometric diffusion framework. Specifically, we design a dual-domain denoising architecture that couples topological connectivity encoded by heterogeneous GNNs with global geometric context modeled by a Transformer. Furthermore, we introduce Physics-Guided Sampling, an inference strategy that actively steers the generation using explicit gradients to ensure both statistical plausibility and physical validity. On the ISPD2005 MMS benchmarks, MacroDiff+ outperforms state-of-the-art baselines with a 6.1–6.2% reduction in wirelength. Notably, it exhibits superior stability and scalability on large-scale designs where prior methods fail to converge. The source code is provided at https://github.com/jhy00n/MacroDiff-plus.
HIV is a retrovirus that attacks the human immune system and can lead to death without proper treatment. In collaboration with the WHO and the University of Witwatersrand, we study how to improve the efficiency of HIV testing with the goal of eventual deployment, directly supporting progress toward UN Sustainable Development Goal 3.3. While prior work has demonstrated the promise of intelligent algorithms for sequential, network-based HIV testing, existing approaches rely on assumptions that are impractical in our real-world implementations. Here, we study sequential testing on incrementally revealed disease networks and introduce Policy-Embedded Graph Expansion (PEGE), a novel framework that directly embeds a generative distribution over graph expansions into the decision-making policy rather than attempting explicit topological reconstruction. We further propose Dynamics-Driven Branching (DDB), a diffusion-based graph expansion model that supports decision making in PEGE and is designed for data-limited settings where forest structures arise naturally, as in our real-world referral process. Experiments on real HIV transmission networks show that the combined approach (PEGE + DDB) consistently outperforms baselines (e.g., 17.3% improvement in discounted reward and 15.4% more HIV detections with 25% of the population tested) and explore key tradeoffs that drive solution quality.
Offline robotic control requires long-horizon reasoning from fixed datasets while avoiding unsafe extrapolation beyond demonstrated behavior. We propose GRALP, a principled framework that resolves this tension by jointly enforcing support preservation and controllability at the level of temporal abstraction. GRALP adopts a deliberate architectural separation: diffusion is used exclusively as a deterministic action decoder for executing fixed latent skills, while planning and value estimation operate entirely in latent space under conservative constraints. This design enables stable value learning, controllable skill composition, and efficient planning without trajectory-level diffusion sampling at inference. Across unified D4RL benchmarks, GRALP achieves the highest average performance on Navigation, Sequential (Kitchen), and Adroit domains while remaining competitive on locomotion tasks. On contact-rich RoboSuite manipulation with human demonstrations (Lift and Pick-and-Place), GRALP achieves consistently high success rates (over 94%). These results indicate that reliable long-horizon offline control emerges when expressivity is confined to execution and decision-making operates over support-aligned latent abstractions.
Fluorescence molecular tomography (FMT) serves as a pivotal modality for preclinical tumor screening. While single-view FMT offers distinct advantages in data acquisition efficiency and cost-effectiveness, the scarcity of projection views severely exacerbates photon scattering-induced depth ambiguity, rendering 3D volumetric recovery a highly ill-posed inverse problem. To address these challenges, we propose a physics-inspired spectral topology aware reconstruction network (STAR-Net). Specifically, STAR-Net establishes a synergistic framework: initially, a frequency domain decoupling strategy is introduced to simulate the physical characteristics of diffuse light fields; building on this, a differentiable inverse spectral gating (DISG) mechanism is utilized to explicitly impose low-pass spectral regularization for precise depth recovery; and further, a dual-domain synergistic module is integrated to dynamically fuse spatial and frequency features, achieving high-fidelity detail preservation. Extensive experiments on the Digimouse benchmark demonstrate that the proposed STAR-Net achieves the highest dice coefficient under single-view conditions, validating that explicit spectral topology modeling is a powerful paradigm for mitigating depth ambiguity.
CT-to-PET synthesis aims to synthesize PET images from the widely available and lower-cost CT scans to address the high cost and additional radiation exposure associated with PET scanning. However, CT-to-PET synthesis faces two key challenges due to the sequential correlation of volumetric imaging: preserving smooth transition between adjacent slices and ensuring style consistency across long-range slices. To overcome these limitations, we propose the Style-aware Bidirectional Stream Diffusion Model, which ensures both inter-slice continuity and global style consistency with low computational cost. Specifically, we first utilize a Vector Quantized Variational Autoencoder to encode bidirectional adjacent CT slices into latent codes. A Neighbor Attention module is then introduced to capture transition patterns among these latent codes, ensuring structural continuity. To enhance style consistency, we further design a Prototype Prompting mechanism to construct a feature pool from long-range slices. Global style prototypes are extracted from the feature pool and dynamically integrated into the generation process, guiding the model’s attention toward consistent stylistic features across slices. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art methods.