Artificial Intelligence
A Hardware-oriented Approach for Efficient Bayesian Inference Computation and Deployment
Nikola Pižurica, Matteo Risso, Nikola Milović et al. (+4 more)
Bayesian inference provides a principled foundation for
reasoning under uncertainty, but its computational cost hinders deployment on resource-constrained edge devices. In this paper, we present a hardware-oriented methodology for accelerating discrete
Bayesian inference on commercial off-the-shelf embedded GPUs. We identify that the latency of a broad class of variational message-passing
algorithms is dominated by tensor contractions. Our approach restructures the
memory layout of these operations using two complementary merging strategies that produce compact, regularly-shaped primitives better suited for efficient GPU execution. We then introduce optional sparse array representations and a tensor-clustering scheme to reduce the
memory footprint. We instantiate the methodology and produce optimized variants of three message-passing
algorithms for Hidden Markov Models (HMMs), namely variational filtering, variational message passing, and marginal message passing. Furthermore, we complement this with a machine-learning-based autotuner that automatically selects the best-performing algorithmic variant for a given generative model specification. Benchmarked on an NVIDIA Jetson Orin AGX across 770 randomly sampled realistic Partially Observable Markov Decision Process (POMDP) configurations, our implementations achieve speedups of up to 5x, with typical gains of 2-2.5x, while producing numerically identical outputs to the baseline implementations.
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All authors Nikola Pižurica, Matteo Risso, Nikola Milović, Alessio Burrello, Igor Jovančević, Conor Heins, Miguel de Prado
Artificial Intelligence · Cross-listed from cs.CV
CaT-GS: Efficient 3DGS Rendering for Large Scale Scenes via Inter-frame Caching and Tile Scheduling
Tingjia Zhang, Bo Chen, Shengzhong Liu et al. (+2 more)
Recent breakthroughs in 3D Gaussian Splatting (3DGS) have advanced neural rendering with high fidelity and speed. However, its performance degrades significantly in large-scale scenes due to the computational burden of tile-based rasterization. Existing optimization efforts either require costly scene re-training or focus on narrow aspects of the pipeline, overlooking critical inefficiencies in real-world deployments. Through a comprehensive analysis, we identify three primary sources of redundancy and low GPU utilization: redundant inter-frame pre-processing, viewpoint-based occlusion redundancy, and severe tile-level load imbalance. To address these issues, we propose CaT-GS, a novel and efficient 3DGS rendering pipeline. CaT-GS introduces a speculative multi-frame preprocessing method to eliminate redundant computations across consecutive frames, and an inter-frame caching mechanism to eliminate viewpoint redundant rendering stages. Furthermore, it refactors rasterization tasks with a dedicated kernel to mitigate tile load imbalance, significantly boosting GPU utilization. Extensive experiments demonstrate that CaT-GS achieves a speedup of up to 10 times over the original 3DGS and up to 70% over previous state-of-the-art methods, establishing a new
benchmark for high-fidelity, real-time rendering of large-scale scenes.
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All authors Tingjia Zhang, Bo Chen, Shengzhong Liu, Fan Wu, Guihai Chen
Artificial Intelligence · Cross-listed from cs.CV
Measuring and Improving Complex-Atomic Answer Consistency in Endoscopic VQA
Yuhao Liu, Cheng Zhao and Guanghui Yue
Endoscopic visual question answering (VQA) increasingly asks complex questions that combine several endoscopic answer components rather than isolated factual queries. Such complex answers may be scored as correct even when the same model fails on associated atomic questions. We introduce EndoCA, a paired complex-atomic answer consistency
benchmark for evaluating whether complex answers remain consistent with same-image atomic answers. EndoCA contains two suites: EndoCA-Core evaluates compact question-complexity patterns commonly seen in practical endoscopic VQA, and EndoCA-Diagnostic supports controlled analysis across increasing question complexity. We evaluate 11 VLMs spanning open, medical, endoscopy-adapted, and closed-source models on EndoCA. Some VLMs achieve high complex-answer accuracy, yet their atomic-answer accuracy and complex-atomic answer consistency remain substantially lower. To reduce this complex-atomic inconsistency, we introduce Atomic-Support Reconciliation (ASR), a training-free mechanism that uses model-generated atomic answers as contextual premises for answer revision and consistency-guided selective answering. On four selected publicly available models, ASR-Revise improves paired complex-atomic correctness with modest changes in complex-answer accuracy, while ASR-Selective improves accuracy on answered cases by allowing the model to abstain from less reliable cases. Together, EndoCA and ASR provide a consistency-aware
benchmark and a training-free mechanism for answer reconciliation and selective answering in endoscopic VQA.
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Artificial Intelligence · Cross-listed from cs.HC
I wanted it to feel more personal: Customization of social AI as AI individualism in practice
Marita Skjuve, Anna Grøndal Larsen, Asbjørn Følstad et al. (+2 more)
Despite the growing availability of customizable social artificial intelligence (AI), such as ChatGPT, Grok, and Character.ai, we know little about how users actively shape social AI to reflect their personal preferences. This study examines why and how users (N = 169) customize social AI through the lens of the newly developed concept of AI individualism. Through reflexive thematic analysis of open-ended responses, we identified several motivations for customization, including (1) enhanced pragmatic support, (2) emotional support or companionship, (3) trust and reliability, (4) pushback, (5) a tailored degree of human likeness, (6) creativity or playfulness, and (7) having the AI function as an extension of the self. In line with the concept of AI individualism, our findings show that, for many users, customization is a co-creative process between the human and the AI that is perceived as strengthening support, autonomy, ownership, and engagement, potentially contributing to a closer and more personal relationship. Through customization users may come to view social AI as a personalized social resource that increases their sense of individualism, freedom, and control. We discuss how these perceptions may foster pseudo-autonomy, whereby customization creates an illusion of individual control over powerful social AI systems.
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All authors Marita Skjuve, Anna Grøndal Larsen, Asbjørn Følstad, Nena van As, Petter Bae Brandtzaeg
Artificial Intelligence · Cross-listed from cs.LG
Phasor Attention: Mean Root Square Normalization for Phase Manifold Preservation
Sungwoo Goo, Hwi-yeol Yun and Sangkeun Jung
While Root Mean Square Normalization has become the de facto standard for accelerating modern sequence models, its reliance on the quadratic accumulation of independent scalars ($\sum x^2$) inherently triggers outlier-induced numerical instability, gradient starvation, and anisotropic phase distortion. We introduce Mean Root Square Normalization (MRSNorm). By structurally pairing channels into 2D phasors, MRSNorm mathematically inverts the traditional scaling paradigm: it computes the localized $L_2$ magnitudes (Root Square) before aggregating them via a global $L_1$ average (Mean). This operational inversion strictly constrains activations to a phasor
manifold, preserving conformal invariance. By sharing a single affine weight across phasor components, MRSNorm halves the total number of learnable parameters, proving that unconstrained spatial scaling in standard norms is a harmful redundancy. We analytically demonstrate that this
geometric constraint yields a built-in, trigonometric gradient clipper governed by the Pythagorean identity, unconditionally equalizing the local gradient norm to ensure Gradient Homogeneity. Empirical evaluations on a ResNet with CIFAR-100 show that despite halved parameters, MRSNorm provides critical structural stability under rigorous stress tests. Under extreme hyperparameter settings where standard normalizations suffer from gradient divergence, MRSNorm successfully prevents numerical explosion and secures stable optimization trajectories. Our findings propose a fundamental paradigm shift toward phasor-based deep representation learning. The implementation of MRSNorm is available at Appendix C.
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Artificial Intelligence · Cross-listed from cs.CV
Vis2Reg: Visibility-Aware Landmark-Free Geometric 3D–2D Registration for Liver Laparoscopy
Jiaming Feng, Xukun Zhang, Shahid Farid et al. (+1 more)
Accurate 3D–2D liver registration, which aligns preoperative 3D models to partial, view-dependent intraoperative surface observations, is critical for AR-guided laparoscopic surgery but remains challenging due to severe occlusion, limited visibility, and the lack of 3D ground-truth supervision. Existing landmark-free approaches perform partial-to-complete
geometric alignment, yet robust self-supervision under extreme partial visibility remains difficult. We propose Vis2Reg, a visibility-aware registration framework that explicitly constrains deformation using mask-consistent visible regions. We introduce a visibility-aware self-supervision that derives a visible-domain 3D supervision signal from intraoperative masks, enabled by differentiable point rasterization and mask-guided back-projection. This formulation improves robustness under severe occlusion while maintaining fully self-supervised learning. Vis2Reg combines a robust
geometric rigid initialization module with an implicit neural deformation field for stable
alignment. Vis2Reg achieves a Dice score of 92.6% and a Chamfer Distance of 1.43 mm on real intraoperative
datasets, with 111 ms per-frame inference time, demonstrating both accuracy and practical efficiency.
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All authors Jiaming Feng, Xukun Zhang, Shahid Farid, Sharib Ali
Artificial Intelligence
PGN: Design and Implementation of a Vision-Language Navigation System Based on Pangu Multimodal Foundation Model
Li Xian, Mingxi Li, Yizheng Wang et al. (+3 more)
Vision-
Language Navigation (VLN) requires an
embodied agent to interpret a natural-
language instruction and predict actions from temporally ordered visual observations. Adapting a
multimodal large
language model to VLN requires visual-
language alignment, compact temporal inputs, action-space grounding, and stable training on the target hardware. This technical report presents PGN (Pangu Navigator), an offline VLN action-prediction system built on OpenPangu-7B. Training proceeds in two stages. First, PGMM aligns a frozen EVA-ViT-G/14
vision encoder with the frozen
language backbone by training a Q-Former and a two-layer MLP projector. Second, PGN adapts the aligned model to expert
navigation trajectories using five-observation windows, epoch-dependent temporal sampling, and a
reasoning-then-action output format; this stage freezes the aligned visual pathway and updates three structural-
token embeddings and LoRA adapters. The implementation combines mixed-precision computation, selective FP32 computation, and DeepSpeed ZeRO-2 on eight Ascend 910B NPUs. Under teacher-forced, open-loop evaluation on 500 held-out expert trajectories, V9 reports a 62.29% Normalized Action Match (NAM) and a 100.00% Non-empty Rate (NER). These metrics quantify offline expert-action
alignment rather than closed-loop
navigation success; evaluating error accumulation, path efficiency, and goal completion remains future work.
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All authors Li Xian, Mingxi Li, Yizheng Wang, Yiming Shen, Qi Chen, Zhuoling Xiao
Artificial Intelligence
Financial Audit Assistance using Misinformation Detection and Explanation
Kshitij Madhav Jadhav, Sushodhan Vaishampayan, Manoj Apte et al. (+3 more)
Financial statements (FS) such as Balance Sheet (BS), Income Statement (IS) and Cash-flow Statement (CS) summarize the annual financial performance of a company. FS are widely used for evaluating corporate governance, credit appraisal, risk analysis, validate taxation, make investment decisions etc. Financial
auditing is a complex and knowledge-intensive discipline whose one important aim is ensuring integrity, accuracy,
fairness and absence of material misstatement in the published FS. Given the importance of FS, there are incentives to hide, omit or falsify information to misrepresent the true financial health of the company; e.g., reduce tax liabilities, or increase investor confidence. Given the complex, time-consuming and expertise-dependent nature of
auditing, auditors would benefit from an AI-assisted system that automatically detects instances of misinformation in the given FS and identify likely sources of this misinformation in the financial data. In this paper, we present unsupervised techniques to identify misinformation in FS, and also generate explanations as to the financial variables that are likely sources of misinformation. The auditor can then explore in more detail the associated data sources and business processes to validate these suggestions. A crucial feature of our approach is the use of past corpus of FS and associated audit reports to generate insights, which help in providing assistance. We demonstrate the efficacy of these techniques on a large corpus of 11,460 FS over 5 years and associated audit reports. This paper integrates and adds more novel contributions over the previously reported research (Shinde et al., 2022)\cite{SVAP22}, (Vaishampayan et al., 2022)\cite{VSPP22}, (Pawar et al., 2023)\cite{PAPV23}, which we have used as the foundation for our AI-assisted Auditor Assistance system.
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All authors Kshitij Madhav Jadhav, Sushodhan Vaishampayan, Manoj Apte, Sachin Pawar, Nitin Ramrakhiyani, Girish Keshav Palshikar
Artificial Intelligence · Cross-listed from cs.CV
ReViV: Reconstructing the Viewer and the View in 4D from Monocular Egocentric Video
Xiaozhong Lyu, Gen Li, Zhiyin Qian et al. (+3 more)
Egocentric devices, such as wearable front-facing cameras, provide a unique perspective for capturing the continuous interaction between a human viewer and the surrounding environment. A holistic and efficient
multimodal model capable of reconstructing this 4D representation is therefore highly desirable. However, existing approaches often rely on auxiliary inputs such as pre-computed camera trajectories, treat scene perception and human ego-motion modeling as separate problems despite their strong interdependency, and suffer from slow inference time. To address these limitations, we present ReViV, the first unified framework for holistic
egocentric 4D reconstruction that extracts both viewer and view dynamics from a single
monocular RGB
video. We formulate the task as learning the full joint probability distribution over
multimodal signals, including RGB
video, camera trajectory, gaze direction, full-body motion, hand motion, and depth. Powered by a Masked Generative
Egocentric Transformer, ReViV operates within a single feed-forward architecture to simultaneously reconstruct the temporally consistent 4D reconstruction across the viewer and the view with fast inference speed. Extensive experiments on diverse
benchmarks, including HoloAssist, HOT3D, ARCTIC, Aria Digital Twin, and TACO, demonstrate that ReViV achieves state-of-the-art accuracy and efficiency across holistic ego-body, hand, and gaze reconstruction, camera tracking, while maintaining highly competitive
egocentric depth estimation without relying on heavy task-specific priors. Code and models are fully open-sourced: https://reviv4d.github.io/.
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All authors Xiaozhong Lyu, Gen Li, Zhiyin Qian, Xucong Zhang, Marc Pollefeys, Siyu Tang
Artificial Intelligence · Cross-listed from cs.CV
Medical Imaging Fusing Vision Transformer: Laryngeal Cancer Screening with Explanation
Haiyang Wang and Luca Mainardi
Early and timely screening of laryngeal cancer is crucial for improving clinical outcomes. In recent years, NBI endoscopy has become a standard diagnostic tool for the detection of laryngeal lesions. However, its effective use requires well-trained clinicians and the procedure is time-consuming and subject to interobserver variability. In this context, the application of artificial intelligence (AI) offers a promising solution to support clinical decision-making. In this work, we proposed applying
transformer and
attention mechanism for analyzing the narrow band imaging and distinguish benign and malignant lesions. Results show it has good classification performance with F1 (82.72%), accuracy(82.33%). In addition, the result of laryngeal cancer screening is explainable for clinicians. The explainability is utilizing the state of art
segmentation method (MedSAM) to provide the useful pathological information area for clinicians. The proposed methodology fusing classification and
segmentation provides a translating on laryngeal cancer screening.
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Artificial Intelligence · Cross-listed from cs.RO
Reasoning as a Double-Edged Sword: Architecture and Cross-Stage Robustness in Vision-Language-Action Models
Tuan Duong Trinh, Naveed Akhtar and Basim Azam
Does adding a
reasoning step make a
Vision-
Language-Action (VLA) model more robust to perturbation? Intuitively, a policy that reasons before acting should absorb a perturbed input better than one that maps observations directly to actions. We test this premise head-on across three models that span the
reasoning spectrum (no
reasoning, a text chain-of-thought, and a latent iterative loop), perturbing each at the
vision,
reasoning, and action stages on LIBERO and SimplerEnv. Two questions organize the study: does the
reasoning design shift robustness, and can the
reasoning be read back at runtime as a safety signal? We find that the latent-iterative model is by far the least robust: under both
stochastic noise and white-box perturbation its task success collapses, while the other two hold. This fragility is structural rather than cumulative: varying the
reasoning depth at inference barely moves it.
Reasoning outputs can in principle be monitored, but the monitors fail under fair tests. A plan–action consistency probe that looks near-perfect under naive evaluation falls to chance under adaptive attack. Under matched-FPR calibration, fusing it with an action-anomaly probe never lifts defended success above undefended. Scoped to these output-level behavioral probes under white-box
vision-stage attack, this ceiling is a precondition that any viable defense must first satisfy.
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Artificial Intelligence · Cross-listed from cs.LG
Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring
Kseniya Sahatova, Rafael Seidi Oyamada, Xuefei Lu et al. (+1 more)
Predictive process monitoring supports the optimization and control of operational business processes by
forecasting the future state or outcome of ongoing cases. While deep neural networks have achieved strong performance for these tasks by modeling sequential dependencies in event logs, their black-box nature limits trust and practical adoption. Feature attribution methods are often used to address this, but applying them directly poses a dilemma: event-level attributions impose high computational complexity for long traces, while explanations based on aggregated trace representations often fail to capture the underlying control-flow dynamics. To address this issue, we propose a local post-hoc explainability method for deep neural networks in outcome prediction. The method relies on a control-flow-aware
segmentation algorithm that partitions a trace into meaningful segments and supports the computation of segment-level SHAP explanations. This makes it possible to identify which parts of a trace influence a prediction and which change points steer the case toward the predicted outcome. We assess the proposed
segmentation method on a synthetic
dataset with known process logic, where meaningful change points can be explicitly verified, and we demonstrate its usefulness on real-world event logs from a loan application process and an administrative process of a Dutch municipality.
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All authors Kseniya Sahatova, Rafael Seidi Oyamada, Xuefei Lu, Johannes De Smedt
Artificial Intelligence · Cross-listed from cs.CV
BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis
Moona Mazher, Abdul Qayyum, Steven A. Niederer et al. (+1 more)
Foundation models pretrained using self-supervised learning have transformed computer
vision by learning transferable representations from large-scale unlabeled data. However, existing foundation models for neuroimaging remain limited by task-specific training, slice-based learning strategies, or relatively small pretraining
datasets, restricting their generalizability across diverse
brain MRI applications. In this work, we present BrainNext, a general-purpose self-supervised foundation model for volumetric
brain MRI analysis. BrainNext combines masked autoencoder (MAE) pretraining with a native three-dimensional Bi-Directional xLSTM-UNet architecture to learn rich anatomical representations from 60,551 unlabeled
brain MRI examinations spanning multiple MRI modalities. The pretrained model is subsequently adapted to downstream tasks through lightweight task-specific fine-tuning. We evaluate BrainNext on the Foundation Models for Medical Imaging (FOMO) 2025 Method Track, encompassing classification,
segmentation, and
brain-age estimation, where it achieved second place overall and ranked first in the meningioma
segmentation task on the official FOMO 2025 challenge leaderboard, demonstrating strong transferability across heterogeneous neuroimaging tasks. These results highlight the potential of large-scale self-supervised pretraining to learn robust and transferable volumetric representations, establishing BrainNext as a scalable foundation model for diverse
brain MRI applications.
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All authors Moona Mazher, Abdul Qayyum, Steven A. Niederer, Daniel C. Alexander
Artificial Intelligence · Cross-listed from cs.PL
ETAS: An Effect-Typed Language for Agent Systems
Huiri Tan, Yikun Wang, Puyang Zhang et al. (+2 more)
ETAS is a programming
language for
agent systems that treats model-backed
agents, tool calls,
prompts, typed
memory, human approvals, policies, and execution traces as semantic program elements rather than library conventions. It separates deterministic computation from agentic nondeterminism and externally visible actions while preserving a direct programming style. We present the core design of ETAS. Its static semantics assigns ordinary types through spec conformance and tracks each computation with two behavioral indices: an escaping effect row and a persistent abstraction of the typed action trace it may request. Specs form a terminating compile-time constraint calculus: type specs provide evidence for polymorphism and resource facts, callable specs constrain function and stage shapes, and trace specs express allow, deny, and temporal constraints. Typing checks requested traces against compiled monitors and emits residual obligations when dynamic resources preclude a complete static proof. The dynamic semantics distinguish requested, handled, denied, and committed events; handlers interpret typed actions without making their requests invisible to authorization or audit. We formalize a core calculus and state preservation, progress, type/effect soundness, handler trace-transparency, and policy safety. We also implement ETAS in Rust with a command-line interface, typed HIR checks, effect and policy diagnostics, handler checks, and trace-aware execution hooks. ETAS provides a programming-
language foundation for
reasoning about authorization, nondeterminism, recovery, and audit evidence before and during
agent execution.
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All authors Huiri Tan, Yikun Wang, Puyang Zhang, Shangyu Li, Jiasi Shen
Artificial Intelligence
Dynamic Defense Profiling Enables Cognitive Jailbreak of Text-to-Image Models
Dongdong Yang, Deyue Zhang, Zhao Liu et al. (+5 more)
Text-to-Image (T2I) generative models have achieved remarkable progress in synthesizing high-quality visual content, yet they remain vulnerable to
adversarial misuse, particularly in generating Not-Safe-For-Work (NSFW) images. Most existing jailbreak attacks primarily rely on heuristic
prompt engineering or black-box optimization, treating model feedback as a binary signal (success or failure). This coarse-grained paradigm overlooks the rich information embedded in diverse failure modes, such as textual refusal, visual blocking, and semantic sanitization, resulting in inefficient exploration and severe semantic collapse. In this paper, we propose MIND, a cognitive jailbreak framework that reframes
adversarial prompt generation as a belief-state inference problem over latent defense mechanisms. Instead of blindly searching for bypass
prompts, MIND actively models the target system's latent defense mechanisms by interpreting multi-modal feedback as high-density signals. Specifically, the framework integrates three core components: (1) a Multi-modal Judge for fine-grained feedback decomposition, (2) a Defense Profiler for iterative belief updating, and (3) a Meta-
Memory module for retrieving historically effective attack strategies. These components are unified within a
reasoning-driven evolutionary optimization process, enabling adaptive and semantically consistent jailbreak generation. Extensive experiments on the I2P
benchmark demonstrate the effectiveness of MIND. Under six representative pre-processing and post-processing defense settings applied to the Stable
Diffusion v1.5 model, MIND achieves an Attack Success Rate (ASR) of 95.62%, significantly outperforming existing methods. Additionally, the effectiveness of the proposed framework is validated across four widely used commercial T2I systems, achieving the highest ASR of 91.58% on Wan-2.5.
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All authors Dongdong Yang, Deyue Zhang, Zhao Liu, Zonghao Ying, Wenzhuo Xu, Jiankai Jin, Xiangzheng Zhang, Quanchen Zou
Artificial Intelligence · Cross-listed from cs.CV
CDIS: Cross-Dimensional Class-Agnostic 3D Instance Segmentation via 2D Mask Tracking and 3D-2D Projection Merging
Juno Kim, Hye-Jung Yoon, Yesol Park et al. (+1 more)
Class-agnostic 3D instance
segmentation is critical for robotic systems operating in unknown environments, enabling perception of previously unseen objects for reliable manipulation and
navigation. Existing approaches typically project per-frame 2D instance masks into 3D and merge them, which often breaks object identities across time and yields fragmented 3D instances. We introduce Cross-Dimensional Class-Agnostic 3D Instance
Segmentation (CDIS), a zero-shot framework that explicitly tracks 2D instance masks across frames and associates them with 3D superpoints, creating a feedback loop between 2D and 3D. This cross-dimensional
reasoning links temporally stable 2D tracks with spatially coherent 3D regions, producing globally consistent 3D instance labels without any 3D-specific training. Experiments on
benchmark datasets demonstrate that CDIS achieves higher accuracy and consistency than state-of-the-art zero-shot methods, while remaining efficient and scalable to diverse real-world environments.
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All authors Juno Kim, Hye-Jung Yoon, Yesol Park, Byoung-Tak Zhang
Artificial Intelligence · Cross-listed from cs.SE
Persona-as-Configuration: Generative Stakeholder Reporting for Agricultural Floods
Oliver Aleksander Larsen, Tiziano Santilli, Francesco Daghero et al. (+1 more)
Cyber-physical systems built on deterministic edge inference, such as on-vehicle flood detection for agricultural fields, produce structured decision logs that must be interpreted differently by heterogeneous stakeholders. Pairing such systems with large
language models (LLMs) to generate stakeholder-specific reports introduces a tension: the generative layer is non-deterministic, while the edge plane must remain replayable and auditable. We propose an architectural pattern resting on two invariants: unidirectional consumption, in which the generative layer is a strict read-only consumer of the deterministic plane and never writes back, and persona-as-configuration, in which stakeholder adaptation is a versioned
prompt-template artifact rather than runtime improvisation. We instantiate the pattern as a context-aware dashboard layer over the JSON decision logs of a previously published edge-based standing-water detection system, and analyse how the integration boundary admits standard generative-reliability mitigations as configuration- or middleware-level extension points. A structured expert review rated the pattern favourably across five ISO/IEC 25010-aligned quality dimensions, with strongest agreement on separation of concerns. End-user evaluation with agricultural stakeholders is planned for future work.
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All authors Oliver Aleksander Larsen, Tiziano Santilli, Francesco Daghero, Mahyar T. Moghaddam
Artificial Intelligence · Cross-listed from cs.CV
Measuring Monosemanticity in Sparse Autoencoders via Latent Activation Coherence
Katarzyna Filus and Sebastian Pokuciński
Within Explainable Artificial Intelligence, mechanistic interpretability uses Sparse Autoencoders (SAEs) to extract more interpretable features from neural representations. However, assessing their monosemanticity, and thus explanation quality, remains challenging. Existing metrics require external concept labels or depend on pretrained
embedding models, making them sensitive to encoder's geometry. We introduce the Tversky Monosemanticity Score (TMS), a label-free metric that operationalizes monosemanticity as activation-set coherence of binarized SAE latents, and does not require external
embedding encoders. We evaluate TMS on SAEs trained on features from pretrained
vision and
vision-
language models (DINOv3, CLIP, BLIP2), two common SAE regimes (TopK, BatchTopK), multiple sparsity levels, and expansion factors. Our results show that TMS is less affected by encoder anisotropy than its
embedding-based alternative, while remaining aligned with established monosemanticity indicators. TMS also reveals distinct SAE training dynamics across base models. Moreover, under encoder anisotropy, TMS provides a stronger indication of probe-based concept deletion effectiveness, while being competitive otherwise.
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Artificial Intelligence · Cross-listed from cs.LG
FIFA World Cup 2026 as a Contamination-Free Benchmark for LLM Forecasting Agents: Four Models, a Bookmaker, and 104 Matches
Jiacheng Ding, Cong Guo and Jason Xu
We introduce WC2026-
Agents, a
benchmark and
dataset for evaluating large
language models (LLMs) as autonomous
forecasting agents on real, future events. For every one of the 104 matches of the 2026 FIFA World Cup, four frontier models – Claude Opus 4.8, ChatGPT (GPT-5.5, high
reasoning), Gemini 3.1 Pro, and Grok (Expert Mode) – ran an identical search-act-reflect loop: gather evidence with a web tool, commit to a 1X2 (team-A win / draw / team-B win) distribution and a virtual 100-USD bet, and, after the match, reflect given only the final score. Because every match kicked off after the models' training cutoffs, the
benchmark is contamination-free by construction. Crucially, we pair the four
agents with a fifth competitor drawn from the same information environment – the pre-match betting
market – collected as per-match 1X2 odds, giving an economically grounded baseline and letting us score not just what an
agent predicts but what it does with money. The release contains 416 forecasts and 414 reflections with verbatim
reasoning, ground truth (including penalty shootouts), odds, and a reproducible evaluation suite. A reference evaluation surfaces findings that raw accuracy hides: the four
agents issue an identical top pick in 92% of matches and none beats the
market's Brier score; indeed, a naive flat stake on the
market favorite out-earns all four
agents. Yet the
agents diverge sharply as decision-makers: betting return-on-investment ranges from -18% to +10%, fading the
market is unprofitable for all four, the share of forecasts that cite the
market ranges from 12% to 100%, and self-reported error rates on wrong picks range from 36% to 86%. The
benchmark thus measures calibration, decision quality, and self-knowledge – axes on which frontier models differ even when their predictions do not. Data and code: https://github.com/graphuofm/FIFA2026LLM
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Artificial Intelligence · Cross-listed from cs.IT
Autonomous Discovery of Wireless Communications Algorithms
Fayçal Aït Aoudia, Jakob Hoydis, Sebastian Cammerer et al. (+4 more)
Large
language model (LLM)-driven evolutionary search is an emerging
algorithm-discovery paradigm that has already produced novel results in several scientific fields. Yet its application to wireless communications remains largely unexplored. To bridge this gap, we introduce The AI Telco Engineer (AITE), a framework to autonomously design
algorithms for complex communication problems, while navigating performance-complexity tradeoffs. We showcase AITE on two challenging physical-layer problems: designing an equalizer for an orthogonal time-frequency space (OTFS) system, and constructing a receiver
algorithm for an orthogonal frequency-division multiplexing (OFDM) system using a custom constellation and operating without pilots. For the first task, AITE develops
algorithms that outperform the best-known solutions while reducing computational latency by a factor of 3.6 compared to the strongest baseline. For the second task, it discovers the first explicit, explainable
algorithms that achieve performance parity with state-of-the-art neural receivers. These results demonstrate the strong potential of LLM-driven evolutionary search for the autonomous discovery of next-generation wireless communications
algorithms.
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All authors Fayçal Aït Aoudia, Jakob Hoydis, Sebastian Cammerer, Gian Marti, Merlin Nimier-David, Nicolas Roussel, Alexander Keller
Artificial Intelligence · Cross-listed from cs.SD
Time-Frequency Consistency Learning for Robust Speech Deepfake Detection
Jun Xue, Zhuolin Yi, Yanzhen Ren et al. (+6 more)
Recently,
speech deepfake detection (SDD) has achieved significant progress. However, its robustness evaluation remains largely confined to controlled additive noise scenarios, lacking systematic investigation of the complex distortions introduced by acoustic front-end (AFE) processing pipelines in real-world deployments. In this work, we simulate a unified AFE pipeline comprising acoustic echo cancellation, noise suppression, automatic gain control, and voice activity detection (VAD), and conduct a comprehensive evaluation of current state-of-the-art models. The results show that the nonlinear and time-frequency coupled distortions introduced by AFE significantly degrade detection performance. To address this issue, we propose a Time-Frequency Consistency Learning (TFCL) framework, which aims to learn invariant spoofing representations that remain stable before and after AFE processing. We observe that AFE not only introduces temporal misalignment (e.g., segment-level shifts caused by VAD), but also weakens or distorts critical frequency-domain cues. To this end, TFCL employs an
attention-driven soft
alignment mechanism to capture cross-temporal dependencies, along with frequency-domain structural consistency constraints to enforce feature invariance. As a result, the model is able to maintain stable representations under both temporal perturbations and spectral distortions. Extensive experimental results demonstrate that the proposed method effectively mitigates the performance degradation caused by AFE processing, significantly improving the robustness of SDD in real-world scenarios. The code is available at https://github.com/JunXue-tech/TFCL.
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All authors Jun Xue, Zhuolin Yi, Yanzhen Ren, Yihuan Huang, Jiayu Xiong, Yi Chai, Guanxiang Feng, Jiajun Liu, Tong Zhang
Artificial Intelligence · Cross-listed from cs.LG
Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning
Ziyi Liu and Grace Zhang
Inverse reinforcement learning (IRL) provides a powerful framework for learning from demonstrations. However, real-world tasks often exhibit substantial natural variations (e.g., picking up mugs with varying shapes), making it impractical to collect demonstrations that fully specify a new task under every possible scenario. In practice, while demonstrations for the target task are limited, it is often easier to obtain
datasets of heterogeneous but related behaviors. This motivates the problem of few-shot IRL with multi-task demonstrations (FM-IRL), where an
agent must learn a new task with substantial variations from only a limited number of target-task demonstrations, together with sufficient demonstrations of related tasks and online
agent experience. To do so, we must both recover the expert distribution of the new task and provide guidance when the
agent deviates from it. We introduce Multitask discriminator Proximity-Guided IRL (MPG), which learns two complementary reward components: (1) a generalizable discriminator that transfers shared structure across related tasks to identify expert behavior in a new task, and (2) a proximity function that measures how far a state deviates from expert behavior and provides corrective guidance during exploration. We demonstrate the effectiveness of our method on multiple challenging
navigation and manipulation tasks under significant variations (e.g., object configurations, table layouts, and initial
robot poses), achieving an average success rate of 81.2%, outperforming the strongest per-task baseline by an average of 24.7 percentage points.
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Artificial Intelligence · Cross-listed from cs.RO
Seg2Grasp: A Robust Modular Suction Grasping in Bin Picking
Hye-Jung Yoon, Juno Kim, Yesol Park et al. (+2 more)
Current bin picking methods that rely heavily on end-to-end learning often falter when confronted with unfamiliar or complex objects in unstructured environments. To overcome these limitations, we introduce Seg2Grasp, a modular pipeline designed for robust suction grasping in dynamic and cluttered bin scenarios. Seg2Grasp is built on a three-step process:
Segmentation, Grasping, and Classification. The
Segmentation module employs a
Transformer-based model to generate class-agnostic object masks from RGB-D images, ensuring accurate detection across various conditions. The Grasping module uses surface normals and mask proposals to determine the optimal suction points, enhancing grasp success. Finally, the Classification module leverages fine-tuned open-vocabulary Mask-CLIP for precise object identification, enabling versatile handling of diverse objects. Real-world robotic experiments demonstrate that Seg2Grasp outperforms existing methods in success rates and adaptability, establishing it as a powerful tool for automated bin picking in industrial settings.
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All authors Hye-Jung Yoon, Juno Kim, Yesol Park, Jun-Ki Lee, Byoung-Tak Zhang
Artificial Intelligence · Cross-listed from cs.CV
DA-Fusion: Deformable Attention-Based RGB-D Fusion Transformer for Unseen Object Instance Segmentation
Yesol Park, Hye-Jung Yoon, Juno Kim et al. (+1 more)
In logistics automation, precise
segmentation of unseen objects is crucial for efficient robotic manipulation in cluttered environments. Tasks such as bin-picking and shelf-picking require robust perception to handle occlusions, varying object shapes, and complex spatial arrangements. Traditional RGB-based methods tend to over-segment objects due to their reliance on texture, while depth-based methods often under-segment by focusing primarily on
geometric features. To address these limitations, we propose DA-Fusion, a deformable
attention-based RGB-D fusion
Transformer designed for unseen object instance
segmentation. DA-Fusion effectively combines the strengths of both RGB and depth data, enhancing
segmentation accuracy in cluttered and multi-layered object environments. We also introduce the Object Clutter Bin
Dataset (OCBD), a
benchmark dataset specifically tailored for evaluating bin-picking scenarios in top-down views. Extensive evaluations demonstrate that DA-Fusion outperforms state-of-the-art methods across diverse environments, making it particularly suited for real-world logistics tasks.
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All authors Yesol Park, Hye-Jung Yoon, Juno Kim, Byoung-Tak Zhang
Artificial Intelligence · Cross-listed from cs.IR
MagicSelector: Joint Optimization for Agent Tool Selection via Counterfactual Decomposition and Progressive Reranking
HONOR Agentic Search Team: Zhengzong Chen, Lei Tang, Lijun Liu et al. (+26 more)
We present MagicSelector, a joint optimization framework integrating Counterfactual task decomposition, Progressive reranking, and Dynamic Top-K, designed to address the fundamental challenges of tool
retrieval in
agents. MagicSelector is a specialized framework capable of translating ambiguous user instructions into executable atomic subtasks and guiding high-precision tool
retrieval, effectively mitigating redundant noise and severe context distraction in out-of-domain (OOD) scenarios. We empower MagicSelector with these capabilities through three key contributions: (1) a preference-guided counterfactual task decomposition mechanism that utilizes a counterfactual reward to quantify the marginal causal gain of decomposition on
retrieval ranking, effectively imposing fine-grained structural supervision on logical coherence; (2) a progressive tool reranking method driven by self-
distillation hard negative mining, which optimizes both point-wise and list-wise relevance to enhance fine-grained discrimination among highly similar tools; and (3) a dual semantic boundary-aware dynamic Top-K strategy that adaptively monitors reranking score cliffs and inter-tool semantic shifts to dynamically truncate the candidate list, maximizing relevant tool recall while filtering long-tail noise. Evaluated on MTDTool, the first task decomposition
benchmark we constructed tailored for mobile multi-turn interactions with process-level annotations, MagicSelector yields promising performance. Extensive experiments demonstrate that MagicSelector significantly outperforms state-of-the-art methods in terms of tool
retrieval accuracy, OOD generalization capability, and overall
token efficiency, thereby demonstrating the effectiveness of our proposed framework.
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All authors HONOR Agentic Search Team: Zhengzong Chen, Lei Tang, Lijun Liu, Chuandi Jiang, Fan Yang, Keyun Chu, Chu Zhao, Shihao Liu, Minghang Li, Bo Liang, Can Wen, Hailong Wu, Jingnan Ju, Mian Liu, Nengbin Zhang, Peiqiang Wang, Penghe Nie, Qinhui Gu, Sijia Lv, Siqi Chen, Wei Zhang, Yang Xu, Yuhao Qian, Yuxiang Zhang, Zeng Cheng, Zhen Wang, Zuan Chen, Yuanyuan Zhao, Fei Huang
Artificial Intelligence · Cross-listed from cs.NI
Mobile Network Control with a World Model
Maxime Bouton, Ioanna Mitsioni, Simon Lindståhl et al. (+1 more)
The increasing complexity of mobile networks necessitates intelligent and dynamic control strategies for efficient, energy-conserving management. We propose a world model-based approach for network control that enables adaptive configuration of crucial parameters. The world model is trained from historical data and predicts the impact of its actions on future network states. Our controller leverages the model's uncertainty estimate to robustly find optimal network configuration changes. Furthermore, the optimization objective can be changed dynamically without model retraining. We demonstrate the effectiveness of the approach in simulated closed-loop control of a mobile network energy-saving feature. Our results show improved performance in balancing energy savings with quality of service, compared to traditional methods and reinforcement learning approaches. Finally, we show the world model performance on real network data from, and evaluate counterfactual actions proposed by the controller under various throughput constraints.
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All authors Maxime Bouton, Ioanna Mitsioni, Simon Lindståhl, Jaeseong Jeong
Artificial Intelligence
WuYu-EnvLE-Bench: A Benchmark for Evaluating Large Language Models in Environmental Law Enforcement
Ziliang Yang, Yi Zhang, Kaijun Lin et al. (+3 more)
Large
language models (LLMs) are increasingly considered for environmental enforcement, but their ability to produce traceable enforcement decisions remains unclear. We introduce WuYu-EnvLE-Bench, a
benchmark built from real enforcement cases, regulatory standards, and expert review. It contains 2,521
benchmark instances, 14 tasks, and 12 pollution-medium subdomains across pre-enforcement, in-enforcement, and post-enforcement workflows. Using Absolute Environmental Enforcement Score (AES) and Intelligent Enforcement Index (IEI), we evaluate open-source and closed-source LLMs across capability, response quality, and resource efficiency. Results show that LLMs perform well on rule-bounded tasks but remain unreliable in evidence-chain construction, contradiction detection, multi-source integration, and procedural judgment. Model scaling also shows diminishing returns: medium-sized models approach leading models in structured tasks, while larger models do not reliably overcome evidence-
reasoning bottlenecks. WuYu-EnvLE-Bench highlights the need for evidence-grounded, rule-aware, and task-adaptive enforcement
reasoning.
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All authors Ziliang Yang, Yi Zhang, Kaijun Lin, Jiachao Ke, Haihong Xu, Zongguo Wen
Artificial Intelligence
Semantically Similar, Yet Not Answerable: Diagnosing the Semantic-Answerability Gap in Table RAG
Jiaming Tian, Liyao Li, Wentao Ye et al. (+6 more)
In
retrieval-augmented generation (RAG), semantic relevance asks whether a source matches a query in meaning, while answerability asks whether it contains sufficient information to answer the query. A Semantic-Answerability Gap (SAG) may arise in
retrieval when a retriever can reach semantically relevant sources yet fail to identify those that are uniquely answerable. We uncover this gap using tables as a controlled setting, where shared schemas and entities provide strong semantic signals while localized content and row-column bindings distinguish answerable from non-answerable sources. Using TCR-Bench, a controlled sibling-table
benchmark, we find that dense retrievers achieve only 18.2% top-1 target
retrieval, reducing QA F1 from 0.755 with the oracle table to 0.330 with retrieved top-5 tables. Controlled diagnostics show that retrievers favor semantic volume over sufficiency, respond weakly to row-column binding disruptions, and struggle to distinguish Targets from Siblings. Explicit answerability assessment substantially improves target identification, while fine-tuning shows that answerability is learnable but difficult to transfer without compromising broad semantic
retrieval.
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All authors Jiaming Tian, Liyao Li, Wentao Ye, Haobo Wang, Peng Lu, Lihua Yu, Zujie Ren, Gang Chen, Junbo Zhao
Artificial Intelligence · Cross-listed from cs.LG
MXSens: Sensitivity-Aware Mixed-Precision Quantization for Efficient LLM Inference
Simla Burcu Harma, Danila Mishin, Zhengyuan Su et al. (+7 more)
4-bit quantization enables efficient LLM inference, but suffers from significant accuracy degradation due to outliers. Prior work addresses this problem via data rotation or mixed-precision integer quantization, but often relies on software-managed scaling and frequent dequantization, incurring substantial overhead. Microscaling formats, such as MXINT, eliminate these inefficiencies by encoding scales in hardware, yet remain incompatible with rotation-based methods. Our analysis reveals that outliers vary in severity, from rare extremes to frequent mild deviations, and that quantization sensitivity is unevenly distributed across layers and columns. These insights motivate a fine-grained, sensitivity-guided approach. We introduce MXSens, a training-free method that assigns mixed mantissa bitwidths (4/6/8) based on column- and layer-wise sensitivity, naturally leveraging the block-wise structure of MXINT. MXSens outperforms state-of-the-art quantization methods across a range of models and tasks. Under the W4A4KV4 setting, MXSens achieves perplexities of 3.77 and 7.63 on LLaMA-2-70B and LLaMA-3-8B, respectively, substantially improving over existing baselines on WikiText-2. Our work establishes a new balance between accuracy and resource efficiency for LLM quantization.
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All authors Simla Burcu Harma, Danila Mishin, Zhengyuan Su, Ayan Chakraborty, Elizaveta Kostenok, Dongho Ha, Babak Falsafi, Martin Jaggi, Yunho Oh, Amir Yazdanbakhsh
Artificial Intelligence · Cross-listed from cs.RO
Lifelong Multi-Subsystem Pickup and Delivery with Buffer-Limited Handover Stations
Chuanlong Zang, Isabelle Barz, Anna Mannucci et al. (+3 more)
Coordinating payload transfers between subsystems is a critical challenge in lifelong Multi-
Agent Pickup and Delivery (MAPD). We study systems where
agents are confined to separate regions and must exchange payloads through shared handover stations. These stations, equipped with single docks and finite buffers, are inherently vulnerable to blocking and starvation. We formalize this problem as Multi-Subsystem MAPD with Buffer-limited Handover Stations (MS-MAPD-BHS). We then propose Handover-Aware Reservation and Routing (HARR), an online controller that couples per-subsystem planners. HARR uses a shared dock reservation calendar and a deterministic rolling-horizon projection of buffer occupancy to coordinate actions. A candidate route is accepted only if its dock interval is free and the resulting buffer occupancy projection remains within capacity. Under perfect execution, these checks ensure collision-free dock use and buffer-safe committed operations within the reservation horizon. In simulation, HARR achieves up to 77% higher throughput and 92% lower backlog than a fixed-dock ablation at moderate load, while also reducing planning time relative to a coupled station-aware
Token Passing baseline. These results show that explicit interface coordination substantially improves stability in modular multi-subsystem transport.
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All authors Chuanlong Zang, Isabelle Barz, Anna Mannucci, Philipp Schillinger, Florian Lier, Wolfgang Hönig