Designing functional protein sequences that satisfy multiple desired properties is a core research focus of protein engineering. Prior methods struggle with inability or inefficiency when dealing with numerous, often conflicting, properties. We propose Multi-Property Protein Diffusion, (MP2D), a unified framework for multi-objective protein sequence optimization that integrates conditional discrete diffusion with constrained MCTS and global iterative refinement. MP2D formulates diffusion denoising as a constrained sequential decision-making process and employs MCTS to explore diverse denoising trajectories guided by Pareto-based rewards. A global iterative refinement strategy further enables repeated remasking and re-optimization of candidate sequences, while a dynamic Pareto constraint prevents candidate bloat and maintains balanced trade-offs across objectives. We evaluate MP2D on two challenging multi-objective protein design tasks: antimicrobial peptide and protein binder optimization, involving four to five conflicting properties. Experimental results demonstrate that MP2D consistently outperforms existing multi-objective baselines, achieving robust and balanced improvements across all objectives without retraining generative models. These results highlight MP2D as a practical and scalable solution for multi-objective functional protein design.
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Similarity-graph-based multi-view clustering is effective in capturing non-Gaussian cluster structures, yet it suffers from an inherent conflict between scalability and geometry-consistent cluster assignment. In particular, cluster labels are typically inferred via secondary clustering rather than being learned directly from the underlying data geometry. Although anchor-based extensions alleviate the computational burden, they introduce the challenge of anchor-number selection. To address these limitations, we propose Multi-view Clustering via Manifold Decomposition (MvMD), which directly infers cluster labels from multi-view data without explicit similarity graph construction or anchor selection. MvMD reformulates multi-view clustering as a unified multi-view regression problem, where cluster labels are optimized as model variables. To improve robustness against missing small clusters, a global balance regularization is incorporated. Meanwhile, local geometric consistency and the low-rank structure of cluster assignments are jointly enforced through a symmetric matrix factorization scheme, which is efficiently realized via a truncated SVD-based low-rank approximation. Extensive experiments on benchmark datasets validate the effectiveness and efficiency of the proposed MvMD. The code is available at https://github.com/Vince-Doit/MvMD.
Bridging the Objective Gap: A Unified Pre-Training Framework for Few-Shot Medical Image Segmentation
Few-shot medical image segmentation relies on dense, boundary-sensitive prototype matching, yet common pre-training objectives mainly optimize global alignment or reconstruction, creating an objective gap that hurts boundary delineation and increases adaptation cost. This raises the question: how to pre-train representations intrinsically matchable for episodic FSS while requiring minimal adaptation-induced re-organization? We introduce a Drift-Gap diagnostic to quantify intrinsic dense-matching misalignment and adaptation-induced feature drift. Guided by this lens, we propose BOG-PRETRAIN, combining Reliability-Gated Alignment to mitigate noisy report supervision, Semantic-Guided MIM to emphasize boundary-informative regions, and Dual Consistency Regularization to stabilize episodic metric geometry. Across five benchmarks (1/4/16-shot), BOG-PRETRAIN improves mean Dice by +15.5/+15.9/+12.5 points over best priors and reduces mean HD95 by 0.9/4.6/10.1; it achieves the lowest Drift (0.052 vs. 0.155 baseline) and Gap_PT (0.256), with ablations confirming the components' complementarity.
Federated optimization under data heterogeneity presents a significant challenge, often leading to suboptimal model performance. While numerous methods aim to replicate the ideal performance of centralized training, they frequently fall short in highly heterogeneous settings. In this paper, we introduce HaFedHo, an adaptive objective rectification method that harmonizes local training with the ideal data-centralized objective, requiring minimal modifications to the standard federated learning framework. HaFedHo operates by first decoupling the centralized objective and then employing a dynamic Taylor series expansion to accurately estimate the global objective for each client. Our theoretical analysis shows that the estimation error provably converges to zero as training progresses. Furthermore, extensive experiments on real-world datasets demonstrate that HaFedHo surpasses state-of-the-art methods, including SCAFFOLD, MimeLite, and FedDyn, in both test accuracy and communication efficiency. Notably, HaFedHo maintains its superior performance even with a client participation rate as low as $0.2\%$ in severely heterogeneous environments.
Knowledge distillation has become a prevalent technique for deploying efficient recommender systems, enabling lightweight student models to approximate the performance of larger teachers. However, we identify a critical issue: distillation systematically amplifies popularity bias, as student models inherit and intensify the popularity-driven shortcuts encoded in teachers trained on interaction data dominated by popular items. To address this limitation, we propose GUIDE (Geodesic aware Unbiased Instructive Distillation with Experts), a collaborative distillation framework that incorporates domain-specific debiasing experts alongside the global teacher. GUIDE tackles two key challenges in this paradigm. First, for expert routing, we introduce Spherical Expert Alignment, which conducts expert-student matching on the spherical manifold with geodesic distance optimization, eliminating magnitude-induced bias and ensuring stable gradient flow. Second, for context fusion, we design a Meta-Debiasing Gate that dynamically arbitrates teacher-expert influence based on real-time user-item context through end-to-end meta-learning. Extensive experiments on multiple real-world datasets demonstrate that GUIDE significantly mitigates popularity bias while preserving recommendation accuracy, with state-of-the-art trade-offs among efficiency, accuracy, and fairness. The code and data are available at:https://github.29com/zx19971219/GUIDE.
Accurate traffic flow prediction is fundamental to Intelligent Transportation Systems (ITS). However, traffic dynamics exhibit inherent multi-scale heterogeneity, where stable global trends are often masked by stochastic local fluctuations. Existing methods struggle to reconcile these conflicting resolutions, leading to sub-optimal forecasting. To address this, we propose the Multi-Scale Spatial-Temporal Diffusion Transformer (MS-STDT). Deviating from previous diffusion approaches that treat generation as a monolithic task, we reframe the forward diffusion process as an intrinsic temporal coarse-graining operation. By leveraging the data's multi-scale hierarchy as a structural anchor, we introduce a coarse-to-fine latent guidance strategy that enables the model to reconstruct stable global trends before refining fine-grained details. This ensures physical consistency and generative stability without requiring external labels. Extensive experiments across six real-world datasets confirm that MS-STDT performs the best, demonstrating significant improvements in predictive accuracy and zero-shot robustness against sensor failures. We provide code and data at https://github.com/ZetaoLiPhD/MS-STDT.
Achieving fairness in machine learning models while maintaining high accuracy is an important but complex task, especially when handling multiple sensitive attributes. Traditional fairness methods often struggle to eliminate bias within subgroups divided by sensitive attributes. Several key challenges have been identified in this context: (1) Multiple sensitive attribute scalability challenge, where methods fail to ensure fairness as the number of sensitive attributes increases, despite scenarios with multiple sensitive attributes being prevalent in real-world applications; (2) Multiple objective optimization conflict challenge, where simultaneously optimizing for accuracy, fairness, and other relevant objectives leads to conflicting gradient updates, causing suboptimal performance. To address these challenges, we propose BAMFair, a Barycenter Aligned Mediation framework for fairness across multiple sensitive attributes. It comprises two core modules: a Global Barycentric Alignment (GBA) module and a Nash Fairness Mediator (NFM) module. Specifically, GBA innovatively introduces a global fair barycenter and minimizes the distances from subgroups divided by sensitive attributes to it, providing a scalable and efficient solution for fairness optimization across multiple sensitive attributes. Subsequently, NFM negotiates an agreement among inconsistent gradient updates between different objectives. Extensive experiments on four real-world datasets validate that BAMFair outperforms state-of-the-art methods in scenarios with multiple sensitive attributes.
Hyperspectral image (HSI) clustering facilitates the unsupervised discrimination of complex surface materials but traditionally relies on the idealized assumption of centralized data availability. In real-world scenarios, however, this assumption clashes with data privacy regulations and the physical distribution of data across isolated silos. While Federated Learning offers a decentralized solution, existing frameworks in remote sensing are predominantly confined to supervised paradigms and struggle to address the heavy reliance on annotations and Non-IID distributions inherent among clients. To overcome these limitations, we propose a novel framework named Robust Federated HSI Clustering(RFHC) that enables collaborative unsupervised learning without requiring raw data exchange. Specifically, we design a dual-encoder architecture that incorporates a Federated Model Weight Augmentation strategy(FMWA), which generates consistent views through local-global network interactions to mitigate the spectral distortion introduced by traditional data augmentation. Furthermore, we develop a hybrid optimization objective that synergizes prototype relationship optimization with contrastive learning. This mechanism utilizes global prototypes to guide local training, effectively stabilizing feature learning against data heterogeneity while ensuring intra-cluster compactness. Extensive experiments on three benchmark HSI datasets demonstrate the effectiveness and superiority of the proposed method against state-of-the-art model(SOTA) federated approaches.
We investigate the interaction between quantization and pruning in the compression of open-weight small language models (SLMs), as these techniques are frequently combined in practice without a clear understanding of whether their effects are antagonistic, synergistic or additive. Prior work has reported ordering effects and studied pruning or quantization in isolation, but rarely quantifies their interaction across models and evaluation metrics. We introduce an interaction coefficient that isolates non-additive effects and apply it to Falcon3-1B-Base and LLaMA-3.2-1B, with a limited study on Qwen2.5-1.5B. Our experiments cover four hybrid application orders, multiple pruning sparsities and 4/8-bit quantization. We find non-monotonic interactions: memory savings are dominated by 4-bit quantization, while pruning alone provides limited benefits. Hybrids exhibit the greatest effects at global unstructured pruning sparsity s ∈ {0.2, 0.3} while getting close to additivity at s=0.5. At low sparsity, truthfulness improves, while reasoning losses diminish at higher sparsity. Future work will extend interaction analysis to hardware-aware sparse kernels and larger models.
Achieving globally desirable outcomes in networked multi-agent systems—such as high social welfare, stable allocations, and widespread cooperation—is a fundamental challenge in AI. This paper outlines a research agenda that explores two complementary pathways to this goal. The first is a top-down approach, where a central mechanism designer proposes rules to guide strategic agents towards theoretically optimal equilibria. The second is a bottom-up approach, where desirable farsighted policies, like cooperation in social dilemmas, emerge from the decentralized interactions of agents via multi-agent reinforcement learning. We argue that the integration of these paths constitutes a promising frontier for creating robust and adaptive multi-agent systems.
Determining and verifying product provenance remains a critical challenge in global supply chains, particularly as geopolitical conflicts and shifting borders create new incentives for misrepresentation of commodities, such as hiding the origin of illegally harvested timber or stolen agricultural products. Stable Isotope Ratio Analysis (SIRA), combined with Gaussian process regression-based isoscapes, has emerged as a powerful tool for geographic origin verification. While these models are now actively deployed in operational settings supporting regulators, certification bodies, and companies, they remain constrained by data scarcity and suboptimal dataset selection. In this work, we introduce a novel deployed data valuation framework designed to enhance the selection and utilization of training data for machine learning models applied in SIRA. By quantifying the marginal utility of individual samples using Shapley values, our method guides strategic, cost-effective, and robust sampling campaigns within active monitoring programs. By prioritizing high-informative samples, our approach improves model robustness and predictive accuracy across diverse datasets and geographies. Our framework has been implemented and validated in a live provenance verification system currently used by enforcement agencies, demonstrating tangible, real-world impact. Through extensive experiments and deployment in a live provenance verification system, we show that this system significantly enhances provenance verification, mitigates fraudulent trade practices, and strengthens regulatory enforcement of global supply chains.
Air pollution is a leading global health threat, yet many developing countries lack the dense monitoring infrastructure needed for accurate exposure assessment and informed policy. Optimal Sensor Placement (OSP) is a foundational challenge in expanding monitoring capacity. While mutual information (MI) offers a principled criterion for selecting informative sensor locations, its computational cost grows with both the number of placements and the density of the candidate grid. We present a scalable, continuous optimization framework that treats sensor coordinates as differentiable parameters and directly maximizes MI. Unlike standard approaches, our method is computationally efficient—its runtime is independent of both the number of placements and the size of the search grid—making MI-based acquisition feasible over large spatial domains. On a continental-scale PM2.5 dataset, our method outperforms random placement and the widely-used Maximum Predictive Variance heuristic. In a focused regional study, it approaches the performance of greedy MI while being orders of magnitude faster. Our framework enables practical, information-theoretic sensor placement for real-world environmental monitoring.
Personalized concept generation by tuning diffusion models with a few images raises potential legal and ethical concerns regarding privacy and intellectual property rights. Researchers attempt to prevent malicious personalization using adversarial perturbations. However, previous efforts have mainly focused on the effectiveness of protection while neglecting the visibility of perturbations. They utilize global adversarial perturbations, which introduce noticeable alterations to original images and significantly degrade visual quality. In this work, we propose the Visual-Friendly Concept Protection (VCPro) framework, which prioritizes the protection of key concepts chosen by the image owner through adversarial perturbations with lower perceptibility. To ensure these perturbations are as inconspicuous as possible, we introduce a relaxed optimization objective to identify the least perceptible yet effective adversarial perturbations, solved using the Lagrangian multiplier method. Qualitative and quantitative experiments validate that VCPro achieves a better trade-off between the visibility of perturbations and protection effectiveness, effectively prioritizing the protection of target concepts in images with less perceptible perturbations.
Opioid overdose is a growing global health crisis that claims more than 120,000 lives annually, of which more than half use opioids alone, without access to bystander intervention. Fatal overdose events are marked by motionlessness, respiratory depression, and hypoxemia, yet current wearable systems often rely on a single biomarker, limiting detection speed and accuracy. We present HypoxSpike, a novel ternary spiking neural network designed for real-time, multi-biomarker overdose detection for low-power neuromorphic hardware, optimized for integration into shoulder-based wearables. HypoxSpike combines motion, respiration, and oxygen saturation signals, while accounting for skin tone and body physiology, thus addressing known racial bias in pulse oximetry. Our research leverages an open-source shoulder-worn dataset from 19 patients experiencing sleep apnea, exploiting the shared physiological mechanisms underlying apnea and opioid overdose. This allows a direct comparison of our model with existing overdose detection approaches. HypoxSpike classifies three stages of hypoxemia with an average accuracy of 94%, outperforming state-of-the-art shoulder-based hypoxemia estimation while reducing false positive alert rates by 23.5%. By minimizing false positives, HypoxSpike supports accurate and power-efficient overdose detection, improving trust and usability for high-risk populations often overlooked by conventional systems.
Sea Surface Temperature (SST) is crucial for understanding upper-ocean thermal dynamics and ocean-atmosphere interactions, which have profound economic and social impacts. While data-driven models show promise in SST prediction, their black-box nature often limits interpretability and overlooks key physical processes. Recently, physics-informed neural networks have been gaining momentum but struggle with complex ocean-atmosphere dynamics due to 1) inadequate characterization of seawater movement (e.g., coastal upwelling) and 2) insufficient integration of external SST drivers (e.g., turbulent heat fluxes). To address these challenges, we propose SSTODE, a physics-informed Neural Ordinary Differential Equations (Neural ODEs) framework for SST prediction. First, we derive ODEs from fluid transport principles, incorporating both advection and diffusion to model ocean spatiotemporal dynamics. Through variational optimization, we recover a latent velocity field that explicitly governs the temporal dynamics of SST. Building upon ODE, we introduce an Energy Exchanges Integrator (EEI)-inspired by ocean heat budget equations-to account for external forcing factors. Thus, the variations in the components of these factors provide deeper insights into SST dynamics. Extensive experiments demonstrate that SSTODE achieves state-of-the-art performances in global and regional SST forecasting benchmarks. Furthermore, SSTODE visually reveals the impact of advection dynamics, thermal diffusion patterns, and diurnal heating-cooling cycles on SST evolution. These findings demonstrate the model's interpretability and physical consistency.
Intrinsic Barriers and Practical Pathways for Human–AI Alignment: An Agreement-Based Complexity Analysis
PDF ↗We formalize AI alignment as a multi-objective optimization problem called -agreement, in which a set of N agents (including humans) must reach approximate (ε) agreement across M candidate objectives, with probability at least 1-δ. Analyzing communication complexity, we prove an information-theoretic lower bound showing that once either M or N is large enough, no amount of computational power or rationality can avoid intrinsic alignment overheads. This establishes rigorous limits to alignment *itself*, not merely to particular methods, clarifying a "No-Free-Lunch" principle: encoding "all human values" is inherently intractable and must be managed through consensus-driven reduction or prioritization of objectives. Complementing this impossibility result, we construct explicit algorithms as achievability certificates for alignment under both unbounded and bounded rationality with noisy communication. Even in these best-case regimes, our bounded-agent and sampling analysis shows that with large task spaces (D) and finite samples, *reward hacking is globally inevitable*: rare high-loss states are systematically under-covered, implying scalable oversight must target safety-critical slices rather than uniform coverage. Together, these results identify fundamental complexity barriers---tasks (M), agents (N), and state-space size (D)---and offer principles for more scalable human-AI collaboration.
Reward-model-based fine-tuning is a central paradigm in aligning Large Language Models with human preferences. However, such approaches critically rely on the assumption that proxy reward models accurately reflect intended supervision, a condition often violated due to annotation noise, bias, or limited coverage. This misalignment can lead to undesirable behaviors, where models optimize for flawed signals rather than true human values. In this paper, we investigate a novel framework to identify and mitigate such misalignment by treating the fine-tuning process as a form of knowledge integration. We focus on detecting instances of proxy-policy conflicts, cases where the base model strongly disagrees with the proxy. We argue that such conflicts often signify areas of shared ignorance, where neither the policy nor the reward model possesses sufficient knowledge, making them especially susceptible to misalignment. To this end, we propose two complementary metrics for identifying these conflicts: a localized Proxy-Policy Alignment Conflict Score (PACS) and a global Kendall-Tau Distance measure. Building on this insight, we design an algorithm named Selective Human-in-the-loop Feedback via Conflict-Aware Sampling (SHF-CAS) that targets high-conflict QA pairs for additional feedback, refining both the reward model and policy efficiently. Experiments on two alignment tasks demonstrate that our approach enhances general alignment performance, even when trained with a biased proxy reward. Our work provides a new lens for interpreting alignment failures and offers a principled pathway for targeted refinement in LLM training.
Generating long, coherent text remains a challenge for large language models (LLMs), as they lack hierarchical planning and structured organization in discourse generation. We introduce Structural Alignment, a novel method that aligns LLMs with human-like discourse structures to enhance long-form text generation. By integrating linguistically grounded discourse frameworks into reinforcement learning, our approach guides models to produce coherent and well-organized outputs. We employ a dense reward scheme within a Proximal Policy Optimization framework, assigning fine-grained, token-level rewards based on the discourse distinctiveness relative to human writing. Two complementary reward models are evaluated: the first improves readability by scoring surface-level textual features to provide explicit structuring, while the second reinforces deeper coherence and rhetorical sophistication by analyzing global discourse patterns through hierarchical discourse motifs, outperforming both standard and RLHF-enhanced models in tasks such as essay generation and long-document summarization.
Black-box algorithms aim to optimize functions without access to their analytical structure or gradient information, making them essential when gradients are unavailable or computationally expensive to obtain. Traditional methods for black-box optimization (BBO) primarily utilize non-parametric models, but these approaches often struggle to scale effectively in large input spaces. Conversely, parametric approaches, which rely on neural estimators and gradient signals via backpropagation, frequently encounter substantial gradient estimation errors, limiting their reliability. Explicit Gradient Learning (EGL), a recent advancement, directly learns gradients using a first-order Taylor approximation and has demonstrated superior performance compared to both parametric and non-parametric methods. However, EGL inherently remains local and myopic, often faltering on highly non-convex optimization landscapes. In this work, we address this limitation by integrating global statistical insights from the evolutionary algorithm CMA-ES into the gradient learning framework, effectively biasing gradient estimates towards regions with higher optimization potential. Moreover, we enhance the gradient learning process by estimating the Hessian matrix, allowing us to correct the second-order residual of the Taylor series approximation. Our proposed algorithm, EvoGrad2 (Evolutionary Gradient Learning with second-order approximation), achieves state-of-the-art results on the synthetic COCO test suite, exhibiting significant advantages in high-dimensional optimization problems. We further demonstrate EvoGrad2's effectiveness on challenging real-world machine learning tasks, including adversarial training and code generation, highlighting its ability to produce more robust, high-quality solutions. Our results underscore EvoGrad2's potential as a powerful tool for researchers and practitioners facing complex, high-dimensional, and non-linear optimization problems.
We propose ECPv2, a scalable and theoretically grounded algorithm for global optimization of Lipschitz continuous functions with unknown Lipschitz constants. Building on the Every Call is Precious (ECP) framework, which ensures that each accepted function evaluation is potentially informative, ECPv2 addresses key limitations of ECP, including high computational cost and overly conservative early behavior. ECPv2 introduces three innovations: (i) an adaptive lower bound that prevents vacuous acceptance regions, (ii) a memory mechanism that restricts comparisons to a fixed-size subset of past evaluations, and (iii) a fixed random projection that accelerates distance computations in high dimensions. We theoretically show that ECPv2 retains ECP’s regret guarantees and expands the acceptance region with high probability. Extensive experiments and ablation studies empirically validate these findings. Using principled hyperparameter settings, we evaluate ECPv2 across a wide range of nonconvex optimization problems and find that it consistently matches or outperforms leading optimizers while significantly reducing wall clock time.