Information Retrieval
Risk-Aware Reranking for Agentic Tool Retrieval
Qinfei Li, Xiaoxuan Dong, Jin Zhang et al. (+6 more)
Tool
retrieval determines which external tools are exposed to an LLM
agent for a user query or task, making
retrieval a critical pre-execution safety boundary. Unlike document
retrieval, tool
retrieval exposes executable actions: a tool that is useful for one task may be unnecessary or risky for another. However, existing tool-
retrieval methods primarily optimize semantic relevance, and safety evaluations often focus on failures after tool execution rather than risks introduced during
retrieval. We study risk-aware tool
retrieval, where the goal is to retrieve useful tools while reducing exposure to higher-risk tools. We propose a lightweight reranking framework on top of a frozen first-stage retriever. The framework models query-conditioned relevance and tool-level exposure risk separately, combines them through an explicit parameter controlling the tradeoff between safety and utility, smooths scores over a ToolGraph, and optionally applies rule-based safety constraints. To support
retrieval-time safety evaluation, we annotate 6,108 tools across UltraTool and Seal-Tools with five ordinal risk levels and define metrics that measure risky-tool exposure in the top-$k$ results. Experiments on UltraTool and Seal-Tools show that our approach improves the relevance–safety tradeoff over relevance-only retrievers and reranking baselines, with the rule-filtered variant providing a conservative operating point for safety-critical deployments. These findings indicate that
retrieval-stage filtering can reduce the candidate action space exposed to
agents before execution, complementing downstream tool-use safeguards. The code and supplementary materials are available at: https://github.com/qli447/risk-aware-tool-
retrieval-release.
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All authors Qinfei Li, Xiaoxuan Dong, Jin Zhang, Dexu Yu, Wenhao Deng, Junchen Fu, Youhua Li, Hanwen Du, Chunxiao Li
Machine Learning
MOSH-WM: Mask-Grounded Soft-Hamiltonian Dynamics for Object-Centric World Models
Zhekai Wang, Haoxiang Huang, Xiang Liu et al. (+6 more)
Object-centric world models forecast future
videos by evolving a set of entity slots, but the variables receiving dynamics supervision are often unconstrained visual features. We introduce \method{}, a mask-grounded soft-
Hamiltonian world model that makes its position-like state explicitly depend on slot-owned image support. A frozen
video-slot encoder produces slots and masks; spatial moments of mask-owned support form a canonical state $Q$, temporal differences form $P$, and a learned energy supplies a soft directional bias to a bounded learned increment. Decoder-relevant appearance and identity are stored separately in a causal visual context. A gated composer and bounded residual then combine this context with the propagated phase state to reconstruct decoder-compatible slots. On OBJ3D, given six observed frames and evaluated over the following 30 frames, \method{} reduces LPIPS by 25.0% and spatial MSE by 33.7% relative to the strongest object-centric baseline. On CLEVRER, given six observed frames and evaluated over the following ten frames, the corresponding reductions are 14.5% and 18.7%. Horizon-resolved visual and object-state measurements show that the complete model accumulates error more slowly throughout the 30-frame closed-loop rollout. Project page:https://github.com/moshwm-anon/-moshwm-anon.github.io.
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All authors Zhekai Wang, Haoxiang Huang, Xiang Liu, Zhikang Chen, Yueqing Sun, Qi Gu, Shiji Zhou, Miao Liu, Sen Cui
Machine Learning · Cross-listed from stat.ML
Generative Neural Networks for Sinkhorn Distributionally Robust Hypothesis Testing
Fenglin Zhang, Teyan Liu and Jie Wang
This paper studies the Sinkhorn distributionally robust hypothesis testing (SDRHT) problem, seeking a robust detector against least-favorable distributions in Sinkhorn discrepancy-based ambiguity sets centered at the empirical distributions. Existing approaches solve this problem by solving large-scale conic programs, which are not scalable. To overcome this, we propose a generative framework that learns least-favorable distributions and supports efficient training and end-to-end sampling. For the Sinkhorn discrepancy-based ambiguity sets, we first derive an equivalent conditional-KL-divergence representation with respect to kernel-smoothed reference distributions. This property allows us to prove strong duality for both constrained and unconstrained minimax SDRHT formulations. Based on the closed-form optimal detector and Brenier's
theorem, we reformulate the max-min dual formulation as a maximization problem over convex potentials whose gradients characterize invertible transport maps between kernel-smoothed distributions and their least-favorable counterparts. We efficiently approximate these potentials using Hyper Input Convex Neural Networks (HyCNNs) equipped with
stochastic gradient
estimators and prove the representation power of HyCNNs and the distributional universality of their induced transport maps. Numerical results show that the proposed method achieves superior accuracy and robustness across different sample sizes and dimensions, while avoiding the scalability limitations of classical SDRHT methods.
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Computation and Language
DiaRelay: Relaying Dialogue Context with a Constant-Size Memory for Emotion Recognition in Conversation
Zihao Zhou, Bin Yang, Jinghui Qin et al. (+1 more)
Emotion Recognition in Conversation (ERC) requires models to identify subtle emotional cues that are often distributed across distant dialogue turns. Existing methods typically incorporate dialogue history through a fixed context window. However, short windows discard potentially useful long-range evidence, while enlarging the window repeatedly re-encodes overlapping utterances, increases computational and
memory costs, and may introduce irrelevant context. Moreover, commonly used parameter-efficient adaptation methods, such as LoRA, mainly introduce fixed low-rank transformations in the feature space and do not explicitly maintain a dialogue-level state or condition their transformations on the evolving conversational context. To address these limitations, we propose a lightweight adapter, DiaRelay, to enable LLMs to explicitly maintain a dialogue-level
memory for accurate ERC. Based on LoRA, DiaRelay introduces two extra tightly collaborative components, Selective Relay
Memory Transition and Dual-axis Relay
Memory Read. Selective Relay
Memory Transition progressively aggregates useful historical evidence into a bounded relay
memory and propagates it across successive utterance predictions. This allows earlier emotional cues to influence later predictions after they leave the local context window, without re-encoding the complete dialogue history or expanding the backbone context length. Dual-axis Relay
Memory Read uses the propagated
memory to dynamically modulate low-rank feature transformations, enabling context-dependent representation adaptation without test-time gradient updates. Extensive experiments show that DiaRelay can achieve SOTA weighted F1 and accuracy on MELD while obtaining competitive results on IEMOCAP with only an extra 7.1M trainable parameters, indicating the effectiveness and generalizability of our DiaRelay in enhancing LLM-based emotional understanding.
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All authors Zihao Zhou, Bin Yang, Jinghui Qin, Kebing Jin
Hardware Architecture
A Process-Aware Hybrid Si/IGO Monolithic-3D 6T SRAM with BEOL Pass-Gates for the 2nm Node
Po-Chuan Wang, Dongwon Jang, Eknath Sarkar et al. (+5 more)
We propose a monolithic-3D (M3D) 6T SRAM at the 2nm node, integrating BEOL IGO pass-gates (PGs) with an all-silicon nanosheet latch, buried power rails (BPRs), and Ru interconnects. TCAD calibrated to a state-of-the-art double-gate IGO transistor with a tri-layer HfO$_2$/ZrO$_2$/HfO$_2$ (HZH) gate stack is combined with virtual fabrication and 3D parasitic extraction to realize the first process-aware layout of this
topology. A novel neighbor-
cell shared source/drain (S/D) bitline (BL) design enlarges the IGO contact area to mitigate contact resistance and restore PG drive without area penalty. The resulting
cell achieves a 25% footprint reduction vs the high-performance (HP) 122 Si baseline while maintaining robust static noise margin (SNM) over a wide supply voltage range. At the 128$\times$256 subarray-level, it reduces write delay by 42.2% and EDP by 9.7% compared to the high-density (HD) 111 Si baseline, owing to reduced
cell parasitics and wordline (WL) loading from the smaller footprint.
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All authors Po-Chuan Wang, Dongwon Jang, Eknath Sarkar, Alexei Svizhenko, Md. Nahid Haque Shazon, Piyush Kumar, Suman Datta, Azad Naeemi
Computer Vision
Seeing the Unseen: Semantic-in-Gaussian for Sparse-View 3D Generalization
Zeyang Bai, Yunpeng Wang, Yunbiao Wang et al. (+1 more)
Generalizable 3D Gaussian Splatting (G-3DGS) has emerged as a promising approach for novel view synthesis undersparse-view settings. However, existing frameworks remain restricted by pixel-aligned Gaussian estimation, whichstruggles in partially observed or occluded regions and often leads to incomplete surfaces or structural collapse. Toaddress these challenges, we propose SeeU (Seeing the Unseen), a novel G-3DGS framework. We frame its core design asSemantic-in-Gaussian: semantic-conditioned refinement in Gaussian space. Specifically, we introduce a Cross-viewEntropy-Aware (CEA) module that aggregates multi-view semantic and
geometric cues into compact
embeddings. Theseembeddings guide the Conditional Gaussian
Transformer, which applies residual updates to coarse Gaussians, helpingrecover under-constrained regions of partially observed structures while preserving surface consistency. Comprehensiveexperiments on multiple
benchmarks demonstrate that SeeU consistently improves rendering quality and structuralcompleteness while retaining efficient feed-forward inference. Especially under challenging extrapolation settings,SeeU achieves an average improvement of 2.44 dB in PSNR compared to recent SOTA G-3DGS methods.
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All authors Zeyang Bai, Yunpeng Wang, Yunbiao Wang, Jun Xiao
Information Retrieval
Rethinking Item Tokenization in Generative Recommenders: From Fixed Atoms to Semantic Subwords
Xinrui Miao, Mingjia Yin, Jiaqing Zhang et al. (+5 more)
In generative recommender systems, items are typically tokenized into fixed-length semantic ID sequences for autoregressive next-item prediction. However, for user-context modeling, this fine-grained representation triggers Intra-item
Attention Overload: excessive
attention is spent on low-level intra-item dependencies rather than high-level inter-item behavioral transitions. To address this, we propose Semantic Subword Tokenization (SST), which represents historical items as variable-length semantic subwords while preserving fixed-length target decoding. SST first applies Item-level Subword Tokenization (IST) to merge stable adjacent atom
tokens into compact semantic subword
tokens, thereby reducing intra-item reassembly in the encoder. It then introduces Behavior-induced Co-occurrence Augmentation (BCA) to inject coarse-grained semantic prefix transition signals, guiding the freed modeling capacity toward inter-item behavioral regularities. Extensive experiments on three public
datasets and three generative recommender backbones show empirical improvements of SST over fixed-length and transferable variable-length SID baselines. Code is available at https://github.com/mxrcandy/Semantic-Subword-Tokenization.
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All authors Xinrui Miao, Mingjia Yin, Jiaqing Zhang, Wei Guo, Yong Liu, Yuyang Ye, Hao Wang, Enhong Chen
Artificial Intelligence
When Words and Bodies Disagree: Realizing Verbal and Nonverbal Mismatch
Parisa Ghanad Torshizi and Stacy Marsella
Traditional nonverbal behavior generation systems for virtual
agents typically take utterances as input and generate behaviors that match or illustrate the spoken words. However, human nonverbal behavior is shaped by more than
speech content. It is influenced by speaker roles, interpersonal relationships, social context, and cognitive and emotional states of interactants. Therefore, the nonverbal channel carries information or
emotions hidden in words. Modeling these complex verbal-nonverbal mismatches is critical for realistic virtual
agents, particularly in training applications. We propose a taxonomy of mismatches, strengthening, mitigating, and leaking, and evaluate whether LLMs can select contextually appropriate mismatched behaviors. Through human-subject studies, we assess whether these context-driven behaviors, realized in a virtual human framework, produce the intended effects. Our results demonstrate that LLMs, when prompted with social context, successfully select targeted mismatched behaviors (e.g., sarcasm). Furthermore, the human-subject evaluation confirms that these nonverbal cues significantly shift observers' perceptions of the
agent's attitude and emotional valence. Participants successfully distinguished between distinct mismatch functions, such as strengthening, mitigating, and leaking, perceiving clear directional shifts in social meaning compared to matching baselines.
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Numerical Analysis
Symmetric Taylor–Hood elements for the linear stress gradient problem
Jun Hu, Ting Lin, Shudan Tian et al. (+1 more)
We develop mixed finite element methods for the linear stress-gradient elasticity problem based on symmetric Taylor–Hood elements. We establish the stability of the symmetric Taylor–Hood pair on simplicial meshes in both two and three dimensions, thereby resolving the stability left open by Brezzi, Fortin, and Marini in 1993. Combing the Nitsche's method, we further construct finite element schemes for linear stress problem in general boundary condition. Numerical experiments confirm the theoretical results.
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All authors Jun Hu, Ting Lin, Shudan Tian, Min Zhang
Machine Learning
Spiking Neural Networks for Continuous Control: Neuromorphic Reinforcement Learning in Conventional Computing
Jessica Hunter, Md Maruf Hossain Shuvo and Krishna Roy
Reinforcement learning (RL)
algorithms have made strides over the past decade applying them to a wide range of problems and control tasks. However, the deployment of RL on neuromorphic hardware for continuous control tasks remains under-validated. Namely it is unclear whether replacing a conventional actor network with a spiking neural network (SNN) affects the performance of an
agent before any hardware-specific benefits manifest. We provide a systematic validation of a minimal, neuromorphically viable spiking actor variant of Soft Actor-Critic (SAC) on conventional hardware, establishing a baseline for future neuromorphic RL research. In this paper, we propose the Spiking Actor Network Soft Actor Critic (SANSAC) to address the use of RL frameworks in continuous environments, designed as a framework that can be implemented on neuromorphic hardware. We compare a traditional Soft Actor Critic (SAC) network to SANSAC in a traditional computer. We demonstrate the near equivalent performance of SANSAC and SAC, while addressing the impact of hidden dimensions. Our results demonstrate the viability of SNN based
algorithms in complex continuous environments, as well as competitive performance to traditional neural networks in traditional computers, providing a basis to continue exploring the use of SNNs in continuous RL frameworks.
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Databases
HAWKEYE: Seeing One Layer Deeper – A Cohesion-Aware Structural Channel for Temporal Link Prediction
Jiacheng Ding and Xiaofei Zhang
State-of-the-art temporal-link-prediction (TLP) models are, in essence, multi-channel information aggregators: they combine an interaction-history channel, a time-encoding channel, and a structure channel. The first two have been refined relentlessly; the structure channel remains a crude afterthought – DyGFormer encodes it as a 1–2-bit neighbour-cooccurrence count. We begin with a measurement: on sparse temporal graphs the classical 1-hop common-neighbour signal is near-random (discriminative AUC $\approx 0.50$), because two nodes almost never share a direct neighbour; the genuinely discriminative signal lies one hop deeper – the 2-hop cohesive bridge, whose discAUC reaches 0.73–0.98, on both bipartite and non-bipartite graphs. Motivated by this, we propose HAWKEYE, a cohesion-aware structural channel that incrementally maintains the classical k-family of cohesiveness indicators (degree $\to$ k-core $\to$ k-truss) and forms 2-hop cohesive-bridge features. HAWKEYE is a drop-in replacement for a temporal-graph model's native structure channel, with no change to the backbone. Swapping HAWKEYE into DyGFormer improves test AP/MRR over the cooccurrence channel by +0.6 to +10.8 points across six multi-seed-validated
datasets (uci, enron, USLegis, CanParl, reddit, mooc). On the bipartite recommendation
benchmark tgbl-subreddit, a 3-seed single-pass struct-only ablation shows HAWKEYE nearly doubling the baseline test MRR (0.103$\pm$0.003 $\to$ 0.204$\pm$0.005, +10.1 points across all three seeds); the streaming pipeline scales to the 67M-edge tgbl-flight in five minutes per pass. We further characterise when it helps: the gain tracks a graph's training-free 2-hop discAUC and vanishes on degenerate or saturated graphs – a predictable boundary. All code, data, and figure-generation scripts are released.
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Artificial Intelligence
SEAM: Shot Entity-Attribute Memory for Consistent Short-Drama Generation at Scale
Jiaqi Liu, Maolin Ran, Xiaoyang Lu et al. (+5 more)
Short-drama generation has grown into a large, industrialized pipeline, and as it scales from isolated shots to the episode level, visual continuity has become a critical bottleneck. Current
agent frameworks generate each shot in isolation, so context drifts across shots and props, character posture, and blocking turn inconsistent. Once assembled, these small discrepancies amplify into severe visual breaks. We present SEAM (Shot Entity-Attribute
Memory), a training-free, model-agnostic
memory graph that repairs continuity entirely at the
prompt-text layer by extracting a multi-dimensional state for every shot, retrieving only causally prior context over the resulting graph, filtering it selectively, and injecting the surviving constraints by natural-
language prompt rewriting. We further release SEAM-Bench, a double-blind continuity storyboarding
benchmark, on which SEAM raises cross-episode continuity recall from 0.700 to 0.946, generalizes across six mainstream text models, and yields consistent, though not yet significant, gains at the generated-image layer. Deployed as a mandatory stage in CreativeFitting's SEAM-
Agent production pipeline over 201 shots, SEAM reaches a 96.5% director-acceptance rate with zero unsafe injections; a conservative counterfactual attributes at least 21.9 percentage points of that rate to its cross-episode
memory.
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All authors Jiaqi Liu, Maolin Ran, Xiaoyang Lu, Jian Wang, Weiwen Liu, Jianghao Lin, Yong Yu, Weinan Zhang
Computational Engineering
Frontiers in FinTech: Multimodal Foundation Models for Financial Reporting and Decision Science
Yulu Huang, Niannian Yu, Yaxin Yang et al. (+1 more)
Financial information no longer arrives in a single format. Research reports come as PDFs, financial statements live in spreadsheets,
market trends are captured in images, and policy documents reach analysts as scans, each carrying part of the picture the others cannot supply. Accounting information systems built around single-modality extraction pipelines and rule-based tools therefore struggle to assemble the full picture, slowing financial statement analysis, complicating audit evidence corroboration, and limiting investment decision support. This study presents FinVision, a
multimodal large
language model that unites
vision-
language models with domain-specific financial
reasoning. Instead of processing documents in isolation, FinVision reads text, tables, and images together, converts them into consistent structured data, and verifies cross-modal agreement, in the same spirit as auditors corroborating evidence from independent sources. The model is trained in two stages, pre-trained on large-scale public financial corpora and fine-tuned on institution-specific investment data, so it can apply established valuation methodologies and audit risk assessment frameworks while outperforming zero-shot and single-stage baselines. A natural-
language decision pipeline lets users describe what they need and turns those descriptions into executable workflows, supporting portfolio optimization, real-time risk monitoring, and refinement through multi-turn dialogue. Across 200 listed companies, FinVision reduced valuation error by 19 percent relative to the strongest baseline; a user study with 48 accounting and investment professionals reported a 51 percent reduction in task completion time. These results carry implications for audit automation, financial reporting quality, and more inclusive access to expert-level financial analysis.
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All authors Yulu Huang, Niannian Yu, Yaxin Yang, Yong Huang
Computer Vision
LoViF 2026 The First Challenge on Unified Removal of Raindrops and Reflections: Methods and Results
Zewei He, Xi Tong, Yu Chen et al. (+49 more)
This workshop paper comprehensively reviews the First Challenge on Unified Removal of Raindrops and Reflections. The challenge aims to address a frequently encountered practical problem in the field of autonomous driving, i.e., raindrop-reflection composite degradation on rainy days. This competition attracted 149 registered participants and received 12 valid final submissions with corresponding fact sheets, significantly contributing to the progress of unified removal of raindrops and reflections. All the methods are developed and evaluated on our real-shot RainDrop and ReFlection (RDRF)
dataset. A detailed analysis of the submitted methods and corresponding results is provided in this report, which highlights effective approaches and provides interesting insights for future research.
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All authors Zewei He, Xi Tong, Yu Chen, Xingyu Liu, Xin Li, Zepeng Wang, Jiagao Hu, Fuhao Li, Yuxuan Chen, Fei Wang, Daiguo Zhou, Minmin Yi, Chuanrui Zhang, Liwen Zhang, Yeongjin Jeong, Hyunjin Cho, Jiwon Lee, Minsang Kim, Jae Woong Soh, Jin-Hui Jiang, Rong-Lin Jian, Chih-Chung Hsu, Youngjin Oh, Junhyeong Kwon, Junyoung Park, Jae Hyun Park, Sung Ju Lee, Nam Ik Cho, Vishwajeet Shukla, Himanshu Baurai, Zhiqi Zhang, Kui Jiang, Zhaocheng Yu, Runzhe Li, Dawei Fan, Hao Li, Zhanshuo Zhang, Fan Ji, Jiangmeng Li, Xiongxin Tang, Fanjiang Xu, Shangquan Sun, Anh-Kiet Duong, Petra Gomez-Krämer, Jean-Michel Carozza, Ruibo Zhang, Dexiang Hong, Xinyan Liu, Shengeng Tang, Weidong Chen, Tzu-Hsuan Weng, Min-Te Sun
Cryptography and Security
EGAMA-RC: Risk-Calibrated Evidence-Gated Adaptive Malware Analysis for Robust and Interpretable Memory-Forensic Triage
Isaac Kofi Nti
Machine-learning malware detectors often achieve high clean-data accuracy, but operational triage also requires evidence about uncertainty, novelty, robustness, interpretability, latency, and review cost. This paper presents EGAMA-RC, a risk-calibrated evidence-gated framework for
memory-forensic malware triage. Building on SHAP-guided feature refinement, EGAMA-RC combines
dataset-specific refinement, model-pool evaluation,
adversarial and open-family testing, novelty scoring, explanation-conditioned evidence, and runtime-aware routing. Low-risk samples are accepted automatically, while uncertain, high-risk, or potentially novel cases are routed to review, escalation, or novelty-aware handling. Across three malware
datasets and a frozen multi-seed protocol, the selected hybrid gate accepts 93.12% of pooled samples with 99.86% accepted accuracy and a 0.136% false-accept rate. Novelty calibration reduces over-restrictive review behavior while preserving a low unsafe-accept profile. XGBoost provides lightweight fast-path inference with p50/p95 latency of 0.0054/0.0059 ms per sample. The results show that dependable malware analysis requires risk-calibrated routing, novelty awareness, and controlled analyst review, not classification accuracy alone.
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Systems and Control
Real-Time Edge-based Detection of Correlated AI Data-Center Load Episodes
Chandan Chaudhary, Abanish Tiwari, Yansong Pei et al. (+2 more)
Artificial-intelligence data centers running bulk-synchronous training can impose sub-second power swings. When several facilities synchronize their training cycles, these load variations become spatially correlated and amplify the aggregate disturbance on the grid. A grid operator without access to data-center telemetry must infer this correlation from electrical measurements alone. However, the required observation time and the feasibility of detection on substation-deployable hardware remain uncharacterized. This paper develops a correlation-based detection method to classify the multi-facility operating regime from cross-facility power measurements. Analytical derivations and experimental validation show that the resulting detection confidence increases with the observation-window length at a rate governed by the load correlation time. The method is demonstrated in a real-time hardware-in-the-loop testbed, where load setpoints generated from a validated semi-Markov data-center load model are applied to an electromagnetic-transient grid simulation on a Real-Time Digital Simulator. A compact classifier built on pairwise power correlations runs on an edge device in this loop and determines whether the data-center load variations are independent or spatially correlated. The cross-facility correlation separates the independent and correlated cases across independent realizations. The held-out detection accuracy improves with the observation window, consistent with the predicted relation. A raw-waveform network fails to generalize, supporting pairwise correlation as the discriminative signal. The detector executes in real time on commodity edge hardware. A closed-loop demonstration against the running simulator tracks a regime change within one observation window.
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All authors Chandan Chaudhary, Abanish Tiwari, Yansong Pei, Mohammed Ben-Idris, Joydeep Mitra
Cryptography and Security
TEE-X: TEE-aware Acceleration Framework for Large Vision Models at the Edge
Kurt M Wilson, Mohaiminul Al Nahian, Abeer Matar A. Almalky et al. (+5 more)
Despite their remarkable success, machine learning models, particularly in
vision applications, are alarmingly vulnerable to a range of security threats. One key factor in the attack landscape is the distinction between white-box and black-box threat models, as the latter poses challenges that limit attack effectiveness when access to model information is limited. As a result, using Trusted Execution Environments (TEEs) enhances security for machine learning applications by protecting model confidentiality and execution integrity, effectively shifting the execution environment from the white-box to the black-box side of the threat model spectrum. While adopting TEEs for large
vision models, e.g.,
Vision Transformers (ViTs), is crucial for enhancing security and
privacy, significant challenges related to
memory constraints and increased computational latency must be addressed, especially in time-sensitive edge applications where safety and
privacy are paramount. The objective of this work is to enable large
vision models to be fully hosted within TEEs, achieving GPU-level inference latency for time-sensitive edge
vision applications while maintaining performance. To this end, we propose TEE-X, a TEE-aware acceleration framework that introduces a sensitivity-aware modularization technique and enables vectorization in TEE inference. This design is validated on OP-TEE for Arm TrustZone, configured to optimize performance on the NVIDIA Jetson AGX Xavier for efficient edge
vision applications using ViT models. The findings reveal that TEE-X delivers an effective TEE-aware acceleration framework that achieves minimal accuracy-latency trade-offs while ensuring fast and secure edge inference for
vision models.
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All authors Kurt M Wilson, Mohaiminul Al Nahian, Abeer Matar A. Almalky, Sadat Shahriyar, Souvik Kundu, Zhishan Guo, Abdullah Al Arafat, Adnan Siraj Rakin
Computation and Language
A Source-Grounded Framework for Constructing and Evaluating Progressive Multimodal Diagnostic Dialogues from Clinical Case Reports
Yufan Wang, Rui Yang, Yi Liu et al. (+2 more)
Clinical diagnosis requires progressive integration of patient history, physical examination, laboratory findings, medical images, and diagnostic-informative tests. However, most
multimodal medical
benchmarks evaluate fixed inputs or endpoint answers, while fully interactive diagnostic
agents conflate evidence selection with evidence interpretation. We present a source-grounded framework to construct progressive
multimodal diagnostic dialogues from case reports and an evaluation strategy for assessing MLLMs on final diagnosis, diagnostic
reasoning, and image-finding interpretation. Evaluation on 24 internal medicine case reports showed that our framework can accurately convert case reports into reference dialogues, achieving a diagnosis F1 of 0.99 and a
reasoning-quality score of 4.79 out of 5. Evaluation on two frontier MLLMs (o4-mini and Claude Haiku 4.5) achieved
reasoning-quality scores of 2.75 and 2.50, respectively, with substantially lower diagnosis,
reasoning, and image-finding F1 scores. The results demonstrate that fluent responses do not necessarily reflect evidence-grounded clinical
reasoning and highlight the utility of the proposed framework for evaluating
multimodal diagnostic
reasoning.
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All authors Yufan Wang, Rui Yang, Yi Liu, Yi Lin, Yifan Peng
Systems and Control · Cross-listed from math.OC
General-Sum Linear Regulator Games for Positive Systems
Alba Gurpegui, Monika Tomar and Takashi Tanaka
This paper studies a continuous-time general-sum non-cooperative game with linear costs, positive linear system dynamics, and elementwise linear input constraints. In the finite-horizon case, we present a verification
theorem characterizing feedback Nash
equilibria, in terms of absolutely continuous solutions of a coupled system of vector-valued ordinary differential equations, realized by time-varying feedback laws. Unlike linear-quadratic differential games, whose Riccati-based
equilibria scale quadratically with the state dimension, this formulation scales linearly. However, the resulting piecewise-constant feedback saturates between its constraint bounds rather than varying smoothly, and additional mathematical challenges arise when characterizing the solutions of the differential equations, which are generally discontinuous due to the switching nature of the feedback gains. In this work, we study the case where switching occurs only at isolated time instants. In the infinite-horizon case, under stabilizability assumptions, the
equilibrium is characterized by coupled vector-valued algebraic equations. For this game, we propose iterative methods to compute both finite and infinite-horizon
equilibria. The approach is illustrated through a large-scale pollution game.
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Artificial Intelligence
CacheRouter: A Dual-Path Tool Routing Architecture with Cache-Preserving Main-Model Isolation for Long-Tail Tool Discovery
Donghui Zha, Lingwei Xu, Linxiao Wu et al. (+2 more)
Tool use in LLM systems faces a structural trade-off. Progressive disclosure keeps the
prompt small by showing only the tools relevant to the current task, while
prompt caching rewards a request prefix that stays fixed across calls; every change to the visible tool list invalidates the cached prefix. This paper treats the trade-off as a problem of request architecture and proposes a dual-path routing design that assigns tool selection and tool delivery to separate channels. The main model always sees a small, fixed set of core tools, so the head of its request is unchanged across calls; all other tools are reached through an independent routing channel, in which a router sub-model searches the full tool list, selects one tool, executes it, and returns the result. Tool registration is automated from source code and supports runtime updates, so the tool set can grow without modifying the main model's request prefix. The design generalizes progressive disclosure: capabilities are disclosed through the routing channel, and the main model's prefix stays stable. A prototype implementation was exercised on 55 functional queries and a 30-turn dialogue;
token-level cache hit rates reached 90.99% and 95.2%, cutting input cost to about 12.0% and 8.0% of a no-cache baseline under DeepSeek's pricing, where cache-hit input
tokens cost roughly 1/30 of cache-miss
tokens.
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All authors Donghui Zha, Lingwei Xu, Linxiao Wu, Yixue Dong, Haochen Li
Computation and Language
WnW: Waxing-and-Waning KV Cache for Long-Form Speech LLMs
Yiming Yao, Chenyang Lyu, Xuanfan Ni et al. (+4 more)
Long-form audio inputs make the KV cache the dominant
memory cost of
speech LLMs. Prefill-only KV compression methods permanently discard audio KV positions once evicted, with no pathway to recover them during decoding. We show this is fragile on long-form audio: prefill
attention concentrates near the audio start (an
attention-sink effect), while decode-time
attention distributes broadly, and the two rankings overlap weakly. We propose WnW (Waxing-and-Waning KV cache), which classifies KV-heads into anchor,
tidal, and fixed roles via offline calibration. Anchor heads keep all audio KV on GPU and yield a decode-time signal of which audio region each
token is read from;
tidal heads keep a CPU-resident complement that is recalled chunk-by-chunk based on aggregated anchor-head scores; fixed heads keep only an on-GPU subset, with the rest permanently discarded. On LibriSpeech-Long with two 3B backbones (Voxtral-mini-3b and Qwen2.5-Omni-3B), WnW preserves near-Full-Cache accuracy while keeping only 20% of audio
tokens on GPU, where prefill-only baselines fail to terminate. Results generalize across
language, task, and domain shifts, and CPU-GPU recall adds little decode-time overhead in our measurements.
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All authors Yiming Yao, Chenyang Lyu, Xuanfan Ni, Longyue Wang, Weihua Luo, Yazheng Yang, Jinsong Su
Numerical Analysis
Diagonal Frog meets ADI: trading matrix exponentials for rational maps in the Fokker–Planck equation
Andrey Itkin and Rakhymzhan Kazbek
A companion paper \cite{ItkinDF2026} introduced the Diagonal Frog (DF) positivity-preserving schemes for anisotropic Fokker–Planck equations, advancing each directional substep by a Krylov-computed
matrix exponential, which dominates the cost. Replacing that exponential by a rational map $r(\gamma L)$ reduces the substep to a banded solve, but the positivity argument no longer applies. We prove that for eventually exponentially positive generators the entrywise sign of $r(\gamma L)$ at large steps is decided by a single number, the value $r(\infty)$ taken on infinitely stiff modes. Nonnegativity holds above a computable threshold when $0\le r(\infty)<1$, and at most on a bounded interval, empty or vanishingly narrow in all our tests, when $r(\infty)<0$. The criterion rejects the Crank–Nicolson (trapezoidal) method, where $r(\infty)=-1$, and selects the subdiagonal Padé$(0,2)$ method, which is second order, L-stable and provably positive above an explicit threshold. The resulting DF-ADI scheme costs $O(N)$ per step, keeps the implicit factorized mixed derivative unchanged, is second order in space and time, and conserves discrete mass exactly. In the strong cross-
diffusion regime, however, the directional factors demand a step larger than the mixed derivative permits, so the composite second-order scheme is only empirically positive there, and the criterion serves to discriminate the well-behaved multiplicative factors from the stabilizing-correction schemes rather than to guarantee positivity. Against the Krylov exponential it runs ten to thirty-two times faster at matched accuracy in our tests with the gain growing with the mesh. We extend the construction to the backward Kolmogorov equation and to jump-
diffusion models.
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Human-Computer Interaction
AffAdapt: AFFect-driven ADAPTive AI Personas for Seamless Conversations
Nishanth Chidambaram, Kaustubh Paliwal, Kayla Hom et al. (+4 more)
AI-generated personas are being increasingly used for support, training and simulations. While generative AI models possess abilities to generate affect-aware responses, their embodiment into visual personas is an active area of investigation. Naturalistic exchanges require understanding of the conversational partners' turn completions, whether the
agent should respond or keep listening and rely on non-verbal cues aligned with one's emotional states. Seamless human-AI conversation in a
multimodal setting requires all modalities being generated to act in coordination. We present AffAdapt, a seamless interaction design framework for AI-personas, which coordinates streaming
speech recognition, proactive turn-management, persona-grounded response generation, a persistent emotional state, and synchronized
embodied output into a single interaction loop. We demonstrate the architecture in the context of practicing sensitive, high-stakes conversations, and report an initial case study showing fluid turn management and adaptive, persona-consistent behavior, alongside open challenges in interruption handling, open-ended dialogue, and
multimodal affective
alignment. AffAdapt's interaction loop is a generalizable pattern for coordinating timing, identity, and affect in real-time AI personas - applicable to training, coaching, education, and simulation contexts wherever believable, responsive interaction matters.
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All authors Nishanth Chidambaram, Kaustubh Paliwal, Kayla Hom, Shaoze Zhou, Chen Chen, Manas Satish Bedmutha, Nadir Weibel
Robotics
Physics Filtering Favors the Generalization of Robot Learning
Jindou Jia, Shixuan Han, Meng Wang et al. (+8 more)
Living organisms exhibit extraordinary adaptability to unseen environments through their intrinsic physical structures and lifelong feedback-driven learning. Endowing
robots with comparable generalization is critical for reliable operation in the real world. While recent approaches attempt to improve generalization by scaling training data, such strategies remain impractical for robotics, where collecting real-world demonstrations at the scale of large
language models is prohibitively costly and slow. Contrary to this reliance on massive
datasets, we show that
robots can generalize effectively under dynamics uncertainties even with limited training data by leveraging a feedback mechanism, namely PhyFilter, that corrects learning outputs with physics-filtered learning residuals. PhyFilter operates as a lightweight, model-agnostic module whose parameters can be automatically optimized through an auto-learning
algorithm, eliminating manual tuning and enabling seamless integration with diverse
robot policies. We validate PhyFilter across four representative robotic systems, demonstrating that it enables quadruped
robots to generalize to unseen terrains, payload variations, and speed ranges; drones to flight under unseen wind disturbances; aerial manipulators to achieve centimeter-level in-air capture despite wind and mass uncertainties; and acceleration differentiators to remain robust with distribution shift. These results show that physics-filtered feedback can serve as a powerful alternative to massive data scaling.
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All authors Jindou Jia, Shixuan Han, Meng Wang, Gen Li, Zihan Yang, Sicheng Zhou, Kexin Guo, Jianfei Yang, Xiang Yu, Wei Wang, Lei Guo
Artificial Intelligence
Does Rank Still Matter? Position Bias When AI Agents Shop on Our Behalf
Davood Wadi and Yu Ma
When shopping is delegated to AI
agents, it is unclear whether the ranking advantage documented for humans persists. Across 7,000 sessions with five large
language models (LLMs) and varying
reasoning effort, we compare AI
agents with human field data. AI
agents search more extensively than human consumers. As with humans, lower-ranked listings are less likely to be inspected, although the effect is smaller for AI
agents. Unlike humans, AI
agents show a pattern consistent with the lost-in-the-middle effect, whereby middle listings have the lowest probability of inspection. At the choice stage, position effects are concentrated at lower
reasoning effort, but higher effort reduces the middle penalty for every LLM that exhibits it. These findings suggest that, when search is delegated, displayed attributes may matter more than placement, and that exposure to position bias depends on how the AI
agent is configured.
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Systems and Control · Cross-listed from math.OC
Unit-to-Plant Stability Shaping of Multi-Electrolyzer ReP2H Plants via Interface Design and Dispatch
Miao Zhang, Yiwei Qiu, Xiaoyu Wang et al. (+5 more)
Alkaline water electrolysis (AWE) units supplied by insulated gate bipolar transistor rectifiers (IGBT-Rs) may experience oscil-lations caused by coupling between rectifier control and electro-lyzer (ELZ) dynamics. Because this risk varies with unit loading and power allocation, production-oriented dispatch may place a multi-ELZ renewable power-to-hydrogen (ReP2H) plant near or exceed its stability boundary. This paper proposes a stability-oriented framework for control design and plant production dis-patch. A three-port admittance model links the ac port, dc link, and electrolysis stack. Unit-level dc-port analysis quantifies the effects of loading, temperature, Buck bandwidth, and dc-link capacitance, while plant-level aggregation evaluates how unit commitment and power allocation affect stability. Results show that higher loading reduces stability, whereas larger dc-link ca-pacitance and higher Buck bandwidth improve it. Under the same plant loading, different power allocations result in different plant-level stability margins, with balanced allocation generally providing a larger margin than concentrated allocation. The plant-level model thus distinguishes the stability margins of ad-missible schedules. Hardware-in-the-loop (HIL) tests validate these trends and the proposed redistribution rule. The resulting operating regions and dispatch rules can be used to screen unit commitment and power allocation decisions in plant production scheduling.
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All authors Miao Zhang, Yiwei Qiu, Xiaoyu Wang, Linlin Wu, Yi Zhou, Shi Chen, Buxiang Zhou, Kaigui Xie
Computation and Language
Enrich-Retrieve-Rank: Scaling Capability Discovery Beyond In-Context Routing
Nazib Sorathiya, Daniel Zhang and Bardiya Akhbari
Agent ecosystems now include thousands of MATS components (Models,
Agents, Tools, and Skills), yet their discovery still relies on in-context routing. These systems read a registry (names, hints, or descriptions, as context budget permits), pick a candidate, invoke it, and retry on failure. This pattern degrades with scale, and registries are growing fast. We recast capability discovery as search over a registry by defining an offline enrichment step that turns sparse metadata into searchable profiles, and an online retrieve-then-rank pipeline that returns a ranked shortlist without invoking any candidates online. We show that from N=10 to 7,278 capabilities, in-context routing's top-1 accuracy (Match@1) collapses (0.85 to 0.12), while retrieve-then-rank degrades more gently (0.81 to 0.39) because its reranker still ranks the right capability first 0.70-0.87 of the time once
retrieval finds it. In the Nova Micro sweep, the crossover is around N=500. We compare against two in-context baselines. Full-Ctx puts the whole registry in the
prompt and asks the LLM to pick. Search&Pick gives the LLM a search tool to narrow candidates before it picks. At full scale the pipeline leads Search&Pick by 6.5 percentage points (pp) on Match@1 at about half the cost. It reduces cost 70x versus Full-Ctx. We use a fixed configuration (same enrichment, retriever, and scorer weights) across
agent, tool, and skill registries. The pipeline runs in production as the default capability-discovery layer of a large-scale multi-
agent platform.
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Computer Vision
Hybrid Generative-Discriminative Object Placement
Siyuan Zhou and Li Niu
As an important operation of image composition, object placement aims to predict the plausible placement (location, scale) for the inserted foreground object. Previous object placement methods can be divided into generative methods and discriminative methods, both of which cannot balance efficiency and effectiveness well. In this work, we propose a semi-generative method in the middle ground between them. In particular, we assign uniformly distributed anchors on the background. Then, we fuse foreground and background features to predict the rationality score for each anchor and predict plausible placement sets for positive anchors. Extensive experiments on the OPA
dataset show that our method can strike a good balance between efficiency and effectiveness.
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Computer Vision
MorphoCLIP: Text-Supervised Contrastive Learning for Perturbation Matching in Cell Painting Images
Sukhrobbek Ilyosbekov, Shubham Gajjar and Rongfei Jin
Cell Painting microscopy captures how
cells change after a chemical or genetic perturbation. Connecting these images to the perturbations that produced them could make large imaging screens easier to search and interpret, but the task remains difficult because biological effects are subtle and technical variation is substantial. We introduce MorphoCLIP, a contrastive model that links
Cell Painting profiles with text descriptions of compounds, CRISPR knockouts, and ORF overexpressions. The model keeps its
vision and
language backbones frozen and trains only a compact cross-channel module and projection layers, so it can be trained on a single consumer GPU. On held-out CPJUMP1 data, MorphoCLIP searches in both directions: from a
cell image to its perturbation description and from a description to matching
cell images. In both cases, a correct match appears among the top ten results much more often than expected by chance. Adding a replicate-
alignment loss makes profiles from repeated experiments more consistent, although this improvement does not yet translate into reliable gene-compound matching. Gene-aware labels and plate correction also show no consistent
retrieval benefit. These findings suggest that text supervision can help organize chemical and genetic
Cell Painting data. Matching compounds with genetic perturbations, however, remains an open problem.
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Information Retrieval
FashionKG-RAG: Knowledge Graph-Enhanced Retrieval-Augmented Generation for Fashion Question Answering
Yujuan Ding, Linyin Luo, Shijie Wang et al. (+5 more)
Fashion is a knowledge-intensive domain in which effective decision-making depends on integrating multiple types of knowledge. Although Large
Language Models (LLMs) have transformed many areas, their application in fashion remains limited by
hallucinations and weak domain specialization. Knowledge Graph (KG)-based
Retrieval-Augmented Generation (RAG) offers a promising way to add structured knowledge to LLMs. However, existing fashion KGs are typically restricted to product-level attributes or item relations, and fail to capture the broader fashion ecosystem. To bridge these gaps, we propose FashionEcoKG, a comprehensive, domain-wide knowledge graph built with expert-level precision and professionalism. It is constructed through a three-stage agentic pipeline that extracts high-fidelity knowledge cores from authoritative textbooks and strengthens structural connectivity through cross-domain augmentation and generative expansion. To leverage this resource, we further develop PG-RAG (
Pruning-Grounding RAG), a training-free framework designed to handle the conceptual density and linguistic noise of fashion queries. Specifically, we introduce a Dual-Granularity Path Re-Ranking (DGPR) module of two stages. The
Pruning-based Semantic Ranking (PSR) module distills each query into a skeleton form to improve
retrieval recall, while the Grounding-based Agentic Ranking (GAR) performs point-wise scrutiny of candidate paths against the original full query to ensure global relevance. Experiments on a curated fashion QA
dataset show that PG-RAG effectively leverages FashionEcoKG to improve
retrieval and answer accuracy, outperforming both non-RAG and existing KG-RAG baselines.
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All authors Yujuan Ding, Linyin Luo, Shijie Wang, Xu Yuan, Yunshan Ma, Yi Bin, Wenqi Fan, Qing Li