Papers

In uncrewed aerial vehicle (UAV)-relayed mobile edge computing (MEC) networks, computation tasks generate traffic with diverse latency requirements and data sizes. Routing decisions therefore need to adapt to both traffic characteristics and changing network conditions. Compared with single-path routing, multipath routing is better suited to such heterogeneous traffic because it provides multiple forwarding options and enables flexible traffic splitting. However, conventional multipath routing usually splits traffic over predefined end-to-end paths, making it difficult to respond quickly to link fluctuations and topology changes in UAV networks. To address this issue, we propose a traffic-adaptive per-hop multipath routing method for multi-hop UAV networks, in which each UAV dynamically distributes traffic among multiple candidate next hops. We formulate the routing problem to improve the on-time packet delivery ratio while reducing the packet loss ratio, and model it as a decentralized partially observable Markov decision process (Dec-POMDP). To solve this problem, we develop a multi-agent reinforcement learning (MARL) algorithm, termed Multi-Agent Proximal Policy Optimization with Dirichlet Modeling (MAPPO-DM). MAPPO-DM follows the centralized-training-and-decentralized-execution framework and models continuous traffic-splitting actions using a Dirichlet distribution. Simulation results show that MAPPO-DM outperforms the baseline methods and maintains robust performance under various network conditions.

Blind and low-vision (BLV) users are increasingly engaging with large language model (LLM) interfaces to access documents, but it is unclear how such systems support or hinder their ability to build interconnected knowledge. To examine this gap, we compared a Question-Answer Interface (QAI) that supports open-ended conversational inquiry, with a Document Interface (DI) based mostly on traditional structured text document navigation. We recruited 16 BLV screen reader users where they used both interfaces to explore two fictional worlds. Data from interaction logs, concept maps, decision-based tasks, and semi-structured interviews provide comparative insights into how interface design supports knowledge construction. Findings show that participants visited more distinct documents with the DI and formed larger and more correct mental models with the DI than with the QAI. They were also more able to apply knowledge they had gained. Simultaneously, many still preferred the QAI and often estimated that they had explored more, formed better mental models and applied their models better when acquiring the information with the QAI, despite this not being the case. Our analysis suggests possible interface design reasons for these differences and highlights some of the risks introduced by using question-answer interfaces to access information spaces.

Cold-start multimodal recommendation faces three coupled challenges: (i) sparse interactions obscure user intent, (ii) cold items remain topologically isolated, and (iii) similarity-based item graphs may cause semantic drift. To address these issues, we propose MOTIF, a Motivation-guided Topology Inference framework for cold-start multimodal recommendation. MOTIF integrates Semantic Motivation Reasoning, Knowledge-enhanced Graph Reconstruction, Weighted Graph Contrastive Learning, and Semantic-Structural Alignment. It uses offline LLM reasoning to infer motivation semantics, reconstructs transferable item-item topology, and learns robust graph embeddings without injecting generated text into prediction. Experiments on three multimodal benchmarks show consistent gains over graph-based, multimodal, cold-start, and LLM-enhanced baselines, with up to 6.07% relative improvement over the strongest recent baseline.

Recent advances in generative AI have significantly increased the demand for high-resolution image and video generation, positioning diffusion models as a core technology. Among them, Diffusion Transformers (DiTs) have emerged as the state-of-the-art (SOTA) models due to their scalability and output quality. However, self-attention in DiTs incurs significant computational overhead, leading to excessively long latency as the complexity grows with the fourth power of the output resolution. While prior works have attempted to mitigate this cost using sparsity and quantization techniques, they fall short of effectively reducing the computational cost in high-resolution DiTs. In this paper, we present APT, a software-hardware co-designed accelerator for high-resolution DiTs. APT leverages attention probabilities as a unified importance metric to jointly optimize computation through fine-grained pruning and adaptive precision scaling. At the algorithm level, we propose Attention Probability-guided Adaptive Dual Thresholding (APDT), which dynamically performs element selection and precision assignment using dual thresholds. To ensure compatibility with memory-efficient FlashAttention, we introduce Timestep-Aware FlashAttention (TAFA), which predicts attention probabilities across timesteps by exploiting temporal similarity. At the architecture level, we co-design a specialized accelerator that efficiently supports irregular sparsity and dual-precision execution, featuring dynamic mask management, address translation, dual-precision compute units, and a tile-based dataflow. Finally, we evaluate APT on SOTA DiT models, including PixArt-$\alpha$, Stable Diffusion 3, and FLUX. APT achieves up to 8.16$\times$ speedup and 14.98$\times$ higher energy efficiency over NVIDIA A100, and up to 3.01$\times$ speedup and 2.04$\times$ higher energy efficiency over EXION, a SOTA diffusion model accelerator.

Chain-of-thought prompting can expose and amplify demographic stereotypes within an LLM's intermediate reasoning and create a failure mode that final-answer debiasing alone cannot address. Mitigating such bias during generation presents a fundamental timing problem: intervening too late allows biased reasoning to propagate, while unnecessarily intervening can disrupt otherwise correct reasoning. Existing approaches largely avoid this decision by either evaluating completed reasoning chains post hoc or intervening at predetermined steps, leaving open when a developing reasoning trajectory provides sufficient evidence to warrant correction. We formulate this decision as an online change-point detection problem. A per-step bias signal updates a CUSUM statistic and a targeted correction is injected only when accumulated evidence crosses a detector-specific threshold calibrated on held-out data. We instantiate the framework with a white-box signal derived from next-token probabilities and a black-box signal obtained from an LLM judge, enabling deployment with both open-weight and hosted models. On gpt-4o-mini adaptive black-box triggering recovers most of the disambiguated-context accuracy lost under fixed-interval intervention while requiring substantially fewer interventions. That result holds even with an independent judge. Across six open-weight models, the white-box signal improves ambiguous-item accuracy on all six but reduces disambiguated-item accuracy on five because it cannot distinguish unsupported stereotype reliance from correct, stereotype-congruent evidence.

As AI companions become increasingly embedded in everyday life, there is an urgent need to detect harms that emerge in social and emotional human-AI interactions. Yet research in this area is constrained by the lack of real-world, multi-turn conversational datasets for operationalizing and evaluating harms that are relational and contextual. In this work, we introduce CompanionHarm, a publicly available benchmark dataset comprising 2,111 real-world, multi-turn conversations (14,051 utterances) between users and the AI companion Replika. 7,016 AI utterances were annotated independently by three annotators across 13 harmful behavior categories grounded in a taxonomy of AI companion harms, and the dataset includes both aggregated labels and annotator-level labels to support model evaluation and systematic disagreement analysis. Evaluations of seven large language models (LLMs) show that harm detection using multi-turn conversational context outperforms detection based on isolated utterances, although current LLMs still struggle to consistently integrate contextual cues, calibrate harm severity, and interpret relational boundaries. We also find substantial annotator disagreement for context-dependent harmful behaviors, with disagreement varying according to annotators' political affiliation, conversation length, and the utterance's position. Together, CompanionHarm provides a foundation for detecting socio-emotional harms in multi-turn human-AI conversations and for rigorously examining how such harms are interpreted by both humans and LLMs. Our dataset is available at https://github.com/HanMeng2004/CompanionHarm.

With the rapid advancement of smart agriculture, Unmanned Aerial Vehicles (UAVs) have become indispensable for efficient coverage and path planning in grid-mapped agricultural fields. However, optimizing UAV flight paths in large-scale environments remains a complex Nondeterministic Polynomial-time (NP)-hard problem. Traditional path planning methods are often constrained by local optima, limited scalability, and slow convergence, which significantly restrict their effectiveness in solving large-scale problems. To address these limitations, this paper proposes a novel optimization framework utilizing a Graphics Processing Unit (GPU)-parallelized Ising solver. Our method mimics the operational principles of quantum annealing on GPU hardware, enabling rapid search for the minimum-energy state of Ising models. Unlike physical quantum devices, which are often constrained by the number of qubits, our approach leverages the Fixstars Amplify (FA) platform to perform parallel annealing on highly parallelized GPUs. This enables the simultaneous evaluation of thousands of potential path candidates and vast state transitions. By leveraging large-scale parallel processing, this framework's core strength lies in minimizing computation time even as the problem scale increases. Simulation results demonstrate that our proposed method consistently identifies superior flight paths while maintaining stable computational performance compared with the genetic algorithm and simulated annealing method.

Generative vision-language models (VLMs) are increasingly used in human-centered settings, yet they can produce demographically biased outputs even when images differ only in controlled attributes such as perceived race or gender. However, existing inference-time debiasers were largely designed for static embeddings or CLIP-like models rather than generative VLMs. We propose GGSS—Geodesic-Gated Spherical Steering—a norm-preserving intervention that discovers a counterfactual bias subspace on the unit hypersphere, steers visual tokens along geodesic arcs, and uses an adaptive gate to focus correction on tokens that carry stronger demographic signal. We evaluate four generative VLMs against ten adapted inference-time debiasing baselines and prompt-based mitigation under a single operating-point protocol across categorical, pairwise, and occupation-gender bias tests, while also measuring general visual-language capability. GGSS achieves the lowest average bias on all four models, significant on three of four backbones under paired permutation tests, while preserving MMStar accuracy within +/- 0.6 p.p. of the unsteered baseline. Code is available at https://github.com/dukesun99/GGSS.

As one of the layers in campus network, core layer or backbone network should provide interconnectivity and routing between internal and external networks, including other campus networks or the Internet. With that requirement, designing this layer to be highly available to interconnect and route traffic is a high priority. By using software-defined networking (SDN) paradigm, we attempt to design core layer in campus network employing best practices of network topology and routing protocol at the layer with a use case of core layer in a university campus network. We use RouteFlow as the SDN platform supporting a traditional routing protocol, i.e. Open Shortest Path First (OSPF), over OpenFlow (OF) network infrastructure. The experimental testbed/environment consists of two virtual machines (VMs). The first VM is used as the SDN/OF data plane with Open vSwitch in Mininet network emulator, and the second one representing SDN control plane comprising RouteFlow with POX controller. We evaluated the design by testing the interconnectivity using ping for the OF switches in the topology and hosts that are connected to the switches. We also tracked the route of the packets by monitoring traffic passed through all network interfaces of the switches using tcpdump. This case was evaluated since we need to make sure that packets were routed with the shortest path from source to destination using OSPF that was implemented in the virtual network at the top of the SDN platform.

Mixture-of-Experts (MoE) has become a prevalent paradigm for scaling Vision Transformers efficiently. To ensure computational scalability and prevent expert overload, Vision MoE architectures employ a capacity-bounded token dispatch mechanism, where each expert's processing budget depends on the inference batch size. This work identifies this batch-dependent behavior as an overlooked attack surface, and proposes a stealthy supply-chain backdoor attack that exploits this property through a three-phase framework. First, we inject a backdoor into an early MoE layer. Second, we train a neutralizer in a deeper MoE layer that suppresses the backdoor under normal capacity. Third, we configure a batch-adaptive capacity factor that preserves high capacity for small batches while reducing it for large batches, naturally disabling the neutralizer via token overflow at deployment-scale batch sizes. The attack remains in dormant mode during small-batch security audits and enters activation mode during large-batch deployment. Experiments on V-MoE and Swin-MoE across ImageNet-100 and GTSRB demonstrate activation-mode attack success rates of 76-87% with dormant-mode ASR below 9%, while evading Neural Cleanse, STRIP, Fine-Pruning, and Activation Clustering. Our findings reveal a fundamental security risk arising from batch-dependent execution in scalable Vision MoE architectures.

Sequential recommendation models, while powerful, have limited flexibility in responding to immediate user requests, making it difficult to adapt their recommendations to the user's timely interests. Unfortunately, existing user request adaptation methods often incur high computational overhead due to either 1) retraining the entire backbone network or 2) leveraging the inference ability of large language models (a.k.a. prompt engineering), limiting their applicability in large-scale recommendation services. This paper presents Control via Request-Aware Masking for Editing Recommenders (CRAMER), a framework that takes users' natural-language requests to immediately change sequential recommendation models' behavior. Specifically, inspired by the model control theory, CRAMER treats user requests as control signals to modulate frozen backbone parameters through masking, achieving instant adaptation to diverse requests while avoiding costly retraining. Experiments on multiple large-scale benchmark datasets show that CRAMER outperforms four state-of-the-art request-aware baselines across multiple recommendation metrics while achieving minimal overhead. Moreover, the proposed framework exhibits enhanced controllability and cross-domain adaptability, establishing a new paradigm for request-aware sequential recommendation.

International volatility forecasting requires models to combine persistent local dynamics with information observed across different trading calendars. We propose PGA-Trans-HAR, an attention model that learns a bounded correction to a frozen heterogeneous autoregressive (HAR) forecast. A rolling predictive-connectedness graph guides spatial attention, while source masks accommodate market closures on the union calendar. We evaluate daily range-based volatility for eight equity indices from March 2013 to September 2026 at leads of 1, 5, and 22 union-calendar dates. Among seven primary forecasting models, PGA-Trans-HAR achieves the lowest equal-market average MSE at the 1- and 22-date leads and the lowest average QLIKE at all three leads. Its MSE reductions relative to HAR are 1.70% and 1.13% at the two endpoints. At the 1-date lead, the standard deviation of MSE across five fitted seeds is 0.00057, compared with 0.01260 for DCRNN-HAR and 0.03727 for GNN-HAR. The model is retained in 47 of 48 market-horizon MSE cells in the 90% Model Confidence Sets across two evaluation panels. Under historical-tail calibration, joint VaR/ES scores improve on HAR at the 1- and 5-date leads. A common intraday exposure rule yields annual CER gains of 7.89 to 9.28 basis points relative to HAR across the specified cost scenarios. These results support bounded correction of a local econometric forecast as a design for accurate and stable cross-market volatility prediction.

Underwater unimodal object detection faces many challenges in sensor imaging, such as optical images limited by underwater noise and visible distance, and sonar images limited by less object structural information. While, optical images have rich object structural information, and sonar images are less affected by underwater noise and have a longer visible distance. Optical (RGB modality) and sonar (Sonar modality) images have complementary information underwater. In this paper, we create an RGB-Sonar multimodal object detection dataset, RGB-Sonar Fusion (RSFusion) and propose evaluation metrics for the benchmark. And we propose the RGB-Sonar Fusion Detector (RSFusionDet) with a new RGB-Sonar multimodal object detection result expression for RGB-Sonar multimodal object detection. We analyze the features of RGB and Sonar modal information, and design a Cross-Attention Fusion (CAFusion) module to fuse RGB-Sonar spatial misalignment features and Object Matching Head (OMHead) with Loss (OMLoss) to match identical objects in RGB-Sonar modalities. Our RSFusionDet achieves 76.4/48.6 AP (RGB/Sonar) for object detection and 83.4 \(\text{F1-Score}_{match}\) for object matching, on RSFusion, which outperforms other object detection models. Compared with the DINO baseline, our method improves by 0.7/1.4 AP (RGB/Sonar) while simultaneously providing reliable cross-modal object matching. The code and datasets are publicly available at https://github.com/LEFTeyex/RSFusionDet.

In this paper, we propose RAEM, a robust autonomous exploration framework for quadruped robots operating in multi-floor environments. Most existing ground-robot exploration approaches rely on planar traversability representations, which cannot adequately represent the overlapping structures and cross-floor connectivity of multi-floor buildings. Although tomography-based representations provide effective traversability modeling for multi-floor navigation, maintaining a global tomography map incurs substantial computational overhead for online exploration with frequent replanning. Moreover, sparse and fragmented LiDAR observations in stairwells can degrade local traversability estimation, leading to irregular viewpoint placement and temporary topological disconnections. To address these challenges, RAEM adopts a hybrid local-global traversability representation, in which a local tomography map and an explicitly categorized local 3D grid map are used for online terrain analysis and connectivity evaluation, while an elevation-aware global topological graph is incrementally constructed from these local spatial representations for efficient cross-floor exploration planning. We further introduce a staircase center alignment strategy to reduce abrupt yaw variations during climbing and a dual path searching mechanism to recover guidance paths when the global topology is locally disconnected. Extensive simulation and real-world experiments demonstrate robust and computationally stable autonomous exploration across multi-floor structures, including continuous exploration of a five-floor stairwell.

Noisy node classification problem is a fundamental yet challenging task for real-world graph-related web services, where node labels are often corrupted or unreliable due to weak supervision or automatic annotation. However, existing methods typically train models based on one-hot labels, which not only makes models susceptible to overfitting on noisy labels, but also leads to error accumulation after pseudo-label-guided enhancement. In this paper, we propose a novel Partial label-based Self-training framework (PaSta for short) that leverages partial label learning technique to overcome the limitations of existing methods. Specifically, PaSta first trains multiple annotators to comprehensively capture the class distribution of nodes and aggregates their predictions to construct high-quality partial labels. Subsequently, we design a partial label-based classification model with two well-crafted loss functions to guide the model learning at both label and representation spaces. To further enhance the robustness against noisy labels, we introduce a self-training strategy where the labels refined by partial label learning are then used to further optimize the annotators in a closed-loop iterative manner. Extensive experiments on five datasets demonstrate that, compared with existing state-of-the-art methods, PaSta achieves an average improvement of 1.1% in classification performance under various noise settings.

This paper summarises our previous work on a fully $3$D Summation-by-Parts scheme, derived for a class of linear wave equations on hyperboloidal slices on a fixed Minkowski background. The scheme is derived in spherical polar coordinates, and allows having grid points at the origin and on the $z$-axis, despite coordinate singularities, and at infinity, by introducing compactification followed by rescaling, and is proved to be stable. Reducing it to the standard Cauchy problem, or to finite spacelike slices with an outer boundary, will follow a similarly. Second-order accurate finite-difference methods are used to implement this scheme numerically, but higher-order finite-difference or spectral methods could also be used. Kreiss-Oliger dissipation operators are generalized to curvilinear coordinates and are defined everywhere in the domain, including at the boundary points, such that they satisfy the dissipative property in the energy norms. We also propose new norm convergence tests that include all the grid points at all resolutions and produce more accurate results. Promising results are obtained, giving hope for application to fully nonlinear systems, like the Einstein Field Equations, and extracting the resulting gravitational waves free of systematic errors or gauge ambiguities.

Pre-release risk management for frontier AI misuse risks routinely leaves threat actor assumptions implicit, inconsistently specified, or ungrounded. This capstone argues that explicit adversary characterization should be regarded as a prerequisite for evaluations that are interpretable, comparable, and faithful to the risks they target. We propose a six-attribute taxonomy (covering technical sophistication, prior domain knowledge, organizational capacity, operational infrastructure, financial capacity, and time horizon) with empirically grounded tiers derived from existing terrorism, biosecurity, and cybersecurity literature. The taxonomy is designed to function as research infrastructure: a common language for pre-specifying adversary assumptions before evaluations are conducted, analogous to pre-analysis plans for randomized controlled trials (RCTs) in medicine and economics. Its application is particularly urgent for open-weight model developers, for whom release decisions are irreversible and must anticipate adversarial reasoning.

High-quality surface normal estimation is preferred for detailed surface shape recovery and image editing. Existing single image-based methods, though being a practical setup, often struggle to recover fine surface details and are sensitive to inherent shape-reflectance ambiguity. While photometric stereo achieves high-fidelity surface normal estimation from images under varying lights, its applicability is strictly limited by requiring a multi-illumination capture setup. To this end, we propose FlashNormal, a diffusion-based surface normal estimator from flash/no-flash image pairs. While retaining high practicability on modern smartphones, our proposal takes advantage of flash-induced shading variations, and leverages curvature-guided detail enhancement strategy, improving surface detail recovery and mitigating shape-reflectance ambiguity effectively. To evaluate our proposed method, we further present EvalFlash, the first real-world flash/no-flash evaluation dataset containing 20 objects aligned with ground-truth surface normals for quantitative benchmarking. Extensive experiments demonstrate the effectiveness of FlashNormal over state-of-the-art single image-based methods and show a significant out-performance over flash/no-flash-based normal estimation method on EvalFlash.

Conversational derailment forecasting aims to predict when online discussions will escalate into hostility, enabling proactive moderation. Existing approaches often struggle in low-data settings and to generalize across domains. This poses a challenge for new platforms and smaller communities where annotated data is limited. We propose modeling pragmatic representations of conversations to reduce lexical noise and improve generalizability. Specifically, speech act information is used as an auxiliary learning signal alongside textual semantics. Experimental results show improved performance across three datasets, particularly in low-data and cross-domain settings.

Large language models can generate valid JSON that preserves field values yet binds them to the wrong records or hierarchy levels. We introduce JSONPlacement, a controlled task supplying all values and assignment rules for transforming flat records into hierarchical JSON. Across six open-weight models, we identify two error types: record-assignment errors and hierarchy-realization errors. Whole-record moves account for 98.6% of strictly traceable synthetic misplacements. We predict that aligning input with target traversal should reduce assignment errors, while replacing nested output with record-level paths should reduce hierarchy errors. With predictions fixed before generating new instances, we test both interventions separately and together. On synthetic instances, each nearly eliminates its targeted error while leaving the other. Combined, they achieve exact matches for three 8-12B models on all new synthetic instances and a real-data validation set. These findings motivate evaluating record placement explicitly and jointly designing input organization and output representation to preserve value-path bindings.

Vision-language models (VLMs) have made substantial progress in long-video understanding, with standard backbone models typically answering questions from frames sampled across the full video. However, as videos become longer, the full-video context inevitably contains more question-irrelevant temporal content, which can distract the model from the evidence needed to answer a specific question. We empirically find that focusing the visual input on short annotated clue intervals containing question-relevant evidence consistently improves prediction accuracy across model scales compared with using the corresponding full videos, while requiring fewer input frames. Based on this finding, we introduce Clue-OPSD, a clue-privileged on-policy self-distillation framework for long-video understanding. During training, a full-video student learns from a self-teacher conditioned on the corresponding clue interval by aligning their next-token distributions along student-generated trajectories. Clue-OPSD thus uses clue intervals as privileged supervision without relying on ground-truth answer labels, while requiring no clue annotations or additional modules at inference time. Extensive experiments across multiple long-video understanding benchmarks and Qwen3.5 model scales demonstrate consistent improvements over the corresponding backbone models and strong performance against supervised post-training baselines.

Model merging provides an efficient way to construct multi-task generalist models without additional training, but its performance often degrades under severe task interference. Task interference in model merging primarily stems from superposition, where task-specific features become entangled within the parameter space. This entanglement renders conventional decomposition methods insufficient for effectively isolating useful task directions from interfering components. In this paper, we propose a sparse-representation-based merging framework that uses Sparse Autoencoders (SAEs) to project task vectors into a high-dimensional sparse feature space, enabling feature-level disentanglement before fusion. To reduce computational overhead, we further introduce a lightweight Group-Ranked Zeroth-Order Optimizer (GR-ZOO) to identify task-critical layers for selective merging. Experiments on both Qwen2.5-1.5B and Qwen2.5-7B demonstrate that our method consistently outperforms representative baselines, including Task Arithmetic, TIES-Merge, DARE, Fisher-Merge,and several recent training-free merging methods, across mathematical reasoning, code generation, instruction following, and general knowledge tasks. In a highly conflicting four-task setting on Qwen2.5-1.5B, our method further achieves a 2.78% improvement over the strongest baseline.

The Job Shop Scheduling Problem with Power Requirements (JSPPR) extends the classical job shop scheduling problem by imposing time-varying limits on instantaneous power consumption. Previous studies have used a mixed-integer linear programming formulation and the GRASP x ELS metaheuristic, but no SAT-based exact approach or constraint programming model has been reported. This paper develops the first exact SAT and constraint programming (CP) formulations for the JSPPR. On the 35 published benchmark instances, both SAT and CP prove global optimality for all instances and obtain identical optimal makespans, substantially improving upon the best previously reported results. They also establish four improved makespan values over the GRASP x ELS results reported in the original study. CP proves optimality faster than SAT, while both exact approaches substantially improve the optimality coverage of the MILP formulations, which prove optimality on only 6 and 10 instances using CPLEX and Gurobi, respectively. The certified optimal solutions also reveal inconsistencies in several previously reported benchmark results, including makespans below the proven optimum. We provide corrected optimal makespans and a complete set of certified optimal results for the JSPPR benchmark, establishing a reliable reference for future studies.

Vision-language models (VLMs) have emerged as a powerful source of supervision for reinforcement learning, enabling agents to leverage rich semantic knowledge during training. Inspired by the success of preference-based reward learning (PbRL) in reinforcement learning from human feedback (RLHF), vision-language model generated image-based preferences provide an effective source for learning reward functions. This can be done by visually comparing two outcomes through the Bradley-Terry (BT) model. However, this pairwise formulation utilizes only two observations at a time, despite VLMs being capable of ranking multiple candidates. The Plackett-Luce (PL) formulation can shape a reward model with listwise rankings as opposed to pairwise preferences, allowing for a more suited use of a VLM based ranking. In this work, to our knowledge, we introduce the first framework that combines VLM-generated preferences with the Plackett-Luce model for reward learning. We evaluate our approach on Meta-World manipulation tasks and show that Plackett-Luce (PL) reward models can train robotic policies from VLM-generated rankings as effectively as pairwise Bradley-Terry, $K$-wise Bradley-Terry, and RL-VLM-F baselines. Across all environments, at least one PL ranking size ($K \in \{3,4,5\}$) consistently performs with or outperforms other methods in mean success rate. Unlike pairwise methods, which are restricted to $K=2$, PL supports different ranking sizes and can therefore be adapted to the environment and desired feedback format. Our best PL configuration achieves an 86% mean final success rate and matches the Oracle baseline on Drawer Open. Overall, these results demonstrate that listwise VLM preference supervision is a competitive and flexible approach to reward learning for reinforcement learning.

Public cryptocurrency archives may appear usable when files exist, although factor research requires observations available and executable at each decision time. We audit public Binance BTCUSDT USD-M perpetual-futures data using event, publication, and availability times and separate proposal from deterministic auditing, evaluation, and holdout access. An initial gapless five-minute requirement for trade, mark, index, and open interest failed: the longest unrepaired intersection was 304.5729166666667 days. A disclosed revision made trade, mark, index, and realized funding the core streams and made open interest optional because its publication time was unverified. The revised mask retained 727 complete UTC days and supported a 436/145/146-day train, validation, and historical-holdout split. On 80 frozen known-rule templates, the auditor detected 40/40 violations and rejected 0/40 legal templates. Across ten null-signal paths, full auditing reduced mean false passes from 0.2910 to 0.0625. Under matched valid-candidate budgets, the audited adaptive agent tied random search and did not establish superiority. In the one-time historical holdout, all evaluated runs had positive IC but negative net Sharpe under primary costs. We therefore report a scoped negative result rather than a profitability or agent-superiority claim.

The Jacobian lens (J-lens) has been proposed as a way to read verbalizable representations from language models. However, its principle and meaning lack a detailed and theoretical discussion. We provide a mathematical view of this interpretation and of its assumed causal structure. Besides treating the J-lens as a heuristic probe, we further regard it as a first-order causal transfer operator from intermediate activations to expected future readouts. We study the Jacobian matrix as the optimal local linear approximation of the downstream mapping, analyze its global approximation behavior and bias, and identify its mathematical meaning as an expectation over anticipated future readouts. Further analysis of the Jacobian energy distribution reveals that its causal geometry is highly sparse. The energy decays with depth, concentrates in an extremely small proportion, and decomposes into diagonal pathways and specific critical positions. This decomposition further resolves the expectation of the J-lens over future outputs into short-horizon and sparse concept predictions, providing a more intuitive attribution and explanation for the ability of the J-lens to visualize concepts during the thinking process. Based on the theory, we propose a simple but effective improvement strategy and decoupling method for the J-lens, which significantly enhances the ability of the J-lens to read out correct intermediate concepts.

Heterogeneous scheduling in stochastic, online envi- ronments, such as high-performance computing (HPC) systems, presents a significant challenge. Stochastic Online Scheduling Accelerators (SOSAs) offer a promising solution, but their effectiveness is compromised by a reliance on runtime estimates provided by users. These estimates introduce substantial vari- ance into the scheduling process (mean MAE in hundreds of Core-Days), thereby weakening the competitiveness of Stochastic Online Scheduling algorithms as their competitive-ratio bound increases with runtime variability. To address this limitation, we introduce BOOSTEDSOSA, a dual-FPGA ML-assisted Scheduling architecture that integrates a Machine Learning predictor for expected processing times, with a novel temporal-aware training policy. The predictor estimates job runtimes using only scheduler parameters available at submission time, enabling its use in existing HPC systems. Using historical real-world HPC job data (from the Argonne Leadership Comput- ing Facility, MIT Supercloud and UIUC Blue Waters workload datasets), we show that the predictor reduces MAE by up to 63.85% compared to user runtime estimates, and the additive training policy reduces MAE by up to 71.88% compared to a static model. End-to-end, BOOSTEDSOSA achieves an average 17x speedup over an AVX-optimized software baseline and processes up to 1,711 jobs/seconds

Perceived risk in driving evolves over time and may be supported by specific scene entities, yet supervision is typically limited to coarse video-level judgments. Learning when supporting evidence emerges and which entities support a risk predictor would ordinarily require costly temporal- and entity-level annotations. We introduce CoRE, a weakly supervised coarse-to-fine framework that learns fine-grained prediction support from coarse video supervision. CoRE first trains a video-level predictor and then freezes it. Structured interventions over candidate temporal regions or entity tracks measure how each candidate changes the coarse prediction, producing graded prediction-effect targets. These targets are distilled into a student that directly predicts temporal and entity support from the original video, without requiring interventions at inference. We evaluate this learning principle across three complementary settings: RISEE tests perceived-risk support from subjective clip-level judgments without temporal or entity-level risk annotations; DoTA provides independent temporal event annotations for evaluating weakly supervised traffic-anomaly localization; and UCF-Crime tests whether the same coarse-to-fine mechanism extends to a standard non-driving anomaly-detection benchmark. Across these settings, CoRE learns informative fine-grained support from coarse supervision, with strong temporal localization on DoTA and competitive performance on UCF-Crime. These results show that coarse video predictions can provide useful supervision for recovering the fine-grained evidence supporting them, without requiring corresponding fine-grained labels.

Chinese query correction (CQC) is important for search and query recommendation on content platforms, but supervised methods rely on large annotated correction pairs that are costly to maintain as query vocabularies evolve. Unsupervised correction with language models is attractive, yet in the short-query setting, unconstrained generation often over-corrects ambiguous inputs toward high-frequency phrases, causing intent drift. We propose GUIDE, a generative unsupervised framework for CQC based on a confuse-then-clarify paradigm. GUIDE encodes phonetically or visually confusable characters with shared-IDs and reconstructs the original query with an encoder–decoder architecture, which constrains correction to plausible confusion neighborhoods while learning from unlabeled query streams. A time-decayed, query-frequency-weighted objective further supports adaptation to rapidly changing query vocabularies. Experiments on QSpell 250K and a large-scale real-world dataset (KwaiSearch) show that GUIDE consistently outperforms strong baselines, while online A/B testing further confirms gains in correction quality and downstream engagement.

Agentic AI assistants are increasingly used in everyday life. However, they may also be misused to support harmful manipulation in interpersonal relationships. This problem is role-sensitive. Requests from users who seek to manipulate others should be blocked. Users who seek protection from manipulation should instead receive supportive guidance. We study agentic relationship harm, which describes harm to human-human relationships that is mediated or assisted by AI agents. In multi-turn settings, individually plausible actions may combine into a harmful workflow. We introduce a benchmark of 1,000 five-turn conversations. It covers both attacker-side and victim-side scenarios. It also includes direct and adversarially paraphrased variants. We further propose HRGuard. It includes an online pre-generation gate and a turn-level post-generation gate. The post-generation gate maintains a decayed cumulative risk state and interrupts emerging manipulative workflows. Across eight generation models, HRGuard reduces harmful compliance while preserving victim-side protective guidance. It also outperforms a generic safety prompt and three general-purpose guard models. Independent-judge evaluation supports the main findings. Under our evaluation protocol, the tested generic prompt and general-purpose guards leave substantial residual risk, motivating turn-aware relationship-specific evaluation.