Artificial Intelligence
Imitation Learning for Connection-Tableau Construction
Fredrik Rømming, Mantas Bakšys, Martin S. Fixman et al. (+1 more)
An automated
theorem prover builds a proof step by step, choosing at each point what to add and what to remove. We cast this construction as a policy acting in a transition system induced by a formal calculus, which fixes which steps are sound: for clausal connection tableaux, leanCoP-style search and plCoP/rlCoP-style planning then become stateful policies over one interface, and policy-learning methods apply directly. We equip such policies with a graph neural network that scores proof edits from structure that transfers across problems, train it by imitation learning from found proofs, and measure how performance holds as we remove search scaffolding, from full symbolic backtracking to a policy the network drives alone. Within a fixed step budget on M2k, MPTP2078-bushy, and TPTP v9.2.1, learned policies solve up to 46% more problems than leanCoP, and reach proofs in an order of magnitude fewer steps.
Read the full abstract ▾
All authors Fredrik Rømming, Mantas Bakšys, Martin S. Fixman, Sean B. Holden
Cryptography and Security
A Self-Evolving Multi-Agent Framework Defense against LLM Jailbreak Attacks
Tongyan Hu and Bryan Hooi
Large
language models (LLMs) remain vulnerable to jailbreak attacks that exploit techniques such as role-playing, obfuscation, code transformation, and multi-step indirection to elicit harmful outputs. As jailbreak strategies keep emerging, defenses have proliferated in an ongoing cat-and-mouse game, yet most remain static: their safety behavior is fixed at deployment, so they cannot accumulate defensive experience or adapt to unseen strategies. We propose a self-evolving test-time defense built around a persistent, cross-interaction rule
memory: when an attack succeeds, the framework abstracts that failure into a method-level rule capturing the structural attack wrapper rather than the harmful topic, and reuses it against future inputs. Because rules are method-level, one induced rule generalizes across an entire attack family, and the label space expands as novel wrappers appear. The mechanism operates entirely through external
memory and prompting, with no parameter updates, and applies to both open-weight and black-box API models. We realize it as four cooperating modules, but the contribution is the
memory-based adaptation mechanism, not the module decomposition. Across four black-box jailbreak families and multiple models, our method substantially reduces attack success rates while preserving benign utility, remains robust under an adaptive composite-wrapper attack, and does not increase over-refusal as the
memory grows.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from eess.AS
VoiceMem: Streaming Dual-Brain Memory for Real-Time Interaction
Zhifei Xie, Jiaqi Lang, Ze An et al. (+7 more)
Conversational systems, such as duplex
speech language models (SLMs), still lack a streaming, accurate, and empathetic
memory system as their soul. We introduce VoiceMem, a simple
memory architecture with a parallel informational left
brain, an emotional right
brain, and streaming
memory I/O mechanisms. We further build a complete pipeline for
memory-aware SLM training, long-horizon evaluation, and decoupled deployment with interchangeable
memory backends. Experiments and real-world deployment show three advantages: i) Accuracy: under top-5
retrieval, the left
brain outperforms classical systems such as Mem0 at top-200 by nearly 30 points; ii) Emotional & Personal: the right
brain, with short- and long-horizon affective attribution and dual-node persona modeling, achieves state-of-the-art performance across three persona
benchmarks and improves the aggregate score by 4.29 points over the previous best system; and iii) Real-Time & Cheap: VoiceMem completes
retrieval in 134 ms, well within standard VAD latency, adding no extra conversational delay while maintaining high accuracy and low cost. These results show that VoiceMem provides a practical
memory foundation for real-time, personalized, and emotionally aware
speech interaction.
Read the full abstract ▾
All authors Zhifei Xie, Jiaqi Lang, Ze An, Yifan Zhao, Dongchao Yang, Kai Li, Ziyang Ma, Mingbao Lin, Chunyan Miao, Shuicheng Yan
Artificial Intelligence
AsymSpec: Context-Asymmetric Speculative Decoding for Agentic LLMs
Sheng Liang, Yongyue Zhang, Nathanael Brian et al. (+4 more)
Agentic LLM pipelines face escalating inference costs as context accumulates across
retrieval, tool use, and multi-turn interactions. To control latency, deployments routinely compress inputs, but this degrades task accuracy. Speculative decoding (SD) accelerates generation losslessly, yet it assumes the drafter and verifier share an identical context, preventing SD from resolving the accuracy-overhead trade-off. We propose AsymSpec, an asymmetric speculative decoding framework that breaks this
symmetry: a lightweight drafter reads the full input while the large verifier operates on the compressed view. The drafter steers the verifier via a contrastive $\delta$-fusion of logits, modulated by a divergence-aware acceptance gate that preserves verification stability and high draft acceptance rates. Evaluated across four agentic capabilities and two end-to-end
agent benchmarks, AsymSpec reaches $\approx 90\%$ of full-context accuracy on average, delivering $1.3$–$1.7\times$ throughput speedups at $0.2$–$0.3\times$ the compute cost on isolated text capabilities. These results show that asymmetric context access yields substantial gains precisely when compression discards critical
reasoning signals.
Read the full abstract ▾
All authors Sheng Liang, Yongyue Zhang, Nathanael Brian, Hang Lv, Hao Wang, Chen Zhang, Yong Liu
Robotics
DESCENT: Directed Edge Scene Encoding for Airport Surface Movement Prediction
Alexander Prutsch, David Schinagl and Horst Possegger
Advanced automation is a key technology for enhancing the safety of ground operations amidst the increasing density of commercial air traffic. While motion
forecasting is a well-studied task in autonomous driving, its application to airport surface movements remains underexplored. To enable efficient and accurate prediction in this domain, we propose DESCENT, a
transformer-based architecture designed to handle heterogeneous dynamics and strict topological constraints. Our approach features a Potential Reachable Set (PRS) context sampling mechanism that adaptively collects airfield environment context across diverse operational phases. Combined with a detection
transformer-based decoder, DESCENT generates accurate trajectory forecasts. Extensive evaluations on the Amelia-10
benchmark demonstrate significant performance improvements over state-of-the-art baselines. These gains are especially pronounced in safety-critical scenarios, where our domain-aware sampling provides critical long-horizon context necessary for safe
navigation.
Read the full abstract ▾
Machine Learning · Cross-listed from eess.SP
CardioFusion-AI: Robust ECG–PPG Fusion for Multimodal Physiological Monitoring Under Signal Degradation
Navaneetha Krishnan Kamalakannan and Janakiraman Kamalakannan
Wearable electrocardiogram (ECG) and photoplethysmogram (PPG) sensors are complementary but individually fragile: motion artifact, poor contact, and sensor dropout can degrade one or both signals. Fusion strategies that assume both modalities are equally trustworthy can become less reliable than a single clean modality under degradation. We present CardioFusion-AI, a framework whose signal-processing front end, including R-peak and systolic-peak detection, an Orphanidou-type signal-quality index, and beat-by-beat pulse transit time estimation, is validated on 53 real intensive-care recordings (848 windows; heart-rate mean absolute error 1.61 bpm for ECG and 2.78 bpm for PPG) and a real annotated fetal ECG database (R-peak F1 0.89-0.98). We then conduct a controlled synthetic degradation study comparing eight ECG-PPG fusion strategies across six degradation regimes spanning graded corruption and complete modality loss, using five independent training seeds.
Attention fusion achieved the lowest descriptive overall error (1.66+/-0.43 bpm). Both adaptive gates reallocated weight toward the healthy modality under complete modality loss, but showed near-zero correlation between gate weight and signal quality under graded degradation (r = 0.10-0.24). Signal-quality conditioning produced a specific improvement under missing-PPG conditions (1.56+/-0.59 bpm), approaching the 1.48 bpm unimodal ceiling. With only five training seeds, no pairwise comparison survives Holm-corrected significance testing; effect sizes and confidence intervals are therefore reported. These results indicate that modality availability and modality quality are functionally distinct problems for adaptive fusion.
Read the full abstract ▾
Computation and Language
Distinct dynamics of conceptual and referential disruptions in human reading and large language model processing
Rui He, Nihal Altay and Wolfram Hinzen
Linguistic meaning is grounded in conceptual content, from which reference to particular entities emerges as words enter discourse. To examine the processing dynamics associated with these two dimensions of meaning, we selectively disrupted conceptual or referential information in short narratives and traced the resulting effects in human self-paced reading and in the predictive and representational processing of large
language models. In human reading, conceptual disruptions produced a strong but localized processing cost, emerging immediately after the distorted word, reaching an early maximum, and then declining rapidly. Referential disruptions produced weaker effects, which decreased more gradually across subsequent words, and were more strongly modulated by sentence boundaries. In the
language model, both disruptions emerged immediately at the manipulated word. Contextual model surprisal showed a pattern closely paralleling human reading: conceptual disruption produced a larger, more locally concentrated effect that decayed rapidly, whereas referential disruption produced a smaller and more gradual downstream effect. Output-layer representations showed a different pattern: referential disruption produced a larger initial displacement, while both distortions were subsequently characterized by power-law decay. Together, these results provide convergent evidence for distinguishable processing dynamics of two types of meaning: conceptual information imposes a more locally concentrated integration cost, whereas referential information engages a more distributed process of maintaining discourse-level identity.
Read the full abstract ▾
Computer Vision
Uncertainty-Guided Latent Diffusion Models for Faithful Super Resolution
Ren Wang and Yung-Yu Chuang
The perception-distortion trade-off poses a fundamental challenge in single-image super-resolution (SR). Although
diffusion-based SR methods excel at generating perceptually realistic images, achieving high fidelity remains a key limitation. Recent advances in
diffusion-based SR have shown promise in improving fidelity, but these methods often compromise perceptual quality due to their high reliance on a high-fidelity image. To address this, we introduce UGDiff, a novel
diffusion guidance paradigm designed to further improve the perception-distortion balance. In particular, we first estimate the reconstruction uncertainty of the latent features corresponding to a high-fidelity image. This uncertainty is then used to guide the
diffusion process to selectively restore high-frequency details in high-uncertainty regions, while preserving fidelity elsewhere. Furthermore, our guidance method adaptively identifies the high-uncertainty regions by considering not only the estimated uncertainty but also the posterior variance of the
diffusion sampler at each timestep. This relaxes the reliance on the high-fidelity image in the later stages of sampling, thereby achieving a better perception-distortion balance. Extensive experimental results demonstrate that our method performs favorably against state-of-the-art
diffusion-based SR methods.
Read the full abstract ▾
Artificial Intelligence
ProgRouter: Online Progress-Guided Orchestration for Multi-Agent LLM Workflows under Quality-Cost Tradeoffs
Songyuan Li, Ahmed M. Abdelmoniem and Shiqiang Wang
Multi-
agent large
language model (LLM) workflows have emerged as a powerful paradigm for solving complex, open-ended tasks through collaborative
reasoning among specialized LLM
agents, but they incur substantial operating costs due to repeated LLM invocations and long-horizon context accumulation. Existing cascade routing methods make one-shot, query-level decisions and cannot adapt to the dynamic, state-dependent nature of multi-step workflows, in which the right LLM at each step depends on evolving task progress, remaining task difficulty, and cost-efficiency requirements. We present ProgRouter, an online progress-guided routing framework that adaptively selects LLM
agents across workflow steps to preserve task-solving quality while adhering to time and cost budgets. ProgRouter introduces a multi-view task progress scorer that combines coarse workflow outcome regimes with fine-grained signals on subtask completion, progress trends, and workflow state quality. Then, a dual-path task progress predictor and an adaptive meta-gating mechanism estimate the progress gain for each candidate routed LLM. ProgRouter makes online step-wise routing decisions that balance progress gain, task time budgets, and long-term operating cost efficiency. Experiments on HumanEval Plus, MBPP, MATH-500, and ASQA, spanning agentic code generation, mathematical
reasoning, and
retrieval-augmented long-form question answering, demonstrate that ProgRouter reduces the operating cost relative to key baselines while maintaining strong task-solving performance.
Read the full abstract ▾
Machine Learning
Spectral Allocation: Why Muon Outperforms Adam, and How to Improve Muon
Xiaodong Wu, Wenyi Yu, Chao Zhang et al. (+1 more)
Orthogonal optimisers such as
Muon can substantially accelerate large
language model pretraining relative to Adam, yet the mechanism remains incompletely understood. We investigate this through an out-of-sample spectral probing analysis of
Transformer loss landscapes. At checkpoints along real training trajectories, we decompose each momentum buffer into its singular directions and estimate the loss-optimal step size along each direction on held-out data. The resulting spectral profile is anisotropic yet stable across batches and training stages, and consistent across the optimisers and model scales: a volatile head operating at the Edge-of-Stability supports a much smaller step size than the tolerant bulk, which permits substantially larger steps. This profile provides a unified spectral allocation account of why
Muon outperforms Adam, which outperforms SGD. It also exposes a limitation of
Muon's uniform scaling: it still underutilises the bulk. Guided by this finding, we introduce Spectral-Aware
Muon (SAMuon), which holds the head at the
Muon scale and amplifies the bulk using a static spectral prior. We provide two variants: the complete SAMuon follows the measured profile using a low-rank randomised SVD and the simplified SAMuon-lite uses a two-level approximation via rank-one power iteration. Neither method adds persistent optimiser state or notable extra FLOPs beyond
Muon at scale, and the idealised exact-whitening versions of both retain
Muon's asymptotic convergence rate under standard assumptions. Across "modded-nanogpt" models from 124M to 1B parameters, both variants outperform tuned AdamW and
Muon (Scion implementation) baselines in all evaluated model-scale and batch-size configurations. SAMuon requires 13.3% to 24.0% fewer training
tokens to reach the same validation loss as
Muon, while SAMuon-lite retains most of this gain with near-zero wall-clock overhead.
Read the full abstract ▾
All authors Xiaodong Wu, Wenyi Yu, Chao Zhang, Philip Woodland
Artificial Intelligence
Multi-Granularity Context-Enhanced RAG over Multimodal Knowledge Graphs
Zongyu Wu, Yilong Wang, Xiaochen Wang et al. (+5 more)
Retrieval-augmented generation (RAG) is widely used to mitigate
hallucination issues in large
language models (LLMs) and
multimodal large
language models (MLLMs). In particular, knowledge graph (KG)-based RAG leverages structured knowledge to provide (M)LLMs with high-quality external information. Building on these works, recent studies have explored
multimodal knowledge graphs (MMKGs) as knowledge bases for GraphRAG. This enables Graph RAG to integrate knowledge across multiple modalities, thereby further enhancing its performance. However, existing MMKG-based RAG methods generally follow a common pipeline in which different modalities are largely processed independently before being fusion. As a result, textual context is only used to a limited extent during visual information extraction and subsequent
multimodal knowledge fusion. This brings a semantic gap between images and text which limits the
multimodal GraphRAG performance. To address this issue, we propose a novel framework for constructing a Context-Enhanced MMKG (CEMMKG) to better support
multimodal GraphRAG. The proposed CEMMKG enriches each image with complementary textual context at both local and global scopes. Local context goes beyond the surrounding text by incorporating sentences that are semantically related to the image, while global context provides a summary of the entire passage. We further introduce a multi-granularity design for the local context, allowing it to capture semantically relevant information at different levels of detail. Extensive experiments on the selected
vision-centric
dataset validate that CEMMKG is effective in leveraging contextual information to improve MMKG-based RAG performance. Moreover, its effectiveness across different MMKG-based RAG methods demonstrates its broad applicability.
Read the full abstract ▾
All authors Zongyu Wu, Yilong Wang, Xiaochen Wang, Minhua Lin, Zhichao Xu, Fenglong Ma, Xiang Zhang, Suhang Wang
Computer Vision
FRAME: Separating sampling variation from performance disparities in medical image analysis
Mahshad Lotfinia, Daniel Truhn, Andreas Maier et al. (+1 more)
Fairness audits of medical imaging models commonly report the largest performance difference between demographic subgroups and treat any positive value as evidence of bias. Yet a perfectly fair model also produces a positive difference, which grows as subgroups shrink. Some mitigation methods remove demographic information from models because they assume that it produces the difference. Here we introduce Fair-model Reference And Mechanism Evaluation (FRAME). Its first step compares each reported difference with the difference expected from a perfectly fair model at the same subgroup counts. Its second step tests whether candidate causes of any excess over this fair-model reference change the difference when they are injected into model features. We evaluated 36 encoders on nine
datasets in three modalities and audited 89 published differences across six modalities. For 10 frozen encoders and 13 chest radiograph findings, the reference is a median 41% of the race difference and 22% of the age difference. It is exceeded by 40 of 53 published differences in sensitivity or false positive rate and by one of 36 in the area under the receiver operating characteristic curve (AUROC). Adding race information to the features of two encoders does not change the race difference significantly. At matched disease performance, mitigation reduces the race difference by less than its spread across three pretraining seeds (medians 0.005 and 0.012) and the age difference by more (0.008 and 0.006). Image-text pretraining raises the worst-group AUROC for race by a median 0.049 over self-supervised pretraining. Reporting the fair-model reference beside every new and published subgroup difference could direct mitigation to the differences above it.
Read the full abstract ▾
All authors Mahshad Lotfinia, Daniel Truhn, Andreas Maier, Soroosh Tayebi Arasteh
Computation and Language
When Personality Meets Quantization: A Layer-wise MBTI Analysis of Quantized LLMs
Yao Fu, Lijia Huang, Xiaomin Li et al. (+3 more)
Personality is increasingly important in large
language models (LLMs), as it shapes users' trust, engagement, and emotional experiences. While the Myers–Briggs Type Indicator (MBTI) has emerged as a common framework for assessing LLMs' personality, existing studies focus primarily on full-precision models and evaluate only final outputs. They overlook the widespread deployment of quantized LLMs requiring low
memory footprints, whose personality traits remain underexplored. In this work, we present a systematic MBTI analysis of open-source LLMs across multiple precisions, including mainstream 4-bit methods (GPTQ, AWQ) and extreme 2-bit settings (AQLM variants). Beyond output-level evaluation, we examine how personality emerges across layers through option-level
entropy and confidence-gap dynamics, and introduce Uncertainty-Amplified Layer Decoding (UALD) to study decoding-induced personality drift at inference time. Our results reveal a key insight: LLMs' personality is not a static property, but an emergent, layer-dependent decision process sensitive to quantization, prompting, and decoding. Specifically, we find that (1) ENFJ remains dominant across model families and precisions; (2) 4-bit quantization largely preserves coarse personality structure, while 2-bit quantization disrupts fine-grained
prompt consistency and cross-precision agreement; (3) personality decisions emerges in upper layers, following substantial ambiguity in early layers; and (4) inference decoding can shift personality, while personality-aligned conditioning improves robustness. These findings provide a new perspective on the behavioral reliability of quantized LLMs and highlight the importance of considering internal dynamics and inference strategies in personality-sensitive chatbot applications.
Read the full abstract ▾
All authors Yao Fu, Lijia Huang, Xiaomin Li, Runchao Li, Yu Yin, Kenneth A. Loparo
Computation and Language
Lost but not erased: Finding traces of a forgotten language in neural speech models
Peter Plantinga, Charlotte Moore, Peter W. Donhauser et al. (+2 more)
International adoptees retain phonological traces of a birth
language they can no longer speak or comprehend, a persistence typically attributed to a biologically-timed critical period. We asked whether it could instead reflect the ordinary dynamics of learning, using automatic
speech recognition models that simulate the international adoptee experience without maturational confounds. Models were trained on one
language and then abruptly switched to a second. We found that traces of the first
language persisted throughout second-
language training, but mainly in the lowest, pre-phonemic layers. These traces were functional, as models with early exposure re-learned their lost first
language 14% faster than naive models; this advantage held even against models adopted early from a related
language and disappeared when the earliest layers were substituted from a non-adopted model. We argue that these critical-period effects reflect entrenchment of foundational representations rather than a maturational loss of plasticity, and that experience plays a central role in critical periods in
language acquisition.
Read the full abstract ▾
All authors Peter Plantinga, Charlotte Moore, Peter W. Donhauser, Krista Byers-Heinlein, Denise Klein
Artificial Intelligence
SciMIF: Understanding Multimodal Instruction Following in Scientific Domains
Ye Shen, Yuting Zheng, Dun Pei et al. (+4 more)
Understanding instruction-following capabilities in scientific domains is essential for effectively leveraging
Multimodal Large
Language Models (MLLMs) to advance the development of scientific fields. In this work, we introduce SciMIF, a novel
benchmark designed to evaluate the capability of MLLMs in following complex scientific instructions. Specifically, based on an extensive analysis of 22 distinct tasks across 5 representative scientific disciplines, we propose a comprehensive taxonomy comprising 10 constraint groups that captures both general functional requirements and discipline-specific characteristics. Guided by this taxonomy, we develop a high-fidelity instruction injection pipeline to systematically augment existing scientific
datasets. We conduct comprehensive experiments on multiple state-of-the-art closed-source and open-source MLLMs. Our findings reveal significant performance disparities across different scientific disciplines, with chemistry posing greater challenges for current MLLMs. Furthermore, we observe that increasing the model scale does not yield corresponding improvements in constraint adherence, and current models still struggle severely with fine-grained constraints and instructions requiring the deep application of disciplinary knowledge. SciMIF fills the current void in evaluating
multimodal instruction adherence within scientific domains, laying a crucial foundation for future enhancements of MLLMs in rigorous scientific applications. Data and code will be released at https://github.com/shenye7436/SciMIF .
Read the full abstract ▾
All authors Ye Shen, Yuting Zheng, Dun Pei, Zijian Chen, Wenlong Zhang, Qi Jia, Guangtao Zhai
Computer Vision
PANDA - Prototype-Anchored Alignment for Partially Unpaired Multimodal Learning, with Applications to Alzheimers MRI and TCGA Pathology
Sheethal Bhat, Mahfuzur Rahman Chowdhury, Paula Andrea Perez-Toro et al. (+4 more)
Multimodal medical prediction often faces incomplete pairing: auxiliary modalities with complementary signal are available for only a subset of subjects (or none) and cannot be assumed at deployment. We introduce PANDA (Prototype Anchored Data
Alignment), a two-stage framework that transfers auxiliary information to a primary-modality model without auxiliary inputs at inference. Stage 1 learns a shared
embedding from the paired subset and estimates class prototypes from auxiliary modalities; Stage 2 trains the primary encoder on all subjects using cross-
entropy plus
alignment to the frozen prototypes. Because supervision is defined at the class-prototype level, PANDA accommodates arbitrary pairing rates, including zero subject overlap. We evaluate PANDA on two applications. On a 1,021-subject multi-scanner ADNI cohort, we perform AD/CN classification with three auxiliary modalities at distinct pairing rates: tabular scores (44.8%), FDG-PET (18.7%), and external handwriting kinematics (0% overlap). Relative to the same-backbone MRI-only baseline, PANDA attains AUC 0.868 +-0.020 (+7.9pp) and reduces 1.5T CN false positives by 24.3pp; on a fully trainable Conv5-FC3 backbone it reaches AUC 0.893 (best overall). A pairing-rate ablation shows that the joint anchor remains within seed noise from 75% to 5% pairing. On TCGA-Lung survival prediction from whole-slide images with RNA-seq as auxiliary data, PANDA improves over WSI-only on 2-year OS (AUC +3.5pp) and Cox PH (C-index +9.0pts) and outperforms full-fusion training, which underperforms WSI-only, while requiring no RNA at inference; wide confidence intervals on this smaller cohort keep the gains below conventional significance. Overall, PANDA provides a deployment-oriented mechanism for leveraging incomplete auxiliary modalities to improve primary-modality prediction.
Read the full abstract ▾
All authors Sheethal Bhat, Mahfuzur Rahman Chowdhury, Paula Andrea Perez-Toro, Stephan Wunderlich, Rose Dawn Bharat, Siming Bayer, Andreas Maier
Artificial Intelligence
Quantitative Analysis of $\omega$-Regular Robust MDPs
Ali Asadi, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady et al. (+3 more)
Robust Markov Decision Processes (RMDPs) generalize classical MDPs by allowing uncertainty in transition probabilities and optimizing against their worst-case realization. We consider $(s,a)$-rectangular RMDPs with linearly defined uncertainty sets and study parity objectives, which are a canonical representation of $\omega$-regular objectives. An uncertainty set is linearly defined if it is described by linear inequalities over the transition distribution together with auxiliary variables, which capture the standard $L_1$ and $L_\infty$ balls as well as general polytopic uncertainty sets. The quantitative value is the supremum, over all
agent policies, of the satisfaction probability guaranteed against the
adversarial environment. Previous work studied the qualitative analysis, namely the almost-sure (resp. positive) problem that asks whether a single
agent policy guarantees satisfaction with probability one (resp. positive probability) against every environment policy. In this work, we solve the exact quantitative problem. Our contributions are threefold. First, we show that both the
agent and the environment admit pure memoryless optimal policies. Second, we give a
polynomial-time
algorithm for quantitative parity on linearly defined robust Markov chains and use it as a subroutine in a policy-iteration
algorithm for RMDPs. The
algorithm combines quantitative one-step improvements with qualitative almost-sure improvements. Finally, we report experiments comparing our approach with the explicit reduction to
stochastic games.
Read the full abstract ▾
All authors Ali Asadi, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Mehrdad Karrabi, Alipasha Montaseri, Ali Shafiee
Numerical Analysis
Risk-averse Optimization in Random Materials: Algorithmic Advances and HPC Acceleration
Niklas Baumgarten, Marcel Koch, David Schneiderhan et al. (+2 more)
We summarize our advances in the algorithmic development and hardware utilization for risk-averse optimization problems in random materials. This includes risk-averse optimization using the entropic risk measure, as well as recently developed sampling techniques for random materials, that are interoperable with the optimization framework. Furthermore, we discuss recent progress in the efficient utilization of modern hybrid hardware architectures for these methods to solve three-dimensional partial differential equations.
Read the full abstract ▾
All authors Niklas Baumgarten, Marcel Koch, David Schneiderhan, Tim Schrader, Robin Weiß
Computer Vision
Less Contouring, More Accuracy: Lesion-Guided ROI Deep Learning for Ovarian Ultrasound Classification
Mehran Ahmad, Ali Abbasian Ardakani, Afshin Mohammadi et al. (+3 more)
Ovarian lesion classification using transvaginal ultrasound remains challenging due to overlapping imaging characteristics and the dependence on expert interpretation. This study investigates whether lesion-guided region-of-interest (ROI) deep learning can achieve competitive diagnostic performance while reducing the annotation burden associated with pixel-level lesion
segmentation. Two publicly available ovarian ultrasound
datasets were evaluated: the Multi-Modality Ovarian Tumor Ultrasound (MMOTU)
dataset for eight-class classification and the Ovarian Ultrasound
Dataset (OUD) for binary classification. Four strategies were compared under a unified framework: global image-based deep learning, lesion-guided ROI-based deep learning, lesion contour-based deep learning, and contour-based radiomics with machine learning classifiers. Four deep learning architectures, MaxViT-Tiny, Swin
Transformer, EfficientNet-B7, and ResNet18, were evaluated. Radiomics models were developed using support vector machine, k-nearest neighbors, and artificial neural network classifiers, with ANOVA-based feature selection applied for the lower-sample OUD
dataset. The lesion-guided ROI strategy achieved the strongest overall performance, with MaxViT-Tiny obtaining 93.10% accuracy and an AUC of 0.99 on MMOTU and 97.56% accuracy and an AUC of 0.99 on OUD. The contour-based approach achieved comparable accuracy but required substantially higher annotation effort. These findings demonstrate that lesion-guided ROI deep learning provides an effective balance between diagnostic performance and annotation efficiency, offering a practical approach for scalable AI-assisted ovarian ultrasound analysis
Read the full abstract ▾
All authors Mehran Ahmad, Ali Abbasian Ardakani, Afshin Mohammadi, Alisa Mohebbi, Gernot Kronreif, Sepideh Hatamikia
Artificial Intelligence
LivingRAG: Augmenting Graph RAG with Experience
Yuzhuo Cui, Zongye Zhang and Qingjie Liu
Graph-based RAG improves multi-hop question answering by organizing evidence as a knowledge graph. However, most existing RAG systems process each query in isolation and discard useful
reasoning from the LLM's response after inference. As a result, later related queries need to retrieve evidence and reason from scratch. We propose LivingRAG, a Graph RAG framework with writable and reusable
reasoning experience. LivingRAG adds a writable experience store to a graph-based
retrieval backbone, enabling verified experiences to be reused during inference in two ways. Stored graph signals help
retrieval find entities and passages that were useful in earlier related queries. Stored summaries provide a reference
reasoning pattern for answer generation. We analyze online QA streams and find reusable signals from shared entities, graph neighborhoods, and question templates. Experiments on multi-hop QA
benchmarks show that LivingRAG improves accuracy over strong RAG baselines and reduces completion-
token use when relevant prior experience is reused.
Read the full abstract ▾
Numerical Analysis
Optimized Multilevel Sampling Methods under Resource Constraints
Niklas Baumgarten
We present recent developments in multilevel sampling methods under resource constraints. Over the past 15 years, multilevel methods have become widely used for uncertainty quantification. However, scaling them to high-dimensional problems and high-performance computing (HPC) environments remains challenging. In this work, we discuss two
algorithms designed to address these issues: the budgeted Multilevel Monte Carlo (MLMC) method and the Multilevel
Stochastic Gradient Descent (MLSGD) method. We demonstrate their effectiveness on HPC systems under consideration of the available computational resources for applications in forward uncertainty quantification (UQ) and optimal control (OC) under uncertainty.
Read the full abstract ▾
Computer Vision
4DGS-WAM: Bridging Past and Future with an Object-Centric World Action Model based on 4D Gaussian Splatting
Yueen Ma, Zenglin Xu and Irwin King
Current world action models (WAMs) typically operate on 2D visual data. These models can achieve exceptional visual quality, but they lack explicit spatial structure for individual objects and repeatedly process redundant background content. Although point clouds can represent the world in 3D space, they can be difficult to align and accumulate across viewpoints. In this paper, we leverage an explicit 4D Gaussian Splatting (4DGS) representation that separately models dynamic objects and the static background of a scene. For dynamic objects, we use a policy model to predict future actor actions and a world model to predict transformations of their observed Gaussian splats. The static background need not be regenerated for future states, as much of it has already been observed in past frames. This forms an object-centric world action model, which we name 4DGS-WAM. It lifts 2D observations into a persistent 4D representation so that previously observed static content can be reused during future prediction. Future-state extrapolation can then focus on modeling the
evolution of dynamic objects. Experiments on KITTI-MOT evaluate short-horizon prediction and past reconstruction.
Read the full abstract ▾
Multiagent Systems
Praxist: From Experimental Artifacts to Solution Lineages
Jin Li, Ahmed Murtadha, Zhiyu Wang et al. (+14 more)
Autonomous R&D
agents now write, run, and improve executable artifacts under automated evaluation-but largely as laboratory instruments: shown on curated
benchmarks, with gains that are hard to trace to a cause and costs well above what sustained engineering practice absorbs. The limitation is structural. Most systems treat each attempt as nearly self-contained, so logs,
memories, and search trees record what happened without establishing which design element produced an improvement, whether its evidence survived validation, or how it recombines with others. Long campaigns therefore keep re-learning the same lessons. We introduce Praxist, a lineage-centered generational system that converts reproducible artifacts and evaluator outcomes into a typed evidence graph of findings, lane-structured frontiers, and agendas. Separating local artifact construction from cohort-level evidence synthesis lets later attempts inherit validated mechanisms, unresolved claims, and useful constraints, and leaves results attached to an inspectable lineage. On the standardized 75-task MLE-bench suite, the finalized official-grader results give Praxist 60 medals (80.0%), 49 of them gold, against 55 medals (73.3%) and 34 gold for a Claude Code baseline on Claude Opus 4.8-at a recorded model spend of $3054 versus $38370, roughly a twelfth of the cost. Five case studies-quantitative trading,
LiDAR-inertial-visual SLAM, tokamak
magnetic control, rocket landing, and job-shop rescheduling-carry the same process into open-ended engineering problems, improving on each task-native baseline in headline accuracy, survival, or resource cost, with the discovery path on record. Stronger artifacts at an order of magnitude less spend, each backed by an auditable lineage, are, to our knowledge, first brought together here: the operating profile production research requires, not the one a
benchmark demonstration establishes.
Read the full abstract ▾
All authors Jin Li, Ahmed Murtadha, Zhiyu Wang, Qiwen Chen, William Chen, Yifei Wu, Guan Wang, Cheng Gong, Andy L. Siy, Jiayi Yang, Mengsha Huang, Wenhao Li, Yixuan Liu, Shuailin Pan, Mingli Yuan, Sen Song, Yuhao Sun
Computers and Society
Spatial-Knowledge-Graph-Grounded LLM Agents for Neighborhood Livability Evaluation
Haiyan Hao
Neighborhood livability is commonly assessed with static built-environment indicators, such as facility proximity, street connectivity, and access to public space. These measures describe available opportunities but do not directly represent how residents with different mobility capacities, household roles, schedules, and care responsibilities experience the neighborhood. This paper presents a prototype framework that uses a spatial knowledge graph (KG) and large
language models (LLMs) to generate and revise household schedules, followed by rule-based feasibility checking and GIS-based network materialization. The spatial KG integrates residents, residences, facilities, neighborhood context, and sampled road hubs; Graph-RAG retrieves each household's nearby spatial context, including candidate POIs and approximate walking times, for the scheduling LLM. The LLM produces structured household schedules, while rules are used for lightweight repairs and auditable feasibility checks. The LLM then revises schedules in response to identified feasibility issues. A routing module derives the actual travel paths, travel times, modes, and event histories from the road network. The resulting events support synthetic resident-
agent interviews about daily convenience, travel burden, activity feasibility, and household coordination. A prototype demonstration in a Shenzhen neighborhood shows that nominal facility availability does not necessarily imply convenient access: residents with limited mobility and households with care responsibilities experience greater travel and coordination burdens. The framework offers an auditable way to connect spatial opportunity, household activity constraints, and resident-specific livability interpretation, while keeping simulated experience distinct from observed perception.
Read the full abstract ▾
Computer Vision
Auditable CT Phenotyping Through Report-derived Radiological Observations
Riga Wu, Walter Witschey, Yicheng Li et al. (+9 more)
Medical image foundation models can predict clinical phenotypes from computed
tomography (CT), but strong performance leaves open whether they read disease-specific findings or shortcuts that correlate with the diagnosis. We tested this in 221 electronic-health-record (EHR) phenotypes using Auditable CT phenotyping (ACT), built on report-derived radiological observations. We trained ACT on 38,317 patients, mined 376,194 observations and evaluated it in 25,183 held-out patients. ACT exceeded five
vision-
language baselines on zero-shot annotation, and CT-CLIP across 221 phenotypes from unseen CT pulmonary angiography, both under zero-shot scoring (0.651 versus 0.572) and under linear probing (0.709 versus 0.662). Reading each probe exposes what accuracy conceals: only 97 observations occupy the 221 rank-1 positions, and one phrase describing aortic and coronary calcification ranks first for 20 phenotypes, including osteoporosis, urinary tract infection and major depressive disorder. Restricting the bank to clinician-specified evidence redirects those probes onto phenotype-related observations in 86 phenotypes at no accuracy cost (0.751 versus 0.741). Accurate CT-based EHR phenotyping can therefore rest on observations that are not valid evidence for the coded phenotype and that ACT can identify and intervene on.
Read the full abstract ▾
All authors Riga Wu, Walter Witschey, Yicheng Li, Felix Barajas Ordonez, Keno K. Bressem, Lisa C. Adams, Gary E. Weissman, Li Shen, Christos Davatzikos, Eduardo Barbosa, Daniel Truhn, Tianyu Han
Computation and Language
Unveiling Spectral Mechanisms in Training-Free LLM Text Detection
Haitong Luo, Xuying Meng, Weiyao Zhang et al. (+5 more)
The rapid advancement of Large
Language Models (LLMs) makes it increasingly difficult to distinguish human writing from machine-generated text. Training-free detection offers a scalable solution, yet common confidence-based metrics mainly measure average
token probabilities and often miss the signal fluctuations that characterize human writing, which we call "generative vitality". Spectral analysis offers a way to capture this vitality, but its mechanism and practical boundaries remain underexplored. In this paper, we analyze spectral detection from both theoretical and empirical perspectives. We connect spectral energy to variance in proxy log-probability trajectories and explain how broader human
token choices create the fluctuations used by frequency-domain indicators. We further show that the strength of this signal depends on text length and sampling range: spectral evidence is clearest for long, continuous, constrained generation, while short, fragmented, mixed, and edited settings require complementary confidence and fluctuation views. These findings clarify when frequency-domain detection works and provide guidance for future multi-dimensional detector design.
Read the full abstract ▾
All authors Haitong Luo, Xuying Meng, Weiyao Zhang, Wenji Zou, Shengfeng Lou, Xuefeng Jiang, Chungang Lin, Yujun Zhang
Human-Computer Interaction
Gaming Together on Discord: Teen Gamer's Cross-Platform Practices
Elena Koung, Xinning Gui and Yubo Kou
Discord is one of the most popular communication platforms among gamers. While prior research has highlighted its role in community building, relatively little
attention has been paid to its original gaming context-how it shapes gameplay and social experiences. To address this gap, we conducted semi-structured interviews with 16 teenage Discord users. Through reflexive thematic analysis, we show how players leverage Discord to create more collaborative and socially enriched experiences that extend beyond the game itself. However, gaming together on Discord also resulted in social and security risks. We conceptualize gaming together on Discord as a cross-platform practice that extends gameplay beyond a game and supports players' social needs. Additionally, cross-platform practice also introduces the 'platform gap,' where fragmented governance between platforms exposed players to risks. To address this tension, we propose design implications aimed at bridging the platform gap, strengthening communication channels, and supporting safer gaming experiences.
Read the full abstract ▾
Machine Learning
When Pruning Meets Interpretability: Preserving Sparse Autoencoder Robustness in LLMs
Suchit Gupte, Xueru Zhang and Mohammad Mahdi Khalili
Sparse autoencoders (SAEs) are widely used to interpret the internal representations of large
language models (LLMs), yet their reliability under post-hoc model compression remains poorly understood. We present a systematic study of how
pruning affects SAE behavior and theoretically show that, for a fixed SAE, its impact is governed by perturbation energy, a covariance-weighted norm. This perspective exposes a key limitation of magnitude
pruning: by ignoring activation geometry, it distorts the learned representation space and degrades SAE functionality. Activation-aware methods such as Wanda and SparseGPT, in contrast, implicitly control perturbation energy and are therefore substantially more robust at preserving SAE behavior. We further reveal a consistent structural vulnerability across all
pruning methods: middle layers are significantly more sensitive to
pruning than early or late layers. Guided by this insight, we propose a layer-wise sparsity allocation strategy, achieving lower perplexity under the same average
pruning sparsity. Experiments across four model architectures validate our theoretical findings. Code is publicly available at https://github.com/osu-srml/sae-robustness-under-
pruning/tree/main.
Read the full abstract ▾
Robotics
A Statistical Audit of Physical AI Benchmark Redundancy
Zaruhi Navasardyan and Hrant Davtyan
Physical AI models are evaluated on suites of
benchmarks that differ across model reports, leaving the model-by-
benchmark matrix sparse and the relationship between
benchmarks unmeasured. We construct a
matrix of 51 models on 12 physical AI
benchmarks, selected from a registry of 51
benchmarks and 152 models by reporting density, combining scores from model cards and
benchmark papers with our own evaluation runs under each
benchmark's official protocol. We measure how much information the
benchmarks share and show quantitative evidence of Redundancy. Redundancy affects reported rankings: collapsing the two substitute pairs into single columns moves 22 of 51 models by three or more places under an equally weighted average. We then select
benchmarks greedily under a utility combining score dispersion with variance not explained by the already-selected set, and obtain a four-
benchmark subset retaining 78.5% of the utility of all 12, on which we fit a Bradley–Terry ranking. The procedure requires only
benchmark-level scores with sufficient overlap and is not specific to physical AI.
Read the full abstract ▾
Software Engineering
XREPOTEST: Benchmarking Multilingual Repository-Level Unit Test Generation for Large Language Models
Dung Le Quang, Dong Cao Van, Nam Le Hai et al. (+3 more)
Large
language models (LLMs) have shown promise for automated unit test generation, but existing evaluations largely rely on standalone settings and a narrow set of programming
languages, overestimating real-world readiness. We introduce XREPOTEST, a
multilingual repository-level
benchmark for unit test generation spanning five underexplored
languages: Rust, Go, Julia, PHP, and Ruby. XREPOTEST evaluates tests under realistic repository constraints using a containerized execution framework and multiple context augmentation strategies, including file-level, LSP-based, and
retrieval-based context. Beyond standard metrics such as test pass rate and coverage, we propose Invocation Rate (IR) to assess whether generated tests meaningfully exercise the intended functionality. Experiments with 14 state-of-the-art LLMs, including Claude 4.5, GPT-5.2, DeepSeek V4-Pro, and Qwen families, reveal a substantial gap between standalone and repository-level performance, as well as trade-offs between richer context and test reliability. Overall, XREPOTEST provides a challenging and informative
benchmark to advance scalable and robust unit test generation in realistic software environments. The
dataset and code are publicly available at: https://github.com/solis-team/XRepoTest
Read the full abstract ▾
All authors Dung Le Quang, Dong Cao Van, Nam Le Hai, Linh Ngo Van, Anh M. T. Bui, Phuong T. Nguyen