arXiv:2508.21424v2 Announce Type: replace
Abstract: Deep learning models have achieved state-of-the-art performance in many computer vision tasks. However, in real-world scenarios, novel classes that were unseen during training often emerge, requiring models to acquire new knowledge incrementally. Class-Incremental Learning (CIL) methods enable a model to learn novel classes while retaining knowledge of previous classes. However, these methods make the strong assumption that the incremental dataset is fully labeled, which is unrealistic in practice. In this work, we propose an unsupervised Incremental Learning method using Confidence-based Pseudo-labels (ICPL), which replaces human annotations with pseudo-labels, enabling incremental learning from unlabeled datasets. We integrate these pseudo-labels into various CIL methods with confidence-based selection and evaluate performance degradation on CIFAR100 and ImageNet100. Then, we compare our approach to popular Class Incremental Novel Category Discovery (class-iNCD) methods addressing similar challenges. Additionally, we apply our method to fine-grained datasets to demonstrate its real-world practicality and measure its computational complexity to validate its suitability for resource-constrained environments. ICPL achieves competitive results compared to supervised methods and outperforms state-of-the-art class-iNCD methods by more than 5% in final accuracy.
Science Journals
arXiv:2607.17858v1 Announce Type: new
Abstract: Reservoir computing exploits nonlinear dynamical systems to encode temporal inputs into high-dimensional state space representations. Although reservoir performance is often characterized through memory, nonlinearity, and their tradeoff, such aggregate measures do not reveal how task-relevant information is organized within the state space. Here, we introduce an eigen-spectral decomposition framework linking the degree-wise information processing capacity to the corresponding state space modes. As a result, we are able to quantify the degree-wise representation energy, and show that in some cases, substantial amounts of information processing capacity may reside in low-energy modes that are vulnerable to experimental noise. These results suggest that useful reservoir computation depends not only on dimensionality expansion, but also on the geometric organization of task-relevant information, with direct implications on physical reservoir computers.
arXiv:2607.17996v1 Announce Type: new
Abstract: Terahertz (THz) wireless communication offers a promising alternative to traditional wired links in data centres (DCs), enabling ultra-high data rates, low latency, and greater scalability. However, THz signals suffer from high path loss, necessitating the use of directional antennas (DAs). While DAs enhance signal strength, they introduce challenges such as deafness and synchronisation, typically addressed through receiver-initiated MAC protocols. Most existing THz MAC protocols use fixed beamwidths, which results in a key performance trade-off: narrow beams improve gain for long-range links but reduce throughput for short distances due to increased alignment overhead, while wide beams benefit short links but degrade performance over longer distances. To overcome this limitation, we propose DBS-ADAPT, a dynamic beamwidth selection-based MAC protocol that adjusts the antenna beamwidth according to the distance between nodes, maximising throughput without compromising link range. We also introduce an enhanced version, EDBS-ADAPT, which further reduces beamwidth switching and control overhead while preserving throughput gains. Both protocols are evaluated using the NS-3 THz module. Simulation results show that DBS-ADAPT improves average throughput by up to 22% and reduces delay up to 10% compared to the baseline ADAPT-3 protocol. EDBS-ADAPT further cuts beamwidth switching overhead by 95%, making it more efficient for scalable and high-performance wireless DC environments.
arXiv:2509.00446v2 Announce Type: replace
Abstract: Recent advances in autonomous digital agents from industry (e.g., Manus AI and Gemini's research mode) highlight their potential for structured tasks through autonomous decision-making and task decomposition, but it remains unclear how well such systems support real-world information-intensive workflows. We study this question in journalism, where newswriting requires iterative planning, contextual reasoning, and active discovery of missing background to produce a coherent article. We introduce NEWSAGENT, a benchmark for evaluating how agents search raw materials, select relevant information, and iteratively revise drafts through core journalistic functions. Given a writing instruction and partial firsthand materials, agents must identify narrative perspectives, issue keyword-based queries, retrieve historical context, and generate complete news articles. Unlike typical summarization or retrieval tasks, essential context is not directly available and must be actively discovered, reflecting real-world reporting constraints. NEWSAGENT consists of 6k human-verified examples derived from real news. We evaluate open- and closed-sourced LLMs with commonly-used agentic frameworks on NEWSAGENT, which shows that agents are capable of retrieving relevant facts but struggling with planning and narrative integration. We believe that NEWSAGENT serves a realistic testbed for iterating and evaluating agent capabilities in terms of web data manipulation to real-world productivity.
arXiv:2509.00930v2 Announce Type: replace
Abstract: Large language models (LLMs) exhibit strong general reasoning, yet the community lacks controllable, scalable, and verifiable tools to analyze and improve these abilities. We present SATQuest, a verifier that generates diverse SAT-based reasoning tasks directly from Conjunctive Normal Form (CNF) instances and checks answers objectively with PySAT. SATQuest factorizes evaluation along three orthogonal dimensions: instance, problem type, and question format, enabling fine-grained, multi-dimensional analysis and reinforcement fine-tuning. Randomized CNF generation mitigates memorization and supports reproducible experiments. Using SATQuest, we benchmark a range of open- and closed-weight LLMs and uncover persistent gaps in logical reasoning, particularly on higher-complexity tasks and in transfer beyond familiar mathematical notation to machine or narrative formats. We further show that reinforcement fine-tuning with SATQuest rewards substantially boosts targeted performance and generalizes to larger instances, while cross-format robustness remains challenging. Collectively, SATQuest provides verifier-backed infrastructure for controlled, scalable, and reproducible empirical research on LLM logical reasoning and its training.
arXiv:2509.01235v2 Announce Type: replace
Abstract: Balancing training accuracy and adversarial robustness has beeen a challenge since the birth of deep learning. Here, we introduce a geometry-aware deep learning framework that leverages layer-wise local training to sculpt the internal representations of deep neural networks. This framework promotes intra-class compactness and inter-class separation in feature space, leading to manifold smoothness and adversarial robustness against white or black box attacks. The performance can be explained by \blue{data-dependent statistical mechanics of integrating out the network parameters}, \blue{supplemented by a phenomenological model} with Hebbian coupling between elements of the hidden representation. Based on the current geometry-aware learning framework, the deep network can assimilate new information into existing knowledge structures while reducing representation interference.
arXiv:2509.02522v3 Announce Type: replace
Abstract: Recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) have empowered large language models (LLMs) to tackle challenging reasoning tasks such as mathematics and programming, however existing RLVR methods often suffer from sparse reward signals and unstable policy gradient updates inherent to RL-based approaches. To address the challenges, we propose PACS, a novel RLVR framework that achieves imPlicit Actor Critic coupling via a Supervised learning framework. By treating the outcome reward as a predictable label, we reformulate the RLVR problem into a supervised learning task over a score function parameterized by the policy model and optimized using cross-entropy loss. A detailed gradient analysis shows that this supervised formulation inherently recovers the classical policy gradient update while providing more stable and efficient training. Extensive experiments demonstrate that PACS significantly outperforms strong open-source models and RLVR baselines, yielding substantial average gains of +8.26% (4B) and +9.57% (8B) over base models offering a promising avenue for LLMs post-training with verifiable rewards. Our code and data are available as open source at https://github.com/ritzz-ai/PACS.
arXiv:2509.07996v4 Announce Type: replace
Abstract: World modeling has become a cornerstone in AI research, enabling agents to understand, represent, and predict the dynamic environments they inhabit. While prior work largely emphasizes generative methods for 2D image and video data, they overlook the rapidly growing body of work that leverages native 3D and 4D representations such as RGB-D imagery, occupancy grids, and LiDAR point clouds for large-scale scene modeling. At the same time, the absence of a standardized definition and taxonomy for "world models" has led to fragmented and sometimes inconsistent claims in the literature. This survey addresses these gaps by presenting the first comprehensive review explicitly dedicated to 3D and 4D world modeling and generation. We establish precise definitions, introduce a structured taxonomy spanning video-based (VideoGen), occupancy-based (OccGen), and LiDAR-based (LiDARGen) approaches, and systematically summarize datasets and evaluation metrics tailored to 3D/4D settings. We further discuss practical applications, identify open challenges, and highlight promising research directions, aiming to provide a coherent and foundational reference for advancing the field. A systematic summary of existing literature is available at https://github.com/worldbench/awesome-3d-4d-world-models
arXiv:2509.08420v3 Announce Type: replace
Abstract: Strong, symmetrically focusing plasma lenses are promising for accommodating the small beams associated with plasma-based accelerators and collider final foci. However, while focusing with active and passive plasma lenses has been experimentally demonstrated, compatibility with high-brightness beams relevant for applications has not. In this work, we show experimentally that passive plasma lenses can preserve free-electron-laser-quality slice emittance while focusing two orders of magnitude more strongly than quadrupole magnets, and that the focal parameters can be controlled.
arXiv:2607.18200v1 Announce Type: new
Abstract: Robots in cluttered indoor spaces often fail not because they cannot generate collision-free paths, but because a fixed safety margin is mis-calibrated: conservative margins cause detours and timeouts, while permissive margins lead to near-boundary shortcuts under perception bias. Diffusion-based planners propose diverse trajectory candidates from egocentric RGB-D, yet reliable selection remains the bottleneck. We propose a context-conditioned safety critic that learns an adaptive clearance preference for ranking diffusion proposals, decomposed into three complementary terms: (i) a safety term with a clearance-budget penalty and a control-barrier-function residual for waypoint- and transition-wise safety, (ii) an efficiency term combining a smoothness penalty with a safety-gated detour-ratio penalty that avoids detours without incentivizing risky shortcuts, and (iii) a distance-constraint matching term that anchors the learned budget to realized ESDF clearances to prevent margin collapse. We train the critic with privileged ESDF geometry in simulation and distill it into a perception-only selector via a two-stage teacher-student procedure. On PointGoal navigation in HM3D and MP3D, including cross-dataset transfer, our method achieves the highest success rate (SR) and success weighted by path length (SPL) among strong diffusion, optimization, and RL baselines. Trained purely in simulation, it transfers to a Unitree G1 humanoid and navigates cluttered indoor scenes without task-specific tuning.
arXiv:2607.17409v1 Announce Type: cross
Abstract: We study the problem of sequentially evaluating a new large language model (LLM) on a fixed question set using historical performance data from prior LLMs. Our goal is to construct a confidence sequence (CS) for the model's capability on this question set and to design active querying rules that shrink the CS width as quickly as possible. For CS construction, we invert a family of test supermartingales and focus on two representative approaches: a reverse information projection (RIPr)-based approach and a testing-by-betting-based approach. We first study these approaches under an oracle setting, and demonstrate the oracle optimality of the RIPr-based construction. We then propose a growth-oriented querying rule that aims to maximize the worst-case one-step expected log-increment over the endpoints of the current CS. In practice, we build these test supermartingales and the querying rule on predictions of question-level correctness learned from historical data. We then analyze the shrinkage behavior of the resulting CSs and identify two key factors that slow the shrinkage rate of CSs: accumulated prediction mismatch and the spikiness of the querying distribution. Finally, motivated by this analysis, we propose several mixture querying rules that combine growth-oriented querying, prediction refinement, and uniform exploration, trying to mitigate the effects that slow the shrinkage rate. We provide experiments comparing different querying rules for the RIPr-based and testing-by-betting-based CSs across several synthetic testing datasets. Interestingly, we observe that the simplest querying rule, uniform sampling, can sometimes outperform more adaptive querying rules for both methods.
arXiv:2607.17661v1 Announce Type: new
Abstract: Remote sensing has become an increasingly valuable tool for agricultural monitoring, particularly through the use of publicly available satellite imagery. However, effectively integrating domain knowledge into machine learning methods remains challenging. This study presents a real-world example of early sugar beet harvest yield forecasting from purely optical Sentinel-2 imagery, demonstrating how a tight integration of domain knowledge and machine learning can lead to synergistic gains. We empirically find that using very small vision transformer patch sizes and all available Sentinel-2 spectral bands improves our model despite being uncommon design choices in the domain. As a practical contribution, we were able to identify a large fraction of low-yield fields in a different year early on in the growth cycle through a modified training setup and a ranking-based detection of underperforming fields.
arXiv:2505.11765v5 Announce Type: replace
Abstract: Agents powered by advanced large language models (LLMs) have demonstrated impressive capabilities across diverse complex applications. Recently, Multi-Agent Systems (MAS), wherein multiple agents collaborate and communicate with each other, have exhibited enhanced capabilities in complex tasks, such as high-quality code generation and arithmetic reasoning. However, the development of such systems often relies on handcrafted methods, and the literature on systematic design and optimization of LLM-based MAS remains limited. In this work, we introduce \textbf{OMAC}, a general framework designed for holistic optimization of LLM-based MAS. Specifically, we identify five key optimization dimensions for MAS, encompassing both agent functionality and collaboration structure. Building upon these dimensions, we first propose a general algorithm, utilizing two actors termed the Semantic Initializer and the Contrastive Comparator, to optimize any single dimension. Then, we present an algorithm for joint optimization across multiple dimensions. Extensive experiments demonstrate the superior performance of OMAC on diverse tasks against recent approaches.
arXiv:2509.14230v2 Announce Type: replace
Abstract: While structured pruning presents a highly effective pathway for accelerating Large Language Model (LLM) inference, existing methods frequently suffer from significant performance degradation and demand computationally retraining to recover capabilities. To overcome these barriers, we present NIRVANA, a novel, hardware-aware structured pruning framework designed to preserve both zero-shot performance and the optimization landscape for downstream fine-tuning. Departing from traditional loss-based heuristics, our approach evaluates structural importance through a first-order function-space saliency inspired by the Neural Tangent Kernel (NTK), effectively safeguarding the model's critical training dynamics. To prevent structural collapse at high compression rates, we introduce a global unit-ranking strategy coupled with an analytically derived allocation mechanism, which optimally balances the pruning aggressiveness between attention heads and MLP neurons. Furthermore, we eliminate the instability typically associated with random data sampling by employing a lightweight, KL-divergence-driven calibration data selection process. Extensive evaluations across Llama3, Qwen, and T5 architectures demonstrate that NIRVANA consistently establishes new state-of-the-art results on different benchmarks, providing a theoretically sound and practical approach to LLM compression. The code is available at https://github.com/iDEA-iSAIL-Lab-UIUC/NIRVANA.
arXiv:2509.14357v4 Announce Type: replace
Abstract: The Freeze-Tag Problem (FTP) asks for the minimum time needed to activate a swarm of robots, starting from a single active robot. When an active robot reaches a frozen robot, the latter becomes active; both robots may then move independently and activate further robots. We prove that FTP is strongly NP-hard in the plane under every fixed rational $L_p$ distance, $1 \le p < \infty$, and under $L_\infty$. The geometric argument also applies to every fixed real $p > 1$ for which the metric admits an effective specification. For $L_1$ and $L_\infty$, the integer-coordinate decision problems are strongly NP-complete. The reduction starts from Numerical 3-Dimensional Matching with distinct integers and also yields NP-completeness for unweighted planar grid graphs.
arXiv:2509.20174v2 Announce Type: replace
Abstract: Higher-order interactions, where groups of nodes interact collectively rather than pairwisely, are central to many complex systems, from neural and ecological networks to social contagion. However, simulating dynamical processes on such higher-order structures remains computationally challenging due to the combinatorial growth of possible interactions. Here, we develop efficient and statistically exact Gillespie algorithms for Markovian spreading dynamics on large and heterogeneous hypergraphs. By incorporating phantom processes $-$events that advance time without altering the system's state$-$, we drastically reduce the computational complexity of standard algorithms ($\mathcal{O}(N^2)$), achieving up to linear scaling with system size. Relying on the susceptible-infected-susceptible model with critical mass thresholds as a benchmark, we show that the optimized algorithms outperform standard approaches by several orders of magnitude, enabling simulations of networks with millions of nodes and broad heterogeneity in both degree and interaction order. Efficient sampling methods, needed to overcome the bottlenecks imposed by either a high maximum order or number of interactions, and other dynamical processes on higher-order networks are tackled. These results establish a general framework for scalable, continuous-time simulations of higher-order contagion and related dynamical processes.
arXiv:2607.18043v1 Announce Type: new
Abstract: Accurately solving partial differential equations (PDEs) on arbitrary geometries and a variety of meshes is an important task in science and engineering applications. In this paper, we propose Adaptive Mamba Neural Operators (AMO), which integrates reproducing kernels for state-space models (SSMs) rather than the kernel integral formulation of SSMs. This is achieved by constructing Takenaka-Malmquist systems for the PDEs. AMO offers new representations that align well with the adaptive Fourier decomposition (AFD) theory and can approximate the solution manifold of PDEs on a wide range of geometries and meshes. In several challenging benchmark PDE problems in the fields of fluid physics, solid physics, and finance on point clouds, structured meshes, regular grids, and irregular domains, AMO consistently outperforms state-of-the-art solvers in terms of relative $L^2$ error. Overall, this work presents a new paradigm for designing explainable neural operator frameworks.
arXiv:2607.17480v1 Announce Type: new
Abstract: Blind recognition of polar codes from noisy observations is a key problem in non-cooperative signal processing. Although existing blind successive cancellation list (BSCL) recognition exploits channel soft information, it performs two-hypothesis path expansion at every source-bit position, resulting in high complexity. In this paper, we first analyze the first recognition-error positions in the blind successive cancellation (BSC) recognition and observe that they are closely related to the corresponding contribution terms in the existing Bhattacharyya-parameter-based upper bounds. Based on this observation, a critical-set-aided simplified blind successive cancellation list (SBSCL) recognition method is proposed. SBSCL performs two-hypothesis path expansion only at the selected critical-set positions and keeps BSC recognition at the remaining positions, thereby reducing complexity. To improve the reliability of critical-set selection and refine the performance analysis, density-evolution (DE)-based bounds are further developed. Under the ideal SC-consistent condition, the synthetic log-likelihood-ratio (LLR) distributions obtained from density evolution are used to compute the optimized Chernoff coefficient for the upper bound and the overlap coefficient for the lower bound. Simulation results show that the DE-based bounds are tighter than the Bhattacharyya-parameter-based bounds. In the considered settings, the gap between the DE upper and lower bounds is within $1$ dB around a recognition-error probability of $10^{-2}$. Furthermore, SBSCL achieves nearly the same recognition success rate as BSCL, and the size of critical set decreases rapidly as the signal-to-noise ratio (SNR) increases.
arXiv:2607.17965v1 Announce Type: new
Abstract: 3D Gaussian Splatting (3DGS) employs Gaussian primitives for explicit scene representation, facilitating real-time, high-fidelity reconstruction and novel view synthesis of complex scenes. However, the explicit modeling inherent in 3DGS introduces a gradient bias during optimization, rendering its non-convex optimization process highly susceptible to convergence toward local suboptimal solutions. This constitutes a fundamental limitation in 3DGS optimization, which we term the Blur Trap. To address this limitation, we integrate simple explicit exploration into the 3DGS optimization framework. First, through rigorous mathematical analysis of the 3DGS optimization formulation, we identify the underlying optimization bias responsible for the Blur Trap and categorize it into two distinct subtypes: the Far-Side Blur Trap and the Near-Side Blur Trap. Subsequently, we propose two highly straightforward exploration strategies (Random Seeding and Random Splitting) to mitigate the far-side and near-side blur traps, respectively. Experimental validation demonstrates that the incorporation of these exploration operators effectively and complementarily overcome the Blur Trap, achieving high-quality rendering performance across multiple datasets. Project page: https://chengbo-wang.github.io/ExploreGS/
arXiv:2607.17519v1 Announce Type: new
Abstract: Classical paintings preserve rich spatial, cultural, and historical content, making their reconstruction as explorable 3D scenes valuable for digital preservation, immersive exhibition, and cultural engagement. Yet, unlike photographs, they often depict scenes in a single-view, stylized manner, with weak perspective, lighting, and depth cues. Existing 3D reconstruction methods are largely built on natural-image priors, making it difficult to recover geometrically plausible and visually faithful 3D representations from such inputs. To address this challenge, we introduce Classical Painting-to-3D (CP3D), a new task that aims to recover a 3D representation from a single classical painting while jointly ensuring geometric plausibility, appearance fidelity to the source artwork, and plausible novel-view synthesis. We further propose GeAR, a training-free two-stage framework for Geometry Grounding and Appearance Restitution. GeAR first converts the input painting into a geometry-grounded representation with more coherent shading and illumination cues, improving the stability of 3D Gaussian reconstruction. It then restores artwork-faithful appearance across views under spatial constraints and multi-view consistency, recovering the painterly textures and details weakened during grounding. In addition, we construct HeriArch, a curated benchmark of 10,160 high-resolution classical artworks for systematic evaluation of CP3D. Extensive experiments and user studies show that GeAR consistently outperforms strong baselines in geometric plausibility, appearance fidelity, and human preference. Code and dataset will be released publicly.
arXiv:2410.18777v1 Announce Type: cross
Abstract: This research presents an online path planner for Unmanned Aerial Vehicles (UAVs) that can handle dynamic obstacles and UAV motion constraints, including maximum curvature and desired orientations. Our proposed planner uses a NURBS path representation and a Differential Evolution algorithm, incorporating concepts from the Velocity Obstacle approach in a constraint function. Initial results show that our approach is feasible and provides a foundation for future extensions to three-dimensional (3D) environments.
arXiv:2503.20813v3 Announce Type: cross
Abstract: This work presents an equivalent circuit model for Magnetic Tunnel Junctions (MTJs) that accurately captures their magnetization dynamics and electrical behavior. Implemented in LTspice, the model is validated against direct numerical solutions of the Landau-Lifshitz-Gilbert-Slonczewski (LLGS) equation. It effectively simulates essential spintronic phenomena, including ferromagnetic resonance, field- and spin-torque-induced switching, and spin-torque-induced oscillations. Simulation results demonstrate strong agreement between LTspice and LLGS solutions, confirming the model accuracy and utility for efficient circuit-level analysis of spintronic devices. The ability to incorporate time-dependent magnetic fields and voltage inputs makes the proposed model suitable for diverse applications such as neuromorphic computing, microwave signal processing, and spintronic memory technologies. By providing a computationally efficient yet physically accurate circuit representation, this work enables seamless integration of MTJs into larger electronic systems, potentially accelerating the development of advanced spintronic circuit architectures.
arXiv:2607.16225v1 Announce Type: cross
Abstract: Brain-Computer Interfaces (BCIs) face a severe calibration bottleneck due to cross-subject spatial covariance shifts and physiological artifacts. To enable zero-calibration BCI, a deep learning pipeline was engineered combining Per-Session Independent Component Analysis, Riemannian Euclidean Alignment, and EEGNet stabilized by Stochastic Weight Averaging (SWA). Evaluated on the strict MOABB BNCI2014-001 benchmark, the proposed architecture successfully isolates true sensorimotor rhythms. For the primary case study (Subject 1), a clinically robust SWA stable accuracy of 90.97% (AUC: 0.976, Cohen's $\kappa$: 0.819) was achieved. Furthermore, expanded 9-fold Leave-One-Subject-Out (LOSO) cross-validation yielded a globally stable mean accuracy of 74.31%, proving hardware-agnostic zero-shot efficacy for binary motor imagery.
arXiv:2607.17450v1 Announce Type: new
Abstract: Handler calculus conflates resumable and non-resumable effect operations through a single do construct, distinguished only by result type annotation. This conflation does not compromise type safety -- progress and preservation hold -- but it permits resumption bindings for non-resumable operations, creating vacuous bindings that the because-calculus eliminates at compile-time. The because-calculus structurally separates registration (non-resumable, void-returning) from attestation (resumable, non-void-returning) using dual effect rows and level-indexed typing, rejecting such clauses at compile-time via the Resumption Subconstraint. We prove the Conflation Theorem: collapsing the adjoint triple of existential, substitution, and universal functors into a single effect operation is non-faithful -- the erasure from the because-calculus to handler calculus maps rejected clauses to accepted ones. Four movements correspond to four natural transformations; categorical semantics maps each judgment to a category-theoretic construct. We establish progress, subject reduction, and tower progress for the full calculus.
arXiv:2607.17784v1 Announce Type: new
Abstract: Nonclairvoyant scheduling is a fundamental online model in which processing times are initially unknown to the scheduler. Unfortunately, for important objectives such as total completion time and makespan, worst-case analysis yields pessimistic guarantees: every nonclairvoyant algorithm has a competitive ratio of at least $2$ for these objectives.
Recent work introduced $\varepsilon$-clairvoyance, where a scheduler receives a signal once an $\varepsilon$-fraction of a job remains (FOCS'25, NeurIPS'25). This model avoids giving the algorithm a priori predictions as done in learning-augmented algorithms, a practice that is often hard to justify in practice. However, existing algorithms and analyses rely crucially on signal times being precise, an assumption hardly justifiable in applications such as task profiling.
We introduce stochastic clairvoyance, a beyond-worst-case model in which each job emits a randomly timed signal during its execution, drawn from a distribution over its processing length. For this model, we design new online scheduling algorithms whose competitive ratios are strictly below $2$ for minimizing total completion time and makespan. On the technical side, we prove a new black-box theorem that converts bounds on expected pairwise job delays into competitive guarantees via a continuous amortized charging argument.
Our results show that stochastic clairvoyance is not merely a curiosity: it yields robust improvements across different scheduling objectives and machine environments. More broadly, stochastic clairvoyance suggests a new direction in beyond-worst-case analysis for online algorithms, and builds a bridge between learning-augmented algorithms and stochastic information models.