Artificial Intelligence · Cross-listed from cs.DC
MANE: A Multi-Path Adaptive Network for Edge Onloading of Deep Neural Networks
Sokratis Nikolaidis, Stylianos I. Venieris, Leonidas Malachias et al. (+1 more)
Split computing constitutes a widely used distributed inference approach, where a lightweight head model is onloaded onto the device and a heavier tail model resides on an edge server, leveraging the growing computational capabilities of modern System-on-Chips while alleviating server load. As intelligent indoor environments such as smart offices grow increasingly populated with diverse IoT devices, a single edge server must simultaneously assist multiple devices, each competing for the same shared inference resources. Without a principled mechanism to manage this shared load, the server is quickly overwhelmed, causing latency SLO violations and rendering server-assisted inference ineffective. In this work, we present MANE, a distributed inference framework that equips the server with a multi-path tail architecture, enabling a dynamic accuracy–throughput trade-off at runtime. By introducing a novel multi-path model architecture, a three-stage training scheme featuring a Joint Head Network
Distillation loss and a hysteresis-based scheduler with an equitable device-fallback policy, MANE maintains over 80% SLO satisfaction rate where state-of-the-art onloading methods fail completely, while preserving accuracy 6pp higher than on-device alternatives, across up to 40 concurrent devices.
Read the full abstract ▾
All authors Sokratis Nikolaidis, Stylianos I. Venieris, Leonidas Malachias, Iakovos S. Venieris
Artificial Intelligence · Cross-listed from cs.CV
Compositional SVG Generation via VLM-Driven Hierarchical Semantic Parsing
Sehwan Park, Taehoon Kim, Geonhee Han et al. (+3 more)
While
Vision-
Language Models (VLMs) excel at visual
reasoning, generating structured, editable Scalable Vector Graphics (SVG) remains a fundamental challenge. Existing pipelines predominantly yield flat, semantically agnostic collections of paths, where editing a single object requires manually identifying its constituent paths. To address this, we propose a VLM-driven agentic framework for semantic compositional SVG generation. Our pipeline recursively parses visual scenes into semantic and
geometric hierarchies via top-down decomposition, visual grounding, and
prompt-driven amodal occlusion recovery, ensuring each component is geometrically complete. Furthermore, we introduce the Semantic SVG
Benchmark with human-annotated semantic groups and novel sub-component metrics (Semantic Recall/Precision, PERE) to explicitly evaluate structural compositionality and functional editability. Experiments show that our natively predicted structures surpass the upper bounds of existing flat-generation methods in both grouping quality and editability, while maintaining state-of-the-art visual fidelity.
Read the full abstract ▾
All authors Sehwan Park, Taehoon Kim, Geonhee Han, Dohyun Kim, Seung Wook Kim, Paul Hongsuck Seo
Artificial Intelligence · Cross-listed from cs.DB
Natural Language Knowledge Graph Query Execution: Leveraging Controlled Semantics in the LLM Context Window
Blake G. Fitch
Large
Language Model (LLM) applications often transfer domain concepts into the model's context informally, through
prompt prose, schema dumps, and examples. We show that for database queries, data model concepts pass to LLMs more effectively through representations whose vocabulary terms carry declared, machine-readable semantics (controlled semantics). NLKGQ is a working system and reusable framework that does this for data modeled in a knowledge graph. A formal OWL ontology serves as the transfer mechanism, concentrating the meaning of the data into semantically precise
tokens the model can use directly. In a single LLM call, NLKGQ places in the context a system
prompt instructing on SPARQL, the complete domain OWL ontology, and a domain-specific
prompt addition, together with the user's natural
language query. The model then generates the SPARQL query directly, zero-shot. Where the native vocabulary of an existing database or federation of databases is opaque, a wrapper ontology substitutes clean terms and a runtime rewriter restores the native forms. Evaluating on DBLP-QuAD 2.0 showed that its scores depend on the graph snapshot, the endpoint used, and the wording of its machine-generated questions, so we propose DBLP-QuAD 3.1, which maintains the intent of 2.0 while making reference results deterministic, revising reference SPARQL where needed, and rewriting the natural
language questions, with a frontier model, to state each reference query's intent clearly and completely. We evaluate on the DBLP-QuAD 2.0
benchmark (57.6% Match under deterministic re-scoring), DBLP-QuAD 3.1 (89.9% Match on 1,000 questions), SemOpenAlex (98% Match against a published baseline's 86% on the identical test set), and neuroimaging metadata (100%).
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.RO
Skill Composition for Legged Robot Reinforcement Learning
Daniel Gigliotti, Flavio Maiorana, Fabio Patrizi et al. (+1 more)
Robots, and
humanoid robots in particular, are increasingly competent at individual behaviors, each obtained by training a specialized controller. A specialized skill is quick to train, converges reliably because the problem it faces is narrow, and can be validated on its own, none of which is true of a single end-to-end policy asked to cover everything. What remains fragile is the transition between them. We argue that the composition of independent sub-policies deserves to be treated as a research problem in its own right, rather than as an implementation detail left to whatever mechanism happens to be at hand. Reliable composition is what turns a collection of separate skills into a repertoire that can be used, extended and shared. More fundamentally, if control can be passed between specialized policies safely, and at any moment, the choice of what the
robot should do next can be delegated to a component of an entirely different nature, such as a planner, an automaton or a symbolic controller, whose behavior can be inspected in advance. The policies would then only ever have to act, and what the
robot can be trusted to do would become verifiable.
Read the full abstract ▾
All authors Daniel Gigliotti, Flavio Maiorana, Fabio Patrizi, Luca Iocchi
Artificial Intelligence · Cross-listed from cs.CL
CompCQR: Compositional Query Generation for Training-Free Conversational Search
Yunah Jang, Kang-il Lee, Joongbo Shin et al. (+1 more)
Multi-turn interactions with LLMs are becoming increasingly common in information-seeking scenarios. However, user queries are often ambiguous and context-dependent, making them ill-suited for direct use as retriever queries. Conversational query reformulation (CQR) addresses this issue by rewriting the current utterance into a stand-alone query grounded in the dialogue history. Recent LLM-based CQR approaches achieve strong performance; however, their repeated LLM invocations and misalignment with downstream retrievers remain challenges. In this work, we begin from the observation that retrievers are highly sensitive to content ordering: simply reordering the same content can lead to changes in
retrieval coverage and performance. Based on this, we propose a novel training-free method that generates a very large number of queries with minimal LLM usage by compositionally combining a small set of atomic components. We further apply LLM
reasoning to construct a high-quality document set that balances precision and recall while capturing the user's core intent. Our framework generalizes across both open- and closed-source LLMs as well as dense and sparse retrievers. It achieves strong performance on four widely used conversational
benchmarks, with up to 22.5% relative MRR improvement over the previous state-of-the-art baseline with far fewer LLM calls.
Read the full abstract ▾
All authors Yunah Jang, Kang-il Lee, Joongbo Shin, Kyomin Jung
Artificial Intelligence
Diffusion-Based Generation of Gait Trajectories
Damian Benasco, Juan Carballeira-Lopez, Jaime Ramos-Rojas et al. (+4 more)
Generation of musculoskeletal gait trajectories conditioned on patient-specific parameters remains a key challenge for wearable robotics and rehabilitation. Assistive systems such as lower-limb exoskeletons require reference trajectories that adapt to individual morphology and therapeutic goals while preserving biomechanical realism. Traditional approaches rely on hand-crafted gait templates or optimization procedures that scale poorly across subjects and walking conditions. In this work, we explore conditional
diffusion models for generating lower-limb joint-angle trajectories conditioned on gait parameters such as step length. We compare a baseline
transformer diffusion model with a controllable
diffusion transformer variant incorporating adaptive normalization and classifier-free guidance. Experiments on a
dataset of 4,590 gait cycles show that
diffusion models can generate realistic periodic gait trajectories while enabling some controllability variation in gait characteristics, highlighting their potential for personalized gait synthesis in assistive robotics.
Read the full abstract ▾
All authors Damian Benasco, Juan Carballeira-Lopez, Jaime Ramos-Rojas, Julio S. Lora-Millan, Antonio J. Del-Ama, David Rodriguez-Cianca, Pablo Lanillos
Artificial Intelligence · Cross-listed from cs.HC
Investigating the Impacts of Generative AI on Information Seeking
Alexi Orchard and Shannon Lodoen
This paper is an encore submission of our 2026 journal article "Expertise and Information Seeking in the Age of Generative AI: New Procedures, New Problematics" with an extended discussion for the CSCW 2026 "Broader Impacts of GenAI in Communication" Workshop on October 10, 2026. In the original article, we employ procedural rhetoric to analyze how generative AI chatbots leverage natural
language signifiers of expertise and intelligence to influence users' perception of their trustworthiness. In this submission, we extend our conversation in the CSCW community with the goal of cultivating a cross-disciplinary vocabulary for describing, analyzing, and mitigating the risks posed by the integration of generative AI into human communication practices. It is important to develop an understanding of how the procedures surrounding information-seeking practices are informed by users' values, experiences, and expectations - and how these procedures might in future be altered by the emerging turn toward AI "experts" and authority.
Read the full abstract ▾
Artificial Intelligence
DynSTEER: Dynamic Stage-wise Trajectory Evaluation and Execution-time Review for Agents
Zhichao Shi, Xuhui Jiang, Wenjie Zhang et al. (+5 more)
Large
language model
agents are increasingly deployed for long-horizon task execution, raising a central granularity question for trajectory evaluation: whole-trajectory verification is too coarse to capture concrete failures and their associated evidence in long trajectories, while atomic-step scoring is too fine-grained, noise-sensitive, and computationally expensive. This granularity gap makes a single-reference trajectory paradigm inadequate for assessing the rich space of valid
agent execution paths and delays timely feedback and early stopping in long-horizon tasks. To address these issues, we propose DynSTEER, a dynamic stage-wise framework for
agent trajectory evaluation. DynSTEER bridges the granularity gap through stage-wise dynamic evaluation that segments rollouts at key execution nodes and adapts its multi-level review strategy based on stage-level results; it compiles a path-tolerant milestone graph from available task inputs to preserve diverse legal paths without reference leakage; and it supports terminating unrecoverable
agent executions to curb resource waste. Experimental results show that DynSTEER improves evaluation discriminability by over 85% compared with whole-trajectory evaluation and saves 17.74% of execution steps. The code is available at https://github.com/zhichao-stone/DynSTEER
Read the full abstract ▾
All authors Zhichao Shi, Xuhui Jiang, Wenjie Zhang, Xiaojun Wu, Cehao Yang, Chengjin Xu, Jian Guo, Yuanzhuo Wang
Artificial Intelligence · Cross-listed from cs.CV
Open-UniMo: Towards Unified Motion-Language Understanding and Generation in the Open World
Guocun Wang, Kenkun Liu, Guorui Song et al. (+7 more)
Unified motion generation and understanding is crucial for
embodied AI systems that can both synthesize and interpret human actions in open-world environments. Existing motion-
language models often treat motion as an auxiliary modality of a
language model, leading to text-dominated representations and limited cross-modal interaction. Moreover, the next-
token prediction paradigm is not naturally suited to long motion sequences, where autoregressive generation may accumulate prediction errors. To address these challenges, we propose Open-UniMo, a unified Large Motion-
Language Model (LMLM) trained on million-scale open-world motion-
language data. Open-UniMo promotes modality parity by extending Qwen's vocabulary of about 150K text
tokens with 64K motion
tokens, enabling motion and
language to share a unified
token space. We further introduce motion-consistent Chain-of-Thought
reasoning as an intermediate representation to bridge
language semantics and motion dynamics. Open-UniMo is trained with a two-stage pipeline, where supervised fine-tuning establishes CoT-guided bidirectional motion-
language mapping and Group Relative Policy Optimization (GRPO) improves semantic
alignment while mitigating cumulative errors in autoregressive motion-
token generation. To support comprehensive evaluation, we propose Open-MoBench, a VLM-guided
benchmark for assessing text-to-motion (T2M) generation, motion-to-text (M2T) understanding, and bidirectional consistency. Extensive experiments show that Open-UniMo achieves state-of-the-art performance on both conventional metrics and Open-MoBench. Furthermore, ablation studies reveal that M2T understanding is not primarily limited by motion-
token vocabulary size; instead, coupling M2T with the learnable T2M generation path yields stronger cross-modal representations, demonstrating that generation can facilitate understanding in AR-based motion-
language modeling.
Read the full abstract ▾
All authors Guocun Wang, Kenkun Liu, Guorui Song, Jing Lin, Zhe Huang, Luyuan Zhang, Dake Zhong, Choo Sin Wai, Xiaoguang Han, Haoqian Wang
Artificial Intelligence
A note on goal-based hierarchical RL
Kevin Murphy
The
agent-centric general value function (ACGVF) construction of \citet{tasse2026goal} lets the
agent make two decisions that are normally imposed by the environment or
agent designer: which goal to pursue and when to declare a goal as finished (in addition to choosing the action). This is a very general framework that subsumes almost all prior work on reinforcement learning, control and planning, as well as more general formalisms proposed in the cognitive sciences. However, it assumes the environment is fully observed, i.e., that the observation is a sufficient statistic. In \citet{murphy2025rl}, a general
agent design was proposed where the policy is based on an internal belief state $z_t$ and an internal goal; however, the goals were assumed to be externally provided. In this note, we unify and extend these two approaches using the formalism of hierarchical hidden Markov models (HHMM) \citep{murphy2001hhmm}.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.LG
Diagnosing Temporal Misalignment in Multichannel Time-Series Classification with Minimum Description Length
Sebastian Buschjäger, Michael Frichert, Daniel Kuhse et al. (+1 more)
Multichannel time-series classification commonly assumes synchronized sensor streams, although latency, clock drift, and preprocessing can introduce relative delays during data collection or after deployment. Existing synchronization solutions are often hardware-specific and difficult to apply retrospectively. Consequently, synchronization problems may remain undetected while classification performance is suboptimal. We introduce a classifier- and label-free diagnostic based on minimum description length (MDL). Our method applies candidate temporal shifts to sensor groups and measures how efficiently one group can be encoded through a representation of the remaining channels. An increased codelength indicates that the shift destroys shared temporal structure, whereas the minimum identifies the
alignment most strongly supported by the data. Unlike learned synchronization methods, the diagnostic requires neither retraining nor a trusted aligned reference and can therefore test both training and deployment data for misalignments. Experiments on two controlled synthetic tasks and nine real-world
datasets show that the metric exposes
alignment structure and can recover accuracy under induced deployment drift. A whole-
dataset audit further identifies stable nonzero MDL optima in established
benchmarks including FordChallenge, Opportunity, PAMAP2, and UCIActivity, revealing potential systematic offsets that conventional model evaluation does not expose. Our method thus provides a general-purpose tool for detecting, understanding, and correcting temporal misalignment throughout the time-series learning pipeline. Our code is available under https://github.com/sbuschjaeger/mdl-temporal-misalignment.
Read the full abstract ▾
All authors Sebastian Buschjäger, Michael Frichert, Daniel Kuhse, Jian-Jia Chen
Artificial Intelligence · Cross-listed from cs.CR
SENTINEL: A Multi-Pathway Architecture for Detecting Living-Off-the-Land APT Attacks on Windows Command Lines
Ahad Bin Islam Shoeb, Kamrul Hasan, Jamal Uddin Tanvin et al. (+4 more)
Living-Off-the-Land (LOTL) is the dominant evasion technique of Advanced Persistent Threat (APT) actors, exploiting legitimate Windows utilities to conduct malicious operations without deploying custom malware and enabling state-sponsored campaigns to maintain persistent access within military and critical defense infrastructure for extended periods. Existing detection methods fail against obfuscated commands and multi-stage attack sequences, as demonstrated by the Volt Typhoon APT campaign, which maintained undetected access to U.S. critical infrastructure for over 18 months using exclusively signed Windows utilities. We present SENTINEL, a multi-pathway architecture integrating BERT-based semantic encoding, character-level CNN for obfuscation invariance, inter-command
attention for multi-stage pattern recognition, and autoencoder-based anomaly scoring. Evaluated on a balanced Volt Typhoon
benchmark derived from Microsoft and CISA threat intelligence advisories, SENTINEL achieves 92.0% accuracy on documented state-sponsored attack commands and 91.2% on obfuscated variants, compared to 74.0% and 72.0% for standalone BERT. Per-class analysis reveals that models achieving over 98% overall validation accuracy on imbalanced data exhibit only 44-58% malicious recall on balanced
adversarial sets. Character-level processing contributes 5.6 percentage points of obfuscation invariance, and the 8.0 percentage point gap over augmentation-only baselines confirms structural architectural value beyond data-driven robustness alone.
Read the full abstract ▾
All authors Ahad Bin Islam Shoeb, Kamrul Hasan, Jamal Uddin Tanvin, Liang Hong, Imtiaz Ahmed, Md Arif Billah, Al Amin
Artificial Intelligence
AI Deployment Accountability Engineering: A Vision for Accountable AI in Safety-Critical Socio-Technical Systems
Murat Kantarcioglu
Artificial intelligence systems are rapidly becoming critical components in healthcare, finance, public services, and other safety-critical domains. Yet the engineering practices used to evaluate these systems remain predominantly model-centric, emphasizing properties such as accuracy, robustness,
fairness, and interpretability before deployment. These properties are necessary but insufficient once an AI system operates within an ever changing socio-technical environment characterized by distribution shifts, institutional constraints, human feedback loops,
privacy requirements, and interactions among multiple AI
agents. This
vision paper introduces AI Deployment Accountability Engineering (ADAE), a proposed AI engineering subdiscipline concerned with establishing measurable, continuous, and actionable accountability for deployed AI systems. ADAE treats accountability as a deployment-layer property rather than solely as a property of an individual model. It seeks to determine whether an AI-enabled system continues to operate within acceptable risk limits, identify the contexts in which failures emerge, attribute failures across interacting technical and human components, translate technical failures into downstream consequences, and support timely intervention. We articulate a research agenda built around four interconnected pillars: structured discovery of context-dependent failure modes,
privacy-preserving accountability measurement, system-level risk analysis for agentic AI, and translation of technical failures into operational, and institutional risks. The broader goal is to establish foundational principles, mathematical tools, and system architectures for accountable AI deployment across safety-critical applications.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CY
AlgoRAG: Retrieval-Augmented Generation for Theoretical Computer Science Education – A Comprehensive Evaluation Framework for Algorithm Analysis and Complexity Theory
Sushan Adhikari
Teaching abstract theoretical computer science (TCS) concepts such as
algorithm analysis and complexity theory is challenging because students must handle formal proofs and asymptotic
reasoning that conventional resources rarely explain in an adaptive, on-demand way. We present AlgoRAG, a specialized
Retrieval-Augmented Generation (RAG) system that couples a large
language model (LLM) with a curated, domain-specific knowledge base to address these challenges. The knowledge base integrates authoritative textbooks, 847 lecture slides, 312 practice problems with solutions, 156 worked proof templates, and 89 complexity worksheets. AlgoRAG incorporates domain-specific optimizations including mathematical entity recognition, notation-aware
retrieval, and pedagogical re-ranking. We evaluate AlgoRAG on 179 curated exam-style questions spanning asymptotic analysis, recurrence relations, dynamic programming, graph
algorithms, NP-completeness, sorting, and divide-and-conquer. The system achieves a 100% success rate with a mean response time of 38.0 seconds. While BLEU-4 scores are zero – a known limitation of n-gram matching on mathematical proofs where equivalent
reasoning may use entirely different notation – AlgoRAG attains ROUGE-1 F1 of 0.0963, ROUGE-L F1 of 0.0683, and a pedagogical quality score of 0.7620, indicating that responses are well-structured and didactically sound even when surface wording diverges from reference answers. Performance is especially strong on NP-completeness (ROUGE-1 F1 = 0.1285, pedagogical quality = 0.7643) and graph
algorithms (ROUGE-1 F1 = 0.1023, pedagogical quality = 0.8086). These results support the conclusion that RAG is an effective architecture for personalized theoretical-CS instruction, providing correct, context-rich explanations even for highly abstract topics.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CL
Disentangling Topology and Diversity in Multi-Agent LLMs for Multilingual Low-Resource Emotion Detection
Ulugbek Shernazarov, Charitha Ruwansiri Weerakon Basnayake, Abdelkhaleq El Jarjini et al. (+2 more)
Multi-
agent LLM systems combine multiple inference calls, but prior work often confounds how calls are connected with how they are diversified. We study these factors independently: inference
topology and source of inter-
agent diversity. In a controlled $2 \times 3$
matrix, we cross parallel aggregation and sequential refinement with
stochastic sampling, role prompting, and learned QLoRA specialization, under a fixed three-call budget and output protocol within each backbone. Using Qwen2.5-14B-Instruct and Llama-3.1-8B-Instruct, we evaluate all six configurations on
multilingual low-resource
emotion detection across nine
languages. Parallel learned specialization is strongest on Qwen at 52.83 Macro-F1 and reaches 52.94 on Llama. On Qwen it also exceeds same-backbone zero-shot, few-shot, CoT, and seven-call self-consistency baselines. The preferred
topology depends on diversity source: sequential refinement helps
stochastic and prompted settings, while the learned Width advantage shrinks from 2.83 points on Qwen to 0.17 on Llama. Depth-wise analysis suggests that later learned specialists can overwrite correct early predictions, although the aggregate effect is backbone-dependent. Overall, how
agents are differentiated produces larger performance shifts than
topology, which should be evaluated jointly with specialization.
Read the full abstract ▾
All authors Ulugbek Shernazarov, Charitha Ruwansiri Weerakon Basnayake, Abdelkhaleq El Jarjini, Noel Crespi, Praboda Rajapaksha
Artificial Intelligence · Cross-listed from quant-ph
Proving olympiad geometry theorems on a superconducting quantum processor
Ning Wang, Zheng-Zhi Sun, Zhengyi Cui et al. (+32 more)
Automated
theorem proving seeks to use computational systems to prove or disprove mathematical and logical statements [1, 2]. It underpins a wide range of applications, and enhancing
theorem-proving capabilities remains a central objective in artificial intelligence [3]. Although recent neuro-symbolic systems have achieved remarkable progress [4-7], their operation is ultimately constrained by classical computational architectures.
Quantum computing [8], by contrast, enables information encoding and coherent parallelism beyond classical limits [9-14], raising the possibility of accelerating structured symbolic deduction [15]. Here we report the experimental realization of automated geometry
theorem proving on a fully programmable superconducting
quantum processor. We develop two complementary
quantum proving frameworks. The first implements Wu's algebraic elimination method using
quantum pseudo-division, with multivariate
polynomials represented in superposition states, enabling
quantum algebraic
theorem proving. The second implements the full-angle method as backward symbolic
reasoning through a hybrid
quantum strategy-guided architecture, demonstrating a general route toward
quantum symbolic proof search. As illustrative examples, we prove two
theorems on a superconducting
quantum processor: the perpendicularity of the diagonals of a square and a 1978 International Mathematical Olympiad geometry problem. Our results establish, at the experimental level, automated logical
reasoning as a viable task for near-term
quantum processors and provide a concrete pathway toward
quantum-enhanced symbolic intelligence.
Read the full abstract ▾
All authors Ning Wang, Zheng-Zhi Sun, Zhengyi Cui, Yiren Zou, Aosai Zhang, Fanhao Shen, Jiarun Zhong, Zehang Bao, Zitian Zhu, Han Wang, Jia-Nan Yang, Jiayuan Shen, Gongyu Liu, Yanzhe Wang, Yihang Han, Yiyang He, Jiahua Huang, Sailang Zhou, Xinrong Zhang, Yaozu Wu, Zixuan Song, Jinfeng Deng, Hang Dong, Qi Ye, Weikang Li, Si Jiang, Yixuan Ma, Shuangyue Geng, Zhide Lu, Chao Song, Hekang Li, Pengfei Zhang, Qiujiang Guo, H. Wang, Dong-Ling Deng
Artificial Intelligence · Cross-listed from cs.CV
Sharing standardized image-derived data in computational pathology using DICOM
Daniela P. Schacherer, Christopher P. Bridge, David Clunie et al. (+13 more)
Development and evaluation of computational pathology methods require access to large and diverse
datasets. Over the past decade, various initiatives invested significantly into collecting, centralizing, and sharing pathology imaging data. In contrast, sharing of image-derived data such as region-of-interest delineations or
segmentation masks is less well developed. In this work, we describe our approach to encoding and sharing image-derived pathology data in a standardized manner within the National Cancer Institute (NCI) Imaging Data Commons (IDC), a platform that hosts and provides public access to de-identified radiology and pathology data. The IDC relies on the Digital Imaging and Communications in Medicine (DICOM) standard for data harmonization, yet the adoption of DICOM for pathology image-derived content has remained largely unexplored until now. Here, we present five representative
datasets harmonized by conversion from their original representations into DICOM and shared publicly in the IDC. We demonstrate the benefits of this harmonization, describe contributions to critical open-source tooling, and discuss technical considerations relevant to broader adoption of DICOM for pathology image-derived data.
Read the full abstract ▾
All authors Daniela P. Schacherer, Christopher P. Bridge, David Clunie, Igor Octaviano, André Homeyer, Markus D. Herrmann, Olivier Gevaert, Tabita Ghete, Markus Metzler, Henning Hoefener, Tahsin Kurc, Curtis Lisle, Kenneth Philbrick, Joel Saltz, Yuanning Zheng, Andrey Fedorov
Artificial Intelligence
OptoAgent: A Trustworthy Multi-Agent Framework for Opportunistic Vision Micro-Screening in Classroom Environments
Toqeer Ali Syed, Ali Akarma, Adeel Ahmad et al. (+1 more)
A child with reduced distance
vision often does not know that anything is wrong. Children adapt, move closer, and rarely report the difficulty, so the problem can survive years of schooling before an adult notices. School screening addresses part of this, but it runs on a schedule, depends on staffing, and is separated from the classroom moments where the difficulty appears. Smartphone and web-based acuity tests have widened access, yet every one of them still needs somebody to start a test. We present SightSentinel, an architecture that turns a wall display a child already reads from into a recurring screening site. Ordinary educational content carries short calibrated optotype probes, and eight specialized
agents divide the work. Perception
agents recover viewing distance, recognition accuracy, approach behavior, gaze stability, response latency, and interocular difference from each encounter. A quality
agent discards observations taken under bad geometry, poor lighting, or inattention. A longitudinal
agent accumulates only the surviving evidence against the child's own baseline, and an orchestrator reports a
Vision Concern Score routed through a safety gate whose output range excludes diagnosis, refraction, prescription, and reassurance. The design question is whether many cheap, noisy, well-gated encounters can reach a referral decision that one scheduled test reaches late or misses. We state the formulation, the architecture, a four-stage validation protocol against clinical reference standards, and the conditions under which the approach should be rejected.
Read the full abstract ▾
All authors Toqeer Ali Syed, Ali Akarma, Adeel Ahmad, Hammad Muneer
Artificial Intelligence
Beyond Scene Description: Multi-Agent Orchestration for Non-visual Access to Virtual Worlds
Toqeer Ali Syed, Ali Akarma, Adeel Ahmad et al. (+1 more)
Virtual worlds now host classrooms, meetings, conferences, shops, and social venues, and nearly every interaction they expose assumes a user who can scan a three-dimensional scene, follow avatars, and read floating panels. Blind and visually impaired (BVI) users are left with assistive tools that each solve one task in isolation: naming an object, reading text, describing a scene, or planning a route. A live virtual room defeats that model: obstacles, speakers, gestures, chat, slides, and notifications arrive together, and a tool that narrates all of them trades a visual barrier for an auditory one. This paper presents MetaBlind, an architecture that distributes nonvisual access across eight specialized
agents, spanning perception,
navigation, social and object interaction, communication, safety and trust,
memory, and personalization, and that places an Accessibility Orchestrator between those
agents and the user.
Agents publish candidate information into a shared accessibility context instead of speaking to the user directly. The orchestrator scores each candidate on safety relevance, goal relevance, urgency, confidence, user relevance, and estimated listening load, then releases only the items it judges relevant at that moment through
speech, structured audio, or haptic output. We give the selection step a formal statement, specify the orchestration cycle as an
algorithm, and define an evaluation protocol against a single-
agent assistant. MetaBlind is reported at the design stage, with no prototype measurement or user study, and the protocol states which outcomes would support the design and which would refute it.
Read the full abstract ▾
All authors Toqeer Ali Syed, Ali Akarma, Adeel Ahmad, Danial Hameed
Artificial Intelligence
When does a scaling result justify a different allocation? A critical review of resource-allocation evidence for AI systems
Seyed Morteza Emadi
AI scaling studies increasingly evaluate systems that combine a pretrained model with
retrieval, search, verification, tools, and interaction. Yet a higher score under a larger budget does not by itself show where additional resources are best spent. This critical integrative review asks when a reported scaling result supports a resource-allocation decision. It compares evidence across pretraining, test-time computation,
retrieval, and
agent evaluation, distinguishing the performance of a tested procedure from the best performance achievable under a resource limit. The synthesis shows that three mismatches recur across this evidence: success counted before an answer is chosen, information a deployed system will not have, and costs left out of the comparison. A capability surface expresses performance as a function of budgets, mechanisms, and available information. Worked analytical examples show how the evaluation metric, deployment volume, selection rule, and stopping policy can alter an allocation conclusion. A resource envelope provides a structured record of the task, development and run-time resources, information access, and procedure behind a reported score. Its application to a published comparison illustrates which conclusions the evidence supports and which deployment questions remain unresolved. The resulting framework specifies the comparisons needed to choose among feasible systems and motivates experiments on the transfer of allocation rules across tasks and operating conditions. It does not propose a universal scaling law or infer general intelligence from
benchmark gains.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.LG
EdgeHAR: An Edge-Native Compact Sensor Foundation Model for Human Activity Recognition
He Zhang, Siyu Yuan, Siyu Liu et al. (+2 more)
Sensor-based human activity recognition (HAR) is fundamental to ubiquitous and wearable computing, yet existing foundation models are largely designed for cloud-scale deployment and struggle with real-world sensing shifts, including unseen users, devices, sampling rates, and sensor placements. We present EdgeHAR, an edge-native compact sensor foundation model designed for wearable intelligence. Unlike conventional models that entangle activity knowledge with acquisition variations, EdgeHAR learns transferable representations by factorizing sensor signals into three latent codes: an (i)Activity-Semantic Code capturing reusable activity knowledge, a (ii)Motion-Dynamics Code modeling temporal patterns, and an (iii)Acquisition-Context Code representing sensor-specific variations. This disentangled design enables efficient adaptation to new users, devices, placements, and activity classes with limited target-domain data. By incorporating lightweight adaptation modules, EdgeHAR achieves foundation-model-level transferability while satisfying edge constraints in computation,
memory, latency, and
privacy. Experiments across heterogeneous HAR
datasets demonstrate that EdgeHAR maintains competitive recognition performance under distribution shifts with substantially reduced deployment cost. EdgeHAR establishes a practical paradigm for compact, edge-first foundation models for ubiquitous sensing systems.
Read the full abstract ▾
All authors He Zhang, Siyu Yuan, Siyu Liu, Sizhen Bian, Bin Guo
Artificial Intelligence · Cross-listed from cs.SD
Bridging the Modality Gap in Long-Form Clinical Audio: A Comparative Study of Lightweight and Heavyweight End-to-End SOAP Generation
Ziyu Zhang, Mingchen Shao, Wenjie Tian et al. (+3 more)
Automating clinical documentation from long-form doctor-patient conversations remains challenging for modern audio-
language models. While cascaded ASR systems perform well, end-to-end (E2E) models often struggle with information loss and
hallucinations on extended audio. For the BeTraC 2026 challenge, the ASLP team presents a fully E2E
multimodal system that generates structured SOAP notes directly from audio, bypassing intermediate transcripts. We constructed a 1.41-million-sample multi-task corpus and applied a multi-stage pipeline: domain pre-training, supervised fine-tuning, and reward optimization. Evaluating the architecture under both Lightweight (3B) and Heavyweight (30B) constraints reveals that each training stage progressively enhances performance. Furthermore, scaling to 30B parameters substantially boosts concept extraction and summarization quality. Ultimately, our E2E systems consistently outperform representative cascaded ASR+LLM baselines, proving the efficacy of direct
multimodal optimization for clinical documentation.
Read the full abstract ▾
All authors Ziyu Zhang, Mingchen Shao, Wenjie Tian, Tianlun Zuo, Longhao Li, Lei Xie
Artificial Intelligence · Cross-listed from cs.LG
Follow the Geometry, Not the Model: Cold Start Semi-Supervised Learning
Itai David and Daphna Weinshall
Modern semi-supervised learning (SSL) couples pseudo-label generation and classifier training, using the classifier's own confidence to select the pseudo-labels that are then used to update the model. In the cold-start regime, where at most a few labels per class are available, this coupling is ill-posed, since the classifier cannot supervise itself before it has learned. To address this problem, we propose VAST (Veracity-Aware Semi-Supervised Training), which decouples these two stages. Probabilistic beliefs over the unlabeled set are first inferred directly from the geometry of a frozen self-supervised
embedding and only then distilled into an inductive classifier. The construction rests on the Veracity
Matrix, a kernel-based structure that aggregates label evidence across the data
manifold and admits an interpretation as a Dirichlet posterior under a per-observation powered-likelihood model. Additionally, we introduce Veracity Propagation, a self-terminating belief-spreading step that extends coverage beyond the kernel neighborhood of the labeled set. Under a controlled protocol in which all methods receive identical frozen
embeddings and labeled sets, VAST outperforms the strongest graph-based SSL baselines at every operating point across three
datasets, with statistically significant gains in 7 of 9 comparisons, while producing a deployable inductive classifier rather than requiring transductive graph inference. Compared with end-to-end confidence-gated SSL, we further find that these methods underperform in this setting and, in our experiments, do not consistently exceed labeled-only performance.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CL
NeuroActiSep: Detecting Factual Hallucinations from Feed-Forward Neurons in a Single Pass
Ali Derogar Odolou, Reza Nazari and Mostafa Salehi
Hallucination in large
language models reduces their reliability and slows adoption. Various white-box studies have used internal representations to detect patterns of truthfulness and factuality. A less-studied approach is to identify feed-forward
neurons correlated with
hallucination. We propose a method to rank feed-forward
neurons at the final
prompt token using a custom
neuron selection
dataset. We transfer the selected
neuron identities to train
hallucination classifiers on other factual question answering
datasets. Our work provides empirical evidence that probes trained using the features from the selected
neurons perform on par with probes trained on internal states. We also analyze the distribution of selected
neurons and the effect of layer depth on detection performance.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CV
From Visual Attribution to Clinical Reasoning: Explainable Parkinson's Disease Screening from Hand-Drawn Patterns
Aritra Dey, Utsav Kumar Nareti, Chandranath Adak et al. (+3 more)
Parkinson's disease (PD) manifests early neuromotor impairments that become observable in controlled hand-drawn patterns such as spirals and meanders, where tremor-induced oscillations, stroke irregularity, and
curvature instability reflect underlying motor degradation. In this work, we present an explainable framework for PD screening from offline hand-drawn patterns that integrates discriminative visual modeling with clinically grounded
reasoning. The predictive model captures distributed structural distortions and fine-grained texture variations. It is evaluated under subject-disjoint protocols to ensure reliable generalization. To move beyond black-box classification, we introduce a multi-stage explainability pipeline that combines visual attribution with structured symptom abstraction. Salient regions are identified using
attention- and gradient-based localization, followed by extraction of clinically meaningful motor descriptors quantifying contour roughness,
curvature irregularity, stroke variability, and tremor-frequency energy. These descriptors are subsequently translated into coherent clinical rationales through a
language-based
reasoning module, linking model evidence to established PD symptomatology. By bridging visual attribution and clinical interpretation, the proposed framework advances interpretable document intelligence for neurological screening using hand-drawn patterns. Experimental results on publicly available Parkinson's disease handwriting
datasets demonstrate competitive predictive performance and clinically consistent explanations.
Read the full abstract ▾
All authors Aritra Dey, Utsav Kumar Nareti, Chandranath Adak, Soumi Chattopadhyay, Krishna Gopal Sasmal, Saeed Anwar
Artificial Intelligence · Cross-listed from cs.CY
A latent dimension of Condorcet's jury theorem for multiple AI advisers
Kazutoshi Sasahara, Aoi Naito and Ryo Fujie
When the same question is asked of multiple AI advisers, as in self-consistency and LLM-as-a-judge panels, Condorcet's jury
theorem predicts that adding independent, competent advisers makes the majority more reliable. The
theorem, however, has a latent dimension when viewed from the user's vantage: adding advisers also makes disagreement more visible. A binomial model reveals that this “visible dissent” becomes nearly inevitable as the number of advisers grows, and that reliability and disagreement approach certainty at rates that cross at an adviser accuracy of 4/5 (0.8); below it, visible dissent eventually becomes more likely than a correct majority. Even ideal panels of independent and competent advisers can be correct in aggregate but appear divided; such disagreement does not by itself indicate aggregation failure. The way advisers split also provides a common basis for predictive multiplicity, reconciliation load, and reliance miscalibration. These results separate aggregation from disclosure and turn the latter into testable questions about how disagreement should be presented and interpreted.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CV
Lightweight Generalized DeepFake Face Detection with WAVIE: Wavelet Augmented Vision Intermediate Embeddings
Arya Pulkit, Aditya Ruhela, Akarshan Kapoor et al. (+1 more)
Deepfake detection systems often exhibit significant performance degradation when deployed on unseen manipulation methods, limiting their reliability in real-world multimedia environments. This lack of generalization poses critical challenges for misinformation mitigation, digital forensics, and human-centric AI systems. Existing detectors perform well on the forgery methods they are trained on, but their accuracy drops sharply on unseen pipelines. To bridge this generalization gap, we propose WAVIE (Wavelet Augmented
Vision Intermediate
Embeddings), an end-to-end architecture that combines complementary spatial and frequency cues on top of a frozen CLIP backbone. WAVIE projects intermediate
transformer embeddings through a lightweight learnable module, applies a three-level Daubechies-6 (db6) discrete wavelet transform (DWT), refines the low-frequency branch while preserving the high-frequency branch, reconstructs the feature via inverse DWT, and performs classification. Trained only on FaceForensics++, WAVIE achieves AUROC = 0.852 on Celeb-DF-v1, 0.852 on Celeb-DF-v2 and 0.831 on WildDeepFake (WDF) at the frame level, outperforming several state-of-the-art generalization baselines. Extensive ablation studies confirm the importance of both the wavelet module and the intermediate-feature aggregation for cross-
dataset performance, highlighting the necessity of jointly leveraging spatial and frequency domains. These results position WAVIE as a strong baseline for deepfake detection in the wild.
Read the full abstract ▾
All authors Arya Pulkit, Aditya Ruhela, Akarshan Kapoor, Arnav Bhavsar
Artificial Intelligence · Cross-listed from cs.DL
Has Scientific Talent Shifted from Depth to Breadth?Evidence across Papers, Knowledge Inputs, Careers, and Teams
Xiaoshn Nee, Haobo Zhong and Xiaomin Ni
Generative artificial intelligence raises a central question for scientific training and organization. Is research shifting from deep specialization toward broad individual knowledge? We examine this proposition across papers, cited knowledge, contributor histories, and teams using 47,959 articles from six fields over 2010-2025, 51,736 resolved cited works, and chronologically reconstructed prior publication histories for 1,754 randomly selected index contributors. From 2010 to 2022, team size increased by an estimated 37.3% (95% confidence interval [34.4%, 40.3%]), while paper topic breadth declined by 0.0144 on a 0-1 hierarchical distance scale. Cited knowledge was stable to modestly broader, revealing a divergence between focused outputs and the reach of knowledge inputs. Established contributors' prior breadth increased by 0.0190 [-0.0078, 0.0459] by 2019-2022, within a +/-0.05 equivalence bound assessed in sensitivity analysis. In mature citation windows, one standard deviation of focal depth was associated with 8.2% higher 1 + FWCI [1.9%, 14.9%]; average breadth and interaction associations were smaller under the specified equivalence bounds. Post-2022 deviations from earlier trends were not systematic, and recent changes did not vary clearly with baseline AI intensity across 83 subfields. The findings support a differentiated structure of scientific expertise in which focused individual accumulation coexists with expanding collaboration and sustained access to diverse knowledge inputs.
Read the full abstract ▾
Artificial Intelligence
Safety Signals to Verify NetOps Agents with Action-Level Granularity
Tobias Labarta, Frederik Pahde, Novak Boškov et al. (+5 more)
Agentic Network Operations (NetOps) are an emerging paradigm promising to enable workload-aware, self-adjustable, and reliable autonomous networks. While
agents have proven their value in incident summarization and telemetry signal extraction, their effectiveness as autonomous control-loop engines heavily relies on their long-horizon reliability. One such setting is the datacenter fabric, where an
agent must respond to alarms and operator intents while abstaining from high-risk actions that may cause or extend downtime. Abstention, however, presupposes that an action's impact is known pre-execution, which necessitates a per-action ground truth that NetOps
agent benchmarks do not provide. We construct such a ground truth for the network repair task of NetArena. A symbolic replay of the emulated network, validated against the environment at every turn, yields the exact value of every action. From the action-level value, we derive two pre-execution targets, namely whether an action reduces the repair distance (progress) and whether it increases it (harm). We show across 10
agent models, that
agent verifiers leveraging internal signals predict both harm and progress more reliably than a baseline using observable signals only. Perspectively, we aim to use these signals as safety feedback to an
agent harness to abstain from risky actions and protect the target system.
Read the full abstract ▾
All authors Tobias Labarta, Frederik Pahde, Novak Boškov, Maximilian Dreyer, David Birkenberger, Manzoor Ahmed Khan, Sebastian Lapuschkin, Wojciech Samek
Artificial Intelligence · Cross-listed from cs.IT
Dependency, Compression, and Synergy: A Unified Information-Theoretic View of Multimodal Learning
Liangjian Wen, Linjie Li, Jiang Duan et al. (+3 more)
Recent advances in
multimodal foundation models have intensified the need to understand how different modalities share, preserve, and complement information. Mutual Information (MI), the Information Bottleneck (IB), and Partial Information Decomposition (PID) provide complementary perspectives, yet existing studies often treat them as isolated tools. This survey presents an information-theoretic perspective connecting these principles as progressively refined views of
multimodal information processing: MI characterizes inter-modal dependency, IB explains task-oriented information preservation under compression, and PID decomposes preserved information into redundancy, uniqueness, and synergy. We review 170 recent studies (2018–2026) and 12 foundational works, organizing
multimodal learning around four challenges: cross-modal
alignment, information-efficient fusion, interaction-type characterization, and scaling to
multimodal foundation models. Rather than using application domains as primary taxonomy axes, we interpret healthcare, robotics, recommendation systems, affective computing, and wireless communications as empirical validations of these information principles. Beyond taxonomy, we organize existing
multimodal paradigms within a single information-theoretic coordinate system – the Generalized
Multimodal Information Lagrangian – in which they occupy exact or approximate parameter corners, and whose unoccupied regions name candidate method families the literature has not yet built. We further discuss how emerging
multimodal foundation models instantiate these principles at scale and identify open challenges including scalable information estimation in high-dimensional settings, standardized evaluation across information-theoretic methods, combinatorial complexity of
multimodal PID, and the transition from post-hoc information analysis toward information-aware
multimodal learning.
Read the full abstract ▾
All authors Liangjian Wen, Linjie Li, Jiang Duan, Yong Dai, Jianzhuang Liu, Zhao Kang