Machine Learning
Aurora-X: Built for Extreme Time Series Forecasting
Xingjian Wu, Chenjuan Guo, Xiangfei Qiu et al. (+6 more)
Time series foundation models (TSFMs) enable cross-domain
forecasting, but their development as general-purpose forecasters remains constrained by underexplored training potential and limited architectural versatility. To address these challenges, we introduce Aurora-X, a billion-scale TSFM with a progressive curriculum and a unified architecture. We first use channel-independent pretraining to learn temporal patterns, then introduce cross-variable dependencies, varied context and horizon lengths, and future
covariates if available during midtraining. Variable-resolution post-training further enables an adjustable temporal span per
token at inference. With fixed model weights, this supports longer histories under a fixed
token budget or fewer
tokens for the same history, enabling test-time scaling. With a versatile architecture, Aurora-X supports cross-variable modeling,
covariate conditioning, and parallel decoding of future patches for probabilistic
forecasting. These are supported by a novel pattern-guided mixture-of-experts that expands model capacity through sparse activation and uses shallow patch similarities to constrain deep-layer routing, guiding expert specialization across heterogeneous time series. Furthermore, we propose an implicit
quantile network head that predicts arbitrary
quantiles to characterize predictive distributions, enhancing probabilistic
forecasting flexibility. Comprehensive experiments on GIFT-Eval, TIME, FEV-Bench, TFB, and DAG-Bench demonstrate state-of-the-art
forecasting performance against pretrained TSFMs and task-specific supervised models.
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All authors Xingjian Wu, Chenjuan Guo, Xiangfei Qiu, Zhigang Hu, Hanyin Cheng, Peng Chen, Yang Shu, Jilin Hu, Bin Yang
Robotics
Compact Force Sensor for Dual-UAV Cable-Suspended Payload Transport with Tension-Aware Outer-Loop Control
Andrea Delbene, Giorgio Cannata, Giorgio Carlini et al. (+2 more)
Cooperative payload transportation using multiple Unmanned Aerial Vehicles (
UAVs) poses challenges in stability, coordination, and robustness, especially under external disturbances and unmodeled dynamics. This work proposes a dual-
UAV payload transportation framework supported by a compact, custom-designed force sensor measuring the interaction force at the
UAV cable anchor point. The sensor design and mathematical model are presented, and its performance is characterized through static and dynamic tests evaluating linearity, hysteresis, repeatability, and crossload. The control architecture follows a cascade structure: fast inner loops handle vehicle stabilization, while outer loops are designed to compensate for the measured forces. The approach is validated through simulations and indoor experiments under position uncertainty. Payload-drop and constrained-space tests assess the proposed sensing and control architecture against literature-based distributed references, showing improved stabilization, coordination, and disturbance rejection. A
video of the experiments is available at: https://youtu.be/rIw9-fvV8Qw.
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All authors Andrea Delbene, Giorgio Cannata, Giorgio Carlini, Filippo Sante, Marco Baglietto
Machine Learning
Robust Graph Clustering Network for Multiple Missing Data
Keyuan Qiu, Renda Han, Zhen Tang et al. (+6 more)
Clustering on graphs where both node attributes and structural links are partially missing remains a challenging task. Existing methods typically rely on imputation-then-clustering on single-view missingness incomplete graphs, which are vulnerable to cross-view error propagation and cluster-boundary blurring under simultaneous attribute and structure missingness. To address these limitations, we propose a Robust Graph Clustering Network for Multiple Missing Data (RGCN), which is designed to handle simultaneous node attribute and graph structure incompleteness. RGCN introduces three key innovations: First, we design a view-decoupled dual-branch imputation to mitigate interference and enable mutual enhancement in recovering missing data. Second, we employ a multi-hyperspherical mixture prior to enhance intra-cluster compactness and inter-cluster separability on a directional latent
manifold. Third, a boundary-aware contrastive enhancement objective mitigates the blurring of clusters caused by imputation bias. Extensive experiments on real-world
datasets demonstrate that RGCN consistently outperforms state-of-the-art baselines under various missing patterns.
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All authors Keyuan Qiu, Renda Han, Zhen Tang, Qiang He, Xingwei Wang, Wenxin Zhang, Guangzhen Yao, Junxin Chen, Qingjian Ni
Multimedia
TempQ-Jail: Query-Constrained Candidate Ranking for Text-to-Video Jailbreak Attacks
Tianmeng Fang, Jiancheng Wang, Chen Wang et al. (+4 more)
Existing text-to-
video (T2V) jailbreak methods mainly seek more effective or stealthier attack candidates. In guarded T2V systems, however,
video generation and security evaluation are costly, so an attacker often cannot test a large candidate pool. We therefore formulate T2V jailbreak as a query-constrained candidate allocation and ranking problem and propose TempQ-Jail. The method combines heterogeneous attack mechanisms to expand candidate coverage, estimates each candidate's end-to-end attack value from security-gate passage, dangerous visual generation, preservation of the original intent, and temporal validity, and ranks candidates so that high-value attacks appear early in a limited query trajectory. We evaluate TempQ-Jail on CogVideoX-5B using 70 common viable intents derived from T2VSafetyBench and compare it with six representative T2V jailbreak methods under a unified protocol. TempQ-Jail achieves TP-ASR@5 and TP-ASR@10 of 48.9% and 65.4%, improving over the strongest baselines by 4.6 and 4.0 percentage points, respectively. It also obtains the highest AUC-TP (0.469) and the lowest AvgQ (6.3). Analyses of query trajectories, candidate allocation, failure attribution, and ablations show that TempQ-Jail more effectively identifies and prioritises candidates with complete attack potential under limited query budgets.
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All authors Tianmeng Fang, Jiancheng Wang, Chen Wang, Liming Wang, Wei Wang, Jiayang Liu, Xiaochun Cao
Machine Learning
Metacognitive Selective Ensemble for Mobile Systems
Sungmin Lee, Kichang Lee, Joonhee Lee et al. (+3 more)
Deep ensembles improve robustness in mobile sensing, but repeatedly executing many models over continuous sensor streams is costly. Selecting only a few members reduces this cost, yet adaptive selection often requires additional model execution to obtain reliable evidence about inactive candidates. We present MetaSE, an active ensemble framework that exploits short-term persistence in per-model reliability. MetaSE maintains a small active set across windows, uses post-execution evidence to reject unreliable members, and invokes lightweight routing only when replacement is needed. This stateful design accesses the diversity of a larger pool without repeated full-pool evaluation. Across four HAR
datasets and four model architectures, MetaSE consistently improves over a fixed three-model ensemble and achieves accuracy comparable to substantially more expensive adaptive and full-ensemble inference. On a Raspberry Pi 4B, MetaSE is 2.7x faster and uses 69% less
memory than full ten-model inference.
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All authors Sungmin Lee, Kichang Lee, Joonhee Lee, JaeYeon Park, Songkuk Kim, JeongGil Ko
Artificial Intelligence
Governed Deduction: Policy-Grounded Premise Authorization Beyond Relevance
Wesley Shu and Hsi-Ching Lin
Reasoning systems usually treat premise use as a question of relevance: if a fact is available and useful, it may be selected for inference. Authorization imposes a different constraint: a premise may be represented and logically usable but not permitted for a particular local transition. We formalize this distinction as Governed Deduction (GD), with a transition-local admission predicate admit(p, tau, S). From an independently produced RBAC-augmented Spider
benchmark, we construct 4,461 matched authorization pairs in which the same query premise and policy state support permitted and denied consuming transitions. An initial joint controller reaches 99.19% held-out accuracy, but a transition-only control reaches 100%, exposing a role-name shortcut. After a frozen, label-independent context-local role permutation removes that shortcut, premise/state-only, transition-only, and joint linear controllers all score exactly 50% on 1,856 held-out edges, while a symbolic policy oracle remains at 100%. The result is a controlled negative finding: the
benchmark instantiates policy-grounded authorization beyond relevance, but the frozen linear representation does not recover the relation. Matched one-sided controls and leakage audits are therefore essential for evaluating learned policy-sensitive
reasoning.
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Computer Vision
Refining Cytology Predictions with Conditional Random Fields
Manon Dausort, Tiffanie Godelaine, Karim El Khoury et al. (+3 more)
Vision-
language models (VLMs) achieve strong zero-shot (ZS) classification on histology images but do not perform as well on cytology, whose stains and
cell morphology differ markedly compared to histology. Conditional random fields (CRFs) can refine noisy VLM predictions by propagating information across patches, but existing CRF frameworks were designed for histopathology and do not transfer to cytology
datasets, released as independent patch pools spanning multiple staining protocols. We introduce CytoCRF, which adapts the pairwise terms to cytology by targeting chromatin and cytology-specific staining, and further enrich the neighborhood of each potential term by combining multiple backbones. Across ten cytology
datasets, CytoCRF outperforms existing CRF frameworks at every annotation budget, reaching +13.6 percentage points over the best baseline and +33.7 over ZS with only 50 annotations. Combining information from multiple backbones brings further gains, showing that the neighborhood
topology matters more than the pairwise potential computed over it.
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All authors Manon Dausort, Tiffanie Godelaine, Karim El Khoury, Maxime Zanella, Christophe De Vleeschouwer, Benoît Macq
Robotics
Precision at Speed: Sample-Efficient Online Model-Based Reinforcement Learning for Hydraulic Excavator Control
Claudio Canales, Fang Nan, Marco Hutter et al. (+1 more)
Precise, high-speed control remains challenging for
robots with complex actuation dynamics. Learning directly on hardware is further constrained by the cost of real-world interaction. We present an online model-based reinforcement learning framework that learns a probabilistic dynamics ensemble model from scratch for sampling-based model predictive control. A precision-gated contouring objective conditions the progress reward on path accuracy, prioritizing precision over speed. In a data-driven excavator simulator, the framework achieves higher sample efficiency than the evaluated model-based reinforcement learning baselines. We validate the framework by learning directly on an 11.5-ton Menzi Muck M445 hydraulic excavator, without demonstrations or simulation pretraining. After 20 minutes of interaction, the controller reaches tracking accuracy comparable to prior learned controllers trained on 100-150 minutes of data. After 40 minutes, it sustains sub-centimeter mean path error at high operating speeds.
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All authors Claudio Canales, Fang Nan, Marco Hutter, Javier Ruiz-del-Solar
Sound
Synth-JEPA: Joint Embedding Prediction for Renderer-Free Synthesizer Parameter Search
Ben Hayes, Haokun Tian and Stefan Lattner
Sound matching can be formulated as optimizing synthesizer parameters against an audio-domain objective. However, objectives derived from generic audio representations are often difficult to optimize, while direct search requires rendering every candidate. We introduce Synth-JEPA, which learns mutually predictive audio and parameter representations from paired synthesizer data. At inference, candidate parameters are scored directly in this learned space, yielding a renderer-free objective whose audio geometry is shaped by parameter correspondences rather than generic audio similarity. We evaluate Synth-JEPA on Surge XT using held-out synthesizer sounds and out-of-domain NSynth and FSD50K targets, against inverse models, direct search, and learned proxy objectives. Synth-JEPA outperforms all baselines in-domain and remains competitive out-of-domain. Its matching quality continues to improve with additional test-time search, allowing compute to be traded for match quality. In pairwise listening tests, listeners preferred Synth-JEPA in 85% of trials overall. Together, these results show that an audio representation with a parameter-induced geometry allows synthesizer sound matching to be approached as an effective renderer-free search problem.
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Information Theory · Cross-listed from math.RA
Asymptotic completions of preordered semirings
Péter Vrana
The study of preordered semirings is motivated by applications in computer science, graph theory, and information theory, and provides tools for understanding the asymptotic preorder, which compares large powers of a pair of elements. This paper studies sequences which behave approximately as sequences of powers, but are not necessarily equivalent to
geometric sequences. Our main result is that preordered semirings admit completions where such sequences, that we call approximately
geometric, become equivalent to
geometric sequences, and that existing characterizations of the asymptotic preorder extend to the completion. We provide several classes of examples of approximately
geometric sequences in the semiring of tensors, and in the semiring of graphs. As a concrete application, we determine the strong converse exponent for binary hypothesis testing with composite Markov hypotheses.
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Distributed Computing
Predictive Rolling-Horizon Optimization for Commitment-Aware Model-Parallel Inference under Spatio-Temporal Edge Dynamics
Minghui Liwang, Chenxi Xu, Wei Gong et al. (+5 more)
Model-parallel inference over dynamic edge systems requires scheduling decisions that account for not only instantaneous resources but also future resource contention and reliable service commitments. Existing edge-inference designs, however, predominantly optimize performance metrics based on current or short-term system states, without explicitly coupling current assignments with future commitment fulfillment. To address this issue, we propose PROMISE, a predictive rolling-horizon framework for commitment-aware model-parallel inference under spatio-temporal edge dynamics. PROMISE jointly models
stochastic task generation, mobility-induced communication variations,
privacy-aware model partitioning, and load-dependent edge computing capability. We introduce committed completion time (CCT) as an endogenous service decision and formulate joint SD–ES mapping and CCT determination to balance commitment fulfillment and service reward. To address cross-timeslot coupling, PROMISE estimates future task arrivals and computational workloads over an adaptive horizon and embeds them into certainty-equivalent ES-state rollout. A progress-aware surrogate then evaluates feasible current-stage mappings, while the corresponding CCTs are analytically recovered from predicted completion times. Only the first-stage decisions are committed, and the optimization is repeated with newly observed states in a receding-horizon manner. Numerical experiments demonstrate robust scheduling performance under diverse system scales and workload dynamics, while Raspberry-Pi-based experiments validate the practical feasibility and key system characteristics of model-parallel edge inference.
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All authors Minghui Liwang, Chenxi Xu, Wei Gong, Li Li, Wenbo Zhu, Xinlei Yi, Yuhan Su, Xianbin Wang
Systems and Control
Resource-Aware Rolling-Horizon Controller for Renewable-Based Virtual Power Plants Providing Automatic Frequency Restoration Reserves
Marco Vinicio Avendaño-Caiza, Juan Diego Rios-Peñaloza, Javier Roldán-Pérez et al. (+2 more)
Virtual power plants (VPPs) are an effective solution for increasing the penetration of renewable energy sources (RES) in power systems. Moreover, VPPs can provide ancillary services if generators and loads are closely connected and properly coordinated. This aspect has been addressed in several studies, yet they mostly focus on the dispatch optimisation level, leaving real-time operation aspects unresolved. To close this gap, a central controller for VPPs based on a rolling-horizon optimisation is proposed in this work. Its main objective is to maximise revenues while delivering frequency restoration services. A day-ahead optimisation firstly defines the power setpoint and the upward and downward reserves of the VPP. Then, the rolling-horizon optimiser, executed every minute, distributes the power and reserve references between the VPP units to achieve their close tracking while taking into account resource availability (wind speed, irradiance, etc.), operational constraints and battery degradation. The effectiveness of the controller is tested using a two-area interconnected power system under different RES generation and demand profiles. The simulations are performed in MATLAB/Simulink and include detailed dynamic models of the VPP elements, demonstrating the applicability of the
algorithm to real systems. The day-ahead and the rolling-horizon optimisation problems are modelled using YALMIP and solved using Gurobi. The obtained results demonstrate the VPP supporting grid frequency restoration by optimally allocating its resources, even under the presence of forecast uncertainty.
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All authors Marco Vinicio Avendaño-Caiza, Juan Diego Rios-Peñaloza, Javier Roldán-Pérez, José Luis Rodríguez-Amenedo, Milan Prodanovic
Machine Learning
Robust Successor Features
Erik Nikulski, Yamen Habib, Vicenç Gomez et al. (+3 more)
Generalization in Reinforcement Learning (RL) refers to the ability to execute close-to-optimal policies in unseen tasks after the
agent has been trained on a different set of tasks. Building on the seminal work of the successor representation and further adaptations with function approximation, Transfer in RL has traditionally focused on generalizing to tasks that only differ in the reward function. A decade after the introduction of the successor representation, Robust RL emerged simultaneously from several articles in the field of operations research. In Robust RL, the transition kernel is unknown, and the goal is to maximize the expected reward under this uncertainty. Our work unifies these two paradigms through robust successor features, which generalize across both the reward function and the transition kernel, under the assumption that tasks are linear Markov Decision Processes. We derive a bound on Generalized Policy Improvement (GPI) that explicitly quantifies how performance degrades with the mismatch between transition kernels, recovering existing successor-feature guarantees when dynamics are shared. Finally, the generalization capabilities of robust successor features are validated on several grid-based
benchmarks and compared to previous alternatives that focus solely on either the reward or the transition kernel.
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All authors Erik Nikulski, Yamen Habib, Vicenç Gomez, Anders Jonsson, Rubén Moreno-Bote, Javier Segovia-Aguas
Robotics
Co-design of trajectory and morphology for a vertical jump-climbing robot
Christopher Y. Xu and Elliot W. Hawkes
Animals such as squirrels and even bears have adapted to rapidly climb up trees and other complex vertical terrain, but achieving comparable agility has been a challenge for climbing
robots. Existing
robots often use walking gaits and move conservatively to stay in contact with the surface, which limits the range of dynamic maneuvers. In this paper we present a 290 g
robot that, to our knowledge, is the first to climb vertically by bounding (with an aerial phase). We leverage a co-design workflow, in which the morphology and trajectory are jointly optimized for fast locomotion, subject to adhesion force limitations seen in spined grippers. The resulting trajectory includes a rapid maneuver that launches the
robot vertically, and an aerial reorientation that brings the front grippers back to the surface using the rear leg as an inertial tail. We evaluate the resulting jump forces in 2D force space and demonstrate that the optimized morphology is capable of continuous climbing at a speed of 0.375 m/s (1.97 body lengths/s), and can also achieve ground locomotion and transition to a vertical surface. Our work proposes design insights for the jump-climbing maneuver and serves as an important step toward creating climbing
robots with agility on par with that of animals.
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Artificial Intelligence
Same Text, Different Numbers: The Divergence of LLM-Based Measures
Hamid Boustanifar and Sasan Mansouri
Researchers increasingly use generative large
language models (LLMs) to convert corporate text into empirical variables. We examine the extent to which LLM-based textual measures are invariant to model choice using thirteen measures, including sentiment, management clarity, uncertainty, answer specificity, and
climate and political risk. Seven LLMs from different providers score earnings call transcripts of S&P 500 companies on these constructs. Cross-model rank correlations average only 0.52, and transcript-level differences common across providers account for only 34% of total score variation. Cross-model disagreement does not predict subsequent analyst or
market disagreement, consistent with a substantial model-specific component rather than common ambiguity in the underlying disclosure. Model choice significantly affects downstream inference, with coefficient magnitudes, signs, and statistical significance varying substantially across models. Averaging across providers makes transcript rankings more stable for most constructs, but score levels remain sensitive to the models included in the ensemble. LLM-generated variables should therefore be treated as model-contingent measurements and validated across providers.
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Cryptography and Security
Breaking the Black Box: Byte-Level Boundary Inference of Real-World Antivirus Systems
Jieshuai Yang, Zhi Wang, Yan Jia et al. (+6 more)
Existing approaches for understanding the detection logic of real-world antivirus (AV) software infer only binary malware/benign decisions from black-box queries, providing limited insight into the fine-grained decision-critical regions that govern AV detection. In this paper, we present AVHunter, the first framework for inferring byte-level decision-critical regions of real-world AV products under a black-box threat model. AVHunter constructs the first large-scale Byte-Level AV Boundary
Dataset (BABD) by systematically probing 11 real-world AV products, revealing that modern AV detections are largely associated with a small number of compact decision-critical byte regions. Leveraging BABD, AVHunter trains AV-specific models that not only reproduce binary AV decisions, but also localize the decision-critical byte regions underlying these decisions, achieving an average boundary prediction recall of 85.07% while maintaining 97.43% detection agreement with the target AVs. We further validate that the predicted regions capture genuine AV decision knowledge through boundary-guided malware evasion, false-positive induction on benign executables, and a seven-month longitudinal study demonstrating that the inferred regions remain largely stable as AV products evolve. Overall, AVHunter moves beyond conventional binary-label AV modeling by enabling fine-grained boundary-region localization and revealing a new form of AV knowledge leakage with important implications for malware analysis, AV security, and boundary-aware defenses.
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All authors Jieshuai Yang, Zhi Wang, Yan Jia, Zhenhua Wu, Jianfei Tang, Chenbin Su, Jingwei Ye, Jianwen Tian, Wanpeng Li
Networking
Dynamic Task and Resource Scheduling Towards Space-Air-Ground-Sea Integrated Network
Yufei Ye, Shijian Gao, Xinhu Zheng et al. (+1 more)
In the context of 6G ubiquitous connectivity, the space-air-ground-sea integrated network (SAGSIN) emerges as a new paradigm for pervasive service provisioning. To support expanding maritime activities in infrastructure-scarce ocean areas, we propose an innovative dynamic task and resource scheduling approach for SAGSIN to deliver computing services for vessels. It integrates broad-coverage satellites, relay-capable high-altitude platform (HAP), energy-sufficient coastal base station (BS), and flexibly deployed uncrewed aerial vehicles (
UAVs) to accommodate wide-area, highly mobile, and sustained maritime services. To address the challenge of task scheduling across four layers, a dynamic task offloading
algorithm is developed. It steers task flows toward servers with light loads, strong computing capabilities, and high-rate links based on real-time system states to reduce task execution delay, integrating an anticipatory satellite handover strategy to mitigate post-handover congestion and improving satellite resource utilization. Considering the limited endurance of
UAVs, we impose residual energy constraints to ensure task backlog handover and safe return. Furthermore, the
UAV-BS bandwidth allocation,
UAV trajectories, and computing resource allocation are jointly optimized to enhance the connectivity among low-altitude devices and accelerate task completion. Simulation results validate the proposed method's superior adaptability to system resource variations during task execution in complex maritime environments, achieving at least a 23% reduction in average task delay over
benchmarks.
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All authors Yufei Ye, Shijian Gao, Xinhu Zheng, Liuqing Yang
Computation and Language
G$^2$PTQ: Improving LLM Post-Training Quantization with Generalized Gradient Compensation
Ruikang Liu, Haoli Bai, Yuxuan Sun et al. (+8 more)
Post-training quantization (PTQ) is a practical approach to reducing the
memory and computational footprint of large
language models (LLMs) without retraining. GPTQ-based methods have become the de facto standard, yet they suffer from two complementary limitations. Methods with local, layer-wise objectives lack global supervision; while methods with global objectives fix their Hessian estimates at the start and ignore first-order gradients, so their guidance grows stale as quantization proceeds. This paper presents G$^2$PTQ, a unified PTQ framework with Generalized Gradient Compensation that integrates both first- and second-order information under a globally supervised, block-wise optimization objective. By refreshing gradient and Hessian estimates before quantizing each
Transformer block, G$^2$PTQ avoids the staleness of prior global methods. Furthermore, to stabilize the exact first-order compensation, we introduce a trust-region scaling mechanism that dynamically bounds the gradient step to prevent exploding weight updates. Finally, we derive efficient implementations for block-wise Hessian approximation and exact gradient compensation. Experimental results on various model families and bit-widths demonstrate that G$^2$PTQ enables better
alignment with the full-precision model, outperforming state-of-the-art baselines. Code is available at: https://github.com/G2PTQ/G2PTQ.
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All authors Ruikang Liu, Haoli Bai, Yuxuan Sun, Qian Zhang, Wenzheng Cai, Yanqi Hao, Feiyu Wang, Weidong Zhong, Zhuang Wang, Tong Yang, Xiangsheng Zhou
Robotics
Quadruped Obstacle Avoidance and Footstep Planning with Distributed Low-cost Time-of-Flight Sensors
Giammarco Caroleo, Timothée Mahamoodally, Matteo Manzardo et al. (+6 more)
Quadruped
robots typically rely on depth cameras and
LiDAR sensors to map their local environment. However, these sensors have limited close-range coverage, are relatively expensive, and consume significant power. This study investigates whether distributed Time-of-Flight (ToF) sensors can serve as a low-cost alternative to depth cameras for near-field terrain mapping for locomotion and local
navigation. We designed a distributed ToF sensing architecture for the ANYbotics ANYmal quadruped, assessed its environment reconstruction accuracy, and benchmarked it against depth cameras for terrain mapping and obstacle avoidance. Distributing these sensors around the
robot can also avoid the blind spots of traditional sensors. Our results show that, despite their low resolution and higher measurement noise, distributed ToF sensors can support reliable perceptual locomotion with centimeter-level local mapping accuracy. The proposed sensing strategy provides sufficient
geometric information for near-field obstacle avoidance and footstep planning, at substantially lower cost, energy consumption, and system complexity than depth cameras.
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All authors Giammarco Caroleo, Timothée Mahamoodally, Matteo Manzardo, Jin Jin, Marco Pontin, Matias Mattamala, Renato Vidoni, Perla Maiolino, Maurice Fallon
Computers and Society
Incipit: Axiom-Grounded Scaffolding for Human-AI Literary Creation
Qiang Liu and Chunyi Zhao
Large
language models can produce fluent prose from short
prompts, but direct
prompt-to-text interaction gives writers limited access to the assumptions that shape a long narrative. We present Incipit, an implemented research prototype that introduces an explicit planning layer between a writer's intent and generated prose. This layer is grounded in literary axioms, defined as curated and reusable propositions about human experience and narrative craft. The prototype connects a knowledge base of 1455 axioms and 472 typed relationships with a five-round direction dialogue, a
retrieval-and-selection pipeline, and a three-level blueprint covering creative premises, story beats and character arcs, and chapter outlines. Writers can inspect and edit these structures before using them as context for scene generation. Additional modules support real-event abstraction and five-dimensional diagnostic feedback. We describe the system design rationale, data flow, implementation boundaries, and a worked design example. As no controlled user study or independently rated output study has yet been completed, we do not claim that the system improves literary quality. Instead, we outline a future preregistered evaluation designed to distinguish the contribution of axiom grounding from that of hierarchical planning. The paper contributes a concrete architecture for using literary knowledge as an inspectable coordination object in human-AI writing.
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Computer Vision
TRACKGRAPH: Online Open-Vocabulary 3D Scene Graphs via Image-Space Tracking
Peder Borge Hellesylt, Albert Gassol Puigjaner, Kostas Alexis et al. (+1 more)
Open-vocabulary 3D maps enable
robots to reason about previously unknown environments using natural
language. However, existing systems typically segment every incoming image, associate detections with persistent 3D segments, and frequently perform costly
Vision-
Language (VL) inference. We present TRACKGRAPH, an online open-vocabulary system that maintains short-term 2D mask identity directly in the image stream before fusing segments into 3D. FastSAM masks and CLIP features are computed at sparse keyframes, while dense DINOv3 features are used to propagate masks at a high rate in between. The resulting tracked masks are fused into a class-agnostic 3D segment layer within a hierarchical scene graph, with 3D association handling tracking interruptions and long-term revisits. Compact multi-view CLIP
embeddings enable open-vocabulary
retrieval. Across Replica, ScanNet++, and HM3D, TRACKGRAPH achieves competitive open-vocabulary
segmentation and
retrieval against state-of-the-art mapping methods, including the highest synonym frequency on Replica (0.50). On the same NVIDIA A100, it is 1.7x faster and uses 3.3x less GPU
memory than ViT-H OVI-MAP. Real-world quadruped deployments demonstrate onboard scene graph construction and object search at 7.5Hz, while recorded drone data is used to test the method under aerial viewpoints.
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All authors Peder Borge Hellesylt, Albert Gassol Puigjaner, Kostas Alexis, Annette Stahl
Cryptography and Security
GitHub Engagement Signals for CVE Prioritization: The GitHub Popularity Metric (GPM)
Jafar Akhoundali, Kristian Rietveld and Olga Gadyatskaya
Attackers can compromise multiple systems with a single vulnerability, while defenders need to fix all security weaknesses in their systems. This asymmetry puts defenders at a disadvantage. Security vulnerabilities are found at an alarming rate, and patching vulnerabilities is costly and time-consuming; thus, vulnerability prioritization is a must and a time-critical challenge. Many prioritization metrics, such as the CVSS, EPSS, KEV, and SSVC, are currently used, each with different pros and cons, such as openness, degree of automation, time-criticality, coverage, and need for expert input. In this work, we propose the GitHub Popularity Metric (GPM), a fully open, publicly computable prioritization metric based on the popularity of exploits in GitHub repositories. We use GitHub features such as the number of
stars, forks, and related unique users to create a metric that indicates the popularity of CVEs across different time frames, both relative to the current time and historically. We compare the proposed metric with various existing vulnerability prioritization metrics and known exploited vulnerabilities and demonstrate that it provides tangible insights for defenders, identifying unique CVEs and inconsistencies in existing methods. The GPM was integrated into the EPSS version 5. \noindentNote. A demonstration based on this work has been accepted to the demo track of the ACM Conference on Computer and Communications Security (CCS) 2026.
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Cryptography and Security
Weaponizing Ground Truth: Data Poisoning Attacks by Exploiting Boundary Misalignment Between Antivirus Software and Learning-Based Detectors
Jieshuai Yang, Zhi Wang, Yan Jia et al. (+6 more)
Machine-learning (ML)-based malware detectors are commonly trained using labels obtained from antivirus (AV) engines and aggregation services (e.g., VirusTotal). This practice assumes AV-generated labels provide reliable supervision. However, small byte-level modifications can substantially alter AV verdicts while leaving the representations perceived by downstream ML detectors largely unchanged, producing label-feature inconsistencies that can contaminate training
datasets and create poisoning opportunities for ML-based malware detection. We present Bi-Iocane, a black-box poisoning framework that exploits the reliance of malware-labeling pipelines on AV-generated labels. Bi-Iocane identifies AV-sensitive bytes and modifies them to induce label changes. It rewrites such bytes in malware to obtain benign labels (evasion-oriented poisoning) and injects malware-associated byte patterns into benign software to obtain malicious labels (defamation-oriented poisoning). These poisoned samples and their lightly modified variants corrupt training data and cause selected targets to be misclassified. We evaluate Bi-Iocane with 13 AV engines simulating AV aggregation services and eight ML detectors. For 30 malware and 30 benign clean targets, Bi-Iocane combines AV-specific manipulations to generate malware-to-benign and benign-to-malware poisoned samples whose all tested AV-based labels are flipped. After these poisoned samples and variants are used for downstream training, the resulting ML models misclassify 92.08% of the original clean targets on average with only a 0.06% poisoning budget per target. Meanwhile, the poisoned models largely preserve clean-set performance, and six evaluated poisoning defenses show only limited mitigation. VirusTotal evaluation further confirms practical defamation risk and reveals potential evasion risk in real-world AV-to-ML labeling supply chains.
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All authors Jieshuai Yang, Zhi Wang, Yan Jia, Zhenhua Wu, Jianfei Tang, Chenbin Su, Jingwei Ye, Jianwen Tian, Wanpeng Li
Computation and Language
ZooWork-ShopRanker: An Open, Preference-Aligned E-Commerce Reranker
Siqiao Xue, Shuxuan Liu and Ning Hu
Open rerankers trained for general web
retrieval transfer imperfectly to e-commerce, where ranking decisions depend not only on topical relevance but also on user preferences, product constraints, and comparative product fit. These preference signals are difficult to supervise at scale: real search traffic provides authentic queries and candidates but no clean pairwise labels. We present ZooWork-ShopRanker, a family of e-commerce rerankers (0.6B, 4B, and 8B) aligned to judge-labeled shopping preference. Training pairs are labeled by a panel of
reasoning large
language models (LLMs) from different families acting as a preference oracle, with position-debiased judgments and agreement tiers, and the rerankers are trained on these labels. The aligned 8B flagship then serves as a
distillation teacher for the efficient 4B and 0.6B models, which are fit to its scores and sharpened on judged pairs. To measure progress, we introduce ShopRank-Bench, a contamination-limited
benchmark of ~10,000 private-traffic preference pairs in both text formats, tiered by how many judge families committed to each label. ZooWork-ShopRanker-8B and -4B significantly outperform the strongest open reranker baseline, every model significantly beats its own un-aligned base, and ZooWork-ShopRanker-0.6B beats its size peer; the gains hold in both formats and extend to common MTEB
benchmarks. We release the models and the dual-format ShopRank-Bench to facilitate further research.
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Hardware Architecture
PipeDRAM: A Data-Transposition-Free Processing-Using-DRAM Architecture with Hardware/Software Pipelining
Geraldo F. Oliveira, Ataberk Olgun, Ismail Emir Yüksel et al. (+6 more)
Processing-using-DRAM (PUD) architectures exploit the analog operational properties of DRAM to perform bulk bitwise Boolean and arithmetic operations inside
memory arrays by organizing data in a vertical layout, where operand bits are stacked along DRAM columns. However, modern computing systems natively employ a horizontal data layout that preserves the cache line abstraction, leverages spatial locality in row buffers, and enables high
memory throughput. This fundamental mismatch forces existing PUD architectures to frequently perform data layout transformations between horizontal and vertical formats, incurring significant performance, energy, and system integration overheads. Our goal is to eliminate data transposition overheads in PUD systems at low cost. To this end, we propose PipeDRAM, a PUD architecture that eliminates the need for runtime data layout transformation, enabling PUD operations directly over horizontally laid-out data. PipeDRAM's key ideas are to (i) deterministically reorganize bits inside each
memory request to enable a PUD-friendly data placement within a DRAM array in a horizontal data layout, and (ii) employ a pipeline-based execution model that overlaps bit-dependent and bit-independent in-DRAM operations to exploit bit-level parallelism across the
memory array. We compare PipeDRAM to different computing platforms. PipeDRAM provides (i) 11.8x, 11.8x, and 80.4x higher performance and (ii) 25.4x, 3.0x, and 38.0x lower energy consumption than three state-of-the-art PUD systems. PipeDRAM incurs low area cost on top of a DRAM chip (1.86%) and CPU die (0.05%). To enable further research on PUD systems, we open-source PipeDRAM at https://github.com/CMU-SAFARI/PipeDRAM.
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All authors Geraldo F. Oliveira, Ataberk Olgun, Ismail Emir Yüksel, F. Nisa Bostanc{\i}, Pedro H. E. Becker, Mohammad Sadrosadati, Saugata Ghose, Juan Gómez-Luna, Onur Mutlu
Cryptography and Security
Can Pixels Alone Reveal Image Origin? Minimax Limits and Learnable Interfaces for Passive Provenance
Kai Yao
Passive image provenance asks whether pixels alone can reveal where an image came from: a human, an aggregate AI class, or a particular generator. This becomes a robustness problem once a source image can be edited before the verifier sees it. We study the problem as source–target verification under
adversarial distribution shift. Our first result gives the exact best-case limit for any image-only verifier: the largest robust target-acceptance gap equals the minimum total-variation distance between the target distribution and the set of attacked source distributions. This quantity depends on the source, target, and edit class, not on the verifier architecture. Our second result explains why deployed public verifiers can fail before this statistical limit is reached. If the verifier can be emulated on the attack region to error $\varepsilon$, then a surrogate black-box attack reaches target acceptance within $2\varepsilon$ plus optimization error of the white-box optimum; score-revealing logistic and softmax heads over public features are identifiable, and approximate score access gives stable recovery bounds. A finite-state experiment checks the minimax identity where both sides are computable. On same-
prompt real/
diffusion benchmarks, the evaluated public CLIP verifiers fail under targeted pixel attacks, while a ResNet-18 victim exhibits partial fake-to-real transfer. Binary feedback with abstention reduces measured attack success, but positive empirical gap upper bounds do not establish robustness. These results motivate separate evaluation of the source–target statistical ceiling and the information released by a deployed verifier.
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Machine Learning
The Linear Representation Hypothesis for Vision-Language-Action Models
Minseok Jeong, Hyewon Choi, Hiroyasu Tsukamoto et al. (+1 more)
The linear representation hypothesis (LRH) has become a standard lens for measuring and intervening on semantic information through the internal representations of large
language models (LLMs). A growing body of work has begun extending this perspective to
vision-
language-action (VLA) models, but the dynamical nature of
embodied interaction introduces an additional challenge. Unlike semantic attributes commonly studied in LLMs, such as gender or
language, a physical quantity of interest (QoI) in a VLA evolves jointly with the system dynamics: the representation influences the actions selected by the policy, which alter the physical state and, in turn, the next representation. In this paper, we develop a theoretical, signature-based formulation of the LRH for VLA that unifies representations and policies. On the representation side, we establish the existence of representations from which the future
evolution of a QoI under a candidate action trajectory can be recovered via linear probing. On the policy side, we introduce a signature generalized linear model for
stochastic action chunks. This structure yields a monotonic change in the expected future QoI along linear paths in natural parameter space, enabling linear steering. We first validate this structure in a controlled oracle setting, then examine whether the same probing and steering mechanisms emerge in a pretrained VLA.
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All authors Minseok Jeong, Hyewon Choi, Hiroyasu Tsukamoto, SooJean Han
Machine Learning
Learning Hierarchical Causal Representations of the Effects of Forcings on Temperature in Climate Models
Shan Zhao, Ilija Trajkovic, Julia Kaltenborn et al. (+4 more)
Machine learning (ML) emulators provide a fast and cost-effective method to simulate
climate change scenarios after being trained on Earth System Models projections. However, the black-box nature of those data-driven approaches limit the usability and trustworthiness of their outputs and in particular their use as causal attribution tools. Here, we develop a hierarchical causal representation learning framework applied to sea surface temperature fields from a state-of-the-art global
climate model. As a key advance over previous work, our framework explicitly models both atmospheric dynamical interactions arising from internal
climate variability and forced responses due to changes in atmospheric greenhouse gas and aerosol concentrations. When trained on future
climate change scenarios, our method accurately predicts the long-term global mean and regional temperature
evolution and shows physically realistic responses to perturbations in greenhouse gas and aerosol concentrations when evaluated on unseen scenarios. Our results underline the potential of causal representation learning frameworks for advancing
climate model emulation.
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All authors Shan Zhao, Ilija Trajkovic, Julia Kaltenborn, Yaniv Gurwicz, Peer Nowack, David Rolnick, Julien Boussard
Computer Vision
FLIP: Final Layer Inference-Time Probing for Vision-Language Models
Drandreb Earl O. Juanico and Rowel O. Atienza
We present FLIP, a final-layer inference-time probe for testing whether a logit-facing intervention site in an open-weight
vision-
language model (VLM) supports structured, task-linked computation rather than generic perturbation. Behavioral change under internal intervention is otherwise mechanistically ambiguous: it may reflect improved use of visual evidence, generic output instability, or outright degradation. FLIP applies elementwise flooring to the final normalized hidden state before logit computation, leaving parameters,
prompts, and decoding unchanged. On a controlled detection/counting probe, sweeping intervention strength reveals three regions: negligible change, a bounded interior regime in which detection recall at IoU 0.50 ($R_{50}$) improves while tolerant counting error ($\mathcal{E}_{\mathrm{count}}$) falls, and over-suppression. We formalize a four-criterion probe-and-sweep protocol for disciplining the interpretation of intervention effects: regime structure, grounding-proxy
alignment, feature-coherence dependence, and failure to reproduce the same positive regime on a performance-based negative control. The post-normalization state passed to the output head is the logit-facing instantiation of this test; under a non-targeted flooring sweep it satisfies the full protocol. Raw decoder-layer interventions, including the last-block output before final normalization, and the singleton-pair left/right control fail to reproduce the Final-site signature, while same-site operators and multiple VLMs replicate it. FLIP is therefore a validation step for intervention-based mechanistic interpretability, not a steering method. Repo at https://github.com/earl-juanico/flip-qwen3vl/
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Data Structures and Algorithms
Deterministic Linear-Time Modular Subset Sum
Phuoc Dinh Le and Kha Le
We give a deterministic $O(m)$-time
algorithm for exact modular subset sum over every modulus $m$. The input gives distinct residues and their multiplicities. The
algorithm reports all reachable residues and answers one target query. It returns a witness when the target is reachable. The
algorithm uses $O(m)$ auxiliary words on an arithmetic word-RAM. This improves Potępa's deterministic $O(m\log m\,\alpha(m))$-time bound under the same input convention. The running time matches the cost of returning a length-$m$ Boolean array. We represent reachable residues as runs along cycles of repeated addition. Newly reached residues pay for scans of partial runs. Packed cycle flags make rebuilding take linear total time at the original modulus. A
theorem on subset sums of distinct units limits the number of adaptive boundary batches to $O(m^{3/4})$. Radix sorting their $O(m)$ total keys also takes $O(m)$ time.
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