Artificial Intelligence · Cross-listed from cs.CL
Dialogue to Detection: A Multimodal Hybrid NLP Pipeline for Insurance Fraud Detection
Muhammad Shakeel Akram, Amal Htait, Abdul Hamid Sadka et al. (+2 more)
Insurance fraud imposes substantial financial losses and operational inefficiencies, raising premiums and impacting trust among legitimate policyholders. Early detection at FNOL remains a persistent challenge. Existing approaches rely largely on private, text-only
datasets, limiting progress on
multimodal methods that integrate linguistic, behavioural, and speaker-based indicators. We introduce a synthetic
multimodal framework that replicates FNOL conditions. It generates
agent-customer dialogue transcripts and two-speaker audios, performs ASR and diarisation. Downstream modules combine NER, regex-based feature extraction, LLM-RAG
retrieval, and speaker
embeddings in a rule-based risk score to flag narrative reuse, structural inconsistencies, and cross-case voice repetition while balancing sensitivity and false positives.
Dataset validation and component-level evaluations show stability and transfer potential, offering a reproducible baseline beyond text-only fraud detection.
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All authors Muhammad Shakeel Akram, Amal Htait, Abdul Hamid Sadka, Emma Meisingseth, Karishma Jaitly
Artificial Intelligence · Cross-listed from cs.CV
Parallel Rollout Approximation for Pixel-Space Autoregressive Image Generation
Jiayi Xu, Di He and Guolin Ke
Pixel-space continuous-
token autoregressive (AR) generation directly models images as sequences of raw pixel patches, avoiding discrete tokenization or a separately pretrained tokenizer. However, it faces coupled challenges: high-dimensional patch generation causes large single-step errors, and teacher-forced training creates a train–inference gap that makes these errors accumulate across AR steps. Existing fixes such as $x$-prediction and input noise injection only partially mitigate these issues. Exact rollout training better matches inference-time conditions, but is impractical due to prohibitively slow sequential sampling. We propose Parallel Rollout Approximation (PRA), a scalable framework that addresses both challenges jointly. PRA generates low-dimensional intermediate states instead of high-dimensional pixel patches, then maps them back to pixel-space
tokens with a pixel decoder, preserving a pixel-in, pixel-out AR interface. It also constructs inference-like pixel inputs through the same intermediate-state-to-pixel path used at inference, independently across positions, approximating the pixel-feedback interface encountered during inference-time rollout while retaining parallel teacher-forced training. On class-conditional ImageNet-1K generation at $256\times256$ resolution, PRA-S with 135M parameters achieves an FID of 2.58, surpassing the previous billion-scale pixel-space AR result of 3.60. Scaling to PRA-L with 511M parameters further improves FID to 1.94, establishing a new state of the art among pixel-space AR models. Beyond generation, PRA achieves higher ImageNet classification probing accuracy than other AR and
diffusion baselines, suggesting its potential for unified pixel-space image generation and understanding.
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Artificial Intelligence · Cross-listed from cs.CR
SHARD: cell-keyed residual splitting for alignment-resistant private dense retrieval
Sergey Kurilenko
Dense
retrieval systems expose document geometry when vector stores are compromised, and a global protective transform can often be aligned from known pairs. We study SHARD, which splits PCA coordinates into a short routing prefix and a residual protected by independent
cell-local orthogonal keys. It supports CKKS ciphertext–plaintext reranking but is evaluated as a leakage trade-off, not a cryptographic document-
privacy guarantee. Corrected scoring uses centered document coordinates and an uncentered scoring query, preserving raw ranking up to a query-dependent constant. Across ten BEIR/MIRACL configurations it reproduces raw nDCG@10 and recall, whereas centering both sides loses up to 0.080 nDCG.
Cell keys spread diffuse known-pair evidence across compartments, but minimum-norm
alignment recovers useful signal far below full key rank, so there is no hard de-anonymization threshold. Real CKKS has maximum score error 2.29e-6 and no top-1 flips; block packing cuts query upload by 74–87% but raises in-process p50 latency by 14–26%. In a strengthened GTR case, an unknown key lowers
token-F1 from 0.665 to 0.242; a wide prefix and eight pairs restore much. Under 25–90% release overlap, the unchanged prefix and clean residual norm link persistent rows with R@1 at least 0.9996, although
cell-Gram linkage degrades under churn. A formally calibrated Gaussian release gives nDCG@10 at most 0.011 at epsilon=1; its only three strict utility matches occur at epsilon=32768 with linkage R@1 at least 0.995. SHARD preserves
retrieval and compartmentalizes
alignment evidence, but does not provide DP, unlinkability, or cancellable templates.
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Artificial Intelligence · Cross-listed from cs.CV
ProMSA:Progressive Multimodal Search Agents for Knowledge-Based Visual Question Answering
ZhengXian Wu, Hangrui Xu, Kai Shi et al. (+8 more)
Knowledge-based Visual Question Answering (KB-VQA) requires models to combine image understanding with external knowledge. Most prior methods use a fixed retrieve-then-generate pipeline with a pre-selected retriever and a static top-k setting, which is not adaptive during
reasoning. We propose ProMSA, a progressive
multimodal search
agent for KB-VQA. Given an image-question pair, the
agent iteratively chooses image search, text search, or stop, under explicit tool-call budgets and with deduplication to avoid redundant
retrieval. For training, we first use rejection-sampling SFT to learn valid tool-use formats, then optimize the
agent with TN-GSPO, a sequence-level RL objective that normalizes updates by both generation length and tool-interaction depth. Experiments on E-VQA and InfoSeek show consistent gains over strong RAG and
agent baselines, and improved
retrieval and end-to-end accuracy. The code is available at https://github.com/DingWu1021/Promsa.
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All authors ZhengXian Wu, Hangrui Xu, Kai Shi, Zhuohong Chen, Yunyao Yu, Chuanrui Zhang, Zirui Liao, Jun Yang, Zhenyu Yang, Haonan Lu, Haoqian Wang
Artificial Intelligence · Cross-listed from cs.CL
From Black-Box to Clinical Insight: A Multi-Stage Explainable Framework for Speech-Based Cognitive Impairment Detection
Yasaman Haghbin, Sina Rashidi, Ali Zolnour et al. (+6 more)
Speech-based cognitive impairment detection offers a noninvasive, accessible alternative to costly biomarker assays, yet
transformer-based models remain clinically uninterpretable. We propose a multi-stage explainability framework that translates black-box
transformer predictions into clinically grounded narratives by integrating SHapley Additive exPlanations (SHAP)-based
token attribution, theory-informed linguistic features, and a four-stage LLM
reasoning pipeline using LLaMA-3.1-70B-Instruct. Built on the SpeechCARE-Adaptive Gating Network
multimodal screening model (F1 = 72.11% on the NIA PREPARE
benchmark), the framework maps model outputs to four cognitive-linguistic dimensions, including lexical richness, syntactic complexity, and semantic coherence. Physician evaluation on 70 stratified English samples demonstrated strong
alignment with patient-level cognitive profiles, and a System Usability Scale score of 82/100 indicated high potential for clinical workflow integration.
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All authors Yasaman Haghbin, Sina Rashidi, Ali Zolnour, Fatemeh Taherinezhad, Ali Fartoot, Hossein Azadmaleki, James M Noble, Maryam Dadkhah, Maryam Zolnoori
Artificial Intelligence
RelBall: Relation Ball with Quaternion Rotation for Knowledge Graph Completion
Yike Liu, Peijia Xie, Chao He et al. (+1 more)
Real-world knowledge graphs are often incomplete, lacking many valid facts. Knowledge Graph Completion (KGC) aims to predict missing links using known triples, thereby enhancing graph coverage. A key challenge is modeling diverse relational patterns such as
symmetry, antisymmetry, inversion, composition and semantic hierarchy. Existing models such as RotatE can capture symmetric, antisymmetric, inverse, and commutative composition patterns, yet struggle with non-commutative composition. Rotate3D addresses this by introducing non-commutativity via three-dimensional rotations, but still fails to capture the semantic hierarchies prevalent in knowledge graphs. Moreover, both models cannot effectively model one-to-many relations. To overcome these limitations, we propose RelBall, which extends Rotate3D with two innovations. First, our model introduces modulus transformation to model hierarchies, driving abstract concepts toward smaller
moduli and concrete instances toward larger ones. Second, it introduces a tail-centric relation ball to model one-to-one, one-to-many, many-to-one, and many-to-many relations. RelBall offers the following advantages: (1) coverage of all relational patterns, including the ones mentioned above; (2) an interpretable hierarchical representation where the modulus directly reflect semantic levels; (3) support for one-to-one, one-to-many, many-to-one, and many-to-many relations. Experiments on multiple
datasets demonstrate RelBall's competitive link prediction performance against various baselines.
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All authors Yike Liu, Peijia Xie, Chao He, Huiling Zhu
Artificial Intelligence · Cross-listed from cs.SE
Reasoning Beyond Prediction: From Data-Driven to Causal Software Engineering
Roberto Pietrantuono, Luca Giamattei and Stefano Russo
Software engineering is an intellectually demanding, creative discipline that juggles a web of interdependent tasks to design, build, and assure the quality of increasingly complex systems. As our expectations from software soar - with demands spanning AI-driven products, pervasively distributed and cloud-native architectures, and deeply embedded cyber-physical environments - its complexity steadily increases. In response, a new wave of co-engineering methods and tools, fueled by deep learning, has emerged to augment the process, enhancing automation and decision support. Yet, these advances remain far from delivering the kind of intelligent support that modern software development demands. We call for a new paradigm of human-machine cooperation: one where machines don't just automate routine tasks or predict from learned patterns, but actively amplify engineers'
reasoning through the lens of causation. As software becomes smarter, a smarter support is needed.
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Artificial Intelligence · Cross-listed from cs.MM
It Lied to a Doctor to Buy Poison Ingredients: Quantifying Real-World Misuse of Phone-use Agents
Yiming Sun, Chen Chen, Zifan Zhou et al. (+1 more)
Phone-use
Agents can execute complex tasks end to end across real mobile applications. By operating a real device on the user's behalf, they reach far more functionalities than CLI
agents, which amplifies the real-world harm they can cause when driven for malicious purposes. We present the first study of this threat on real phones and 27 commercial apps, and find that
agents built on 9 mainstream commercial and open-source models readily carry out serious misuse, ranging from procuring drug and explosive precursors to fraud, online harassment, and review manipulation. Across the
agents we run on real devices, the average refusal rate to harmful requests stays low while the average task-completion rate reaches 68.8%, and in some scenarios an
agent finishes a violation faster than a human would. These results suggest that Phone-use
Agents already meet the practical conditions for automated misuse at scale. In one observed real-device execution, Claude-Opus-4.8 fabricated a medical history, deceived an online doctor into issuing a prescription, and completed the order and payment on its own to purchase a precursor for a highly toxic substance. To our knowledge, this is the first documented real-world case of an AI
agent procuring controlled precursor materials. We trace this behavior to a Safety Awareness-Execution Gap, where an
agent recognizes that a request is harmful yet still executes it. Simple defenses curb the overt cases, but the more covert and arguably more damaging threats, such as coordinated review manipulation and fake traffic, remain largely unsolved. We hope these findings push the community toward safer Phone-use
Agents.
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All authors Yiming Sun, Chen Chen, Zifan Zhou, Mi Zhang
Artificial Intelligence · Cross-listed from cs.CL
VASAE: Naming SAE Dictionary Directions with Vocabulary-Aligned Anchoring
Kairui Zhang, Ziwen Yu, Zahraa S. Abdallah et al. (+1 more)
Sparse autoencoders (SAEs) provide useful decompositions of
Transformer residual streams, but their learned features are usually named post hoc rather than directly connected to the
Transformer's
token vocabulary. We introduce Vocabulary-Aligned Sparse Autoencoder (VASAE), a method that trains SAE features under vocabulary-aligned anchoring and assigns each feature an intrinsic
token name: the
token string whose
embedding is nearest to that feature. Without reducing reconstruction quality compared with a standard SAE, VASAE produces dictionaries with vocabulary-aligned features. Using a 0.8 cutoff on the nearest-
token alignment score, dictionaries trained on GPT-2-small post-residual streams align about 90% of features in layers 0–10. In Llama-3.1-8B, representative shallow and middle-layer dictionaries contain strongly aligned features, including 92.8% in the shallow layer, while the representative final-layer dictionary shows limited
alignment. After subtracting the sentence-level mean sparse code, case studies show that many remaining intrinsic
token names are relevant to nearby input
tokens. These results suggest that vocabulary-aligned anchoring can connect learned features to intrinsic
token names during training, complementing post hoc interpretation of learned dictionaries.
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All authors Kairui Zhang, Ziwen Yu, Zahraa S. Abdallah, Martha Lewis
Artificial Intelligence · Cross-listed from cs.LG
Two-Stage Fine-Tuning for Protein Sequence Generation with Targeted Amino-Acid Composition
Violeta Basten-Romero, Rubén Muñoz-Tafalla, Anna María Díaz-Rovira et al. (+3 more)
Protein language models are standard priors for biological sequence generation, but steering them toward explicit distributional design targets remains largely unexplored. We study a constrained
protein generation problem in which sequences must match a desired amino-acid (AA) composition profile while preserving plausible sequence statistics and diversity. The motivating application is synthetic feed
protein design, where the AA composition of dietary
proteins directly determines their nutritional value. We propose a two-stage pipeline in which domain-adaptive fine-tuning (FT) on an in-domain
protein dataset is followed by iterative reward-weighted FT via reinforcement learning (RL) anchored against the FT model as a frozen reference. We evaluate the pipeline on two AA compositions and find that FT brings the average composition close to the target, while the subsequent RL enforces specific sequence constraints that FT alone cannot satisfy. We additionally evaluate the design choices of the proposed composition reward term against two baselines and an ablated variant, isolate the contribution of each training stage, and verify that AA composition
alignment is achieved without degrading sequence quality.
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All authors Violeta Basten-Romero, Rubén Muñoz-Tafalla, Anna María Díaz-Rovira, Bertran Miquel-Oliver, Isaac Filella-Merce, Víctor Guallar
Artificial Intelligence · Cross-listed from cs.CR
Agentic AI-Powered Re-Identification: An Emerging, Scalable Threat to Mobility Microdata Privacy
Oscar Thees, Roman Müller and Matthias Templ
The widespread collection of fine-grained location data by commercial data brokers creates a re-identification risk that is not widely recognised by the public. While prior research has established that mobility traces are highly unique and that individuals can, in principle, be identified from a handful of spatio-temporal points, such attacks have historically required significant manual effort from skilled analysts, limiting their practical scale. In this feasibility study, we demonstrate in a real world setting that agentic AI fundamentally changes this threat model. We present an end-to-end pipeline in which large
language model
agents autonomously search the open web, cross-reference public records and social media, and resolve raw coordinate sequences to candidate identities - without human intervention. We evaluate the pipeline on a spatio-temporal
dataset containing simulated location points anchored at and around true home and work addresses, focusing on a high-risk disclosure scenario. Our results demonstrate that, from spatio-temporal data and public sources alone, our agentic AI successfully re-identified 18 of the 25 re-identifiable individuals (72%) and 18 of 43 cases overall (41.9%). We discuss implications for Statistical Disclosure Control (SDC) practice and outline the near-future escalation that data custodians and regulators must anticipate. De facto anonymity - an implicit foundation of SDC practice - is shifting. Agentic AI strengthens the case that re-identification is reasonably likely by any means under the GDPR Recital-26 standard, at costs of minutes-and-dollars per target.
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Artificial Intelligence
Verifiable Geometry Problem Solving: Solver-Driven Autoformalization and Theorem Proposing
Can Li, Ting Zhang, Junbo Zhao et al. (+1 more)
Geometry Problem Solving have increasingly adopt the neuro-symbolic paradigm, combining neural intuition with symbolic rigor. However, current frameworks suffer from severe bottlenecks in two core stages: autoformalization, which treats
multimodal translation as a static task decoupled from downstream solver compatibility, and
theorem prediction, where solvers frequently hit a deductive impasse due to fixed rule libraries. To address these, we propose SD-GPS, a solver-driven framework that treats the symbolic solver as an execution oracle throughout both formalization and deduction. First, Solver-Driven Autoformalization unifies supervised formal-
language adaptation and solvability-guided reinforcement learning into a single module built on QwenVL3-2B, making executability the central training signal. Second, Verified
Theorem Proposing introduces an impasse-aware
agent that proposes local auxiliary lemmas from current proof states, ensuring soundness by filtering all proposals through symbolic verification. Empirical evaluations on Geometry3K and PGPS9K demonstrate that SD-GPS consistently outperforms existing MLLM, neural, and neuro-symbolic methods across standard completion, multiple-choice, and cross-modal reference regimes, proving that closing the loop between
multimodal perception and symbolic execution significantly improves
geometric reasoning, offering profound insights into how neural
agents can be grounded by formal systems to achieve verifiable problem-solving capabilities.
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All authors Can Li, Ting Zhang, Junbo Zhao, Hua Huang
Artificial Intelligence · Cross-listed from cs.CV
Home3D 1.0: A High-Fidelity Image-to-3D Asset Generation System for Interior Design
Yiyun Fei, Guoqiu Li, Jin Song et al. (+11 more)
We present Home3D 1.0, a modular image-to-3D generation system that produces high-quality 3D assets from a single reference image, targeting interior design and e-commerce applications. Given a photograph of a furniture or decor item, the system outputs a mesh with physically-based rendering (PBR) materials, and the mesh can be decomposed into material-specific components. The pipeline is organized into four tightly coupled modules: Geometry reconstructs a watertight mesh through latent SDF modelling with a geometry VAE and a coarse-to-fine flow-matching DiT; Texture predicts multiview albedo observations, reprojects them onto the mesh, and completes unseen surface regions with a 3D texture field; Material uses MatWeaver to obtain component masks through
video-based
segmentation and UV-space voting, then retrieves and bakes PBR maps from a curated material library through hierarchical multi-modal matching; and Parts generates material-editable semantic part meshes with a PartVAE and PartDiT, decoding multi-head part-specific SDF fields in one pass. Each module is evaluated independently with dedicated metrics, highlighting both the current system capability and the remaining gaps toward broader deployment.
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All authors Yiyun Fei, Guoqiu Li, Jin Song, Chuqiao Wu, Delong Wu, Hong Wu, Ziru Zeng, Haohui Chen, Yindong Kong, Jing Li, Qi Wu, Feng Zhang, Jianan Jiang, Ruigao Yang
Artificial Intelligence · Cross-listed from cs.CV
Reflect-R1: Evidence-Driven Reflection for Self-Correction in Long Video Understanding
Shuimu Chen, Yuteng Chen, Yuanshen Guan et al. (+7 more)
Current
multimodal reflection mechanisms for long
video understanding predominantly rely on closed-loop self-reflection within internal parameters. Lacking objective external evidence, models are frequently trapped in blind confidence and often fail to correct errors. Furthermore, applying reinforcement learning to multi-stage reflection pipelines introduces severe policy coupling, which is exacerbated by a critical scarcity of dedicated training data. To address these limitations, this work proposes Reflect-R1, the first Evidence-Driven self-correction framework for long
video understanding. The framework constructs a three-stage pipeline consisting of intuition, verification, and arbitration. By dynamically retrieving objective visual evidence to verify initial intuitions and autonomously executing multiple temporal searches to resolve conflicts, it completely breaks the
hallucination loop. To overcome policy coupling, we design a stage-decoupled reinforcement learning
algorithm named SD-GRPO that independently computes advantage functions across different
reasoning stages. Concurrently, we construct a
dataset of 120K samples to bridge the training data gap. Extensive experiments on
benchmarks such as VideoMME and LongVideoBench demonstrate that Reflect-R1 achieves state-of-the-art performance. Our method significantly improves the genuine rectification rate and enables authentic self-correction strictly grounded in objective evidence.
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All authors Shuimu Chen, Yuteng Chen, Yuanshen Guan, Zebang Cheng, Zeyu Zhang, Shengqian Qin, Bin Xia, Jiaran Li, Wenming Yang, Fei Ma
Artificial Intelligence · Cross-listed from cs.CV
Every Step of the Way: Video-based Parkinsonian Turning Step Counting
Qiushuo Cheng, Jingjing Liu, Catherine Morgan et al. (+2 more)
As a prominent symptom of Parkinson's disease (PD), turning impairment is evaluated through parameters such as turning angle, duration, and particularly, the number of steps required to complete a turn, which directly reflects motor dysfunction. Accurate step counting is challenging due to variability in real-world turning movements and atypical shuffling patterns in parkinsonian gait. Existing methods are predominantly wearable-based, requiring users to wear and manage dedicated devices, which can be inconvenient for continuous daily use. To address this, we propose a passive,
video-based framework that estimates step count in a coarse-to-fine manner using diverse motion representations. Specifically, an initial step count is estimated from foot movement signals derived from 3D human mesh recovery, providing high-level motion structures. To incorporate fine-grained motion details, a motion encoder learns complementary gait dynamics from mesh and optical flow to refine the initial estimate. In this process, coarse foot movement signals query the pixel-level motion cues via cross
attention to capture subtle parkinsonian gait dynamics. To handle varying
video lengths, we partition each
video into clips and integrate clip-wise motion
embeddings via multiple instance learning (MIL) for step count residual prediction. Extensive experiments show our method consistently outperforms existing step counting methods on real-world PD turning
datasets.
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All authors Qiushuo Cheng, Jingjing Liu, Catherine Morgan, Alan Whone, Majid Mirmehdi
Artificial Intelligence · Cross-listed from cs.CL
Triadic Werewolf: A Jester Role for Multi-Hop Theory of Mind in LLMs
Avni Mittal
Theory-of-mind evaluations of large
language models typically use dyadic social-deduction games, where every observable cue points to a single hidden side, so a model with strong
language priors can score well without ever simulating opponents' incentives. We extend the Werewolf game with a Jester, a third faction whose utility on peer suspicion is inverted because it wins by being voted out, so optimal play requires
reasoning across three opposing utility functions. Across 60 games on GPT-4.1, DeepSeek-V3.1, and Llama-3.3-70B with Jester self-learning on and off, the Jester wins 60-70% of games while Werewolves never exceed 20%, and GPT-4.1 wolves vote the Jester out on day 1 in 60-70% of games, a strictly self-defeating action. Self-learning helps DeepSeek and Llama but hurts GPT-4.1, with the cost landing on Villagers rather than Werewolves. Only DeepSeek learns the subtle strategy of looking suspicious without looking intentionally suspicious, and it gains the most from the loop. Triadic incentive structure exposes a layer of multi-
agent reasoning that dyadic deduction games leave invisible.
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Artificial Intelligence · Cross-listed from cs.AR
SEADA: An efficient methodology for optimizing mixed-precision DNNs on multi-precision spatial architectures
Leandro Fiorin, Marco Ronzani and Cristina Silvano
Mixed-precision computation has been introduced in deep neural networks (DNNs) as an effective approach to reduce latency, energy consumption, and
memory footprint. However, efficiently mapping mixed-precision networks onto multi-precision spatial architectures poses several challenges. These include determining the appropriate precision for each layer, balancing layer-wise accuracy sensitivity to quantization against architectural heterogeneity and system-level constraints, and accurately estimating the system-level cost of heterogeneous precision assignments. This work presents SEADA, an efficient methodology designed to address these challenges. SEADA comprises: (i) a configurable system-level analytical cost model of a multi-precision spatial accelerator architecture; (ii) a fast mapping tool that identifies near-optimal mappings of DNN workloads onto the target integer accelerator; (iii) analytical models for floating-point layers to estimate the overall benefits of mixed-precision execution; and (iv) a per-layer precision selection methodology based on bit-level
entropy, enabling efficient assignment across multiple numerical precisions. SEADA's efficiency provides designers with a robust framework for the design-space exploration of multi-precision architectures.
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Artificial Intelligence · Cross-listed from cs.CL
A Study of Temporal Fusion Strategies for Named Entity Recognition in Historical Texts
Emanuela Boros
Temporal variation poses a unique challenge for named entity recognition (NER) in historical texts, where entities drift in surface form and salience across time. While
language models (LMs) have made progress in various NLP tasks, their ability to reason about temporality, especially in diachronic contexts, remains limited or at least, questionable. In this paper, we systematically study how temporal metadata can be structurally embedded into NER models using a range of lightweight fusion strategies. We experiment with both absolute and relative temporal representations, injected into
Transformer-based architectures via early or late fusion mechanisms such as cross-
attention, adapters, and concatenation. Our evaluations on French and German historical
datasets reveal that late fusion strategies yield more robust and temporally generalisable performance, particularly in early and noisy periods.
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Artificial Intelligence · Cross-listed from cs.CV
SpatialUAV: Benchmarking Spatial Intelligence for Low-Altitude UAV Perception, Collaboration, and Motion
Haoyu Zhang, Meng Liu, Qianlong Xiang et al. (+3 more)
Spatial intelligence is essential for low-altitude unmanned aerial vehicle (
UAV) perception, collaboration, and
navigation. However, existing
UAV benchmarks often emphasize image-level recognition, single-view understanding, or narrow answer formats, leaving 3D spatial inference, multi-view collaboration, scene dynamics, and diverse task formulations insufficiently evaluated. To address these gaps, we introduce SpatialUAV, a real low-altitude
UAV benchmark comprising 4,331 curated instances across 14 fine-grained task types, covering semantic discrimination, spatial relation, aerial–aerial collaboration, aerial–ground collaboration, and motion understanding. SpatialUAV organizes all samples into a unified visual-input–question–answer schema, while supporting seven input configurations and nine answer formats, including option labels, region identifiers,
geometric values, cross-view correspondences, and free-form motion descriptions. To ensure reliable and grounded evaluation, our data construction pipeline integrates detector-assisted regions, depth supervision, metadata-derived rules, extensive manual annotation, blind filtering, and multi-turn human validation, together with task-specific metrics for heterogeneous outputs. Evaluating representative
vision-
language models across three categories, we show that current models remain far from human-level performance, with pronounced bottlenecks in cross-view association, structured grounding,
geometric reasoning, and temporal viewpoint understanding. These results offer empirical guidance for advancing low-altitude
UAV spatial intelligence. Code and data are available at https://github.com/Hyu-Zhang/SpatialUAV.
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All authors Haoyu Zhang, Meng Liu, Qianlong Xiang, Kun Wang, Yaowei Wang, Liqiang Nie
Artificial Intelligence · Cross-listed from cs.RO
S$^2$-VLA: State-Space Guided Vision-Language-Action Models for Long-Horizon Manipulation
Zhipeng Xie, Zongyi Han, Xiangyi Wei et al. (+3 more)
Vision-
Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation, but their performance degrades significantly in long-horizon tasks due to cumulative error propagation. This limitation largely arises from static feature fusion mechanisms that rely on fixed weights to combine visual,
language, and action representations, preventing the model from adapting to different phases of task execution. To address this limitation, we propose S$^2$-VLA, a framework that introduces a State-Space Guided Adaptive
Attention (SSGAA) mechanism. SSGAA maintains a belief state that tracks task progression and generates dynamic gating weights to adaptively fuse information from three complementary sources visual features for spatial perception, task intents for high-level task planning, and temporal action sequences for execution consistency. This adaptive fusion allows the model to shift its focus throughout task execution, aligning with the evolving requirements of different task stages. Despite its compact 2B parameter size, S$^2$-VLA consistently outperforms larger 7B-scale models and achieves state-of-the-art performance on long-horizon manipulation
benchmarks, including LIBERO and SimplerEnv. highlighting the importance of adaptive feature fusion for long-horizon robotic manipulation.
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All authors Zhipeng Xie, Zongyi Han, Xiangyi Wei, Shiliang Sun, Yang Li, Jing Zhao
Artificial Intelligence · Cross-listed from cs.LG
GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets
Janak M. Patel, Anirudh Deodhar and Dagnachew Birru
Demand
forecasting at the bottom of a retail hierarchy requires predicting tens of thousands of correlated long-horizon series across products, stores, and regions. Modern systems must scale across massive catalogs, capture shared demand dynamics, and remain interpretable enough to be trusted. Classical statistical methods need a separate model per series and are hard to manage at scale; deep autoregressive models struggle as the joint state grows to tens of thousands of dimensions; and recent graph-based forecasters, while capturing cross-entity dependencies, often produce opaque long-horizon forecasts. We propose GNBAN (Graph Neural Basis
Attention Network), an end-to-end architecture combining heterogeneous graph representation learning with an interpretable basis-decomposition head. Retail data are represented directly as a heterogeneous graph derived from the relational schema, so a single model serves the entire catalog. Rather than predicting the horizon directly, GNBAN decomposes each forecast into trend, seasonal, and generic components. Its key innovation is a per-basis
attention mechanism: each basis function keeps its own learnable query and retrieves information independently from the entity's historical neighborhood, letting different bases specialize to distinct temporal patterns while preserving interpretability. On two large-scale
benchmarks, M5 Walmart and Favorita Grocery Sales, evaluated under matched protocols, GNBAN improves volume-weighted WRMSSE by roughly 4-5% over a matched graph baseline. Qualitative analysis shows the learned decomposition exposes trend, seasonal, and residual demand drivers without post-hoc explanation methods. These results demonstrate that scalable relational
forecasting and interpretable forecast decomposition can be achieved together in a unified graph-based framework.
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Artificial Intelligence · Cross-listed from cs.LG
Applicability of memorization indicators for early spotting of overfitting while recalibrating sEMG-decoders on low sample sizes
Stephan J. Lehmler, Tobias Glasmachers and Ioannis Iossifidis
Deep learning models for surface electromyography (sEMG) can benefit substantially from subject-specific (re-)calibration, since no sufficiently large and diverse
datasets are available to train fully generic decoders. However, for user acceptance, the number of repetitions that can realistically be collected during calibration is severely limited, which increases the risk of overfitting and, in extreme cases, can even degrade performance compared to the uncalibrated model. Classical overfitting indicators such as validation performance and regularization with early stopping are difficult to apply in this low-sample regime, as they require additional held-out data that is rarely available in practical calibration scenarios. In this work, we investigate a recently proposed class of memorization indicators based solely on the activation statistics of rectified linear units (ReLU) in deep neural networks, which can be computed directly from training data without any extra validation set. We conduct a transferlearning experiment on a
benchmark sEMG
dataset, where a convolutional neural network is first pre-trained on multiple subjects and subsequently fine-tuned on individual users using only a small number of repetitions. During calibration, we monitor both decoding performance and the activation behaviour of the last hidden layer. Our results provide first evidence that decreases in test accuracy during fine-tuning are ac companied by characteristic changes in activation rates, indicating that activation-based memorization indicators are a promising tool for early spotting of unsuccessful learning in low-sample sEMG calibration settings.
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Artificial Intelligence · Cross-listed from cs.LG
WattLayer: Get Layers Right to Estimate Inference Energy of Neural Networks
Adrien Sardi, Marie-Line Alberi Morel, Sara Alouf et al. (+2 more)
The widespread adoption of Artificial Intelligence (AI) has led to increasing concerns about energy consumption, yet there is a lack of standardized methodologies to accurately estimate AI inference energy consumption, particularly across various tasks and architectures. In this study, we propose a task independent, layer-wise energy estimation model for AI architectures. Our model is evaluated on a large
dataset of more than 100,000 layers for 295 neural network architectures across 3 widely-used tasks and 3 distinct hardware platforms. Our approach achieves a median error of 19.6%, outperforming state-of-the-art methods. We further show that layer-wise decomposition generalize to new tasks without complete retraining, by leveraging shared layers across architectures. It offer tools, insights and a precise methodology to empower stakeholders in designing energy-efficient AI systems.
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All authors Adrien Sardi, Marie-Line Alberi Morel, Sara Alouf, Frédéric Giroire, Joanna Moulierac
Artificial Intelligence · Cross-listed from cs.CV
Hippocampus-DETR: An Explicit Memory Object Detection Framework Based on Hippocampus Modeling
Zhaoning Shi, Bo Ma, Hao Xu et al. (+2 more)
This paper addresses the lack of explicit
memory mechanisms in current object detection models and proposes Hippocampus-DETR, a novel detection framework based on biological hippocampal
memory modeling. This framework integrates a hippocampal
memory network module, HipNet, into the DETR architecture and systematically simulates the anatomical structure and functional organization of hippocampal subregions, including the entorhinal cortex, dentate gyrus, CA3, CA1, and subiculum. Through this design, Hippocampus-DETR realizes pattern separation, pattern completion, importance filtering, and information integration of visual encoding features. During training, different
memory submodules are optimized using a layer-wise training strategy, ultimately forming a
memory system with
memory retrieval and completion capabilities. Experimental results demonstrate that Hippocampus-DETR achieves higher detection accuracy than current mainstream models. More importantly, models equipped with this framework also exhibit excellent generalization ability and data efficiency in tasks such as few-shot image classification,
multimodal feature construction, and image restoration. Subsequent experiments further validate the functional necessity and internal interpretability of each
memory submodule. This study not only provides a novel object detection framework, but also offers a feasible technical pathway for integrating neurocognitive mechanisms with deep learning models, highlighting its significant value in improving model learning efficiency and task robustness. The project is available at https://github.com/2186cloud/hipnet.
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All authors Zhaoning Shi, Bo Ma, Hao Xu, Zepeng Yang, Bo Liang
Artificial Intelligence
NormAct: Benchmarking Embodied Agents' Proactive Compliance with Unspoken Social Norms
Shiyun Zhao, Xinwei Song, Tianyu Guo et al. (+7 more)
Embodied agents driven by
multimodal large
language models (MLLMs) can often complete everyday tasks from visual observations, but goal achievement does not establish whether they proactively respect unstated social norms. Existing
benchmarks assess explicit norm judgments or constrained behavior, but rarely test whether
agents infer and apply scene-relevant norms during ordinary tasks. We introduce NormAct, a
benchmark of 550 TongSim scenarios in which the same goal permits norm-compliant or norm-violating action sequences. Norm-relevant evidence is embedded in each scenario while the applicable rule is omitted from the goal instruction. By progressively increasing normative guidance while holding the goal and scene fixed, NormAct tests whether compliant behavior emerges autonomously or only after prompting. Across three MLLM planners, goal achievement substantially exceeds norm compliance without guidance (67.4% versus 24.7%), while both broad and rule-specific guidance improve compliance, indicating that planners can often comply when prompted but not reliably on their own. With a fixed planner, general-norm
retrieval is less effective than norm-relevant scene descriptions or generated norm cues, suggesting that identifying relevant visual evidence is a greater challenge than accessing general norm knowledge. NormAct therefore supports the development of
embodied agents that pursue everyday goals while proactively respecting unstated social norms.
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All authors Shiyun Zhao, Xinwei Song, Tianyu Guo, Xiaomeng Gao, Mingyuan Liu, Xu Han, Yuanyuan Zhang, Zhenliang Zhang, Xue Feng, Bo Dai
Artificial Intelligence · Cross-listed from cs.LG
Pepti-drift: Scalable Safe-Active Peptide Generation Without Inference-Time Guidance
Takashi Fujiwara, Hikaru Shindo, Kaushalya Madhawa et al. (+6 more)
Therapeutic peptides are a promising drug modality, but their generation must satisfy multiple therapeutic constraints. We introduce BindSafe-PepBench, a fixed-budget
benchmark that jointly evaluates target binding and four major safety metrics on the same generated candidates. We reveal that peptide length is a major confounder of joint binding-safety evaluation: longer peptides tend toward stronger predicted binding but less favorable predicted safety. This creates an apparent trade-off and can bias comparisons among models with different output-length distributions. We report absolute Safe-Active yield and exact-length-matched gains to distinguish generative improvements from output-length effects. High Safe-Active yield remains challenging, while the strongest multi-property methods rely on costly inference-time guidance. We therefore introduce Pepti-drift, a one-step generation framework that incorporates attraction toward target-specific binders and repulsion from liability-associated regions, requiring a single latent refinement followed by parallel decoding without inference-time guidance. Across 88 held-out targets, Pepti-drift achieves an 18.37% predicted Safe-Active yield while retaining positive exact-length-matched gains. The resulting gains are competitive with multi-property-guided baselines while requiring 468 times lower generation cost, enabling scalable and fair high-throughput peptide design.
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All authors Takashi Fujiwara, Hikaru Shindo, Kaushalya Madhawa, Jun Jin Choong, Shuan Chen, Yuna Oikawa, Yiming Zhang, Gyubok Lee, Keisuke Ozawa
Artificial Intelligence · Cross-listed from quant-ph
Parameter-Efficient Quantum-Inspired Fast Weight Programmers for Traffic-Matrix Forecasting
Kuo-Chung Peng, Jiun-Cheng Jiang, Chun-Hua Lin et al. (+3 more)
Traffic
matrices (TMs) capture network-wide origin-destination demand and are central to traffic engineering, yet accurate whole-
matrix forecasting remains challenging when prediction must be performed under the
memory, update, and training-budget constraints of online network control. This paper investigates whether compact
quantum-inspired recurrent models can provide effective TM forecasts without relying on dedicated graph,
transformer, or
diffusion modules. We adapt gated
quantum-inspired Kolmogorov-Arnold network fast-weight programmers (QKAN-FWPs) to direct multi-step Abilene TM
forecasting, where each model predicts the next 20 five-minute frames of a 144-channel origin-destination (OD)
matrix from a two-hour history. We
benchmark three QKAN placement variants against a matched-size long short-term
memory (LSTM) network, a larger LSTM, and a classical gated fast-weight programmer under a shared fixed-budget training protocol. Among the evaluated recurrent models, G-QKANFWP achieves the best pooled root-mean-square error (RMSE), while using only 22.4% of the larger LSTM. It also outperforms both the matched-size LSTM and the classical G-FWP baseline, indicating that the gain is not due to gated fast-weight framework alone. Convergence and channel-wise analyses further show that the
quantum-inspired variants obtain lower validation-loss area under the learning curve (AULC) than matched-size recurrent baselines, while G-QKANFWP and GQKAN-FWP achieve substantially more OD-channel wins. These results identify a classical slow programmer with a
quantum-inspired fast programmer as a promising accuracy-efficiency design for resource-conscious network traffic-
matrix forecasting.
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All authors Kuo-Chung Peng, Jiun-Cheng Jiang, Chun-Hua Lin, Tai-Yue Li, Nan-Yow Chen, Samuel Yen-Chi Chen
Artificial Intelligence
ATOD: Annealed Turn-Aware On-Policy Distillation for Multi-Turn Agentic Tasks
Qitai Tan, Zefang Zong, Mo Li et al. (+3 more)
Training small
language-model
agents for long-horizon interactive tasks requires both fast imitation and reward-driven improvement. On-policy
distillation (OPD) provides dense teacher guidance and typically improves rapidly in the early stage, but its gains saturate once the student approaches the teacher, limiting the final performance ceiling. Reinforcement learning (RL) directly optimizes environment rewards and encourages exploratory improvement toward a higher reward-defined ceiling, but sparse and delayed feedback makes early-stage learning much less efficient than OPD. In this paper, we propose ATOD (Annealed Turn-aware On-policy
Distillation), a hybrid online
distillation algorithm that explicitly exploits this complementarity. (1) ATOD uses an annealed OPD-RL schedule: OPD dominates early training to approach teacher-level behavior, while RL is gradually strengthened to drive reward-based exploration. (2) ATOD introduces Turn-level Disagreement-Uncertainty Reweighting (T-DUR), which softly gates the
distillation sig- nal to prioritize turns with high disagreement or uncertainty in long trajectories. Experiments on ALFWorld, WebShop, and Search-QA show that ATOD consistently outperforms competing post-training baselines: across the three student sizes, ATOD improves average success rate by 4.16 points over OPD and 23.62 points over GRPO, while surpassing the corresponding teacher models by 2.16 points.
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All authors Qitai Tan, Zefang Zong, Mo Li, Yipeng Shi, Yang Li, Peng Chen
Artificial Intelligence
Agentic vs. Parametric World Models: Hybrid Planning for Reliable Language Agents
Xinyuan Song and Zekun Cai
Language agents, such as ReAct, Reflexion, and RAP, implicitly predict how the world will change as they plan and act, using imagined state transitions to guide their decisions. These predictions are encoded in the
agents' backbone LLMs as internal beliefs about the environment, effectively forming an implicit world model for multi-step planning. However, hallucinated state information can become embedded in this world model, leading to persistent inaccuracies that influence subsequent decisions. Traditional parametric world models, such as Dreamer and MuZero, learn environment dynamics through explicit transition models, but their limited model capacity makes them difficult to apply directly to modern
language-
agent planning. We study this tradeoff in graph-structured planning environments and seek to combine the strengths of both paradigms. We propose Hybrid World-Model Planning (HYBRID-WM), which preserves the
language agent's semantic planning capabilities while leveraging a lightweight parametric world model for more accurate transition predictions. Specifically, the parametric model constructs a compact planning skeleton to guide action generation while maintaining consistency between the
agent's imagined transitions and the model's predictions. Experiments across four graph-structured planning
benchmarks demonstrate that HYBRID-WM consistently improves task success and reduces hallucinated-state propagation with modest computational overhead. These findings highlight the effectiveness of combining semantic planning with lightweight parametric grounding for reliable long-horizon
language-
agent planning.
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Artificial Intelligence · Cross-listed from cs.DC
Optimizing Teacher-Student Partitioning for Scalable Knowledge Distillation on HPC Systems
Adrian P. Dieguez, Victor Conchello Vendrell, Alex Batlle et al. (+3 more)
Knowledge
Distillation (KD) enables training smaller student models under the guidance of larger teacher models, and the widely adopted TRL library implements it. Yet, TRL treats both models symmetrically, missing opportunities to exploit their pronounced asymmetry in
memory footprint, and communication requirements. This paper presents an HPC-aware methodology for KD that decouples teacher and student partitioning efficiently. Our approach achieves up to 67% higher samples-per-second than TRL by avoiding unnecessary teacher-model data structures and selecting the best split strategy. We combine vertical and horizontal partitioning of models, deriving an analytical expression that identifies the existence of inflection points between splitting regimes. These results showed that exploiting teacher–student asymmetry through
topology-aware parallelism notably accelerated GKD training on production HPC clusters at our company
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All authors Adrian P. Dieguez, Victor Conchello Vendrell, Alex Batlle, Vinnam Kim, Jordi Ros-Giralt, Harris Teague