Game Theory
Delegating Authorization to Misaligned Agents: Coalitional Alignment and Safe Control
Natalie Collina, Surbhi Goel, Aaron Roth et al. (+1 more)
Long-running AI
agents create a control problem: each action they take changes the state, which in turn affects the trajectory of future actions. If the
agent is not fully aligned, then guaranteeing safety requires approving consequential actions before allowing them to be executed. But requiring human approval at every step makes
attention a bottleneck. Delegating review to other AI
agents raises the same
alignment problem: the reviewers may themselves be misaligned. We identify a condition on a reviewing panel that is weaker than individual
alignment yet necessary and sufficient for a guarantee that the principal fares at least as well in expectation as under a designated baseline policy. Each reviewer
agent reports whether an action proposal made by a proposer
agent improves its own utility relative to the baseline. We show that a threshold rule tolerating $k$ disapprovals is safe exactly when, after any $k$ reviewers are removed, the principal's utility can be written as a nonnegative combination of the remaining reviewers' utilities, plus a term that is nonnegative on every feasible proposal. We call this property $k$-robust coalitional
alignment. The characterization lifts to sequential control: in a discounted MDP with an arbitrary proposer
agent, safety at every state is both necessary and sufficient for the induced policy to match or improve on the baseline. When reviewers vote strategically, full-panel coverage in reward-function space guarantees that every Nash
equilibrium is safe under the unanimous approval rule; in contrast, more permissive thresholds can admit unsafe
equilibria even when reviewers are individually aligned. Experiments with existing reviewer models show that collective review can remain sound without an aligned individual, even when some disapprovals are tolerated.
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All authors Natalie Collina, Surbhi Goel, Aaron Roth, Sikata Bela Sengupta
Artificial Intelligence
When Should a World Model Move? Loss-Conditioned State Execution
Jintao Xu, Zhengyu Chen, Ben Zhang et al. (+2 more)
We introduce loss-conditioned state execution, a model-agnostic method that decides whether to execute a world model's fixed feasible proposal or retain the current state. Predictive informativeness alone, however, does not establish whether an update will reduce downstream loss. Occurrence ranking can approach perfection while persistence remains the unique absolute-loss Bayes action. Two transition laws can also share occurrence information and conditional variance yet require opposite absolute-loss decisions. We formalize state movability as the existence of a loss-reducing feasible correction and distinguish it from the benefit of a particular proposal. Our method constructs a loss-specific feasible proposal from a predictive distribution and evaluates its groupwise bounded-loss gain over persistence on independent calibration units. The proposal is executed only in groups with a positive simultaneous lower confidence bound. For fixed proposals and groups with bounded unit losses, we prove that every accepted group has lower expected loss than persistence with high probability when calibration units are i.i.d. draws from the target population. Experiments on public
forecasting and action-conditioned dynamics
benchmarks show supported updates and a trade-off between certification and coverage. On 28,684 held-out M4 Monthly series, the method executes the proposal for 14.0% of series and achieves bounded loss 0.588, compared with 0.599 for persistence and 0.621 for always executing the proposal. The paired 95% bootstrap intervals for both comparisons lie below zero. In constrained
forecasting of six unhealthy-inventory types from JD$\mbox{.}$com, a leading e-retailer in China, strong occurrence-ranking signal coexists with a loss-based preference for persistence, illustrating why event predictability and state execution must be evaluated separately.
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All authors Jintao Xu, Zhengyu Chen, Ben Zhang, Yongzhi Qi, Jianshen Zhang
Artificial Intelligence
Navigating Sparse Evidence: Agentic Visual RAG via Explicit Context Selection and Consolidation
Yucheng Shen, Lingyong Yan, Jiulong Wu et al. (+4 more)
Visual
Retrieval-Augmented Generation (VRAG) empowers models to navigate and answer queries about visually rich documents by retrieving relevant page images as visual evidence and
reasoning over their content. However, effectively utilizing this visual evidence is usually impeded by two main challenges. First, answer-relevant evidence is sparse and may be concentrated in a small region of one page or dispersed across multiple pages. Second, existing agentic methods often generate answers based on raw exploration trajectories or compressed textual
memories rather than an explicitly organized set of supporting images, making answers susceptible to exploration noise and obscuring the evidence-backed
reasoning trace. We argue that the bottleneck lies not only in evidence discovery but also in its preservation and organization before answer generation. We propose SCoRE (Selection and Consolidation for Robust Evidence), a unified
agent loop for explicit evidence selection and consolidation. During exploration, SCoRE retains only query-relevant observations and their source pointers in a maintained textual ledger, preserving earlier evidence while keeping the visual context bounded. At termination, it reloads the referenced original images and consolidates the visual evidence for answering, arranging it into a logical sequence. This decouples final
reasoning from exploratory trial-and-error while ensuring strict visual grounding via indexed claim-to-image linkages. To enable end-to-end optimization of this unified rollout, our training paradigm combines filtered cold-start trajectory
distillation with evidence-aware reinforcement learning, whose reward promotes evidence coverage, consolidation compactness, and answer correctness.
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All authors Yucheng Shen, Lingyong Yan, Jiulong Wu, Shuaiqiang Wang, Jianmin WU, Dawei Yin, Min Cao
Cryptography and Security
RAPID: A Real-Time Defense Against Unauthorized Model Distillation for Text-to-Image Services
Zihan Wang, Boheng Li, Rui Zhang et al. (+5 more)
Diffusion-based text-to-image (T2I) models are increasingly used for visual content creation, making their generation capability a valuable intellectual property asset. However, this capability is vulnerable to black-box output-based
distillation, where an adversary queries the service, collects
prompt-image pairs, and trains an unauthorized substitute model that mimics its generation behavior. Existing perturbation-based defenses apply sample-wise optimization to make generated images disruptive to unauthorized training, but introduce substantial computation and latency that reduce the usability of online T2I services. A natural solution is to integrate defensive perturbations into the VAE decoder, allowing the protected model to generate defended images directly without online sample-wise optimization. However, existing sample-wise objectives struggle to transfer to the shared decoder setting. We empirically find that a defensive shared decoder induces a substantially smaller latent shift than sample-wise optimization, suggesting that objective reachability matters more than destructiveness in this setting. To overcome this limitation, we propose RAPID, a self-referenced latent maximization framework that removes external dependencies and encourages the same model update to induce consistently disruptive effects across training samples, thereby improving reachability. We further introduce reconstruction-guided color regularization that blocks the latent shortcut and reinforces visual disruption. Extensive experiments on four T2I models and four
datasets, with comparisons against five representative baselines, show that RAPID consistently degrades substitute-model generation quality while preserving service visual fidelity. Our work establishes a paradigm for real-time protection against unauthorized
distillation in deployed T2I systems.
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All authors Zihan Wang, Boheng Li, Rui Zhang, Wenshu Fan, Qingchuan Zhao, Tianwei Zhang, Hongwei Li, Guowen Xu
Computer Vision
SURE-Map: Self-Correcting Streaming Geometric Foundation Models
Mingkai Liu, Hao Zhao and Xingxing Zuo
Streaming
geometric foundation models are emerging as a compelling alternative to SLAM systems. Yet this streaming nature introduces a fundamental issue: each prediction is made from limited context, which is vulnerable to dynamic objects and weak textures. Small local errors accumulate into severe
geometric distortion and long-horizon scale drift. We argue that reliable streaming reconstruction requires
geometric foundation models to be not only predictive, but also self-correcting. We introduce SURE-Map, a self-correcting framework built upon two complementary principles. First, we explicitly model cross-view
geometric uncertainty. Unlike conventional depth or point confidence, which primarily reflects the reliability of individual-view prediction, our uncertainty directly measures whether the jointly predicted pose and depth induce geometrically consistent cross-view pixel correspondences. Second, because local correction alone cannot eliminate slowly accumulating scale errors, we introduce multi-timescale self-correction: fast consecutive-frame inference preserves streaming efficiency, while sparse keyframe-window inference provides longer-range
geometric evidence to periodically recalibrate the scale of recent trajectories. SURE-Map establishes new state-of-the-art performance for online feed-forward reconstruction across long-horizon
benchmarks, reducing ATE-RMSE from 24.00 to 17.24 m on KITTI, 5.11 to 4.74 m on Oxford Spires, and 31.37 to 28.58 m on VBR, with further improvements to 15.17, 4.63, and 22.12 m when incorporating loop-closure refinement. Project page: https://mingkai-liu.github.io/projects/sure-map/.
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Artificial Intelligence
KnowBench: Effort Reduction as a Unified, Deployment-Grounded Benchmark for Clinical AI
Jocelyn Kang and Caroline Zhang
Clinical AI systems are evaluated with instruments built for research settings (reference-based similarity metrics and expert rubric panels) that measure resemblance to an artifact rather than reduction of a burden. We introduce KnowBench, pioneered by Knowtex, whose unifying metric is Effort Reduction (ER): the proportion of system-generated clinical work product accepted by the responsible clinician under expert and safety review. ER is defined once and instantiated per task across the administrative workload clinical AI automates: visit notes, diagnosis and billing codes, orders, EHR chart summarization, patient after-visit summaries, and clinical decision support. In every instantiation the construction is identical: the clinician's review-and-attestation event is the ground truth, every accepted unit is work the system completed, and every correction is residual effort returned to the clinician. The primary contribution of this paper is the
benchmark itself: the metric, its degenerate cases, and a reporting protocol under which ER claims are auditable and cross-system comparable. Alongside it we report an initial headline measurement from the documentation instantiation: over one million signed encounters across a production window exceeding six months and thirteen medical specialties, Knowtex's proprietary fine-tuned clinical foundation models operating inside a closed feedback architecture achieve an aggregate ER of 97.99%, with per-specialty aggregates spanning 96.8-98.9%. This release reports the protocol's checklist partially, and states which companion statistics are withheld; the
benchmark is offered so that this figure, and every figure reported after it, can be held to the same standard.
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Machine Learning · Cross-listed from stat.ML
Learning under Target Shift: Optimal Density Ratio Estimation and Importance-Weighted Regression
Ren-Rui Liu and Zheng-Chu Guo
We study density ratio estimation and importance-weighted regression under target shift with continuous outputs. Under target shift, the conditional distribution of the inputs given the outputs remains invariant across the training and test distributions, while the output marginal distribution may change. Although this problem has been extensively studied for discrete outputs, the continuous setting is substantially less understood: the importance weights are determined by an unknown density ratio function, for which existing estimation methods lack explicit finite-sample convergence rates. We propose a spectral regularization method in a reproducing kernel
Hilbert space (RKHS) for estimating the continuous density ratio from labeled training samples and unlabeled test inputs. Under a source condition with regularity parameter $\iota>0$, we establish high-probability finite-sample guarantees and show that the
estimator achieves the capacity-independent minimax-optimal RKHS-norm rate $O(n_\eta^{-\iota/(2\iota+2)})$. We then incorporate the estimated density ratio into importance-weighted regression and characterize the propagation of density-ratio estimation error to the final predictor. When sufficiently many samples are available for density ratio estimation, the resulting regression
estimator attains the minimax-optimal rates of standard kernel regression. These results establish a finite-sample theory for continuous density ratio estimation and importance-weighted learning under target shift.
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Cryptography and Security
When the World Lies: Backdoor Attacks on Latent World Models for Downstream Control
Roberto Riaño, Gorka Abad, Stjepan Picek et al. (+1 more)
Pretrained world models, learned simulators that encode an observation into a latent state and predict how it evolves under actions, are beginning to be reused as off-the-shelf dynamics backbones for control, like pretrained encoders and
language models are reused today. We show that this reuse opens a supply-chain
backdoor: an adversary who controls only a released checkpoint can hijack the downstream controller, even though the victim trains and evaluates entirely on clean data and never sees the trigger. The attack encodes no explicit trigger-to-action rule. Instead, the poisoned model routes trigger-bearing observations into a chosen latent region and reshapes the local dynamics there, so that the victim's own optimization (Dreamer-style actor training in imagination, or MPC/CEM planning over predicted futures) re-discovers the attacker's target action on its own. Across several control tasks and trigger families, the trigger steers the controller's action toward the attacker's target, controlling every action dimension and hijacking 100% of triggered steps on the strongest settings. The checkpoint still passes the clean-data diagnostics a victim would run before deployment, with clean-task success retaining at least $\sim$75%. The effect is temporally gated: it appears only while the trigger is present and disappears when the trigger is removed. Trigger-blind repair is budget-dependent: moderate clean fine-tuning can preserve clean utility while leaving the triggered failure intact, whereas sufficiently aggressive adaptation can remove it only after substantially degrading clean control. The world-model backbone itself is therefore an emerging and underexamined attack surface for control. The full code and artifacts are available in our repository.
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All authors Roberto Riaño, Gorka Abad, Stjepan Picek, Aitor Urbieta
Machine Learning
MoveBench: A Benchmark for Global-Scale Wildlife Movement Forecasting
Justin Kay, Shir Bar, Ellen O. Aikens et al. (+28 more)
Understanding and predicting wildlife movement is critical for ecology and conservation. While trajectory
forecasting has advanced for human and vehicle movement, wildlife trajectories present distinct challenges: they are unconstrained in space, highly
stochastic, and influenced by environmental conditions. We introduce MoveBench, the first large-scale
benchmark for probabilistic wildlife movement
forecasting, containing 2.6M GPS locations from 800+ individuals across 110 species in 127 countries, paired with 1.6B environmental raster tiles capturing 160
covariates known or hypothesized to influence movement. We propose a probabilistic evaluation protocol for movement trajectory forecasts, addressing limitations of point-prediction metrics for inherently
stochastic phenomena. Through comprehensive empirical evaluation of four method families across multiple temporal and spatial scales, we reveal that: (1) existing predictive methods generalize better to future timepoints than to unseen individuals, (2) deep learning approaches do not consistently outperform simpler baselines, and (3) environmental
covariate selection significantly impacts performance. MoveBench enables standardized evaluation of movement
forecasting methods and provides a foundation for methodological advances on this ecologically important task.
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All authors Justin Kay, Shir Bar, Ellen O. Aikens, Martin Becker, Francesca Cagnacci, Juliet Cohen, Scott W. Forrest, Jessica Kendall-Bar, Madeleine Lucas, Macon Overcast, Meredith S. Palmer, Will Rogers, Nicholas J. Russo, Christian Rutz, Larissa T. Beumer, Michael Brown, Ying-Chi Chan, Sarah C. Davidson, Diego Ellis Soto, Anne G. Hertel, Roland Kays, Benjamin Koger, Guram Mikaberidze, Thomas Mueller, Ruth Oliver, Thorsten Papenbrock, Robert Patchett, Jared A. Stabach, Dane Taylor, Scott W. Yanco, Sara Beery
Artificial Intelligence
EvoOntology: A Self-Evolving Ontology Layer for Data Agents
Meiduo Chong, Shaolei Zhang, Ju Fan et al. (+1 more)
Data
agents aim to fulfill natural-
language instructions over heterogeneous data, including tables, files, and databases. However, data
agents face a challenging
agent-data gap: heterogeneous data resides outside the
agent, while the
agent can access it (e.g., column names and file paths) only through generic tools. Existing approaches either let
agents directly explore raw data sources or inject manually constructed semantic layers into
prompts. However, neither scales well to large heterogeneous data sources nor adapts to different
agent behaviors. In this paper, we introduce EvoOntology, a self-evolving ontology layer for data
agents. EvoOntology encapsulates the ontology as an MCP server comprising a schema layer, a content layer, and a tool layer, enabling
agents to actively query and interact with the ontology at runtime. To this end, we introduce a builder
agent for autonomous ontology construction and a self-
evolution loop that continuously refines the ontology through attribution-guided typed edits that are accepted only after a backbone-conditional paired evaluation. Experiments on three well-adopted data-
agent benchmarks with four LLM backbones demonstrate that EvoOntology consistently outperforms strong baselines and existing semantic-layer approaches, effectively bridging the
agent-data gap and enabling more effective interaction with heterogeneous data. Code: https://github.com/ruc-datalab/EvoOntology
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All authors Meiduo Chong, Shaolei Zhang, Ju Fan, Xiaoyong Du
Machine Learning
Transfer Learning for Socioeconomic Estimation in Forced-Displacement Settings
Steven Ndung'u, Adel Daoud, Ismael Yacoubou Djima et al. (+2 more)
Progress in inclusive household surveys has strengthened socioeconomic evidence for forcibly displaced populations, providing indispensable
benchmarks on living conditions and welfare. However, these surveys remain resource-intensive and periodic, while conditions can change between rounds, particularly in settings affected by fragility, conflict, and violence. More frequently updated, spatially granular complementary evidence is therefore needed to identify where socioeconomic conditions may be changing between survey rounds and to inform operational prioritization. Earth observation and machine learning offer a scalable source of spatially explicit socioeconomic information. However, tools developed for general populations have not been systematically adapted and evaluated in forced displacement settings, where living conditions, settlement patterns, and displacement impacts may differ substantially. We address this gap by adapting a
multimodal spatiotemporal
vision transformer, pretrained on Demographic and Health Survey data from approximately 1.2 million households across 36 African countries, to forced displacement and host community settings in South Sudan, Cameroon, and Zambia. We develop and evaluate the updated, adapted model using socioeconomic indices derived from UNHCR FDS and RMS data. Our results show that satellite-derived geospatial
covariates explain up to 66% of the variation in socioeconomic outcomes in camp-intersecting grids, with a mean absolute error (MAE) of 4.37 index points, and 41% in non-camp-intersecting areas, with an MAE of 5.41. The framework complements and adds value to periodic household surveys by filling critical spatial and temporal data gaps with regularly updated, model-based socioeconomic estimates. These estimates sustain insight between survey rounds and support timely humanitarian prioritization and field verification.
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All authors Steven Ndung'u, Adel Daoud, Ismael Yacoubou Djima, Hai-Anh H. Dang, Patrick Michael Brock
Neural and Evolutionary Computing
Event-Native Symbolic-Temporal Spike Encoding Framework for Heterogeneous Cyber Streams
Dalton Diez, Peyton Andras, Max Shroyer et al. (+1 more)
Spiking neural networks (SNNs) have shown promise for sparse, event-driven computation through stateful processing that is naturally compatible with low-power edge hardware. These properties align with cyber monitoring, where data arrives asynchronously, and malicious behavior often emerges through temporal patterns across event sequences. However, cyber streams are not composed solely of continuous numeric signals: their informative structure is also carried by categorical identifiers, irregular timing, and local behavioral context. Traditional rate- and population-based spike encodings are not naturally suited to these heterogeneous semantics, while conventional intrusion detection system (IDS) pipelines typically resolve the mismatch by converting raw events into flows, fixed aggregation windows, or dense tensors. Although useful for conventional classifiers, these transformations introduce buffering latency, obscure native temporal structure, and weaken the computational advantages of event-driven neuromorphic processing. We introduce an event-native symbolic-temporal spike encoding framework that maps heterogeneous cyber events directly into sparse, spike-compatible inputs. By assigning encoding roles to semantic identity, local frequency context, and inter-event timing, the framework preserves categorical semantics and temporal dynamics. We validate the approach on packet-level Network IDS and extend it to message-level CAN IDS, using both domains to evaluate whether the encoding exposes usable structure for recurrent SNNs operating directly on native event streams. Under edge-oriented, $\mu$Caspian-aligned hardware constraints, compact recurrent SNNs achieve strong anomaly detection performance, with an operational hybrid metric ($J_{hybrid}$) of 0.987 on Network IDS and 0.980 on CAN IDS.
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All authors Dalton Diez, Peyton Andras, Max Shroyer, James Ghawaly Jr
Robotics
JEPLO: Joint-Embedding Predictive Learning for LiDAR-Based Legged Locomotion
Qihao Yuan, Yixuan Qiu, Ziyu Cao et al. (+2 more)
Light detection and ranging (
LiDAR) remains less explored than RGB-D sensing for perceptive legged locomotion, and existing
LiDAR-based approaches often rely on explicit mapping. We present JEPLO (Joint-
Embedding Predictive learning for legged LOcomotion), a single-stage learning framework for mapping-free,
LiDAR-based perceptive locomotion for legged
robots. We introduce a proprio-exteroceptive JEPA (PE-JEPA) world model to learn predictive
egocentric terrain representations from onboard observations, including raw
LiDAR scans. A concurrent JEPA-teacher-student (CJTS) pipeline is further proposed to train a locomotion policy informed by JEPA latent representations in simulation using deep reinforcement learning with a simple reward formulation. The framework achieves successful sim-to-real transfer, enabling omnidirectional traversal of diverse terrains, including long staircases and high boxes, with lightweight onboard computation. Evaluations demonstrate greater robustness than existing perceptive locomotion frameworks, particularly under degraded perception caused by occlusion, sparsity and noise. Further analysis validates JEPLO's ability to retain task-relevant information under these challenging conditions. We open-source our implementation, experimental
datasets, and hardware setup designs https://github.com/ASIG-X/JEPLO.
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All authors Qihao Yuan, Yixuan Qiu, Ziyu Cao, Ming Cao, Kailai Li
Numerical Analysis
Nested QMC designs on spheres
Hao-Ning Wu and Xiaosheng Zhuang
Nested cubature rules, in which each refinement retains all previously used nodes and thus reuses earlier function evaluations, are natural in multilevel and adaptive integration. For every fixed $s>d/2$, we show that the equal-weight QMC integration rate on $\mathbb S^d$ is compatible with such nested point sets. In the subcritical range $d/2<s<d$, cumulative unions of geometrically growing QMC blocks yield nested QMC design sequences whose successive cardinality ratios converge to any prescribed $\rho>1$. At and above the critical index $s=d$, where the block-averaging estimate no longer yields the optimal rate, we prove an equal-weight completion
theorem based on low-frequency discrepancy cancellation. A block-sensitive estimate sharpens the iteration and yields nested QMC designs for all $s\ge d$ with $N_{j+1}\lesssim(j+1)^{2s/d-1}N_j$. Every prescribed finite point set also admits an optimal-rate completion.
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Machine Learning
Sylvas: Synergistic Learning Value based Device Scheduling in Federated Continual Learning
Yuxuan Sun, Yuxuan Bai, Tan Chen et al. (+2 more)
Federated continual learning (FCL) enables shared global models to continuously adapt to distributed and non-stationary data streams, making it important for Internet of Things applications such as intelligent transportation, industrial monitoring, and unmanned systems. Under spatio-temporal data distribution dynamics and label scarcity, a key challenge is how to quantify the contribution of each edge device to global learning performance and schedule the most valuable devices under resource constraints for timely model updating. This article presents Sylvas, a synergistic learning value based device scheduling framework for FCL at the wireless edge. Sylvas evaluates the learning value of distributed data from two perspectives: distributional value, which characterizes the contribution of device data to global model learning from a spatio-temporal distribution perspective, and label value, which captures the quantity and reliability tradeoff of pseudo-labeled data. By integrating these factors into a synergistic learning value metric, Sylvas schedules devices with high learning value while satisfying communication and computation resource constraints. Case studies demonstrate that Sylvas supports timely model adaptation under spatio-temporal distribution dynamics and effectively exploits unlabeled data.
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All authors Yuxuan Sun, Yuxuan Bai, Tan Chen, Sheng Zhou, Zhisheng Niu
Computation and Language
Enabling Streaming User Transcription in Full-Duplex Speech-to-Speech Models
Ke Hu, Nourchene Ferchichi, Edresson Casanova et al. (+9 more)
Full-duplex
speech-to-
speech (S2S) models enable natural conversational AI by allowing simultaneous listening and speaking. However, these models typically lack inherent user
speech transcription, which is essential for applications such as conversation logging, accessibility features, and quality monitoring. In this work, we propose an efficient method to add streaming ASR capabilities to an existing duplex S2S model by introducing a lightweight ASR head in parallel to the
agent text head. Our approach requires minimal additional parameters and no significant architectural changes to the base S2S model, enabling real-time user transcription while preserving full-duplex conversational capabilities including turn-taking and barge-in handling. Experimental results demonstrate that our method achieves streaming average WER of 10.21% on the HuggingFace Open ASR Leaderboard within the duplex S2S framework. Additionally, we show that the same architecture trained as a standalone streaming ASR model achieves competitive results (7.73% WER) compared to current SOTA models. We will open-source our training and inference code to facilitate further research in joint streaming ASR and S2S modeling.
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All authors Ke Hu, Nourchene Ferchichi, Edresson Casanova, Ankita Pasad, Elena Rastorgueva, Chen Chen, Nithin Rao Koluguri, Piotr Zelasko, Yifan Peng, Hainan Xu, Zhehuai Chen, Boris Ginsburg
Computation and Language
Sequential Adapter Stacking for Cross-Lingual Low-Resource ASR
Thai Thi Thanh Thao Dang, Mengjie Qian and Kate Knill
Extending large-scale
multilingual automatic
speech recognition (ASR) models to low-resource
languages remains challenging. Model performance is skewed toward high-resource
languages and degrades sharply for
languages with limited labeled data and pre-training exposure. To address this, we investigate parameter-efficient approaches for transferring knowledge from resource-rich source
languages to low-resource target
languages on Whisper. Alongside warm initialization and
attention-based fusion, we propose Sequential Adapter Stacking, which places a trainable target-
language adapter on top of a frozen source-
language adapter. Under controlled experiments, these approaches are evaluated on three target
languages unsupported by Whisper – Asturian, Assamese, and Xhosa – using source
languages with varying degrees of relatedness. Sequential Adapter Stacking with the closest related source consistently and significantly outperforms full fine-tuning across the three targets, with 5–8% relative WER reductions. These gains largely persist with only one hour of target training data.
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Systems and Control
Extended Version: Storage-Based Strategic Manipulation of Constraint-Binding Patterns in Power Networks
Mehdi Davoudi, Minghao Mou and Junjie Qin
This paper studies the strategic
market participation of a monopolistic energy storage aggregator (ESA) in a day-ahead electricity
market. The ESA coordinates geographically distributed storage units, submits a coordinated bid for its portfolio, and may hold financial transmission rights (FTRs). The system operator clears the
market through a network-constrained, multi-period economic dispatch, determining generation and load schedules, nodal prices, energy-
market payments, and FTR payoffs. We formulate the ESA–system-operator interaction as a Stackelberg game and characterize its
equilibrium through a constraint-binding-pattern decomposition of the
market-clearing problem. Beyond enabling
equilibrium computation, the framework reveals how the ESA can increase its profit by strategically inducing or avoiding particular constraint-binding patterns. It also establishes a novel welfare result: although strategic storage without FTRs is known to weakly improve social welfare relative to the no-storage case, certain FTR positions can overturn this guarantee by strengthening the ESA's incentive to induce particular patterns, causing social welfare to fall below the no-storage level. Motivated by these findings, we develop two system-operator mechanisms for limiting undesirable ESA behavior and its adverse effects on
market outcomes and social welfare. Finally, a three-bus study illustrates the theoretical findings, while IEEE test systems demonstrate the scalability of the proposed method.
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Graphics
TopoRig: Topology-Agnostic Facial Rigging via Multi-Source Supervision
Andrew Fleet, Soroush Mehraban, Vida Adeli et al. (+2 more)
Automatic facial rigging across heterogeneous mesh
topologies remains challenging because high-quality expression supervision is often tied to canonical templates, while deformation transfer to arbitrary meshes can introduce
geometric artifacts and correspondence errors. We present TopoRig, a
topology-agnostic facial rigging framework that predicts FACS-conditioned deformations directly on input mesh vertices while preserving the original
topology. Starting from the ICT FaceKit expression model, we construct complementary supervision from accurate but template-biased common-
topology rigs,
topology-diverse but noisier transferred rigs, and targeted image-based cues for controls poorly captured by
geometric transfer. TopoRig combines local surface geometry, landmark-relative semantic features, global shape context, and FACS controls to predict per-vertex displacements. We train on 3,496 generated identities using 45 non-gaze expression controls from the 53-control ICT FaceKit vocabulary. On held-out identities and unseen mesh
topologies, TopoRig more faithfully reproduces the reference expression space than prior neural facial-rigging methods, while qualitative results show consistent localized deformations across diverse character geometries. Ablations demonstrate that semantic landmark features and complementary supervision improve cross-identity and cross-
topology generalization. Overall, TopoRig amortizes heterogeneous and imperfect expression supervision into a single
topology-preserving deformation model.
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All authors Andrew Fleet, Soroush Mehraban, Vida Adeli, Cole Clifford, Babak Taati
Computation and Language
Look Before You Leap: Factual Decoding with Internal Attribution Signals
Hayeong Ryu, JungMin Yun, Byeonggeuk Lim et al. (+2 more)
Hallucination remains a critical challenge in large
language models (LLMs), where early factual errors compound through autoregressive generation in a snowballing effect that neither post-hoc correction nor weight-level intervention can effectively preempt. We propose DescaPE (DEcoding Signal Control Against Path Error-snowballing), a decoding framework that leverages internal model signals to suppress
hallucination-prone trajectories at inference time. Through sliding-window MLP ablation, we identify a factual-salient layer span within LLMs whose derived signal is selectively elevated for factual
tokens and exhibits anomalous spikes at
hallucination-prone steps. We train a lightweight probe to approximate this signal from a single forward pass and integrate it into candidate scoring to penalize high-risk continuations while rewarding factually grounded ones. Experiments across five factuality
benchmarks on three LLMs demonstrate that DescaPE achieves factuality improvements over decoding-time baselines in multiple settings, while incurring only 1.10x latency overhead in our efficiency evaluation. Our code is available at https://github.com/hayeonggg/DESCAPE.
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All authors Hayeong Ryu, JungMin Yun, Byeonggeuk Lim, Sunhee Jo, YoungBin Kim
Artificial Intelligence
Design of a Deep Learning Credit Risk Early Warning System Integrating Multi-source Heterogeneous Data
LiYang Wang, Zhen Zhong, Zhen Tian et al. (+1 more)
Advancements in data fusion and real-time analytics technologies have opened new avenues for addressing complex domain challenges. Financial risk early warning systems often suffer from inefficiency due to information silos and monitoring delays. This paper proposes a credit risk early warning system based on heterogeneous information fusion. The system employs a model architecture integrating deep neural networks and
attention mechanisms to extract multidimensional features from diverse data sources such as transaction behaviors and social networks, thereby establishing an early identification mechanism for corporate and individual credit risks. System testing demonstrates that this approach significantly enhances the accuracy and timeliness of risk warnings, outperforming traditional rule-based engine solutions. The findings offer innovative insights for early intervention in financial risks, holding practical significance for safeguarding financial stability.
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All authors LiYang Wang, Zhen Zhong, Zhen Tian, Keyu Chen Keyu Chen
Computation and Language
Merging the Knowledge of LLMs for Automatic Speech Recognition
Hayato Futami and Tatsuya Kawahara
Automatic
speech recognition (ASR) systems, trained on paired
speech-text data, have been improved by leveraging
language models (LMs) trained on text-only data. LM fusion methods such as shallow fusion and density ratio are well-established methods that incorporate external LMs during ASR decoding. However, they incur additional computational costs due to LM inference, which is particularly problematic for recent larger LMs. In this study, we propose incorporating external LMs via model merging. This method integrates the LMs directly into the parameters of an LLM-based ASR model, requiring no additional computational cost at inference. We formulate domain extension and transfer via arithmetic operations on LoRA parameters. Experimental evaluations were conducted for the domain adaptation of LLM-based ASR trained on CSJ and LibriSpeech. We show that our LM merging consistently improved the ASR performance in the target domains, without degrading inference speed or
memory footprint.
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Machine Learning
A Language-Guided Multimodal Foundation Model for Zero-Shot and Multi-Task Brain Signal Analysis
Mingzhi Chen, Yiyu Gui, Guibo Luo et al. (+1 more)
Brain signal analysis is essential for both neuroscience research and clinical diagnostics, yet current approaches face critical limitations. End-to-end models require task-specific retraining and exhibit limited generalization, while pre-trained models lack semantic depth and still depend on extensive fine-tuning. Meanwhile, general-purpose
multimodal foundation models, though powerful in other domains, struggle to interpret
brain signals due to representational misalignment and lack of domain knowledge. This study introduces a
multimodal foundation model for zero-shot and multi-task
brain signal analysis (METIS) through a unified
language-signal
alignment framework. METIS is pretrained on the largest and most diverse
brain-signal corpus to date, comprising over 70,000 h of recordings from more than 11,000 subjects across 20
datasets. In a comprehensive zero-shot evaluation across 12
datasets, METIS outperformed the leading generalist model by over 20.9% in average accuracy. Remarkably, without any fine-tuning, METIS's performance matches or exceeds that of supervised, task-specific models. Furthermore, METIS demonstrates exceptional data efficiency and strong generalization, achieving an average AUROC advantage of over 16.0% in few-shot settings and 15.9% in cross-
dataset transfer. This work establishes a new paradigm for general-purpose
brain signal analysis, paving the way for next-generation neurotechnology.
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All authors Mingzhi Chen, Yiyu Gui, Guibo Luo, Yuchao Yang
Computer Vision · Cross-listed from eess.SP
Transforming harmonic coefficients for 3D splat compression
Tam Thuc Do, Philip A. Chou and Gene Cheung
We address the problem of color attribute compression for 3D splats. We show that all images generated by 3D splats are linear in the coefficients for each color channel, each spherical harmonic, and each splat, and we identify a basis for the space of all such images. We identify an inner product for the coefficient space that induces the squared error loss on images. We show that orthonormalizing the coefficients with respect to this innner product before coding can yield over 2 dB gain.
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Artificial Intelligence
Are LLMs Good Financial User Simulators? Multi-view Investor Logic Alignment (MILA)
Jiajie He, Jiangyuan Hong, Xintong Chen et al. (+2 more)
Large
language models (LLMs) are increasingly used as user simulators, yet it remains unclear whether their predictions faithfully reproduce the evolving decisions of individual users. We investigate this question in a controlled longitudinal paper-trading study with 80 participants, where user interactions, simulated transactions, virtual portfolio states, and point-in-time
market information are aligned under a rolling next-day prediction protocol. We evaluate behavioral fidelity hierarchically, from trade occurrence to action structure, asset selection, and downstream portfolio consequences. Across 1,239 aligned user-days, no evaluated LLM reliably outperforms a simple recent-activity persistence baseline for predicting whether a user trades. Fidelity further deteriorates at finer levels: models struggle to recover buy–sell structure and traded assets, and similar activity-level predictions can lead to substantially different portfolio trajectories. Controlled evidence ablations show that recent trading history strongly governs activity prediction, whereas asset selection is substantially more sensitive to the available evidence. An observational analysis further finds that intensified ticker-specific research predicts imminent trading, but diagnostic tests do not support a causal interpretation. These findings suggest that current LLMs capture useful short-term behavioral regularities without yet recovering a stable individual decision mechanism.
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All authors Jiajie He, Jiangyuan Hong, Xintong Chen, Dongling Ni, Wenjin Liu
Robotics
Bench2Dex: Benchmarking Visuo-Tactile Bimanual Dexterous Manipulation Across Dexterous Hands
Zhenjie Yang, Yideng Zhang, Dongjie Zhang et al. (+21 more)
Tactile sensing provides contact information that can be difficult to infer from
vision alone, but tactile hardware for dexterous hands has not converged to a common design. Dexterous hands differ in finger structure, contact surfaces, and sensor layouts, while simulated tactile signals still differ from measurements produced by physical sensors. These factors make it difficult to study visuo-tactile manipulation across diverse dexterous hands within a consistent experimental setting. We present Bench2Dex, a simulation
benchmark for visuo-tactile bimanual manipulation across 12 dexterous hands. We adapt existing
robot models with a shared simulated tactile interface that converts local contact geometry into image-like tactile observations. The interface provides a consistent observation format across different hand morphologies without attempting to reproduce the output of a specific physical tactile sensor. Bench2Dex includes 26 bimanual manipulation tasks that involve tool use, articulated-object interaction, and multi-stage manipulation, together with about 1.3K human-teleoperated demonstrations. The
benchmark provides synchronized visual, tactile, proprioceptive, action, and object-state observations, together with executable task metrics. For robustness, we group seven perturbation types into invariance axis, where the correct action does not change, and equivariance axis, where the correct action changes together with the perturbation. We evaluate ACT,
Diffusion Policy, pi0.5, and GR00T N1.5 on Bench2Dex and report their performance and failure modes. Bench2Dex is meant as a platform for studying visuo-tactile learning across dexterous hands. It does not assume that simulated tactile observations can replace real tactile sensing; it offers a shared setting for
algorithm development while tactile hardware and simulation models are still evolving.
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All authors Zhenjie Yang, Yideng Zhang, Dongjie Zhang, Chenyu Jiang, Xianshuai Liu, Yufeng Li, Zuhao Ge, Xingyu Jiao, Zheng Zhang, Kaiyu He, He Wang, Yuwen Zhong, Yi Deng, Muyun Jiang, Xianliang Huang, Haisheng Su, Donghang Zhang, Jian Zhang, Xue Yang, Hongyang Li, Zuxuan Wu, Yu-Gang Jiang, Xiaosong Jia, Junchi Yan
Machine Learning
Solving Finite-sum Coupled Compositional Optimization via Multi-block-Single-probe Estimator
Wei Jiang, Sifan Yang, Yibo Wang et al. (+2 more)
Traditional variance reduction methods (e.g., SPIDER, SARAH, STORM) have been extensively investigated for improving the convergence rates of
stochastic optimization. These techniques typically maintain a sequence of
estimators for a single function (or gradient) across iterations. However, what if we need to track multiple functions, but can only access
stochastic samples of $\mathcal{O}(1)$ functions at each iteration? This scenario arises in an important emerging family of finite-sum coupled compositional optimization (FCCO) problems of the form $\frac{1}{m}\sum_{i=1}^m f_i(g_i(\mathbf{w}))$, where each $g_i$ is accessible only through a
stochastic oracle. The key challenge is to track $\mathbf g(\mathbf{w})=(g_1(\mathbf{w}), \ldots, g_m(\mathbf{w}))$ over time, where $\mathbf g(\mathbf{w})$ has $m$ blocks but only $\mathcal{O}(1)$ blocks can be probed for their
stochastic values at each step. To address this challenge, we propose a novel Multi-block-Single-probe Variance Reduction (MSVR)
estimator to efficiently trace $\mathbf g(\mathbf{w})$ under partial block sampling. Building on the MSVR
estimator, we develop several
algorithms for FCCO problems, achieving improved sample complexities for non-convex, convex, strongly convex, and Polyak-Łojasiewicz (PL) objectives. We further obtain an improved dependence on $m$ when the outer function gradients $\nabla f_i$ are linear. Empirical studies on multi-task deep AUC maximization further demonstrate the superior performance of the proposed
estimators.
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All authors Wei Jiang, Sifan Yang, Yibo Wang, Lijun Zhang, Zechao Li
Artificial Intelligence
Data storytelling meets interpretable machine learning: Decoding AI decisions for non-experts without revealing sensitive data and model details
Lemen Chao, Zixuan Yang, Anran Fang et al. (+2 more)
AI-driven automated decision-making requires both predictive performance and interpretability. Recent advances in interpretable machine learning (IML) provide tools for explaining model predictions, but the technical complexity of these explanations may hinder accessibility to non-experts. To address this challenge, this study integrates data storytelling with IML to enhance the explainability of AI-generated decisions for a broader audience. Following the design science research (DSR) paradigm, this study proposes a formal definition of data storytelling in IML, introduces the DIST Pyramid to align data storytelling with IML, and presents the I-P-O Model to describe their interactions. It further develops an architecture to explain AI decisions through distinct "What-if" and "Why-not" event-generation processes. The architecture also employs data desensitization to protect sensitive input data. To validate the approach, a case study is conducted with the Boston Housing
dataset, using SHapley Additive exPlanations (SHAP) values and large
language models (LLMs) to generate data stories with And-But-Therefore (ABT) structures. An empirical evaluation shows that 76.4% and 74.3% of respondents rated the "What-if" and "Why-not" data stories as more comprehensible, with significantly higher accessibility scores than traditional SHAP visualizations. The paper concludes with the presentation of a narrative interpretation framework that integrates IML and data storytelling, thereby expanding the research scope as well as the practical applicability of AI decision-making.
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All authors Lemen Chao, Zixuan Yang, Anran Fang, Mingran Sun, Ming Lei
Machine Learning
Assembling the CREW: A Collaborative Multi-agent Reinforcement Learning Framework for Automated Related Work Generation
Hai-Dang Dang, Bao-Yen Pham, Bao Nguyen et al. (+2 more)
Automatic Related Work Generation (RWG) significantly reduces the human time and effort required to author the Related Work Section (RWS) of a research paper. However, prior methods leveraging multi-
agent Large
Language Models (LLMs) typically rely on a predefined workflow, where each
agent is responsible for a specific step in the entire process. This rigid, static inter-
agent coordination limits the adaptive collaboration required to synthesize complex scientific literature. To address this limitation, we propose CREW (Collaborative Reinforcement Learning for Related Work Generation), a novel framework where LLM
agents bypass heuristic pipelines to dynamically coordinate by autonomously selecting actions, such as Retrieve, Disseminate, Compose, and Critique, driven by a policy optimized via Independent Proximal Policy Optimization (IPPO). Extensive experiments on a standard RWG
benchmark demonstrate that our approach yields substantial quality improvements over strong existing baselines, while significantly reducing
token costs. Code is available at https://github.com/YenPBao/CREW-Collaborative-MARL.git
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All authors Hai-Dang Dang, Bao-Yen Pham, Bao Nguyen, Tran Thi Huong, Huynh Thi Thanh Binh
Robotics
Continuous Manifold-Decomposed Impedance Retargeting for Contact-Rich Imitation Learning
Jiahao Liu, Kento Kawaharazuka, Tasuku Makabe et al. (+1 more)
CMDIR extends
Manifold-Decomposed Impedance Retargeting (MDIR) to transform fixed-impedance demonstrations into continuous variable-impedance controllers, which can also serve as structured supervision for imitation learning. Continuous Task-
Manifold Impedance Representation (TMIR) pairs an evolving task frame with controller instructions. Demo-relative Compromise dynamics retain moving-basis transport and control/physical metric mismatch, yielding displacement, reaction-impulse, and perturbation-sensitivity criteria. Quality-to-Fast automatically compiles a solver structure from development paths within a predefined finite space, re-instantiates that structure for each demonstration, and certifies the resulting candidate by multi-resolution evaluation. Across 225 retargeted-controller trials in three real contact tasks, full CMDIR improves mean task-proxy retention and reduces mean pose deviation, force fluctuation, and peak force relative to discrete MDIR. FastMPO achieves a $5.8$–$9.4\times$ speedup over C-MPO with comparable closed-loop outcomes. Downstream experiments demonstrate learnability of the complete TMIR supervision interface; lower force fluctuation and peak force are observed among successful executions, while completion reliability remains uneven across tasks and environments.
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All authors Jiahao Liu, Kento Kawaharazuka, Tasuku Makabe, Kei Okada