Artificial Intelligence · Cross-listed from cs.SE
For Coding Agents, the Repository Matters
Daniel Russo
In July 2026, about 1,200 AI
agents in an internal OpenAI evaluation, meant to run in isolation, used a shared package server as a message board, and about 700 of them joined an attack on Hugging Face. An automated scorer judged each
agent on its own task, and the attack was coordinated through infrastructure the
agents shared. Coding
agents meet shared infrastructure every day, in the software repositories that receive their pull requests. Yet field studies still judge coding
agents by outcomes pooled across repositories, such as merge rates and merge times. We asked how much these outcomes depend on the receiving repository, and whether they depend on it more than human contributions do. Using multilevel models, we compared
agent pull requests from the AIDev
dataset of public GitHub repositories with human pull requests in the same repositories. About two-fifths of the variation in how long
agent pull requests take to be resolved lies between repositories, after accounting for the pull request, the
agent, and the repository's observable attributes. In 280 repositories, with
covariates common to both groups, the between-repository variance is 1.76 times as large for
agent as for human pull requests (95% CI 1.41 to 2.18). For waits of several hours, a repository one standard deviation slower than average multiplies the typical wait by about 2.8 for
agent and 2.2 for human pull requests. Rejection shows the same pattern, while for review rounds and comments the difference is not detectable. A proxy for the role of the developer who started the
agent accounts for little of the difference, and stated
agent policies were too rare to test. These results suggest that the oversight and evaluation of coding
agents need the repository, and not only the
agent, as a unit.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CV
Exposure Bias Can Alleviate Itself via Directional and Frequency Rectification in Flow Matching
Guanbo Huang, Jingjia Mao, Fanding Huang et al. (+9 more)
Flow Matching (FM) has achieved remarkable generative performance, yet it suffers from exposure bias due to discrepancies between training and inference. Existing mitigation strategies typically rely on static constraints or external heuristics. In this work, we propose that exposure bias itself inherently contains dynamic signals that can guide its own rectification. To leverage this, we introduce DEFAR (DirEctional-Frequency Adaptive Rectification). This framework simulates the single-step inference process during training to identify exposure bias. It utilizes directional and frequency-adaptive feedback signals from the bias itself to enhance the model's bias tolerance. It consists of two key components: (1) Anti-Drift Rectification (ADR). ADR treats inference-time drift as a signal to learn the direction to steer deviated states back toward the target. ADR endows the model with intrinsic active self-rectification capabilities; (2) Frequency Compensation (FC). Empirically, we observe that accumulated bias often stems from a lack of low-frequency components in high-noise stages, and exposure bias carries the missing frequency. FC leverages the bias itself as a self-feedback weighting factor to reinforce the missing frequency components. Experiments on CIFAR-10, CelebA-64, and ImageNet-256/512 show that DEFAR outperforms prior baselines and further demonstrates favorable scalability, compatibility, and inference robustness.
Read the full abstract ▾
All authors Guanbo Huang, Jingjia Mao, Fanding Huang, Fengkai Liu, Xiangyang Luo, Yaoyuan Liang, Jiasheng Lu, Xiaoe Wang, Pei Liu, Ruiliu Fu, Ruqi Huang, Shao-Lun Huang
Artificial Intelligence · Cross-listed from cs.LG
Towards Value-Constrained Credit Assignment in Fully Delegated AI Cooperatives
Young Yoon, Jimin Kim and Soyeon Park
We propose a framework for reward allocation in fully delegated AI cooperatives where humans are represented by
agents that contribute data and participate in model updates under heterogeneous value constraints. The key idea is to credit only those updates that remain admissible after screening them against each principal's value profile. We formulate value-conditioned gradient filtering, online marginal contribution signals, and cumulative revenue settlement within a traversal learning (TL) substrate. TL is especially attractive here because it performs decentralized backpropagation without the quality loss associated with aggregation-centric distributed learning and, we argue, offers a finer attribution substrate than FedAvg-style
federated learning by preserving explicit traversal and gradient paths. The framework is positioned against data valuation,
federated contribution estimation, personalized
federated learning, and pluralistic
alignment.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CV
HAT-4D: Lifting Monocular Video for 4D Multi-Object Interactions via Human-Agent Collaboration
Jiaxin Li, Yuxiang Wu, Zhenkai Zhang et al. (+11 more)
Extracting dynamic 4D object interactions from massive, in-the-wild
monocular videos offers a highly efficient data collection pathway for scaling
Embodied AI and training VLAs. However, existing
monocular 4D reconstruction methods primarily focus on isolated objects, often failing under the severe occlusions and complex dynamics inherent in multi-object interactions. To bridge this gap, we propose HAT-4D, the first agentic framework designed to reconstruct the 3D geometry, temporal dynamics, and physical interactions of multiple objects from a single
video. By integrating VLMs with a multi-level human-in-the-loop feedback mechanism, HAT-4D efficiently resolves depth ambiguities and interaction-induced occlusions during 3D generation and 4D propagation, yielding physically plausible assets without relying on expensive multicamera rigs. As a scalable data engine, HAT-4D facilitates the creation of MVOIK-4D, an open-world
benchmark for
monocular 4D interaction reconstruction, accompanied by a novel multi-dimensional evaluation protocol focused on physical plausibility and temporal consistency. Extensive experiments demonstrate that HAT-4D achieves SOTA performance on most evaluation metrics, while maintaining competitive semantic
alignment. Ablation studies show that introducing a small amount of human feedback improves interaction reconstruction. Moreover, the data produced by HAT-4D effectively improves baseline performance when used for fine-tuning. Our data and code are available at https://lijiaxin0111.github.io/HAT4D/
Read the full abstract ▾
All authors Jiaxin Li, Yuxiang Wu, Zhenkai Zhang, Xinrui Shi, Haoyuan Wang, Yichen Zhao, Su Linxiang, Chenyang Yu, Mingyu Zhang, Yifan Ding, Boran Wen, Li Zhang, Ruiyang Liu, Yong-Lu Li
Artificial Intelligence · Cross-listed from cs.LG
The Remittance Blueprint: Data-driven Intelligence for Sri Lanka
Dhinanjaya Fernando, Dinura Ginige, Kalana Lakshan et al. (+6 more)
This study analyzes Sri Lankan migration and remittances over 32 years (1994-2025). Using a 384-month harmonized
dataset, we apply exploratory data analysis, stationarity corrected time-series modeling (ADF, Johansen, VAR/VECM), and supervised learning. Results reveal remittance inflows are primarily driven by external macroeconomic variables, specifically exchange rate dynamics and global oil prices, rather than domestic indicators. Impulse response analysis confirms the asymmetric impact of currency depreciation and oil price shocks. Predictively, multivariate machine learning models outperform traditional univariate approaches; Ridge Regression achieves a 73.8% accuracy improvement over SARIMA (Annualized RMSE: USD 494.8 Mn). The optimized framework projects 2026 remittances at USD 9,001 million under stable conditions. These findings highlight the structural dependence of remittances on global economies, emphasizing the need for robust exchange rate policies, skilled migration, and formal financial channels to enhance long-term economic resilience.
Read the full abstract ▾
All authors Dhinanjaya Fernando, Dinura Ginige, Kalana Lakshan, Chanupa Gurusinghe, Lasana Pahanga, Subavarshana Arumugam, Sandeepa Weerasekara, Sandareka Wickramanayake, Nisansa de Silva
Artificial Intelligence · Cross-listed from cs.CL
Cognitive Episodes in LLM Reasoning Traces Enable Interpretable Human Item Difficulty Prediction
Chenguang Wang, Ming Li, Xinyue Zeng et al. (+4 more)
Predicting human item difficulty is central to educational assessment, where reliable estimates support
fairness and effective test construction. Existing methods often depend on costly human calibration or item-level textual representations, providing limited evidence about the cognitive processes that make items difficult. We argue that difficulty should be viewed not only as a property of item text, but also as an observable consequence of the problem-solving burden an item induces. Large
Reasoning Models (LRMs) offer scalable process evidence through
reasoning traces, but such evidence must be structured to support interpretable modeling. To this end, we introduce Epi2Diff (Episode to Difficulty), a framework that maps LRM
reasoning traces into cognitively grounded episode sequences. These episodes group trace segments into functional problem-solving states, enabling difficulty to be modeled through
reasoning scale, effort allocation, and state transitions. Epi2Diff extracts compact episode-dynamic features and combines them with semantic item representations for human difficulty prediction. Experiments on four real-world human difficulty
datasets show that Epi2Diff consistently outperforms strong baselines, including fine-tuned small
language models, LLM in-context learning, and supervised LLM adaptation. On SAT-derived classification
benchmarks, Epi2Diff achieves an 8.1% average relative gain over supervised LLM fine-tuning baselines. Further analyses show that harder items induce more effortful, iterative, and implementation-centered episode dynamics, rather than merely longer responses. These results demonstrate that cognitive episodes in LRM
reasoning traces provide a predictive and interpretable process representation for human item difficulty, offering a new lens for educational measurement with
reasoning models.
Read the full abstract ▾
All authors Chenguang Wang, Ming Li, Xinyue Zeng, Zhuochun Li, Hong Jiao, Tianyi Zhou, Dawei Zhou
Artificial Intelligence · Cross-listed from cs.LG
LLawCo: Learning Laws of Cooperation for Modeling Embodied Multi-Agent Behavior
Qinhong Zhou, Chuang Gan and Anoop Cherian
Embodied agents operating in decentralized and partially observable environments have attracted growing
attention in recent years. However, existing large
language model (LLM)-based
agents often exhibit behaviors that are misaligned with their partners or inconsistent with the environment state, leading to inefficient cooperation and poor task success. To address this challenge, we propose a novel framework, Learning Laws of Cooperation (LLawCo), that enables
embodied agents to autonomously align with both their partners and task objectives. Our framework allows
agents to reflect on past failures to extract misaligned behavioral patterns, which are used to derive high-level behavioral laws, such as "Talk when necessary" and "Wait for partner." These laws are explicitly incorporated into the
agents' chains of thought via supervised fine-tuning, aligning their
reasoning with task requirements and the behavior of other
agents. To evaluate our approach, we introduce PARTNR-Dialog, a large-scale multi-
agent communicative and cooperative planning
benchmark built on the PARTNR environment. Experiments on existing tasks and our new
benchmark demonstrate significant improvements in cooperative efficiency and task success rates. Across four backbone LLMs, our method achieves average success rate improvements of 4.5% on the PARTNR-Dialog
benchmark and 6.8% on the TDW-MAT
benchmark over state-of-the-art open-source communicative
agent frameworks. See the LLawCo project page for details: https://www.merl.com/research/highlights/LLawCo
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.LG
CPAgents: Agentic Composite Phenotype Generation for Cardiac Disease Association
Zuoou Li, Wenlong Zhao, Kelly Yu et al. (+5 more)
Identifying robust associations between cardiac imaging phenotypes and clinical diseases is fundamental to population-scale cardiovascular research and reliable risk stratification. However, current phenome-wide association studies rely on pre-defined, single-variable phenotypes or expert-crafted features, which limits their ability to capture clinically meaningful non-linear effects and cross-phenotype interactions. To address this, we propose CPAgents, an iterative phenotype-Composition framework for cardiovascular Phenome-wide association study (PheWAS) that automatically constructs and validates interpretable composite phenotypes (e.g.,
polynomial, ratio, and interaction forms) from base imaging features. Specifically, our system coordinates three
agents: (i) an Analyst that identifies statistical pathologies and nominates candidate transformations; (ii) a Proposer that generates constrained, medically and statistically motivated expressions under numerical safety rules; and (iii) a Verifier that evaluates candidates using multi-stage criteria and produces transparent evidence trails for accepted phenotypes. Evaluated on a population-scale cardiac imaging cohort, the discovered composite phenotypes markedly improve disease discrimination: across 72 classifier-disease-metric combinations, our variants achieve the top rank in 56 cases versus 18 for baselines, with gains observed across all nine clinical disease categories. Our framework yields compact, clinically interpretable phenotype formulas with transparent evidence trails, enabling scalable discovery of stronger phenotype-disease associations beyond expert-driven feature selection.
Read the full abstract ▾
All authors Zuoou Li, Wenlong Zhao, Kelly Yu, Weitong Zhang, Paul M. Matthews, Wenjia Bai, Bernhard Kainz, Mengyun Qiao
Artificial Intelligence
Tandem Reinforcement Learning with Verifiable Rewards
Difan Jiao, Raghav Singhal, Robert West et al. (+1 more)
Reinforcement learning with verifiable rewards (RLVR) has driven substantial gains in
language-model
reasoning. However, models fine-tuned with RLVR need not remain compatible with weaker partners: gains achieved alone may erode under collaboration, while policy drift may place
reasoning beyond a weaker model's predictive reach. To address both problems, we introduce Tandem RLVR (TRLVR), bringing the recently proposed tandem-training paradigm beyond proof-of-concept settings and into modern RLVR. In TRLVR, a senior learns from rewarded trajectories co-generated with a frozen junior, while the standard GRPO objective is applied only to senior-emitted
tokens. Training Qwen3-4B-Instruct on competition math, TRLVR performs on par with a matched GRPO senior when
reasoning alone. We characterize compatibility through the complementary lenses of handoff robustness and junior legibility. Under
reasoning-step handoffs, the TRLVR senior retains nearly all of its solo performance with the junior, reducing the communication tax to a negligible loss. The senior's
reasoning is also more legible to the junior: its
token distribution stays more closely anchored to the junior's, and the junior predicts the senior's chain-of-thought more readily
token by
token. An ablation that explicitly regularizes GRPO toward the junior does not recover the same combination of capability and compatibility. These results provide initial evidence that tandem rollout structure is a promising direction for keeping RLVR
reasoning gains accessible to weaker partners.
Read the full abstract ▾
All authors Difan Jiao, Raghav Singhal, Robert West, Ashton Anderson
Artificial Intelligence · Cross-listed from cs.CR
Robust Harmful Features Under Jailbreak Attacks: Mechanistic Evidence from Attention Head Specialization in Large Language Models
Yanchen Yin, Dongqi Han and Linghui Li
Jailbreak attacks bypass LLM safety
alignment, yet their mechanisms remain poorly understood. We provide evidence that attacks do not comprehensively eliminate safety features, but instead selectively suppress specific
attention heads. We identify two functionally differentiated types: Adversarially Compromised Heads (ACHs) concentrated in early layers, which are suppressed under attacks, and Safety-Aligned Heads (SAHs) in mid-layers, which maintain robust activations even when attacks succeed. Ablation studies support the causal role of ACHs and the contribution of SAHs to robust activations: suppressing a small number of ACHs is sufficient to induce jailbreak-like behavior on normally refused inputs, while removing SAHs substantially weakens mid-layer safety activations.
Token-level attribution further shows that ACH suppression is driven specifically by attack-template
tokens, providing a mechanistic account of why attacks can bypass refusal decisions through ACH suppression while leaving internal safety signals sustained by SAHs – a phenomenon we term Robust Harmful Features. To validate the practical significance of this robustness, we show that simply reading these persistent activations – without any training – yields competitive aggregate detection performance with strong
adversarial robustness.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CV
Toward Robust In-Context Segmentation via Concept Guidance
Zhigang Chen, Xiawu Zheng and Rongrong Ji
In-context
segmentation (ICS) requires a model to segment target regions in a query image using only a few reference images and their corresponding masks, without updating any parameters. Despite recent progress, prior ICS studies have largely overlooked a critical aspect: system robustness, ie, whether the model can produce stable
segmentation results for the same query under different references. In this work, we revisit ICS from the robustness perspective and introduce a novel paradigm, Concept-Guided In-Context
Segmentation (CG-ICS), which performs
segmentation by extracting high-level semantic concepts from references rather than relying solely on low-level visual matching. Specifically, CG-ICS introduces a concept
reasoning module that uses an MLLM to propose candidates and a SAM3-driven scoring function with tree-search refinement to select reliable textual concepts, together with a parallel visual exemplar route that provides query-side spatial grounding via a simple context construction. Both the textual concept and the visual exemplar are then used to activate the
segmentation capability of a frozen SAM3 backbone. Extensive experiments on standard ICS
benchmarks demonstrate that CG-ICS not only achieves state-of-the-art accuracy but also substantially improves robustness, yielding a more reliable ICS system with significantly reduced variance across diverse reference choices. Code is available at https://github.com/Kakarot1103/CG-ICS.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.LG
Beyond Sparse Supervision: Diffusion-Guided Learning for Few-Shot Graph Fraud Detection
Liming Liu, Chao Hu, Mingfei Lu et al. (+3 more)
Graph-based fraud detection is essential for safeguarding large-scale transaction systems, where undetected anomalies may lead to substantial financial losses and security risks. Real-world fraud graphs pose two coupled challenges: sparse and imbalanced supervision, where verified fraudulent labels are scarce and heavily skewed toward benign accounts, and representation dilution, where spatial message passing may oversmooth camouflaged anomalies while spectral filters may suppress fraud-relevant mid- and high-frequency irregularities. To address these challenges, we propose ADC-GNN, short for
Attention-guided
Diffusion-Contrastive Graph Neural Network, a unified framework that combines
diffusion-guided feature augmentation, contrastive representation learning, and multi-hop spectral
attention for few-shot graph fraud detection. The
diffusion component is formulated as a feature-space denoising augmentation mechanism rather than a full
topology-generative graph
diffusion model: it constructs noise-perturbed node-feature views under a cosine schedule and uses contrastive learning to stabilize node representations across perturbations. The spectral
attention module further adaptively emphasizes fraud-relevant hop-level and relation-level cues. We evaluate ADC-GNN primarily on three public
benchmarks and additionally report a proprietary real-world telecom transaction
dataset with approximately 60,000 records as a private case study. Under the 1% training setting, ADC-GNN achieves consistent improvements over original graph fraud baselines and four protocol-consistent recent graph anomaly/fraud baselines on the public
benchmarks. Additional analyses on split stability, training ratios, oversampling alternatives, module-level ablations,
diffusion schedules, and runtime and
memory-consumption comparisons further characterize the effective operating regime of ADC-GNN.
Read the full abstract ▾
All authors Liming Liu, Chao Hu, Mingfei Lu, Yiwei Ge, Xingle Li, Heyuan Shi
Artificial Intelligence · Cross-listed from cs.CV
PhysisForcing: Physics Reinforced World Simulator for Robotic Manipulation
Peiwen Zhang, Yufan Deng, Shangkun Sun et al. (+11 more)
Video generation models have emerged as a promising paradigm for
embodied world simulation. However, both general-domain
video generators and
robot-specific data fine-tuned models can still produce physically implausible manipulations, including discontinuous motion trajectories and inconsistent
robot-object interactions, which limits their reliability as world simulators. Through extensive experiments, we find that such physical instability mainly arises from two factors: deformation of moving objects and implausible spatio-temporal correlations among interacting entities, particularly during contact. Building on this observation, we propose PhysisForcing, a scalable training framework that strengthens physical consistency by focusing supervision on physics-informative regions through joint optimization of pixel-level and semantic-level features. The framework consists of a pixel-level trajectory
alignment loss, which supervises DiT features using reference point trajectories, and a semantic-level relational
alignment loss, which aligns DiT features with inter-region relations extracted from a frozen
video understanding encoder. Extensive experiments on R-Bench, PAI-Bench, and EZS-Bench show that PhysisForcing consistently improves
embodied video generation over strong baselines, improving the Wan2.2-I2V-A14B and Cosmos3-Nano base models on R-Bench by 22.3% and 9.2% (7.1% and 3.7% over vanilla finetuning), with the Cosmos3-Nano variant attaining the best overall score. Beyond generation, as a world model under the WorldArena action-planner protocol it raises the closed-loop success rate from 16.0% to 24.0% and further improves downstream policy success, indicating that physically aligned
video models yield stronger representations for robotic manipulation.
Read the full abstract ▾
All authors Peiwen Zhang, Yufan Deng, Shangkun Sun, Juncheng Ma, Duomin Wang, Jonas Du, Zilin Pan, Ye Huang, Hao Liang, Songyan Huang, Ruihua Zhang, Enze Xie, Ming-Yu Liu, Daquan Zhou
Artificial Intelligence · Cross-listed from cs.CL
From Tokens to States: LLMs as a Special Case of World Models and the Continuous Path Beyond
Paul Dubois
The AI community has framed the relationship between large
language models (LLMs) and world models as a dichotomy: LLMs predict
tokens; world models simulate reality. Yann LeCun argues in 2022 that reaching general intelligence requires abandoning autoregressive
token prediction in favour of latent-space architectures. This framing is unnecessarily binary. Two claims will be defended. First, LLMs are a degenerate special case of world models: the state space is the set of all
token sequences, the only action is appending one
token, and world models are therefore a strict generalisation of LLMs, not a replacement. Second, there is a natural continuous spectrum from NTP to JEPA, with multi-
token prediction, future-summary prediction, and next-latent prediction as intermediate stations already populated by current research. Moving along this spectrum relaxes the LLM constraints one by one. It also progressively surrenders the two practical advantages that make LLMs trainable at scale: internet-scale self-supervised data, and a
transformer architecture co-designed for discrete
token prediction. Both are examined as open research questions: the data question (the cliff from self-supervised text to instrumented action-labelled environments) and the architecture question (whether the
transformer generalises to continuous-state prediction, or whether a new primitive is needed).
Read the full abstract ▾
Artificial Intelligence
AI-Driven Synthesis for High-Tech System Design: Automating Innovation
Luuk Oerlemans, Steven Westerhof and Theo Hofman
This article addresses the combinatorial complexity inherent in modern high-tech system design by presenting automation-in-design (AiD) as a transformative paradigm. We propose computational design synthesis (CDS), a framework utilising deep learning and generative AI to automate the creation of novel systems. Two case studies (e-drive system design and spatial dimensioning problem) serve as proof-points for this approach. The AI-driven methods used in the case studies represent a fundamental shift in engineering, advancing from simulation-based optimisation towards autonomous design with minimal human supervision.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CE
Higher-Order Fourier Neural Operator: Explicit Mode Mixer for Nonlinear PDEs
Alex Colagrande, Paul Caillon, Eva Feillet et al. (+1 more)
Neural operators provide deep neural networks for learning mappings between function spaces. Among them, the
Fourier Neural Operator (FNO) is particularly effective: its spectral convolution relies on low-dimensional
Fourier-domain representations and can handle inputs at different resolutions. This design aligns well with settings where the
Fourier basis diagonalizes the underlying operator, such as linear, constant-coefficient PDEs on periodic domains, in which
Fourier modes evolve independently. However, nonlinear PDEs may benefit from an additional inductive bias, as they exhibit structured interactions between modes, governed by
polynomial nonlinearities. To capture this inductive bias, we introduce the Higher-Order Spectral Convolution, a spectral mixer that extends FNO from diagonal modulation to explicit n-linear mode mixing, aligned with the dynamics of nonlinear PDEs. Our experiments on standard
benchmarks show that the proposed Higher-Order FNO (HO-FNO) retains the efficiency of FNO-based architectures and consistently improves over other spectral neural operators. HO-FNO also performs on par with or better than state-of-the-art
transformers and state-space models on several
datasets, with stronger gains in highly nonlinear regimes, such as the Poisson equation with
polynomial forcing, where a single HO-FNO layer outperforms FNO models with up to 16 layers. We open-source our code for reproducibility at: https://github.com/AlexColagrande/HO-FNO.
Read the full abstract ▾
All authors Alex Colagrande, Paul Caillon, Eva Feillet, Alexandre Allauzen
Artificial Intelligence · Cross-listed from cs.CV
BiDeMem: Bidirectional Degradation Memory for Explainable Image Restoration
Xinrui Wu and Lichen Huang
Degradation-aware
prompts, conditions, and latent priors are increasingly used in image restoration, yet they are usually judged by a single endpoint: whether the restored image obtains higher PSNR. This is a weak test of semantics. A condition can help by adding capacity, acting as a global correction bias, or exploiting
dataset shortcuts, without becoming an interpretable degradation prior. We propose BiDeMem, a bidirectional degradation
memory for explainable image restoration. A query built from restoration features and input statistics retrieves a compact top-k subset of
memory slots. The same selected slot identity supports the restoration path at inference time and a training-only forward-degradation explanation path. The study centers on verifiability in a controlled multi-degradation NAFNet setting. New controls separate the gain from a correction head alone, a dense query prior, and a static global prior: these variants are 0.2588, 0.2586, and 0.2839 dB below BiRank, respectively. Strong residual supervision and a wider degradation head also remain below the full bidirectional
memory model. Intervention probes show that BiRank preserves restoration quality while increasing wrong-prior and native-prior sensitivity, framing degradation
memory as both a restoration module and a falsifiable explanation mechanism.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CV
OSOR: One-Step Diffusion Inpainting for Effect-Aware Object Removal
Qinming Zhou, Chenxi Sun, Deyang Kong et al. (+6 more)
Real-world object removal is challenging due to two key difficulties: the target object's non-local effects, such as shadows and reflections, which are difficult to model, and the fact that user-provided masks are often inaccurate or incomplete. With billions of parameters and tens of denoising steps,
diffusion-based models achieve strong removal performance at the expense of substantial computational cost, limiting their use in interactive applications and on edge devices. To address these challenges, we present OSOR (One-Step Object Removal), which simultaneously achieves efficient, effect-aware, and mask-robust object removal. Concretely, OSOR introduces: (1) an occupancy-guided discriminator for precise boundary supervision, enabling stable single-step
diffusion training; (2) an alpha head that leverages knowledge from pretrained
diffusion models to predict appropriate removal regions with minimal overhead, thereby handling imperfect masks; and (3) a semantic-anchored verification pipeline (SAVP) that filters noisy instruction-based triplets to produce effect-aware supervision at scale. Using SAVP, we curate CORNE, which contains 280K verified removal pairs, and further annotate AnimeEraseBench and TextEraseBench to evaluate performance on more complex removal tasks. Experiments show that OSOR surpasses strong multi-step
diffusion baselines in perceptual quality while achieving $4\times$ to $30\times$ faster inference.
Read the full abstract ▾
All authors Qinming Zhou, Chenxi Sun, Deyang Kong, Junhao He, Xiangheng Tang, Peike Yu, Haotian Wu, Leilei Cao, Linfeng Zhang
Artificial Intelligence · Cross-listed from cs.CV
STAG: Spatio-temporal Evolving Structural Representation of Action Units for Micro-expression Recognition
Nandani Sharma, Varun Sharma and Dinesh Singh
Micro-expression recognition is challenging due to subtle and short-lived facial muscle movements. Existing methods rely heavily on apex-onset frames, overlook fine-grained inter-frame dynamics, and separately model spatial and temporal information, limiting generalization across
datasets. To address these challenges, we propose STAG, a dynamic ROI-AU-coupled spatial-temporal network that jointly models motion flow and adaptive facial connectivity. The framework extracts optical flow from discriminative frames using magnitude-based selection and temporal
attention. A dual-branch architecture combines an enhanced graph
attention network for structured spatial
reasoning with a
transformer encoder for temporal modeling. A bidirectional cross-
attention module enables mutual refinement of spatial and temporal features, while AU-guided dynamic connectivity adapts facial region interactions according to muscle activation patterns. The
transformer captures subtle temporal dynamics beyond apex-based approaches, improving semantic consistency and interpretability for explainable micro-expression recognition. The fused representation is optimized using focal loss and evaluated on CASME II, 4DME, DFME, NaME, SAMM, and SMIC-HS. Extensive experiments demonstrate improved robustness, generalization, interpretability, and computational efficiency, confirming the effectiveness of adaptive relational
reasoning, AU-guided dynamic connectivity, and deep spatial-temporal feature fusion for accurate cross-
dataset micro-expression recognition.
Read the full abstract ▾
Artificial Intelligence
Ontology-Guided Evidence Path Inference for Multi-hop Knowledge Graph Question Answering
Yongxue Shan, Meihan Wu, Cundi Fang et al. (+2 more)
Knowledge graph question answering (KGQA) aims to answer natural-
language questions by
reasoning over structured facts. Existing multi-hop KGQA methods mainly rely on topic-centered expansion, which faces two key challenges: the search space rapidly grows with noisy mixed-type paths, and retrieved paths may fail to satisfy the semantic constraints of complex questions. To address these challenges, we propose OPI, an ontology-guided evidence path inference framework for multi-hop KGQA. OPI introduces a relation-centric ontology graph to capture the head-tail type constraints of relations, providing a compact interface for answer-side constraints. Based on this ontology graph, OPI first introduces a bidirectional
retrieval mechanism by mapping the predicted answer type to compatible final-hop relations and combining topic-side prefix expansion with answer-side final-hop matching, thereby suppressing noisy mixed-type expansion. OPI further adopts an iterative refinement strategy to reassess retrieved paths and candidate answers under the question context, filtering type-compatible but question-irrelevant evidence for more reliable answer prediction. Experiments on WebQSP, CWQ, and MetaQA show that OPI substantially reduces the search space, improves Hit@1/F1 by 4.6/5.0 points on WebQSP and 8.9/3.3 points on CWQ over the strongest prior results, and achieves near-saturated Hit@1 on MetaQA with the
retrieval module alone.
Read the full abstract ▾
All authors Yongxue Shan, Meihan Wu, Cundi Fang, Jie Peng, Xiaodong Wang
Artificial Intelligence
JD Oxygen AI Item Center (Oxygen AIIC) V1: An Industrial-Scale LLM/VLM-Centric Solution for Item Understanding, Management, and Applications
Oxygen AIIC, Chan Long, Chao Liu et al. (+52 more)
JD$.$com, one of the world's largest e-commerce platforms, serves over 700 million active users and millions of merchants, with a catalog of tens of billions of SKUs. At this scale, high-quality, structured item knowledge underpins a better consumer experience, lower management costs, and higher operational efficiency-yet producing and serving it poses three industrial-scale challenges: fast-emerging concepts, high-quality knowledge production for massive SKUs, and diverse downstream requirements. To address these challenges, we present the JD Oxygen AI Item Center (Oxygen AIIC), an industrial-scale platform built on LLMs/VLMs for item-knowledge production and service. Oxygen AIIC is built around four core pillars: (i) ontology engineering driven by efficient human-AI collaboration, which supports the dynamic
evolution and agile expansion of an ontology with millions of entries; (ii) a "Semantic Search then Discrimination"(S2D) knowledge identification architecture that, combined with throughput improvement strategies, enables scalable, extensible, and high-throughput AI Item Library production for tens of billions of SKUs; (iii) self-evolving item-understanding LLMs/VLMs that improve in a stable and controllable manner, enabling knowledge production with 94.2% precision and 82.8% recall; and (iv) a unified item tunnel that serves as the data and service hub. Oxygen AIIC now covers tens of thousands of JD categories and processes hundreds of millions of item updates per day on Huawei Ascend NPUs. It has accumulated hundreds of billions of item-knowledge assets. Deployed across core business scenarios-including search, recommendation, operations, category planning-Oxygen AIIC has delivered measurable gains at scale. Search-traffic coverage reaches 80.4%, item-information quality issues drop by 37%, the automated fill rate of core attributes during item listing exceeds 80%.
Read the full abstract ▾
All authors Oxygen AIIC, Chan Long, Chao Liu, Chaofan Chen, Chaohui Dong, Chunyuan Guo, Danping Liu, Debin Liu, Deping Xiang, Fulai Xu, Guangyue Liu, Hao Li, Huichun Hu, Jian Yang, Jianan Wang, Jianbo Zhao, Jiaoyang Li, Jiaxing Wang, Jinglong Li, Jinjin Guo, Jun Fang, Jun Liu, Kai Zhou, Li Wang, Lili Gao, Liying Chen, Luning Yang, Mengdi Zhou, Pengzhang Liu, Qi Lv, Qianyun Wang, Qixia Jiang, Ruyue Li, Shimu Liang, Shuxing Wang, Sijie Zhang, Siqi Li, Tianhao Gao, Wang Ke, Weihu Huang, Wencan Lai, Wenjie Zhang, Xiaohui Zhang, Xiaojing Dong, Ya Liu, Yifeng Zhang, Yixiang Wang, Yongtai Zhang, Yongyi Liao, Zhaoru Chen, Zhen Chen, Zhiyong Ma, Zhiyuan Liu, Zhongwei Liu, Ziyan Xing
Artificial Intelligence · Cross-listed from cs.LG
OperatorSHAP: Fast and Accurate Shapley Value Estimation for Neural Operators
Joshua Stiller, Santo M. A. R. Thies, Felix Czaja et al. (+1 more)
Understanding model predictions is essential for physical applications, where outputs often inform safety-critical decisions, such as structural load assessment, weather warnings, and clinical diagnosis. Shapley values satisfy many desirable properties as an attribution method, but their computational cost during inference hinders their practical use. Current amortized explainers, such as FastSHAP, are limited to homogeneous inputs, which is problematic for physical applications where data often comes from irregular grids and geometries. We introduce OperatorSHAP, a grid-agnostic attribution method and training procedure that allows us to train FastSHAP-like explainers for neural operators. We establish a theoretical framework for attributions in function space, connecting to Aumann-Shapley values. We further show that OperatorSHAP's explanations are consistent with state-of-the-art discrete Shapley values across resolutions and transfer across grid sizes without retraining.
Read the full abstract ▾
All authors Joshua Stiller, Santo M. A. R. Thies, Felix Czaja, Eyke Hüllermeier
Artificial Intelligence · Cross-listed from cs.DB
Single and Multi Truth Data Fusion using Large Language Models
Hira Beril Kucuk, Norman W Paton, Jiaoyan Chen et al. (+1 more)
Data fusion, also known as truth discovery, is a data integration problem that aims to determine the correct value or set of values for each attribute of an object when presented with potentially conflicting values from multiple sources. Data fusion tasks belong to two main categories: single-truth scenarios, where each attribute has only one correct value, and multi-truth scenarios, where multiple values can be valid simultaneously. This paper investigates the use of Large
Language Models (LLMs) in data fusion tasks for tabular data. Various prompting strategies, encompassing both single-truth and multi-truth scenarios, are investigated empirically. Domain-dependent, domain-independent, zero-shot and one-shot
prompts are evaluated on three different
benchmark datasets. Experimental results demonstrate that LLM-based approaches outperform traditional unsupervised truth discovery methods, such as DART and LTM, across all
datasets. The codebase of this study has been made publicly available on GitHub.
Read the full abstract ▾
All authors Hira Beril Kucuk, Norman W Paton, Jiaoyan Chen, Zhenyu Wu
Artificial Intelligence · Cross-listed from cs.CR
ToolPrivacyBench: Benchmarking Purpose-Bound Privacy in Tool-Using LLM Agents
Shijing Hu, Liang Liu, Zhu Meng et al. (+1 more)
Large
language models (LLMs) have increasingly moved from standalone text generation systems to
agents that invoke external tools, access environments, and execute multi-step tasks. However, conventional function-calling
benchmarks mainly evaluate task completion and API correctness, while
privacy evaluation
benchmarks typically focus on final responses or
privacy judgments. Neither perspective captures purpose-bound information flow across an executed multi-tool trajectory. Motivated by this limitation in current
agent evaluation, ToolPrivacyBench audits whether task-private atoms are routed only to authorized tools and downstream sinks, thereby evaluating both task completion and
privacy over-disclosure during tool use. The
benchmark contains 2,150 cases, including 1,150 fully synthetic
privacy-sensitive business workflows and 1,000 cases adapted from existing multi-tool and function-calling
benchmarks. Each case is represented by a policy knowledge base. After an
agent executes against mock business backends, the evaluator compares recorded tool arguments and backend audit logs with this policy knowledge base. The evaluation covers nine widely used
agents to characterize purpose-bound
privacy over-disclosure. The results show that successful tool execution does not imply appropriate
privacy disclosure: an
agent may complete a task while transmitting unnecessary private information through intermediate tool calls. ToolPrivacyBench therefore formalizes a need-to-know disclosure boundary, under which each tool should receive only the information necessary for its stated purpose, and uses trajectory-level
auditing to identify
privacy over-disclosure in multi-tool workflows.
Read the full abstract ▾
All authors Shijing Hu, Liang Liu, Zhu Meng, Zhicheng Zhao
Artificial Intelligence · Cross-listed from cs.CL
MultiHashFormer: Hash-based Generative Language Models
Huiyin Xue, Atsuki Yamaguchi and Nikolaos Aletras
Language models (LMs) represent
tokens using
embedding matrices that scale linearly with the vocabulary size. To constrain the parameter footprint, prior work proposes hashing many
tokens into a single vector within encoder-only models. While this offers parameter efficiency, many-to-one collisions prevent its use in causal LMs. In this paper, we propose MultiHashFormer, a new framework that allows hash-based autoregression. Each
token is represented as a unique hash signature, a short sequence of discrete hash IDs, generated by multiple independent hash functions. A Hash Encoder compresses this signature into a single latent vector for processing by a
Transformer decoder. Then, a Hash Decoder generates the hash signature of the next
token, which is then mapped back to text. We evaluate our approach at the 100M, 1B and 3B parameter scales, demonstrating that MultiHashFormer consistently outperforms standard
Transformer LMs across multiple
benchmarks. Furthermore, we show that our model handles
multilingual vocabulary expansion with a constant parameter footprint without any modifications.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CL
Can LLMs Judge Better Than They Generate? Evaluating Task Asymmetry, Mechanistic Interpretability and Transferability for In-Context QA
Sambaran Bandyopadhyay
LLM-as-a-Judge and self-evaluation pipelines implicitly assume that evaluation is easier than generation. We test this in a controlled in-context QA setting where a context passage is the sole information source and each model judges the answer it generated, removing the parametric-knowledge confound of open-domain comparisons. Across four
benchmarks (SQuAD 2.0, DROP, HotpotQA, MuSiQue) and two models, evaluation is not uniformly easier: generation accuracy exceeds self-evaluation on three of four, with multi-hop MuSiQue the exception.
Attention analysis reveals why: evaluation attends to context 3–5x less than generation does and barely reads the candidate answer. LoRA fine-tuning confirms the asymmetry is not a training artifact: generation fine-tuning induces over-acceptance and evaluation fine-tuning degrades generation. These findings challenge core assumptions in self-evaluation pipelines.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.SD
DG^VoiC: Speaker Clustering for Fraud Investigation under Real Call-Centre Conditions
Muhammad Shakeel Akram, Amal Htait, Abdul Hamid Sadka et al. (+2 more)
Insurance fraud remains costly and operationally difficult, particularly in call-centre workflows where many customer interactions begin at FNOL. While recent fraud detection methods mainly rely on structured data, text, or images, repeated speaker identity across calls remains underused as an investigative signal. This paper presents DG^VoiC, a voice clustering framework for customer verification and cross-profile speaker linking on anonymised real call-centre audio. The approach combines sensitive information-aligned anonymisation,
speech-focused preprocessing, sliding-window speaker
embedding extraction, and cosine similarity based clustering to identify repeated speakers under real telephony conditions. The method was evaluated on 121 recordings, with a curated reference subset of 56 samples in 22 human-agreed speaker clusters. used for validation. The best configuration achieved 96% AMI, 95% ARI, 98% completeness, 100% homogeneity, and 99% V-measure. These results show that speaker clustering can provide a strong additional signal for fraud investigation by helping analysts verify speaker consistency and surface repeated voices across customers.
Read the full abstract ▾
All authors Muhammad Shakeel Akram, Amal Htait, Abdul Hamid Sadka, Emma Meisingseth, Karishma Jaitly
Artificial Intelligence · Cross-listed from cs.CV
Mind the Gap: Quantifying the Domain Gap in Cross-Sensor Diffusion Super-Resolution
Dawid Kopeć, Katarzyna Jabłońska, Wojciech Kozłowski et al. (+1 more)
Demand for high-resolution satellite imagery has increased interest in super-resolution (SR) to bridge the spatial resolution gap between freely available missions such as Sentinel-2 and commercial systems like PlanetScope. Because no sensor provides true paired low- and high-resolution observations, SR models are usually trained on synthetically degraded data, creating a domain gap on real cross-sensor imagery. In this work, we provide the first systematic study of how this synthetic-to-real mismatch affects the performance of modern
diffusion-based SR models. Using a large, geometrically and temporally aligned
dataset of Sentinel-2 and PlanetScope imagery, we evaluate five state-of-the-art
diffusion architectures under controlled experimental settings. We also introduce LPIPS-Sat, a domain-adapted perceptual metric based on Sentinel-2 self-supervised features. Our results show two persistent challenges: synthetically trained models degrade sharply on real pairs, while models trained on real cross-sensor data exhibit optimisation difficulties and struggle to adapt to the physical and radiometric diversity. These findings highlight a key limitation of current SR and motivate methods that disentangle super-resolution from domain adaptation.
Read the full abstract ▾
All authors Dawid Kopeć, Katarzyna Jabłońska, Wojciech Kozłowski, Maciej Zięba
Artificial Intelligence · Cross-listed from eess.IV
MLVC: Multi-platform Learned Video Codec for Real-World Deployment
Tanel Pärnamaa, Martin Lumiste, Ardi Loot et al. (+3 more)
Neural
video codecs have surpassed classical codecs in coding efficiency but remain impractical for deployment due to cross-platform incompatibility and high computational cost. Existing quantization-based solutions fail to produce deterministic results across diverse hardware platforms, leading to catastrophic decoding failures. We introduce MLVC, a hardware-robust neural
video codec designed for practical cross-platform inference. The key idea is to explicitly transmit scale parameters through the hyperprior, which guarantees
entropy coding consistency across devices without requiring bit-exact arithmetic. While this increases bitrate overhead, we recover most of the coding efficiency through architectural improvements (gated
memory, ReGLU activation), a long-term reference recovery mechanism, and domain-specific perceptual training. On the VCD
video conferencing
benchmark, MLVC achieves >70% BD-rate (MOS) improvement over hardware HEVC, the strongest deployable baseline, while reaching subjective quality competitive with DCVC-RT, which cannot operate across diverse platforms. Both the encoder and decoder run at 100 FPS on average on commodity NPUs from Apple, Intel, and Qualcomm. MLVC is the first neural
video codec to combine competitive compression performance, real-time speed, and cross-platform robustness across diverse consumer devices, making it suitable for widespread deployment. Code is available at https://github.com/microsoft/mlvc.
Read the full abstract ▾
All authors Tanel Pärnamaa, Martin Lumiste, Ardi Loot, Evgenii Indenbom, Andrei Znobishchev, Ando Saabas
Artificial Intelligence
Lifted Causal Inference
Malte Luttermann, Tanya Braun, Ralf Möller et al. (+1 more)
Lifted inference exploits indistinguishabilities in probabilistic graphical models by using a representative for indistinguishable objects, thereby speeding up query answering while maintaining exact answers. In this article, we show how lifting can be applied to efficiently compute causal effects in relational domains. More specifically, we introduce parametric causal factor graphs (PCFGs) to incorporate causal knowledge in lifted models and give a formal semantics of interventions therein. We further present the Lifted Causal Inference (LCI)
algorithm to compute causal effects on a lifted level, thereby drastically speeding up causal inference compared to propositional inference, e.g., in causal
Bayesian networks. In addition, we present partially directed parametric causal factor graphs (PD-PCFGs) as a generalisation of PCFGs to handle partial causal knowledge and extend LCI to perform lifted causal inference in a PD-PCFG, thereby extending the applicability of lifted causal inference to a broader range of models requiring less prior knowledge about causal relationships.
Read the full abstract ▾
All authors Malte Luttermann, Tanya Braun, Ralf Möller, Marcel Gehrke