Artificial Intelligence
OneLA: Scaling Linear-Attention Decoding to Large Beams in Generative Recommendation
Xiangrui Yang, Cheng Peng, Yunfeng Zhao et al. (+9 more)
Generative recommendation (GR) relies on large-beam decoding to generate hundreds of candidate items, creating a new scaling challenge for recurrent linear
attention. Existing linear
attention serving systems either materialize a full recurrent state for every beam or repeatedly replay shared history, incurring substantial
memory and traffic overhead. To address this, we present OneLA, a linear-
attention decoding framework that exploits the shared
prompt and short divergent suffixes of GR workloads. Specifically, OneLA represents all beam states using a single shared
prompt-derived state and compact, append-only records of their divergent transitions. Using this representation, OneLA computes only the state information required at each decoding step, without reconstructing a full recurrent state for every beam. Furthermore, OneLA uses a lightweight ancestry index to track the transition records that make up each beam's history, allowing beams to be updated without moving or copying existing records. A fused GPU kernel further reuses the shared state across beams. Our analysis shows that OneLA achieves 1.54-2.46x end-to-end decode speedups while substantially reducing recurrent-state
memory use and data movement.
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All authors Xiangrui Yang, Cheng Peng, Yunfeng Zhao, Liang Zeng, Ao Hu, Jiawei Yang, Shengzhe Wang, Jingshan Lv, Xiao Liang, Chen Yang, Jiaqiang Liu, Yiming Qiu
Artificial Intelligence
Niching Agents in The Core
Gary B. Parker, Jim O'Connor and John Asaro
The Core is a unique competitive co-
evolution algorithm that allows
agents to evolve autonomous control without utilizing a traditional fitness function. The
agents evolve via local interactions through tournament selection, crossover, and mutation, producing offspring by evolving better controllers. Previous works have shown The Core's ability to evolve
agents capable of combat and
navigation in the Xpilot
video game. This research expands upon that premise by niching
agents to specific subsets of the original environment The Core was tested in. Our results demonstrate the niched
agents capacity for success over
agents niched to the entire system and
agents niched to different sub-environments.
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Computer Vision
UFO: Chain-of-Evaluation for Omni-Condition Alignment in Multi-Modal Image Generation
Danning Zhang, Yijing Lin, Shuhan Zhuang et al. (+4 more)
Multi-modal image generation, particularly subject-driven customization, has garnered growing
attention in recent years. Despite the rapid advancement of generative models, their evaluation remains largely lagging. Existing methods, whether
embedding-based or Multi-modal Large
Language Model (MLLM)-based, evaluate
alignment with each modal condition in isolation, which contradicts the simultaneous condition
alignment objective of multi-modal image generation, leading to poor consistency with human judgments. To address this challenge, we propose UFO, the first unified framework for omni-condition
alignment simultaneous evaluation. Specifically, UFO introduces a novel Atomized Chain-of-Evaluation paradigm, i.e., it first decomposes omni-condition
alignment into a sequential chain of fine-grained, disentangled Atomic Evaluation Units (AEUs), categorizes them into distinct modality-relevance classes, and then employs general or dedicated functional calls for accurate verification of different AEU types. Experimental results demonstrate that UFO achieves the highest correlation with human evaluation preferences, delivering an average improvement of 15.25%. Furthermore, we present UFO-Bench, a dedicated
benchmark designed to holistically evaluate the performance of existing customization models under the diverse mutual interactions of textual and visual conditions.
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All authors Danning Zhang, Yijing Lin, Shuhan Zhuang, Mengqi Huang, Shaojin Wu, Shancheng Fang, Zhendong Mao
Numerical Analysis
Early stopping of stochastic variance reduced gradient for linear inverse problems by the discrepancy principle
Bangti Jin and Zehui Zhou
Stochastic variance reduced gradient (SVRG) is a variant of
stochastic gradient descent and is a promising iterative method for solving large-scale inverse problems. Nevertheless, the development of theoretically grounded a posteriori stopping rules for SVRG remains an open challenge. In this work, we provide a convergence analysis of SVRG equipped with the discrepancy principle, the most well-known a posteriori stopping rule, for solving a class of linear inverse problems in
Hilbert spaces. We establish the regularizing property of SVRG, and moreover, under suitable source conditions, we derive convergence rates of SVRG iterates. To the best of our knowledge, these are the first convergence rate results of any
stochastic iterative method for inverse problems under the a posteriori stopping rule. The theoretical findings are supported by numerical experiments.
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Artificial Intelligence
Is Gaussian Splatting Becoming Neural Again? A Taxonomy and Controlled Study of Learned Parameterization
YuanHang Wang, Xin Cao and Yi Zhang
Three-dimensional Gaussian Splatting (3DGS) combines explicit primitives with efficient rasterization, yet recent systems increasingly use neural networks to generate or share Gaussian parameters. We characterize this trend along five axes: attribute decoding, spatial sharing, view-conditioned decoding,
topology generation, and amortized inference. An analysis of 19 representative methods shows that these choices address different limitations and cannot be reduced to a binary neural label. We also isolate three forms of neural parameterization in a controlled mip-NeRF 360 study. Sharing appearance and opacity improves reconstruction quality, while decoding
geometric structure offers no further gain. The evidence favors selective neuralization: shared functions help when they capture reusable correlations without sacrificing the local
geometric freedom of explicit splats.
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Artificial Intelligence
BlueLM-GUI Technical Report: A Real-Device-Centric Flywheel for Self-Improving Mobile GUI Agents
Tong Ye, Kunyang Han, Guozhi Wang et al. (+40 more)
Mobile GUI
agents are shifting from multi-module frameworks to native models trained end-to-end, yet industrial deployment faces three persistent gaps. Sandbox training produces a distribution mismatch with production environments; expensive real-device failures remain underutilized; and fixed
benchmarks saturate, losing the power to guide iteration. We present BlueLM-GUI, a 35B-A3B mobile GUI
agent built as a real-device-centric flywheel that closes these gaps through three principles. Every Sample Matters: a dual-track pipeline with Heterogeneous Triple-System Consensus evaluation and an Error Correction & Derivation Module salvages every trajectory into usable supervision. Every Rollout Is Real: a three-stage recipe—continual pre-training, supervised fine-tuning, and agentic reinforcement learning on hundreds of real phones—grounds every rollout in real production environments, so the capability the model learns transfers directly to deployment. Every Query Evolves: a quota-driven
benchmark methodology with three orthogonal axes enables precise attribution and allows the
benchmark to be systematically upgraded as the model improves. BlueLM-GUI achieves 87.4 on MobileGUI-VBench, surpassing the best closed-source model by 5.1 points, and 84.9 on AndroidWorld, the best result among open-source models and competitive with closed-source models. These results demonstrate that grounding model training and iterative improvement in both real devices and the three Every principles yields strong, robust, and transferable mobile GUI capability.
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All authors Tong Ye, Kunyang Han, Guozhi Wang, Longqiang Luo, Zhifeng Ding, Yongxiang Zhang, Xiaolei Shen, Yuxuan Zhang, Zhuping Zhang, Tao Xu, Yue Pan, Yucheng Zhao, Yupei Hu, Yuanjiang Ouyang, Danfeng Shen, Runqi Lin, Hongda Cai, Zhaoxiong Wang, Mengjia Yan, Yingjie Zhong, Chen Zhou, Zeyu Zhang, Xuwen Zhu, Penggang Shi, Mingcheng Luo, Ziyang Wu, Min Jin, Mingfu Shen, Zairong Xu, Fan Zhang, Hao Wang, Liang Liu, Zhulin Xie, Lijun Yao, Xiao Liang, Liangmin Wen, Liqiang Feng, Feilong Wu, Min Hu, Min Chen, Guanjing Xiong, Xiaohu Ruan, Xiaoxin Chen
Information Theory
Error-Rate Reduction in LDPC Decoding via Bit-Aligned Temporal Reinforcement in Parallel Probabilistic-Bit Dynamics
Naoya Onizawa and Takahiro Hanyu
Probabilistic bits (p-bits) provide a physical and algorithmic primitive for
stochastic inference, but highly parallel updates can alter their collective dynamics. We study the decoding of random-regular (3, 6) low-density parity-check (LDPC) codes using Jacobi-type p-bit annealing with
stochastic partial activation. An additive response-path rule stores each bit's saturated response and reuses it before
stochastic readout. High-statistics simulations with independent parameter optimization for each method show pooled bit-error-rate reductions of 33.5%, 74.8%, and 81.8% relative to memoryless probabilistic simulated annealing (pSA) for representative codes of block lengths 96, 192, and 288, respectively. Static gain, normalized averaging, response shuffling, and same-bit binary-state feedback with only its coefficient tuned under the same nonmemory parameters do not reproduce the full saturated-response benefit. Trajectory analysis links the improvement to acquisition of the channel-consistent valid-codeword basin and enhanced post-acquisition stability; the acquisition advantage persists from random and channel-hard-decision starts under the tested conditions. Across all 30 independent code realizations, fixed additive parameter-and-readout packages achieve lower bit- and frame-error rates than separately optimized pSA-specific packages, with neither package retuned for individual codes. These results show that the computational effect of temporal state depends on the retained quantity and its reinjection into
stochastic dynamics.
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Artificial Intelligence · Cross-listed from eess.SP
RF-VoID: Towards Bandwidth-Efficient Exterior Tile Void Detection via Narrowband Radio-Frequency Representation Learning
Xinyan Chen, Ruiqin Ma, Shunsuke Shoda et al. (+5 more)
Hidden debonding behind exterior ceramic tiles is a falling-tile hazard, and millimeter-wave radar offers a non-contact way to find it. Conventional interpretation first reconstructs a range profile, so its reliability is bounded by the available bandwidth, yet bandwidth is what sets the cost, the acquisition time, and the regulatory footprint of a deployed system. This work asks whether that bandwidth can be traded for computation. A 4-40 GHz stepped-frequency system scans twelve exterior-wall specimens containing 0.5-1.0 mm air voids at different depths and interfaces, and the bandwidth dependence of A-scan, B-scan, and C-scan interpretation is analyzed to establish the resolution bound. RF-VoID is then proposed, which decides directly on the narrowband complex response: the sub-band is kept in its measured frequency order with amplitude and phase alongside the in-phase and quadrature channels, a dual-branch encoder reads it along the physical frequency axis using relative position encoding and a distance-dependent locality bias, and an inspection-oriented objective handles the class imbalance and the asymmetric error cost of facade screening. Under a mixed-sample protocol the method attains 98.61% accuracy and a 95.84% F1-score with 0.5 GHz of bandwidth, a seventy-two-fold reduction relative to the full sweep, without range-profile reconstruction, deconvolution, or depth-slice selection; on specimens held out entirely from training it remains the strongest of the compared models, with a mean macro F1-score of 62.12% at 0.5 GHz that rises to 68.57% at 1 GHz.
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All authors Xinyan Chen, Ruiqin Ma, Shunsuke Shoda, Changyu Zhou, Ryo Natsuaki, Akira Hirose, Jianfei Yang, Li Yi
Machine Learning
Split Conformal Prediction with Label-Shift-Adjusted Bayesian Scores
Hyeonsu Lee, Juyeon Kim, Erkhembayar Jadamba et al. (+2 more)
Conformal prediction provides distribution-free uncertainty quantification under exchangeability. However, this assumption is violated by label shift, where the marginal distribution of labels changes while the conditional distribution of inputs given labels remains stable. Under such shifts, standard conformal procedures no longer maintain their intended coverage behavior. Existing approaches address this via importance weighting. They pair the reweighting with residual-based nonconformity scores that ignore predictive uncertainty. The resulting intervals have uniform width.
Bayesian conformal methods produce adaptive intervals by leveraging predictive distributions. They evaluate conformity under the source predictive, which is misaligned with the target domain under label shift. We propose the Label-Shift-Adjusted
Bayesian Score (LSA score), a nonconformity score derived from a posterior predictive tilting identity. This identity shows that the target predictive is an importance-weighted transformation of the source predictive. We use it to derive a direct correction to the
Bayesian score. We evaluate the method on molecular property prediction under controlled label shift. The LSA score consistently yields shorter intervals than residual-based and source-based
Bayesian scores. Coverage in the target domain remains comparable. Under stronger shift, all methods incur some coverage loss due to pseudo-label-based density-ratio estimation. The LSA score is defined for any source predictive with a tractable log-density. We instantiate it with
Bayesian Ridge Regression, where the correction admits a closed form.
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All authors Hyeonsu Lee, Juyeon Kim, Erkhembayar Jadamba, Seungjin Choi, Hyunjin Shin
Robotics
Understanding Whole-Body Robot Teleoperation Strategies Under Diverse Task Objectives and Constraints
Tsung-Chi Lin, Juo-Tung Chen and Chien-Ming Huang
This work investigates the control strategies of complex whole-body
robot teleoperation that coordinate active perception, bimanual manipulation, and
navigation. We developed a hybrid control framework, combining the free-form and constrained control, for the whole-body teleoperation of the TIAGo mobile manipulator. We conducted a user study to explore people's control strategies under different task constraints such as limited time and low tolerance of errors. Our results highlight the effective use of coordinated control in improving task efficiency and reducing the risk of reaching individual joint limits. We discuss our results and their implications for designing future whole-body
robot teleoperation systems.
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Computation and Language
Representation-based Masked Diffusion Model
Yangrong Hu, Ding Huang, Xueyu Zhou et al. (+1 more)
Masked
Diffusion Models (MDMs) have emerged as a compelling paradigm for
language modeling, offering the capability for efficient parallel text generation. However, existing parallel sampling methods typically update multiple masked
tokens independently and ignore the complex mutual dependencies among the masked
tokens. This independent updating mechanism lacks global coordination and might lead to incoherent outputs. To address this limitation, we propose Representation-based Masked
Diffusion Model (RMDM), a framework that leverages the text representation to explicitly encode global semantics and help to parallel update
tokens more precisely. Specifically, we first encode text into a continuous semantic space using a pretrained encoder and learn an invertible transformation that normalizes the representation distribution to a Gaussian prior, facilitating efficient sampling during generation. Conditioned on this latent semantic representation, we train a masked
diffusion model to learn the conditional text distribution, where the representation serves as global semantic guidance to coordinate parallel
token updates and faithfully approximate the target distribution. Empirical results demonstrate that RMDM significantly improves generation quality, particularly in aggressive few-step sampling regimes.
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All authors Yangrong Hu, Ding Huang, Xueyu Zhou, Jian Huang
Distributed Computing
ForgeMegakernel: A General Framework for Efficient Auto-Regressive Model Decode Megakernels
Leshan Li, Zhui Zhu, Xianglong Deng et al. (+6 more)
Auto-regressive model decode is bandwidth-bound, since every weight and key/value-cache byte crosses high-bandwidth
memory once per
token. A megakernel is an ideal solution, but existing automatic megakernel generation approaches cannot achieve both generalization across models and correctness guarantees. We present ForgeMegakernel, which generates a per-model high-performance decode megakernel using coding
agents. ForgeMegakernel pairs a universal knowledge base of ten progressive milestones with an independent mid-state test oracle. The milestones provide the megakernel's structural properties: a fine-grained instruction stream for each SM, dependency counters replacing the global synchronization, and a shared-
memory buffer pool for workload balance across SMs and greater parallelism. The test oracle derives the mid-states of the megakernel and checks the performance, error and precision during the generation process, guaranteeing a correct and trustworthy forged megakernel. We evaluated ForgeMegakernel on 14 representative decoding operations across eight model families spanning 0.6B-13B parameters. The generated megakernels achieved 50.5-85.9% MBU and
geometric mean speedups of 1.21x over SGLang 0.5.18 and 1.54x over a megakernel compiler under identical configurations. Inside SGLang, evaluated on GSM8K with ragged
prompts, all 14 megakernels decoded faster than the SGLang engine at comparable answer accuracy.
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All authors Leshan Li, Zhui Zhu, Xianglong Deng, Yaojian Chen, Qingfeng He, Yuxuan Li, Rong Zhao, Xu Han, Zhiyuan Liu
Cryptography and Security
An Open-Source End-to-End FHE Implementation for Privacy-Preserving Llama 3 8B Inference
Yuhang Fan, Yusi Chen, Kanyu Ye et al. (+1 more)
Cloud LLM services typically require users to send
prompts to a model provider, creating a
privacy risk. Fully homomorphic encryption (FHE) lets a server perform inference without decrypting the input, but representing data as ciphertexts adds storage and computational overhead. In CKKS-based LLM inference, the packing scheme maps logical tensors to ciphertexts and slots. It therefore determines the ciphertext count and the homomorphic cost of linear layers, and it constrains how data pass between linear layers,
attention, and nonlinear computation. As models and sequences grow, inefficient layouts accumulate encoding, compute, and layout-conversion overhead. We present Odin, an FHE inference system that co-designs ciphertext packing and model execution for Llama. Starting from a THOR-style baseline whose bottleneck is weight encoding, Odin uses a feature-major cross-layer layout to unify residual connections and layer interfaces, and builds transient intra-operator layouts for linear projections and
attention. This reduces redundant plaintext encoding of weights in wide projections. Within
attention, QK^T produces scores that Softmax can consume directly, and PV consumes the resulting probabilities, avoiding intermediate repacking. For nonlinear ops, we use minimax
polynomial approximation with input-range control and joint error allocation guided by model quality, reducing
polynomial degree and multiplicative depth. To our knowledge, Odin is the first open-source end-to-end GPU CKKS implementation of Llama-3. With Llama-3-8B weights and a 128-
token input, Odin evaluates all 32
Transformer layers on a single NVIDIA H100 80 GB GPU. Server-side end-to-end FHE evaluation takes 366.4 s and 58.9 GiB peak device
memory. Under the same model, input, CKKS parameters, and hardware, THOR takes 1651.9 s, a 4.51x speedup.
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All authors Yuhang Fan, Yusi Chen, Kanyu Ye, Zhuoran Ji
Information Retrieval
ChronicleRec: Pre-training Temporally Anchored Tokens for Lifelong User Modeling
Chengkai Huang, Yubin Sheng, Liang Guo et al. (+9 more)
Modeling ultra-long user behavior sequences is crucial for industrial recommendation and online advertising, yet directly feeding thousands of historical actions into ranking models is computationally prohibitive, while truncation discards long-range signals. Existing lifelong-interest methods retrieve target-relevant behaviors for each candidate, coupling long-sequence modeling with candidate scoring and repeated online cost. Recent target-independent compression methods enable cached user summaries, but often append query
tokens at the sequence end and use bidirectional encoding, producing unordered and redundant summaries that overlook temporal structure. We propose ChronicleRec, a pre-train-and-transfer framework that compresses an ultra-long behavior sequence once into a chronologically ordered set of Chronicle
Tokens. ChronicleRec applies a recency-aware multi-granularity merge, preserving recent behaviors while coarsening distant history. It then interleaves query
tokens with the merged sequence and uses a causal encoder, so each query summarizes only the history before its temporal anchor. A multi-horizon design masks different recent-history windows across parallel branches to learn complementary long-range interests. The compressor is pre-trained with a mask-and-predict objective that reconstructs held-out recent behaviors from compressed older history, aligning historical signals with near-present intent. Since Chronicle
Tokens are target-independent, they can be cached per user, decoupling ultra-long sequence modeling from online candidate scoring. Experiments on KuaiRand and Tencent AdLive show that ChronicleRec outperforms recent-window and single-pass compression baselines while approaching full-
attention performance.
Token analyses reveal temporally organized and complementary representations, and a seven-day online A/B test confirms significant production gains.
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All authors Chengkai Huang, Yubin Sheng, Liang Guo, Haoxi Liu, Junwei Pan, Shangyu Zhang, Zhixiang Feng, Chao Zhou, Chengguo Yin, Lina Yao, Haijie Gu, Jie Jiang
Computer Vision
Context-Aware Causal Gaze Forecasting for Human-Vehicle Interaction During In-Cabin Tracking Dropouts
Shabnam Shabani and Ghazal Farhani
Dashboard-mounted gaze trackers often lose sight of the driver's eyes during large head rotations, including shoulder checks, mirror glances, and intersection scanning. These maneuvers occur when information about the driver's visual
attention is most useful. Offline gap-filling methods may reconstruct a missing interval using observations from both sides, but an online driver-monitoring system cannot rely on measurements that have not yet occurred. We therefore formulate causal gaze recovery:
forecasting unavailable gaze at time t without target-tracker gaze at t or later. We introduce the Causal Context-Gated Forecaster (CCGF), which encodes a 60-frame pre-dropout history of gaze and head pose and combines it with DINOv3 scene features. A learned reliability gate controls the contribution of the history and scene representations as the dropout progresses. We evaluate two scene conditions: Live, in which the scene representation continues to update during tracker loss, and Frozen, in which the final pre-dropout representation is used throughout the missing interval. We evaluate CCGF on 2,047 eligible, naturally occurring GazeSense head\_lost events drawn from 10.5 h of naturalistic driving by ten drivers. Across all recordings, head\_lost accounts for 8.5 percent of GazeSense recording time. Synchronized gaze coordinates from a head-mounted Neon tracker provide supervision and evaluation targets but are never used as model inputs. Under leave-one-driver-out evaluation, CCGF achieves a mean per-driver median error of 175.7 px (10.5 deg) with Live scene updates, a 33 percent reduction relative to history-only causal
forecasting. With Frozen scene input, the error increases to 210.8 px (12.9 deg), indicating that scene observations acquired during the dropout provide useful predictive information. We will release the
dataset, evaluation protocol, and causal baselines.
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Artificial Intelligence
Toward Robust Personalized Alignment for LLMs: Mitigating Persona Drift in Multi-Turn Dialogue
Youyuan Zhang, Siyuan Li, Fangming Liu et al. (+1 more)
Persona drift remains a central challenge for personalized
language models, as user profiles evolve over long interactions rather than remain permanently fixed. Models must therefore revise persistent persona states when preferences genuinely change, while avoiding updates driven by transient, ambiguous, or unresolved observations. We propose CORE, which separates turn-local evidence from persistent persona-state revision and selectively updates grounded user preferences through uncertainty-aware belief revision. We also introduce PERSIST, a held-out post-anchor
benchmark for persona-state robustness under sequential interaction stress, covering ambiguity, conflict, and controlled social influence. Across ALOE, PersonaChat, and PERSIST, CORE improves personalized
alignment and robustness, with complementary gains in normalized closed-slot state fidelity. Human evaluation and mechanistic controls further support explicit update control beyond stronger generation or persistent
memory alone.
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All authors Youyuan Zhang, Siyuan Li, Fangming Liu, Jing Li
Robotics
READ: Learning Risk-Informed Fields for End-to-End Autonomous Driving
Zhiyuan Liu, Yuanxin Tian, Zehong Ke et al. (+5 more)
Autonomous driving requires more than recognizing what is present in a scene: a planner must determine how road structure, surrounding
agents, and their motion states should influence a future maneuver. Existing learning-based planners can capture these influences through latent scene features and trajectory decoders, but the relationship between environmental factors and candidate actions often remains implicit. This limits the ability to inspect, diagnose, or refine how scene context affects the safety of a predicted trajectory. Classical safety fields provide an explicit spatial representation of this relationship, but their risk shapes and relative weights are prescribed in advance and do not adapt to each scene. We introduce READ, a framework that learns an explicit, planning-aligned risk representation from complementary
geometric and behavioral constraints. READ instantiates this representation as a continuous spatiotemporal field, enabling differentiable queries along candidate trajectories. The learned field connects scene understanding with action selection by encouraging predicted trajectories to align with low-risk regions, while retaining a differentiable interface for trajectory evaluation and refinement. READ integrates with both end-to-end planners and
Vision-
Language-Action models. Experiments on NAVSIM show consistent gains across matched end-to-end backbones and strong performance in a VLA setting; READ also achieves competitive results on NAVSIM v2. These results establish learned spatial risk as an explicit, adaptable representation for safe planning.
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All authors Zhiyuan Liu, Yuanxin Tian, Zehong Ke, Jinhao Li, Hao Cheng, Zhenhua Xu, Wenhao Yu, Jianqiang Wang
Numerical Analysis
A spectral-element method for computing many eigenvalues and their asymptotics of the Schrödinger operator with Robin boundary condition
Weizhu Bao and Fukeng Huang
We propose spectral and spectral-element methods for accurately computing many
eigenvalues of the Schrödinger operator with Robin boundary condition on simple and complex geometries, respectively. Due to their spectral-type accuracy in approximating those eigenfunctions corresponding to high-index
eigenvalues, which are usually highly oscillatory, the proposed approaches have excellent resolution in computing thousands of
eigenvalues accurately and efficiently with the resolution property that the number of
eigenvalues with reasonable accuracy is proportional to the number of degrees of freedom. Using a standard HPC node with 128 GB of
memory, we can obtain numerically more than 5,000 reliable
eigenvalues with relative errors below $10^{-8}$ for different complex domains in two dimensions (2D). Based on these computed
eigenvalues, we first confirm some theoretical results on the Robin-to-Neumann (RtN) gaps of the
Laplacian operator in 2D, which were recently studied by Rudnick et al. [Comm. Math. Phys. 388 (2021)]. Then we systematically study the RtN gaps of the Schrödinger operator and their convergence rates. Based on our extensive numerical results, we formulate a unified
conjecture on the cumulative averages of the RtN gaps of the Schrödinger operator.
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Machine Learning · Cross-listed from stat.ML
Membership Inference via Pairwise Likelihood Ratios
Shengjie Niu, Zebin Yun, Yeheng Ge et al. (+1 more)
Membership inference attacks (MIAs) are the standard tool for
auditing the
privacy risks of machine learning models. Given a query point, an MIA aims to determine whether that point was used to train the target model. In practice, such inference must rely on the statistical signals exposed by the model's outputs, such as confidence scores, logits, and intermediate feature representations. However, existing methods often fail to efficiently summarize and combine these statistical signals. To address this limitation, we propose Pairwise Likelihood MIA (PL-MIA), a unified method that combines a Gaussian likelihood-ratio (GLR) statistic with population calibration and the Cauchy combination test. We characterize theoretically how the GLR retains variance-contraction signals and establish conditions under which population calibration and Cauchy combination improve attack power. We obtain $p$-values from pairwise comparisons between the query point and reference points not used for training, and aggregate these continuous signals using the Cauchy combination test. This preserves the evidence strength that is discarded when each pairwise comparison is reduced to a binary vote. Extensive experiments demonstrate that PL-MIA outperforms strong baselines, improving the true positive rate (TPR) by over 25% in the critical low-false-positive regime, corroborating our theoretical findings. These results demonstrate how statistical principles can turn noisy model outputs into more powerful, calibrated, and reproducible evidence for membership
privacy auditing.
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All authors Shengjie Niu, Zebin Yun, Yeheng Ge, Jian Huang
Computation and Language
ORQA: An Occupation-Realistic Question and Answer Framework for LLM Professional Knowledge
Shreyas Krishnan, Serina Chang and Abhishek Nagaraj
We present ORQA, a method for testing occupation-level knowledge in large
language models. Prior methods either map abstract LLM skills to occupations via task definitions or utilize expert knowledge which is difficult to obtain at scale and expensive. ORQA complements both of these methods by connecting O*NET occupations to trusted occupation-specific websites (such as regulatory agencies, licensing bodies, professional organizations, and government publications) and converting these into source-traceable question-answer pairs. A combination of an automated pipeline and human review produces a set of high quality questions about occupations. The question set created via our method covers 116 occupations from all 21 major groups in the SOC, with 480 questions sourced from 187 different websites. Each question is designed to probe a real-world skill question that is relevant to the occupation in question. We test 15 state-of-the-art frontier and open-weight models via this method. Claude Opus 4.6, GPT-5.4 and Claude Sonnet 4.6 all perform the best at approximately 58-62% while smaller open-weight models achieve approximately 33-41% performance. Performance varies significantly across occupations. Healthcare-related occupations achieve the highest performance (78%) while Office and Administrative Support achieve approximately 40%. Performance on individual occupations (e.g. Sheet Metal Workers and Fish and Game Wardens) is essentially zero. We also find that open-ended questions and weighting by wage bill do not significantly affect the ranking of models on this
benchmark. We believe that leveraging existing trusted occupation-specific information to test LLM knowledge in professional domains may be a scalable and useful method for evaluating occupation-level AI performance in the future. Results and data are available at orqabench.org.
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Machine Learning
Certified AI Triage of ICU Alarms
Mohammed Sameer Syed and Rozhin Yasaei
In the VTaC
benchmark 71% of ventricular-tachycardia alarms are false, but silencing a real one can delay recognition of a dangerous arrhythmia. We reframe alarm reduction as three-way triage (retain, suppress, or defer) and bound the decision this analysis treats as harmful: among suppressed alarms, the fraction that were genuine stays below a user-set budget with 95% confidence, under i.i.d. event sampling. Alarms sharing a waveform record are dependent, so the clustered analysis is a sensitivity check. On the official split a 5% budget certifies in all three seeds, suppressing 74.8% of false alarms while silencing 1.5% of genuine ones, at AUROC 0.953 and Challenge Score 83.33, numerically comparable to the strongest of the eleven published systems. Our central finding measures what multiplicity costs: the correction charges for every candidate, so a finer grid can certify strictly less. Under held-out calibration the 885-
cell grid we declared certifies 1 of 15 fold-runs, while choosing the grid on a separate selection partition certifies 8. We project the calibration volume each budget needs, making an uncertifiable budget a design parameter. Finally, adding a learned reliability dimension to the policy grid did not sharpen the certified frontier.
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Machine Learning
LatentVerse: A Framework for Understanding Shared and Modality-Specific Information in Multimodal Latent Representations
Majd Alafrange, Samuel Friedman, John Kitonyo et al. (+2 more)
Latent
embeddings have become a central data abstraction in modern machine learning, especially in biomedicine, where foundation models are increasingly used to encode
multimodal data like clinical text, medical images, omics, and physiological signals. However, the utility and value of these representations depends on understanding their quality, structure, and the information they encode. Existing analysis workflows for evaluating representations remain fragmented across custom scripts, isolated metrics, and most importantly lack
multimodal analysis, limiting accessibility and reproducibility. We present LatentVerse, a representation analysis resource that combines a web-based visual analytics platform for accessible, report-driven exploration with a command-line interface for scalable technical workflows. LatentVerse unifies diagnostics for various representation quality metrics and extends to
multimodal settings by decomposing
embeddings into shared and modality-specific components. We evaluate LatentVerse through controlled unimodal and
multimodal simulations, discovery-oriented analyses on real biomedical
embeddings, and a user study across diverse use cases. By supporting thorough and interpretable evaluation of latent spaces, LatentVerse makes foundation model representations more understandable in biomedical and data science applications.
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All authors Majd Alafrange, Samuel Friedman, John Kitonyo, Sana Tonekaboni, Mahnaz Maddah
Cryptography and Security
An Evidence-First Multi-LLM Framework for Auditable Critical-Infrastructure Dependency Modeling
Nurjahan, Mst Eshita Khatun, Lamine Noureddine et al. (+1 more)
Critical-infrastructure knowledge is distributed across heterogeneous, incomplete, and weakly structured evidence, making dependency models difficult to construct automatically and difficult to trust. Large
language models (LLMs) can extract structured knowledge from such evidence, but direct LLM-to-graph generation risks unsupported relationships, inconsistent terminology, incorrect entity identities, and erroneous dependency endpoints. We present an evidence-first multi-LLM framework for constructing Infrastructure Knowledge Bases (IKBs) and Infrastructure Dependency Graphs (IDGs) from heterogeneous infrastructure documentation. Multiple open-weight LLMs independently extract candidate entities and dependencies from normalized evidence, after which the framework separates evidence verification, ontology grounding, entity resolution, dependency
alignment, validation, fusion, and human review. Evidence support, ontology reconciliation, endpoint resolution, model agreement, and human validation remain distinct states, while provenance and unresolved cases are preserved throughout. The validated IKB is then projected deterministically into the IDG without introducing new LLM-generated knowledge. Evaluation in nine infrastructure projects shows that entity recovery achieves substantially higher recall than complete directed dependency recovery and that canonical endpoint resolution is a major constraint in dependency construction. Cross-model overlap is also much lower for dependencies than for entities, indicating that the models often produce non-overlapping candidate assertions rather than a stable majority consensus. These findings support an auditable evidence-to-IKB-to-IDG process in which uncertainty is preserved and resolved progressively rather than collapsed into a single confidence or voting decision.
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All authors Nurjahan, Mst Eshita Khatun, Lamine Noureddine, Aisha Ali-Gombe
Machine Learning
When Connected Does Not Mean Similar: Charting the Homophily Boundary of SNAP-KG for Streaming Entity Integration
Jui-Chien Lin and Oshani Seneviratne
SNAP-KG is a framework for assigning newly arriving entities to semantic communities in a growing knowledge graph (KG) using only their raw features, with no graph access and no retraining at inference time. It was evaluated on five multi-view
benchmarks and a 2.4M-node OGB-WikiKG2 KG. In each of these
datasets, at least one graph view is homophilous, meaning that connected nodes usually belong to the same class, and SNAP-KG performs well on all of them. This paper asks what happens outside that setting. We extend the evaluation to three heterophilous graphs (Texas, Wisconsin, Chameleon) and measure the edge homophily of every view. When no homophilous view is available, clustering quality drops sharply for both SNAP-KG and the transductive baselines used in its original evaluation. What decides this is the homophily of the relation, not the number of relations. Multi-view fusion still helps, but only when at least one homophilous relation provides a reliable foundation. The homophily assumption is therefore shared by the whole method family, not specific to SNAP-KG. We argue that heterophilous multi-view clustering is a separate research problem, outside the scope of this work. As future work, we outline how a heterophily-aware teacher could be distilled into SNAP-KG's projector to serve both homophilous and heterophilous KGs.
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Computation and Language
CueMem: Cue-Guided Context Reconstruction for Long-Term Conversational Memory
Changjian Wang, Rongzhen Li, Weili Guan et al. (+3 more)
Long-term conversational
agents must answer user queries by recalling information from extended dialogue histories, yet directly using the full history is costly and often unreliable, while compressed
memory units may lose fine-grained evidence needed for question answering. Motivated by the reconstructive view of autobiographical
memory, we propose CueMem, a cue-guided framework that treats extracted
memory records as
retrieval cues rather than self-contained evidence and reconstructs query-relevant dialogue context from their source turns. During
memory construction, CueMem extracts fine-grained
memory cues from dialogue turns and links each cue to its source turn. At query time, it retrieves query-relevant cues, maps them to source-turn anchors, and expands from these anchors over a turn graph that captures temporal proximity and semantic relatedness, reconstructing a compact evidence context from the original dialogue for LLM answer generation. Experiments on LoCoMo and LongMemEval show that CueMem consistently outperforms representative long-term
memory baselines. Further analyses show that graph-based context reconstruction helps recover supporting dialogue evidence while reducing query-time input
tokens and latency compared with the full-history LLM setting. These results highlight
retrieval cues as an effective alternative to self-contained
memory evidence for long-term conversational question answering.
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All authors Changjian Wang, Rongzhen Li, Weili Guan, Shuming Shi, Quan Lu, Ning Jiang
Computation and Language
SynthSentry: Detecting Synthetic Data Contamination in Language Model Training Data
Praveen Kumar Myakala, Ravichandra Namburi, Sowmya Keragodu Jayaramu et al. (+1 more)
Large
language models trained recursively on their own or other models' outputs undergo model collapse, in which distributional tails and factual accuracy deteriorate while fluency survives. Prior work diagnoses collapse after training; the actionable problem is screening a corpus of unknown provenance before training. We introduce SynthSentry, a corpus-level, model-agnostic contamination signal requiring no access to the generating model, no generation history, and no synthetic labels. The score is a distributional divergence over three statistics: lexical diversity collapse, n-gram tail truncation, and perplexity variance across reference models. We evaluate on corpora contaminated by small open-weight generators and an instruction-tuned open-weight model under a leave-one-generator-out protocol. A domain-stratified study measures false positives on naturally repetitive human text (legal, clinical, source code). The score ranks corpora by severity with little loss when whole generator families are held out. Per-domain calibration holds near its nominal false-positive budget once covariance shrinkage and a bootstrap threshold replace a naive
quantile, which runs four times over budget. A downstream fine-tuning check showed no contamination-driven accuracy deficit at our scale, so whether
pruning recovers one remains open; the same run shows over-
pruning risk once
pruning exceeds the true contamination fraction. We frame screening as a data-curation defense rather than a post-hoc diagnosis and release the scoring toolkit. All results are small-scale; scope is English-
language, batch-mode corpus screening. Contamination sources are single-generation or hand-authored rather than recursively generated, so results speak to synthetic contamination generally and not to recursion depth.
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All authors Praveen Kumar Myakala, Ravichandra Namburi, Sowmya Keragodu Jayaramu, Sooraj George Thomas
Data Structures and Algorithms
Spatial Mixing and Deterministic Approximate Counting of Multi-spin Systems beyond Bounded Degree Graphs
Zhidan Li and Kuan Yang
We develop a framework for deterministic approximate counting of multi-
spin systems beyond bounded-degree graphs. The
algorithm recursively constructs rational polytopes containing the true marginal vectors and uses linear-fractional programming to obtain certified bounds on marginal ratios. For positive interactions on graphs of
polynomial connective constant $D$, we establish strong spatial mixing whenever $Dc<1$, where $c$ bounds the Birkhoff contraction coefficients of the interactions. Under the same condition, we also give a deterministic fully
polynomial-time approximation scheme (FPTAS) for the partition function. We further extend the framework to proper colorings of sparse Erdős-Rényi random graphs using recursion on permissive blocks. For every fixed $\eta\in(0,1)$, sufficiently large fixed $d$, and fixed integer $q\ge(2+\eta)d$, we obtain an FPTAS for counting proper $q$-colorings of $G\sim\mathcal G(n,d/n)$ with high probability over $G$. This improves the leading constant $3$ in the earlier counting guarantee of Yin and Zhang (APPROX/RANDOM, 2016) to $2$, and asymptotically matches the spatial mixing regime established by Yin (ICALP, 2014).
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Computer Vision
EgoMaize: A First-Person Maize Instance Segmentation Benchmark under Severe Field Occlusion
Jiayi Li, Zihan Zhang, Erhankang Yan et al. (+4 more)
Close-range first-person field images are important for mobile maize phenotyping because many plant-level traits depend on in-canopy structures that are difficult to ob serve from overhead views. However, post-seedling maize fields create a difficult in stance
segmentation setting: stems, leaves, tassels, and neighboring plants are elon gated, repetitive, and strongly occluded. We introduce EgoMaize, a compact
benchmark for first-person maize instance
segmentation, where the task is to predict ownership consistent plant masks and plant-owned stem/tassel cues from close-range field images with severe same-class overlap. Existing visible-only labels can fragment one physi cal plant into disconnected supervision, while full-amodal labels may require unverifi able completion behind neighboring plants or field objects. EgoMaize therefore uses an evidence-closed annotation workflow for occluded maize regions and assigns unreli able maize regions to ignore rather than background. Baseline results show that pre trained query-based grouping, boundary refinement, and high-resolution crop refine ment help different aspects of the task, but no architecture solves the coupled chal lenges of fine structure recovery, same-class instance ownership, and occlusion reason ing; occlusion-level analysis further shows that performance decreases as plant visi bility becomes more limited. The
dataset and code are publicly available at https: //github.com/JaaaaaaaD/EgoMaize.
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All authors Jiayi Li, Zihan Zhang, Erhankang Yan, Yitian Chen, Yuze Li, Chengzhang Ding, Jianxin Cao
Robotics
A Deployable Architecture for Robot-Mediated Tasks (DART): Evaluation in Socially Assistive Robot-Guided Cognitive Behavioral Therapy Exercises
Mina Kian, Lydia Ignatova, Jiong Wang et al. (+12 more)
Socially assistive
robots (SARs) can support structured health and well-being interventions, but hardware and cost constraints limit interaction complexity and longitudinal real-world deployments. We present DART: Deployable Architecture for
Robot-Mediated Tasks, an architecture that extends SARs through a web application and cloud infrastructure, enabling visual content, user input, remote computation, and persistent data storage synergistically with the
robot's physical embodiment,
speech, and movement. We evaluated DART by instantiating it in an interatively-developed full-stack HRI system for helping university students with elevated generalized anxiety to complete cognitive behavioral therapy (CBT) homework exercises. The resulting system, which used the low-cost open-source Blossom
robot platform, was refined and evaluated through a participatory design process and multiple user studies, and finally evaluated in an in-lab study with 103 participants, and then a six-week in-home deployment with four participants. In the in-lab evaluation, participants showed significant within-session reductions in stress, state anxiety, and negative affect, and gave the platform a mean System Usability Scale score of 78.89. In the home deployment, the mean System Usability Scale score was 87.5, with positive qualitative feedback on usability. Participants across both groups identified
speech input, visual presentation, and web-
robot synchronization as priorities for improvement. These findings validate DART as an effective architecture for extending the capabilities of a low-cost SAR in both in-lab single-session and in real-world longitudinal deployments.
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All authors Mina Kian, Lydia Ignatova, Jiong Wang, Ji Min Lee, Jiancheng Li, Qianwei Guo, Emily Weiss, Amy O'Connell, Kaitlin Zareno, Jiani Li, Reyna Patel, Leyaa George, Minyu Huang, Justin Yang, Maja J. Matarić
Systems and Control
e-Traceroute: Physically traceable electricity routing for carbon-free energy utilization
Shiu Mochiyama, Ryo Takahashi and Yoshihiko Susuki
Maximizing the self-consumption of residential photovoltaic generation is a promising pathway to meet urgent decarbonization targets for 2030 (and even for 2035). A viable solution is to create a sharing economy for idle battery capacity within a community. To achieve this, utilizing shared physical assets necessitates complete physical traceability of power flows—the capability to strictly trace the ownership of stored energy among multiple participants. Furthermore, physical traceability is an essential function for demonstrating the use of carbon-free energy resources. Such tracing is impossible in conventional systems owing to two fundamental limitations: the mixing of power flows in a common bus and the decoupling of power and information delivery. This study presents a novel physical-layer technology, called e-Traceroute, that overcomes these limitations and realizes physically traceable electricity exchanges. Specifically, physically distinguishable power routing and data transmission are unified over the same power lines. Prototyping experiments demonstrate successful integration of power transfers and information transactions, validating that the proposed system serves as a physical foundation for a reliable and scalable sharing economy that drives bottom-up decarbonization.
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