Artificial Intelligence · Cross-listed from cs.CV
SUMO: Segment and Track Any Motion with Nonlinear State Space Models
Kexin Tian, Sixu Li, Keshu Wu et al. (+2 more)
Visual Object Tracking (VOT) and Moving Object
Segmentation (MOS) are two fundamental tasks in computer
vision that involve both spatial and temporal object dynamics. Existing methods rely predominantly on visual cues and thus often falter in real-world scenarios where object motions are inherently complex and nonlinear. To address this limitation, we propose SUMO, a zero-shot, training-free, unified framework integrating nonlinear dynamics with
vision-based
segmentation for accurate and consistent VOT and MOS. Specifically, we develop a nonlinear State Space Model (SSM) inspired by robotics principles to capture the complex object dynamics. Building on this model, we propose a Selective Unscented Filter (SUF) for accurate state estimation, which features a joint scoring mechanism and dynamically fuses multi-source predictions to identify the most plausible object state over time. Furthermore, we apply a
memory selection mechanism to evaluate the reliability of
memory frames. Our extensive experimental results show that SUMO achieves state-of-the-art performance on both VOT and MOS tasks.
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All authors Kexin Tian, Sixu Li, Keshu Wu, Yang Zhou, Zhengzhong Tu
Artificial Intelligence
Beyond Triplet Plausibility: Relation Set Completion in Knowledge Graphs
Zihao Zheng, Borui Cai, Yao Zhao et al. (+2 more)
Knowledge graphs (KGs) organize real-world knowledge as triplets and underpin many downstream applications. Due to their inherent incompleteness, knowledge graph completion (KGC) is widely studied and is typically formulated as triplet prediction, with link prediction as the dominant paradigm. However, this formulation focuses on the incompleteness of triplet-wise information and overlooks the incompleteness of entity-relation compatibility information. To address this limitation, we introduce a relation set completion task (RSC), which complements the link prediction task and aims to reason about missing relations that are semantically compatible with a given entity. We further propose a Relation Set
Embedding model (RelSetE), which models latent patterns among the observed relations of entities to infer missing ones. To evaluate RelSetE, we derive three
benchmark datasets from standard KG
benchmarks. Extensive experiments demonstrate that RelSetE effectively captures entity-relation compatibility patterns and performs favorably in inferring missing relations of entities. Code and data are publicly available.
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All authors Zihao Zheng, Borui Cai, Yao Zhao, Xin Han, Mengqi Ji
Artificial Intelligence · Cross-listed from cs.CL
Exploring Motivations for Algorithm Mention in the Domain of Natural Language Processing: A Deep Learning Approach
Yuzhuo Wang, Yi Xiang and Chengzhi Zhang
With the rise of data-intensive science,
algorithms have become central to scientific research. In academic papers,
algorithms are mentioned for different purposes, such as describing, using, comparing, or improving methods for specific research tasks. Identifying these purposes can reveal relationships among
algorithms and help assess their roles and value. Taking natural
language processing (NLP) as an example, this study proposes a sentence-level framework for identifying, analyzing, and tracing the
evolution of motivations for mentioning
algorithms. We first identify
algorithm entities and
algorithm-related sentences from full-text papers through manual annotation and machine learning. We then classify mention motivations using pretrained models and data augmentation, and analyze their distribution and temporal
evolution. The results show that deep learning models trained with augmented data outperform traditional machine learning models in motivation classification. In NLP papers, more than half of
algorithm-related sentences express direct use, whereas improvement is the least frequent motivation. The diversity of motivations has increased over time. For specific
algorithm categories, grammar-based
algorithms are more often mentioned for description, while machine learning
algorithms are more often mentioned for use. Over time, use motivations have gradually replaced description motivations across different
algorithms, and the number of motivation types associated with individual
algorithms has declined significantly. This study reveals how authors mention
algorithm entities in academic writing and provides a basis for future research on
algorithm relationship identification and
algorithm impact evaluation.
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Artificial Intelligence · Cross-listed from cs.CL
Linrui Ma, Chun Hei Lo, Xinyu Wang et al. (+12 more)
The quadratic computational cost of traditional
attention mechanisms poses a major bottleneck to the scalability and practical deployment of large
language models (LLMs), particularly in long-context scenarios. To improve efficiency, existing approaches often enforce rigid structural constraints such as local
attention windows. However, these strategies typically lead to substantial performance degradation on tasks requiring precise long-range recall. In this work, we propose MATCH, a scalable and efficient framework that augments sparsified
attention mechanisms with dynamically integrated in-context information through an efficient
retrieval system. Empirical results show that MATCH significantly improves the performance of sparse-
attention models on both synthetic and real-world natural-
language tasks. These findings highlight the versatility of MATCH as a general approach for enhancing in-context
retrieval capabilities while maintaining the efficiency benefits of sparse
attention architectures.
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All authors Linrui Ma, Chun Hei Lo, Xinyu Wang, Peng Lu, Xihao Yuan, Hanting Chen, Kai Han, Xinghao Chen, Chengjun Zhan, Hanlin Xu, Yichun Yin, Lifeng Shang, Feng Wen, Boxing Chen, Yufei Cui
Artificial Intelligence · Cross-listed from cs.CL
Neural Procedural Memory: Empowering LLM Agents with Implicit Activation Steering
Chengfeng Zhao, Yuqiao Tan, Shizhu He et al. (+3 more)
While Large
Language Models (LLMs) excel as static solvers, transforming them into autonomous
agents remains challenging. This transition requires continuous environmental interaction, yet current
agents lack the necessary persistent procedural
memory. Existing approaches predominantly employ
Retrieval-Augmented Generation (RAG) to inject explicit textual guidelines into model contexts. However, relying solely on symbolic instructions can introduce a text-action disconnect, frequently failing to activate the internal representations necessary for correct task execution. To address this, the paper introduces Neural Procedural
Memory (NPM), a training-free framework that represents
agent memory through implicit activation steering rather than explicit instructions. By distilling procedural skills from historical contrastive experiences into steering vectors in the activation space, NPM directly activates the task-relevant neural mechanisms to guide task execution. Evaluations across four
agent benchmarks show that NPM performs comparably to baselines using explicit textual instructions. Furthermore, the results show that combining implicit steering with explicit workflows provides complementary advantages, leading to more robust task execution. Representational analyses indicate that these steering vectors encode consistent task logic, forming organized structures within the activation space. These findings suggest that implicit activation steering provides a promising approach for managing
agent memory.
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All authors Chengfeng Zhao, Yuqiao Tan, Shizhu He, Yequan Wang, Jun Zhao, Kang Liu
Artificial Intelligence · Cross-listed from cs.DB
Experience Graphs: The Data Foundation for Self-Improving Agents
Gang Liao, Yujia He, Abdullah Ozturk et al. (+22 more)
The database community has repeatedly advanced the state of the art by recognizing that new workloads demand new system architectures. We argue that long-horizon agentic tasks – code generation, scientific discovery, hardware design – are such a workload. These
agents explore: they generate artifacts, execute tools, observe failures, branch, and repair over hundreds of steps. This search produces a structured object we call an experience graph: executable artifacts, tool outputs, rewards, sibling comparisons, and causal lineage. Yet existing
agent frameworks treat this experience as disposable state – JSON checkpoints and session logs that cannot be recovered after a crash, queried across users, or materialized into training data. We propose Trellis: a data foundation that treats the experience graph as first-class, governed, queryable database state. The core insight is that search over experience graphs is a database access pattern. Frontier selection is a query, cross-session reuse is vector-seeded graph
retrieval, training-data extraction is a materialized view, and reconstructing what an
agent knew at any past step is a time-travel query. When the database owns the experience graph,
agents become stateless compute, and crash recovery, horizontal scaling, and a closed-loop training flywheel emerge as architectural byproducts. We ground the design in KernelEvolve, a production accelerator-kernel optimizer at Meta, where cross-session reuse reaches a target speedup roughly 10x faster at 52% lower
token cost. More broadly, Trellis turns inference-time search from disposable computation into a durable institutional asset: logs made databases reliable; experience graphs may make
agents cumulative.
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All authors Gang Liao, Yujia He, Abdullah Ozturk, Zhouyang Li, Ying Wang, Zhitong Guo, Hongsen Qin, Yaobin Qin, Tao Yang, Zewei Jiang, Dianshi Li, Jort Gemmeke, Jiangyuan Li, Liyuan Li, Nathan Yan, Masha Basmanova, Uladzimir Pashkevich, Matt Steiner, Pedro Pedreira, Rob Fergus, Anirudh Goyal, Carole-Jean Wu, Gaoxiang Liu, Andrew Witten, Daniel J. Abadi
Artificial Intelligence · Cross-listed from cs.LG
Dual-Flow Reinforcement Learning with State-Aware Exploration
Qijun Li, Zheng Fu, Qi Song et al. (+4 more)
In complex continuous-control reinforcement learning tasks,
multimodal optimal actions often coincide with uncertain,
multimodal return distributions, making reliable value estimation and
multimodal exploration challenging. Existing value estimation methods using unimodal Gaussians restrict expressiveness and yield biased estimates. Recent generative policies can represent
multimodal actions but often collapse to a few modes and under-explore high-value areas of the action space. Motivated by these challenges, we propose Dual-Flow RL, a unified actor-critic framework that jointly models a continuous return distribution and a
multimodal policy distribution using conditional flow matching (CFM). This design supports reliable value estimation and sustained
multimodal exploration. To further enhance exploration, we introduce an
Entropy-Covariance Exploration Regulator (ECER) that enables state-aware exploration regulation leveraging policy
entropy and action-uncertainty covariance. Experiments on DeepMind Control Suite and
Humanoid-Bench show that Dual-Flow RL achieves state-of-the-art performance on most tasks, significantly outperforming prior
diffusion-based and flow-based methods.
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All authors Qijun Li, Zheng Fu, Qi Song, Yifei He, Weitao Zhou, Kun Jiang, Diange Yang
Artificial Intelligence · Cross-listed from cs.CL
How Far Can You Get Without a GPU? A Systematic Benchmark of Lightweight Hallucination Detection Across Question Answering, Dialogue, and Summarisation
Kriti Faujdar and Smit Kadvani
Hallucination detection has become a pressing requirement for trustworthy AI deployment at scale. The most accurate detection methods depend on GPU-intensive inference, proprietary API calls, or white-box access to the generating model, putting them out of reach for resource-constrained researchers and practitioners. We explore a practical alternative: how well can
hallucination detection perform using only lightweight, CPU-feasible methods built on public models? We
benchmark four such detectors, ROUGE-L, semantic similarity, BERTScore, and a Natural
Language Inference (NLI) detector based on a FEVER-trained DeBERTa model, together with a score-level ensemble of similarity and NLI. We evaluate them across all three tasks of the HaluEval
benchmark: question answering (QA), dialogue, and summarisation. We calibrate on a held-out validation split, evaluate on 2,000 test instances per task, and report bootstrap confidence intervals. The similarity-NLI ensemble is the most consistent method, but absolute performance is highly task-dependent. It ranks best on QA (F1 = 0.792, AUC-ROC = 0.873) and on dialogue (F1 = 0.694, AUC-ROC = 0.749), where NLI is the strongest standalone method; on summarisation every method performs near chance (AUC-ROC between 0.469 and 0.574). We then ask whether that failure is intrinsic to lightweight detection or an artifact of our single-pass design, and find it is largely the latter. Raising the premise budget from 800 to 1600 characters lifts summarisation AUC-ROC from 0.567 to 0.629, and replacing single-pass scoring with sentence-level chunk aggregation reaches 0.683, still on CPU with the same model, though at roughly twenty times the NLI inference. Summarisation remains by far the hardest task, but our results do not support treating lightweight detection as intrinsically unsuited to it.
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Artificial Intelligence · Cross-listed from cs.HC
Making Multimodal LLMs Reliable Chart Data Extractors: A Benchmark and Training Framework
Yuchen He, Peizhi Ying, Liqi Cheng et al. (+4 more)
Chart data extraction, which reverse-engineers data tables from chart images, is essential for reproducibility, analysis,
retrieval, and redesign. Existing interactive tools are reliable but tedious, and mixed-initiative systems, while more efficient, lack generalizability. Recent
multimodal large
language models (MLLMs) offer a unified interface for chart interpretation, yet their ability to extract accurate data tables, especially without visible labels, remains unclear. We build a
benchmark featuring diverse real-world charts without data labels to evaluate this capability. Results show that, while current MLLMs reliably reconstruct table structures, they struggle with precise value recovery. To address this, we revisit chart data extraction from a human-centered perspective and argue that extraction should follow a progressive learning process similar to how people read charts. Our training framework substantially improves numerical accuracy, achieving state-of-the-art performance with a 7B-parameter model. A user study further shows that our model effectively supports mixed-initiative workflows for reliable chart data extraction.
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All authors Yuchen He, Peizhi Ying, Liqi Cheng, Kuilin Peng, Yuan Tian, Dazhen Deng, Yingcai Wu
Artificial Intelligence · Cross-listed from cs.LG
Accelerating Q-learning through Efficient Value-Sharing across Actions
Prabhat Nagarajan, Brett Daley, Martha White et al. (+1 more)
Action values are foundational to many control
algorithms such as Q-learning. Therefore, efficient action-value learning is central to reinforcement learning (RL). However, learning them can be slow, requiring many updates to move values from their initialization, typically near zero, to their true values, which may be far from zero. Moreover, action-value learning
algorithms typically update each state-action pair independently, without learning a value that is common to all actions within a state. In this paper, we address these inefficiencies by introducing the mean-expansion layer, which accelerates action-value learning by sharing values across actions within a state and by changing the problem from directly learning potentially large action-values to learning a lower-norm representation of them. In deep RL, this layer can be applied as a parameter-free addition to Q-network architectures without altering the underlying
algorithm. Applied to deep Q-networks and implicit
quantile networks, it improves aggregate performance across 57 Atari 2600 games while increasing action gaps and dramatically reducing value overestimation.
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All authors Prabhat Nagarajan, Brett Daley, Martha White, Marlos C. Machado
Artificial Intelligence
The CRISTAL Method: Neurosymbolic analysis from AI-synthesized world models
Rafael Kaufmann, Felix Neubürger, Michael Walters et al. (+2 more)
This project introduces the CRISTAL Method (Coherent Reliable Intentional Synthesis of Truthful Analysis Logic), a neurosymbolic framework for automating complex analysis workflows, with fundamental investment analysis as a primary use case. This domain poses major challenges: high structural uncertainty, noisy and subjective data, tight
attention budgets, and the need for justified, reproducible decisions. Human analysts often struggle in this domain due to cognitive biases and limitations, suggesting significant value in automation. But while LLM-based
agents have been proposed as analytical aids, their limitations – poor numerical
reasoning, unawareness of uncertainty, and lack of reproducibility – hinder their effectiveness in this context. CRISTAL addresses these gaps through a principled blend of statistical model synthesis, continuous learning, and active learning. Starting from a natural-
language prior knowledge curriculum, CRISTAL builds a dynamic, interpretable probabilistic program that enables full
Bayesian inference, including uncertainty quantification and budget-aware data acquisition. CRISTAL continually refines its world model during analysis, leveraging LLMs for code synthesis and learning. We validate CRISTAL on a novel
benchmark of synthetic equities with rich financial and textual data. On a company classification task, CRISTAL achieves Bayes-optimal accuracy with just 5 examples and a 5-second budget, outperforming state-of-the-art LLMs that plateau around 40% accuracy even with order-of-magnitude more input data and compute.
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All authors Rafael Kaufmann, Felix Neubürger, Michael Walters, Thomas Kopinski, Dimitrije Marković
Artificial Intelligence · Cross-listed from cs.CR
Multi-Level Distributional Entropy from Flow Summary Statistics for Explainable Network Intrusion Detection
Mohamed Aly Bouke, Md Shohel Sayeed, Swee-Huay Heng et al. (+2 more)
Machine learning network intrusion detection systems (IDS) operate on aggregate flow statistics that discard the distributional structure of traffic, and although information-theoretic measures capture that structure, established
entropy estimators require raw packet sequences that pre-aggregated flow
datasets do not contain. No prior method derives
entropy from the summary statistics those records already hold. We introduce Multi-Level Distributional
Entropy (MDE), which computes interpretable information-theoretic features analytically from flow-level summary statistics at three levels, within-flow Gaussian differential
entropy, cross-directional Jensen-Shannon divergence (JSD), and Transmission Control Protocol (TCP) flag-incidence Shannon
entropy, with closed-form properties and no raw packet access; only imputation medians and score bounds are fitted on the training split. We pair the features with a leakage-free, fold-local evaluation protocol that reports the full operational metric suite across cross-validation, temporal, pseudo-live, cross-
dataset, and unseen-attack-family settings, on four
benchmarks (NSL-KDD, CICIDS-2017, CICIDS-2018, UNSW-NB15) with tree-ensemble classifiers and SHAP. The protocol exposes failure modes that aggregate weighted F1 conceals: on CICIDS-2018 an F1 of 0.73 hides a detection rate (DR) of 0.44, on held-out attack families F1 exceeds 0.998 while DR falls to zero, and a 703K-flow pseudo-live replay reveals a threshold-ranking divergence in which score ranking is largely preserved (area under the ROC curve, AUC, 0.84 to 0.86) while fixed-threshold detection collapses (DR 0.08). The
entropy features match conventional features within 0.1 percentage points of F1 and receive reproducible SHAP attributions (Spearman 0.84 to 0.94), so their contribution is a grounded, interpretable representation and an evaluation methodology rather than an accuracy gain.
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All authors Mohamed Aly Bouke, Md Shohel Sayeed, Swee-Huay Heng, Azizol Abdullah, Mohamed Othman
Artificial Intelligence · Cross-listed from cs.LG
What Drives the Inlier-Memorization Effect? A Theory of Outlier Detection via Early Training Dynamics
Kunwoong Kim and Dongha Kim
Outlier detection (OD) aims to identify anomalous instances by learning the underlying structure of normal data (inliers), and is particularly challenging in fully unsupervised settings where no information about anomalies is available during training. Recent advances have leveraged the inlier-memorization (IM) effect, a phenomenon in which deep models memorize inlier patterns earlier than those of outliers, as a powerful signal for distinguishing outliers. However, despite its empirical success, the theoretical understanding of the IM effect remains limited. In this work, we present a theoretical study of the IM effect. Focusing on a simple autoencoder, we show that, under mild assumptions, the model can successfully memorize inliers while failing to memorize outliers during certain stages of early training. In particular, we characterize not only the emergence of the IM effect, but also its strength and persistence, and analyze how these properties depend on the data distribution and parameter initialization. In addition, building on these insights, we derive simple yet practical guidelines for enhancing the IM effect, including data preprocessing and parameter initialization schemes, achieving state-of-the-art performance on the ADBench
datasets. Our findings provide a theoretical foundation for the IM effect and offer actionable directions for improving IM-based outlier detection methods.
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Artificial Intelligence · Cross-listed from stat.ME
HERO: Improving the Reliability and Sensitivity of Generative Model Evaluation Using Historical Data
Xinrui Ruan, Zhenyu Zhao, Waverly Wei et al. (+4 more)
Reliable generative AI models critically rely on expert human annotations to evaluate output quality, yet these "gold" labels are expensive to collect and limited in quantity. Organizations thus often turn to collecting vast but noisy "silver" labels from crowdsourced workers or vendor annotators as proxies for gold labels. Because gold remains the evaluation target, naively aggregating noisy silver labels may introduce bias, and
estimators built on sparsely observed gold labels may have high variance to resolve the model performance gaps that guide practical decisions. Model evaluation has become an ongoing operational practice rather than a one-time exercise, with evaluation rounds repeating across model versions, releases, and content domains. A natural question is whether the previous historical evaluation data can be used to improve each new round of evaluation. We introduce HERO (History Enhanced RObust model evaluation), a novel framework that uses historical data to suppress bias (improve reliability) and reduce variance (improve sensitivity) in model performance evaluation. HERO calibrates silver labelers' performance learned from historical gold annotations, and stabilizes the resulting
estimator by anchoring it to
covariate information measured with high precision in the historical data. HERO can be broadly applied across multiple common evaluation tasks, and remains valid when only a subset of historical labelers appears in the current round. We establish conditions under which the bias and variance reductions hold, showcase HERO's performance in simulation studies, and demonstrate its effectiveness on real-world model evaluation benchmarking
datasets.
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All authors Xinrui Ruan, Zhenyu Zhao, Waverly Wei, Yueshan Zhang, Zeyu Zheng, Sui Huang, Jingshen Wang
Artificial Intelligence · Cross-listed from cs.RO
FalconTrack: Photorealistic Auto-Labeled Perception and Physics-Aware Vision-Based Aerial Tracking
Yan Miao, Karteek Gandiboyina, Noah Giles et al. (+4 more)
Vision-based aerial tracking is critical in GPS-denied environments. Reliable perception for tracking depends on large-scale labeled data, yet most photorealistic
datasets rely on heavy manual annotation and are time-consuming to produce. We present FalconTrack, a unified perception-and-tracking framework that (i) leverages a photorealistic editable simulator for automated label generation and (ii) combines multi-head perception with physics-aware tracking for zero-shot sim-to-real transfer. FalconTrack provides an automated labeling pipeline in a Gaussian Splatting simulator that isolates target Gaussians from short object
videos and composites them with randomized backgrounds to generate RGB, mask, class, and 6-DoF pose labels, producing about 10k labeled images in under 20 minutes. Using this
dataset, we train a multi-head perception module with staged learning and reprojection consistency, and fuse its outputs with class-conditioned dynamics priors in an EKF for tracking. Our perception model outperforms two baselines and reaches 96-100% class accuracy in zero-shot sim-to-real transfer on three geometrically diverse objects and two environments, while maintaining consistent performance in unseen simulated and real scenes. In real hardware closed-loop visual tracking, the onboard system runs at about 25 Hz and achieves 100% success in sim-to-real F1-tenth and gate tracking in five trajectories across two environments, while a mask-centered
vision baseline drops to 60% success on F1-tenth during fast out-of-view scenarios.
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All authors Yan Miao, Karteek Gandiboyina, Noah Giles, Hideki Okamoto, Bardh Hoxha, Georgios Fainekos, Sayan Mitra
Artificial Intelligence · Cross-listed from cs.DB
Mandol: An Agglomerative Agent Memory System for Long-Term Conversations
Yuhan Zhang, Zhiyuan Guo, Ziheng Zeng et al. (+3 more)
Long-term conversational
agents need to remember and query cross-session, multi-typed information with complex correlations. Existing
agent memory systems rely on heterogeneous vector and graph databases, which fragment
memory information and cause high cross-database I/O latency. For
retrieval, common RAG-style methods tend to introduce noise, miss correlated clues, and lack
token budget control, degrading LLM accuracy and efficiency. We propose Mandol, an agglomerative
memory system that consolidates fragmented
memory representations and storage into a unified
memory-native architecture. Its core components include: (1) a hierarchical
memory model that organizes
memory into a basic layer representing raw
memory information and a high-level abstract layer that agglomerates basic
memories into traceable abstract
memories, both uniformly represented as structured semantic graphs; (2) an agglomerative semantic data structure combining SemanticMap and SemanticGraph, which natively fuses key-value, vector, and graph structures and provides unified hybrid
retrieval operators to eliminate cross-database I/O; and (3) a quantitative query mechanism with query-adaptive routing, quantitative denoising and conflict resolution, and
token-constrained context generation, all without involving LLMs during
retrieval. Experiments on two widely used long-term conversation
benchmarks, LoCoMo and LongMemEval, show that Mandol achieves the best overall accuracy among representative
agent memory systems. For performance comparison, Mandol also obtains a 5.4x
retrieval speedup and a 4.8x insertion speedup under 10 QPS concurrent load, while still maintaining low latency on consumer-grade hardware.
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All authors Yuhan Zhang, Zhiyuan Guo, Ziheng Zeng, Wei Wang, Wentao Wu, Lijie Xu
Artificial Intelligence · Cross-listed from cs.LG
Towards Generalizable and Evidential Nuclear Magnetic Resonance-Based Molecular Structure Elucidation via Large Language Model Agent
Zheng Fang, Chen Yang, Yusen Tan et al. (+9 more)
Nuclear Magnetic Resonance (NMR) spectroscopy is the gold standard for molecular structure elucidation, yet interpreting complex spectra for unknown molecules remains a bottleneck reliant on human expertise. While artificial intelligence has advanced this field, current methods face a critical trade-off: database
retrieval cannot identify novel scaffolds, while de novo molecular structure elucidation models operate as black boxes, lacking the atom-level interpretability required for rigorous scientific validation. Here, we present NMRAgent, an evidential
reasoning agent powered by large
language models (LLMs) that bridges this gap by integrating specialized spectral analysis tools with chemical knowledge graphs. Unlike previous approaches, NMRAgent mimics the deductive
reasoning of human experts: it takes experimental NMR spectra and molecular formula as input, plans the elucidation process, proposes candidate structures, verifies peak-atom consistency, and refines misaligned substructure through formula-aware fragment optimization. Enabled by its evidential
reasoning, NMRAgent outperforms state-of-the-art methods, improving top-1 accuracy by 46.5% and Tanimoto similarity by 0.502 on a scaffold-split
benchmark with novel scaffolds in the test set. Besides, we demonstrate the
agent's practical utility by elucidating the structures of two previously unknown natural products isolated from Hydrangea davidii and Vitex trifolia, and by correcting structural misassignments in established literature. By combining high-accuracy prediction with transparent and evidence-based
reasoning, NMRAgent establishes a new paradigm for interpretable AI in analytical chemistry.
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All authors Zheng Fang, Chen Yang, Yusen Tan, Yunpeng Zhao, Fanjie Xu, Hongxin Xiang, Hanyu Sun, Hanyu Gao, Xiaojian Wang, Wenjie Du, Yuqiang Li, Jun Xia
Artificial Intelligence
CLQT: A Closed-Loop, Cost-Aware, Strategy-Consistent Benchmark for Diagnostic Evaluation of LLM Portfolio-Management Agents
Bo Qu and Mingguang Chen
LLM
agents are increasingly cast as autonomous portfolio managers, yet the dominant evaluation idiom, a leaderboard of returns over a fixed window, certifies neither the soundness of an
agent's process nor the durability of its edge: one period's return is dominated by the
market path, and apparent alpha can dissolve once look-ahead bias and trading costs are controlled. We introduce CLQT, a closed-loop
benchmark that reframes LLM trading evaluation as diagnosis rather than ranking. CLQT enforces point-in-time data access through a hard TimeGate, models institutional transaction and financing costs, scores strategy consistency across rounds, and seals every gather-analyze-decide-execute-reflect cycle into a recompute-verifiable audit chain; the same model runs as a constrained investment committee or a single autonomous orchestrator, making scaffolding an experimental variable. From the audit trail CLQT computes a five-axis capability scorecard (Coherence, Acuity, Composure, Discipline, Reliability), with coherence scored partly by a held-out LLM judge to curb self-preference bias. We validate CLQT on a contamination-controlled, year-long multi-model backtest campaign with a 13-configuration ablation grid and a four-week live broker paper-trading track on post-cutoff data. The diagnosis-first read shows that the capability leader is not the Sharpe leader; that
agents' allocations systematically fail to follow their own stated analysis, a stating-versus-doing gap stable across both tracks (+0.30 backtest, +0.23 live); and that module value registers on the capability and behavioral axes when returns alone cannot separate it. Net of realistic costs,
agents clear defensive baselines but do not cleanly beat the index. Credible evaluation of LLM investment
agents must therefore diagnose the process rather than rank a period's return, the standard CLQT operationalizes and makes auditable.
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Artificial Intelligence · Cross-listed from cs.CV
TopoAgent: An Agentic Framework for Automated Topology Learning in Medical Imaging
Guangyu Meng, Pengfei Gu, Xueyang Li et al. (+3 more)
Topological data analysis (TDA), particularly persistent homology (PH), captures
geometric structural properties in medical images (e.g., connected components, loops, shape characteristics), which conventional pixel-level deep learning approaches often neglect. While many topological descriptors are known for converting persistence diagrams (PDs) or raw images into topological feature vectors, existing methods mostly default to a single fixed descriptor (e.g., persistence images), leaving the diversity of topological representations largely unexplored. To the best of our knowledge, there is no known large
language model (LLM)-based agentic framework that can automatically determine the most suitable topological descriptors for a given image
dataset and produce the corresponding topological feature vectors for downstream tasks. To fill this gap, we propose TopoAgent, an LLM-based agentic framework that automates
topology learning for medical image analysis.TopoAgent operates through a Perception–
Reasoning–Action–Reflection loop supported by 21 domain-specific tools and dual
memory that accumulates experience across runs. Its skill set is distilled from systematic evaluation of 15 topological descriptors across 26
datasets with six classifiers. TopoAgent analyzes input images and their topological characteristics, reasons about which topological descriptors best suit the input, and determines the optimal descriptor and its configuration, all without task-specific training.
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All authors Guangyu Meng, Pengfei Gu, Xueyang Li, Yiyu Shi, Erin Wolf Chambers, Danny Z. Chen
Artificial Intelligence · Cross-listed from cs.LG
PS-PPO: Prefix-Sampling PPO for Critic-Free RLHF
Doo Hwan Hwang and Kee-Eung Kim
Reinforcement Learning from Human Feedback (RLHF) for Large
Language Models increasingly relies on critic-free methods as a practical alternative to actor–critic training. Despite their simplicity, existing critic-free approaches propagate a trajectory-level learning signal uniformly across all
tokens in a trajectory. This requires full-trajectory policy updates for every rollout, leading to substantial optimization cost for long
reasoning traces, even though intermediate prefixes often contain enough information to largely determine the final outcome. We propose Prefix-Sampling Proximal Policy Optimization (PS-PPO), a compute-efficient critic-free method for RLHF that exploits this temporal redundancy. PS-PPO introduces a
prompt-conditioned cutoff distribution and samples a cutoff timestep for each trajectory. During the update pass, PS-PPO backpropagates only through the sampled prefix of each trajectory and applies an importance-weighting correction so that the resulting truncated gradient
estimator remains unbiased with respect to the full-trajectory objective. Experiments on mathematical
reasoning and RLHF
benchmarks show that PS-PPO achieves large reductions in training compute and peak GPU
memory, while maintaining accuracy comparable to strong critic-free baselines.
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Artificial Intelligence
Rethinking Generative Reconstruction Attacks against Graph Neural Network Models
Adebayo Keji and Sayanton Dibbo
The application of graph data in numerous disciplines raises the need for gathering and analyzing huge volumes of data, some of which is private and sensitive. The non-Euclidean nature of the graph data makes the analysis computationally challenging, leading to the use of Graph Neural Networks (GNNs) in the age of AI. GNNs may inadvertently leak sensitive data they are trained on, which raises serious data security issues, including the model inversion attack. In this study, we analyze GNNs' vulnerabilities by introducing two novel graph inversion (i.e., reconstruction) attacks: graph-label conditioned (GLC) attack and
embedding-label conditioned (ELC) attack, utilizing targetmodel predictions and their intermediate representations, respectively. We perform a comprehensive analysis of our introduced
privacy attacks and compare them with existing baselines across three
benchmark graph
datasets (i.e., NCI1,
PROTEINS, and AIDS) and four graph distributional/structural metrics (i.e., FGD, EGD, MMD, and GKS). Our work demonstrates that an adversary can use the generator-discriminator technique to reconstruct high-quality graphs in real-world black-box attack scenarios against GNNs. Additionally, we present a variant of our attacks (Ours–) with 50% reduced queries, achieving good or comparable reconstruction attack performance. In addition, we show that GNNs are highly vulnerable to
privacy attacks, varying
Laplacian noise-scales.
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Artificial Intelligence
DEEPMED Search: An Open-Source Agentic Platform for Medical Deep Research with Introspective Verification
Maolin Liu, Fanyu Xu, Ruoqing Xu et al. (+3 more)
Navigating the deluge of heterogeneous medical data, from academic literature (PubMed) to clinical guidelines (Web) and private knowledge bases, remains a critical bottleneck for evidence-based medicine. While commercial black-box tools lack transparency, standard open-source RAG implementations frequently suffer from
reasoning drift when handling complex, long-tail queries. We present DEEPMED Search, a fully open-source, agentic platform designed for transparent medical deep research. Built on a high-performance Next.js architecture, DEEPMED Search features a source-adaptive router that autonomously dispatches sub-queries to PubMed, web search, or local graph-based knowledge bases based on information density. Crucially, the platform integrates an introspective verification module, powered by a causal-consistent multi-
agent debate framework, to validate retrieved evidence against diagnostic logic before synthesis. To demonstrate its robustness, we showcase DEEPMED Search's ability to autonomously decompose high-difficulty rare disease queries, filter out confounding noise, and generate structured, citation-backed research reports in minutes. By open-sourcing this software, we provide the community with a robust infrastructure to democratize access to trustworthy, glass-box medical
reasoning in research and prototyping settings.
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All authors Maolin Liu, Fanyu Xu, Ruoqing Xu, Jiahang Zhang, Hao Wang, Rui Wang
Artificial Intelligence
DeepTrans Studio: Turning Expert Interventions into Shared Team Knowledge in Agentic Translation Workflows
Ziyang Lian, Qingya Zhang, Hao Wang et al. (+5 more)
Professional translation is often a team-based process: translators, reviewers, and project managers must coordinate terminology, legal force, and accountability across documents. Yet many LLM-based translation tools treat human corrections as isolated edits. Expert decisions made in one segment or by one member are rarely captured as reusable knowledge for the rest of the team. We present DeepTrans Studio, a collaborative translation workspace that lets professionals intercept selected nodes in an agentic translation workflow, review evidence, revise AI outputs, and save approved decisions to a shared team
memory. During the demo, attendees will role-play translators and reviewers, resolve preset terminology and legal-modal risks, and see how their decisions are propagated to downstream segments and surfaced in a teammate's workspace as reusable precedents. The demo illustrates how human interventions in AI-mediated work can become shared, traceable knowledge rather than one-off corrections.
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All authors Ziyang Lian, Qingya Zhang, Hao Wang, Huiwen Xiong, Qi Yang, Lingyi Meng, Xiaoyi Gu, Rui Wang
Artificial Intelligence · Cross-listed from cs.HC
From Trait to Behavior: A Cognitive-Affective Personality System (CAPS) Perspective on Multi-Homing Intention in AIGC Platforms
Xuchao Zhang and Jihye Lee
With the rapid development of Artificial Intelligence Generated Content (AIGC) platforms, users increasingly show cross-platform usage intentions. Existing research focuses on adoption and usage intentions in single-platform AIGC contexts. A theoretical gap still exists in studies on cross-platform usage. This paper constructs and verifies a three-stage multiple mediation model based on the personality trait-perception-behavioral response framework. The model integrates the optimum stimulation level (OSL) theory, complementarity theory, and perceived value theory, and it sets social influence and use experience as control variables to examine users' multi-homing intention. The results show that: (a) OSL significantly enhances users' perceived complementarity; (b) perceived complementarity positively affects perceived epistemic value; (c) perceived epistemic value significantly and positively predicts multi-homing intention; (d) OSL influences multi-homing intention through a chain mediation path of perceived complementarity and perceived epistemic value; and (e) social influence has a significant positive effect on multi-homing intention, while the effect of use experience is not significant.
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Artificial Intelligence · Cross-listed from cs.LG
Redefining Maritime Anomaly Detection via Equation-Grounded Synthetic Anomalies
Youngseok Hwang, Sungho Bae, Dohun Lee et al. (+4 more)
Maritime anomaly detection is essential for ensuring maritime safety, security, and efficient traffic management at sea, with Automatic Identification System (AIS) data serving as a primary data source. Despite its importance, most publicly available AIS
datasets lack predefined anomaly labels, forcing prior studies to rely on either distribution-based rarity or domain rule/expert-assisted labeling. These approaches, however, face fundamental limitations: statistical rarity often fails to reflect practically critical events, while expert-based labeling is costly, subjective, and difficult to scale. Moreover, both paradigms tend to overlook interaction-driven hazards such as near-miss approaches between vessels. To address these challenges, we propose an equation-grounded anomaly taxonomy that is implementable under a limited AIS observation schema and extensible to other AIS
datasets. Specifically, the taxonomy defines three anomaly types: unexpected AIS activity (A1), route deviation (A2), and close approach (A3), covering both single-vessel and inter-vessel anomalies. Building on this taxonomy, we introduce a unified score-synthesize-label pipeline that produces LLM-guided plausibility scores, uses them to synthesize anomalies, and assigns timestamp-level labels. To rigorously assess detection performance, we further design
benchmark evaluation settings that account for variations in temporal-window length and anomaly-type composition, and evaluate a broad range of time-series models and anomaly detection models. Together, these contributions provide a systematic basis for evaluating maritime anomaly detection methods across different anomaly types. Our code is available at https://github.com/snudial/open-maritime-anomaly-detection.
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All authors Youngseok Hwang, Sungho Bae, Dohun Lee, Jaeeun Seo, Jeehong Kim, Wonhee Lee, Hyunwoo Park
Artificial Intelligence · Cross-listed from cs.IR
Diagnosing and Mitigating Context Rot in Long-horizon Search
Shijie Xia, Yikun Wang, Zhen Huang et al. (+1 more)
Extensive context has become the norm as Large
Language Models (LLMs) are increasingly deployed in long-horizon search tasks. The concern that increasing context length degrades model capabilities, known as context rot, has become a widely recognized issue for these applications. However, in deep search scenarios, it remains unclear how models actually fail under extensive context, and to what extent existing methods can mitigate such failures. Through a systematic study of four flagship models across three
benchmarks, we identify a previously overlooked phenomenon: under extensive context, models give up or provide uncertain incorrect answers long before exhausting the context window. We refer to these two failure modes as inconclusive search failures. We show that these are common failure modes even for easy and moderately difficult queries, suggesting premature termination on queries that the model is capable of solving. Based on these findings, we revisit methods to mitigate context rot, including context management and parallel sampling. For context management, we analyze seven methods across three categories and show that they are inherently test-time scaling strategies that redirect models from inconclusive search failures toward further exploration by decreasing the peak context length. We also provide model-dependent principles for method selection. For parallel sampling, we develop a behavior-aware filtering strategy and observe a performance gain of 2.6% to 4.9% across three aggregation methods.
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All authors Shijie Xia, Yikun Wang, Zhen Huang, Pengfei Liu
Artificial Intelligence · Cross-listed from cond-mat.mtrl-sci
Optimizing Expert-Designed Crystal Graph Networks for Band-Gap Prediction with an Autonomous LLM Research Loop
Chenmu Zhang and Boris I. Yakobson
Predicting a material's properties from its structure is a central, fast-advancing problem in computational materials science. A decade of work has produced standard public
benchmarks and many published machine-learning models for the task (Dunn et al., 2020). The task's fixed metric and these baselines make it a natural setting for autonomous
agent research (Karpathy, 2026). On the MatBench band-gap
benchmark ($>$100k
crystals), a general-purpose coding
agent autonomously built the most accurate model trained without external pretraining, ahead of all seventeen expert-designed models reported for the task. A closer analysis shows it reached this by implementing known methods: either already standard in
crystal neural-network models, or borrowed from other areas of machine learning. The contributing implementations include element-pair features on each message-passing edge and a
crystal space-group
embedding. The work not only demonstrates that LLM-
agent autonomous research can optimize an expert-designed machine learning model for material property prediction, but also investigates the limitations of such autonomous research.
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Artificial Intelligence · Cross-listed from cs.CL
SEVA: Self-Evolving Verification Agent with Process Reward for Fact Attribution
Aojie Yuan, Yi Nian, Haiyue Zhang et al. (+2 more)
Hallucination is the reliability bottleneck for LLM-based
agents, and fact attribution verifiers are the last line of defense – yet today's verifiers emit only opaque binary labels, leaving
agents unable to self-correct and operators unable to audit. We present SEVA, a structured verification
agent that emits evidence
alignments, step-by-step
reasoning chains, calibrated confidence, and a six-category error diagnosis with actionable fixes. Training such an
agent with RL is non-trivial: standard binary reward on multi-component output triggers advantage collapse – within-group reward variance vanishes and the GRPO gradient disappears. We resolve this with a process reward that decomposes verification quality into five independent components weighted 70/30 toward process signals, restoring the gradient and inducing an implicit curriculum – the
agent first masters verification behavior (
alignment 0.917 -> 0.997, format 72% -> 100%), then outcomes (F1 64.9 -> 69.0). Structured output further enables a Verify -> Reflect -> Probe -> Refine self-
evolution loop, which over four rounds on a 7B model surfaces an unexpected structural finding: each round produces a
benchmark-specialist, not a generalist (+15 pp on HaluEval, -10 to -14 pp on TruthfulQA in the same model, persistent at 4x data). On ClearFacts, SEVA-3B matches GPT-4o-mini (69.0 vs. 69.8 F1) while producing substantially richer, auditable output – confirming a principle that should generalize: for any RL task with multi-component generation, reward granularity must match output granularity.
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All authors Aojie Yuan, Yi Nian, Haiyue Zhang, Zijian Su, Yue Zhao
Artificial Intelligence · Cross-listed from cs.IR
ARMOR: Adaptive Retriever Optimization for Low-Resource Telecom Question Answering
Heshan Fernando, Quan Xiao, Yan Xin et al. (+1 more)
Telecom question answering (QA) is a challenging setting for
retrieval-augmented generation (RAG): evidence is fragmented across standards, papers, encyclopedic resources, and web documents, and answers often hinge on technical tables, equations, and specialized protocol
language. In low-resource subdomains, generator fine-tuning can over-specialize and degrade general capability, making query-side retriever adaptation an attractive alternative. To this end, we ask whether a fixed-generator, query-adapted RAG system can outperform generator-side adaptation, and which retriever objectives best support that setting. We motivate
retrieval, rather than generator fine-tuning, as the adaptation target through a capacity comparison: under bounded-parameter and soft-
retrieval assumptions, query-encoder tuning can have a smaller estimation term than supervised fine-tuning when its effective dimension is smaller. We identify two particularly relevant objectives – the latent-document RAG likelihood, which optimizes generation utility, and the InfoNCE contrastive objective, which improves semantic
retrieval geometry – and leverage them jointly through a retriever optimization method targeting downstream QA performance in the telecom domain. Specifically, we introduce ARMOR, Adaptive Regularized Mixture Optimization for Retrievers, which learns separate temperatures for the RAG
retrieval distribution and InfoNCE softmax and regularizes the adapted query encoder toward the frozen base query encoder. Across telecom-specific
retrieval and generative QA
benchmarks, we show that ARMOR improves evidence
retrieval and answer generation in several in-domain settings. Code is available at https://github.com/heshandevaka/ARMOR.git.
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All authors Heshan Fernando, Quan Xiao, Yan Xin, Tianyi Chen
Artificial Intelligence
GUICrafter: Weakly-Supervised GUI Agent Leveraging Massive Unannotated Screenshots
Sunqi Fan, Lingshan Chen, Runqi Yin et al. (+4 more)
Data, as the fundamental substrate of modern intelligence, has greatly driven the development of current foundation models. Naturally, researchers aim to extend this paradigm to the domain of GUI
agents, hoping to build strong GUI
agents through a similar paradigm. However, GUI
agent data cannot be directly harvested from the internet, making it costly and difficult to collect at scale. As a result, current GUI
agents suffer from poor cross-device generalization and limited visual grounding ability for fine-grained GUI elements. As an attempt to address data challenge in GUI
agents, we propose GUICrafter, a weakly-supervised GUI
agent leveraging massive unannotated screenshots to substantially reduce the reliance on expensive human annotations. GUICrafter explores a curriculum learning framework for training GUI
agents through two progressive stages. First, the model learns visual grounding from large-scale unannotated screenshots and webpages, leveraging the rich contextual signals inherent in GUI interactions without human annotations. Then, in Stage 2, we leverage a small amount of high-quality data to calibrate the model via reinforcement learning. Experiments show that GUICrafter achieves competitive, or even superior, performance to advanced systems like UI-TARS while using only 0.1% of its data. Furthermore, under the same amount of annotated data, GUICrafter surpasses all previous methods such as GUI-R1. Code, data, and models are available at https://github.com/fansunqi/GUICrafter.
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All authors Sunqi Fan, Lingshan Chen, Runqi Yin, Qingle Liu, Yongming Rao, Meng-Hao Guo, Shi-Min Hu