# Research Papers — 2026-02 · bundle page 130 of 210

> Full-text markdown of papers #6451–6500 of 10493 published in 2026-02, newest first. AI-readable mirrors of the full 10493-paper 2026-02 archive (50 papers per file).

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### Collective Behavior of AI Agents: the Case of Moltbook

Authors: Giordano De Marzo, David Garcia
Published: 2026-02-09
Categories: physics.soc-ph, cs.CL, cs.MA
arXiv: https://arxiv.org/abs/2602.09270v1

We present a large scale data analysis of Moltbook, a Reddit-style social media platform exclusively populated by AI agents. Analyzing over 369,000 posts and 3.0 million comments from approximately 46,000 active agents, we find that AI collective behavior exhibits many of the same statistical regularities observed in human online communities: heavy-tailed distributions of activity, power-law scaling of popularity metrics, and temporal decay patterns consistent with limited attention dynamics. However, we also identify key differences, including a sublinear relationship between upvotes and discussion size that contrasts with human behavior. These findings suggest that, while individual AI agents may differ fundamentally from humans, their emergent collective dynamics share structural similarities with human social systems.

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### Measuring Inclusion in Interaction: Inclusion Analytics for Human-AI Collaborative Learning

Authors: Jaeyoon Choi, Nia Nixon
Published: 2026-02-09
Categories: cs.CL
arXiv: https://arxiv.org/abs/2602.09269v1

Inclusion, equity, and access are widely valued in AI and education, yet are often assessed through coarse sample descriptors or post-hoc self-reports that miss how inclusion is shaped moment by moment in collaborative problem solving (CPS). In this proof-of-concept paper, we introduce inclusion analytics, a discourse-based framework for examining inclusion as a dynamic, interactional process in CPS. We conceptualize inclusion along three complementary dimensions -- participation equity, affective climate, and epistemic equity -- and demonstrate how these constructs can be made analytically visible using scalable, interaction-level measures. Using both simulated conversations and empirical data from human-AI teaming experiments, we illustrate how inclusion analytics can surface patterns of participation, relational dynamics, and idea uptake that remain invisible to aggregate or post-hoc evaluations. This work represents an initial step toward process-oriented approaches to measuring inclusion in human-AI collaborative learning environments.

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### Rethinking Global Text Conditioning in Diffusion Transformers

Authors: Nikita Starodubcev, Daniil Pakhomov, Zongze Wu, Ilya Drobyshevskiy, Yuchen Liu, Zhonghao Wang, Yuqian Zhou, Zhe Lin, Dmitry Baranchuk
Published: 2026-02-09
Categories: cs.CV
arXiv: https://arxiv.org/abs/2602.09268v1

Diffusion transformers typically incorporate textual information via attention layers and a modulation mechanism using a pooled text embedding. Nevertheless, recent approaches discard modulation-based text conditioning and rely exclusively on attention. In this paper, we address whether modulation-based text conditioning is necessary and whether it can provide any performance advantage. Our analysis shows that, in its conventional usage, the pooled embedding contributes little to overall performance, suggesting that attention alone is generally sufficient for faithfully propagating prompt information. However, we reveal that the pooled embedding can provide significant gains when used from a different perspective-serving as guidance and enabling controllable shifts toward more desirable properties. This approach is training-free, simple to implement, incurs negligible runtime overhead, and can be applied to various diffusion models, bringing improvements across diverse tasks, including text-to-image/video generation and image editing.

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### Generalizing GNNs with Tokenized Mixture of Experts

Authors: Xiaoguang Guo, Zehong Wang, Jiazheng Li, Shawn Spitzel, Qi Yang, Kaize Ding, Jundong Li, Chuxu Zhang
Published: 2026-02-09
Categories: cs.LG
arXiv: https://arxiv.org/abs/2602.09258v2

Deployed graph neural networks (GNNs) are frozen at deployment yet must fit clean data, generalize under distribution shifts, and remain stable to perturbations. We show that static inference induces a fundamental tradeoff: improving stability requires reducing reliance on shift-sensitive features, leaving an irreducible worst-case generalization floor. Instance-conditional routing can break this ceiling, but is fragile because shifts can mislead routing and perturbations can make routing fluctuate. We capture these effects via two decompositions separating coverage vs selection, and base sensitivity vs fluctuation amplification. Based on these insights, we propose STEM-GNN, a pretrain-then-finetune framework with a mixture-of-experts encoder for diverse computation paths, a vector-quantized token interface to stabilize encoder-to-head signals, and a Lipschitz-regularized head to bound output amplification. Across nine node, link, and graph benchmarks, STEM-GNN achieves a stronger three-way balance, improving robustness to degree/homophily shifts and to feature/edge corruptions while remaining competitive on clean graphs.

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### STaR: Scalable Task-Conditioned Retrieval for Long-Horizon Multimodal Robot Memory

Authors: Mingfeng Yuan, Hao Zhang, Mahan Mohammadi, Runhao Li, Jinjun Shan, Steven L. Waslander
Published: 2026-02-09
Categories: cs.RO, cs.AI
arXiv: https://arxiv.org/abs/2602.09255v2

Mobile robots are often deployed over long durations in diverse open, dynamic scenes, including indoor setting such as warehouses and manufacturing facilities, and outdoor settings such as agricultural and roadway operations. A core challenge is to build a scalable long-horizon memory that supports an agentic workflow for planning, retrieval, and reasoning over open-ended instructions at variable granularity, while producing precise, actionable answers for navigation. We present STaR, an agentic reasoning framework that (i) constructs a task-agnostic, multimodal long-term memory that generalizes to unseen queries while preserving fine-grained environmental semantics (object attributes, spatial relations, and dynamic events), and (ii) introduces a Scalable Task Conditioned Retrieval algorithm based on the Information Bottleneck principle to extract from long-term memory a compact, non-redundant, information-rich set of candidate memories for contextual reasoning. We evaluate STaR on NaVQA (mixed indoor/outdoor campus scenes) and WH-VQA, a customized warehouse benchmark with many visually similar objects built with Isaac Sim, emphasizing contextual reasoning. Across the two datasets, STaR consistently outperforms strong baselines, achieving higher success rates and markedly lower spatial error. We further deploy STaR on a real Husky wheeled robot in both indoor and outdoor environments, demonstrating robust long horizon reasoning, scalability, and practical utility. Project Website: https://trailab.github.io/STaR-website/

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### VLM-Guided Iterative Refinement for Surgical Image Segmentation with Foundation Models

Authors: Ange Lou, Yamin Li, Qi Chang, Nan Xi, Luyuan Xie, Zichao Li, Tianyu Luan
Published: 2026-02-09
Categories: cs.CV, cs.AI, cs.MA
arXiv: https://arxiv.org/abs/2602.09252v1

Surgical image segmentation is essential for robot-assisted surgery and intraoperative guidance. However, existing methods are constrained to predefined categories, produce one-shot predictions without adaptive refinement, and lack mechanisms for clinician interaction. We propose IR-SIS, an iterative refinement system for surgical image segmentation that accepts natural language descriptions. IR-SIS leverages a fine-tuned SAM3 for initial segmentation, employs a Vision-Language Model to detect instruments and assess segmentation quality, and applies an agentic workflow that adaptively selects refinement strategies. The system supports clinician-in-the-loop interaction through natural language feedback. We also construct a multi-granularity language-annotated dataset from EndoVis2017 and EndoVis2018 benchmarks. Experiments demonstrate state-of-the-art performance on both in-domain and out-of-distribution data, with clinician interaction providing additional improvements. Our work establishes the first language-based surgical segmentation framework with adaptive self-refinement capabilities.

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### Optimal Estimation in Orthogonally Invariant Generalized Linear Models: Spectral Initialization and Approximate Message Passing

Authors: Yihan Zhang, Hong Chang Ji, Ramji Venkataramanan, Marco Mondelli
Published: 2026-02-09
Categories: math.ST, cs.IT, cs.LG, math.PR, stat.ML
arXiv: https://arxiv.org/abs/2602.09240v1

We consider the problem of parameter estimation from a generalized linear model with a random design matrix that is orthogonally invariant in law. Such a model allows the design have an arbitrary distribution of singular values and only assumes that its singular vectors are generic. It is a vast generalization of the i.i.d. Gaussian design typically considered in the theoretical literature, and is motivated by the fact that real data often have a complex correlation structure so that methods relying on i.i.d. assumptions can be highly suboptimal. Building on the paradigm of spectrally-initialized iterative optimization, this paper proposes optimal spectral estimators and combines them with an approximate message passing (AMP) algorithm, establishing rigorous performance guarantees for these two algorithmic steps. Both the spectral initialization and the subsequent AMP meet existing conjectures on the fundamental limits to estimation -- the former on the optimal sample complexity for efficient weak recovery, and the latter on the optimal errors. Numerical experiments suggest the effectiveness of our methods and accuracy of our theory beyond orthogonally invariant data.

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### Feature salience - not task-informativeness - drives machine learning model explanations

Authors: Benedict Clark, Marta Oliveira, Rick Wilming, Stefan Haufe
Published: 2026-02-09
Categories: cs.LG
arXiv: https://arxiv.org/abs/2602.09238v3

Explainable AI (XAI) promises to provide insight into machine learning models' decision processes, where one goal is to identify failures such as shortcut learning. This promise relies on the field's assumption that input features marked as important by an XAI must contain information about the target variable. However, it is unclear whether informativeness is indeed the main driver of importance attribution in practice, or if other data properties such as statistical suppression, novelty at test-time, or high feature salience substantially contribute. To clarify this, we trained deep learning models on three variants of a binary image classification task, in which translucent watermarks are either absent, act as class-dependent confounds, or represent class-independent noise. Results for five popular attribution methods show substantially elevated relative importance in watermarked areas (RIW) for all models regardless of the training setting ($R^2 \geq .45$). By contrast, whether the presence of watermarks is class-dependent or not only has a marginal effect on RIW ($R^2 \leq .03$), despite a clear impact impact on model performance and generalisation ability. XAI methods show similar behaviour to model-agnostic edge detection filters and attribute substantially less importance to watermarks when bright image intensities are encoded by smaller instead of larger feature values. These results indicate that importance attribution is most strongly driven by the salience of image structures at test time rather than statistical associations learned by machine learning models. Previous studies demonstrating successful XAI application should be reevaluated with respect to a possibly spurious concurrency of feature salience and informativeness, and workflows using feature attribution methods as building blocks should be scrutinised.

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### RAPID: Risk of Attribute Prediction-Induced Disclosure in Synthetic Microdata

Authors: Matthias Templ, Oscar Thees, Roman Müller
Published: 2026-02-09
Categories: cs.LG, stat.AP, stat.ME
arXiv: https://arxiv.org/abs/2602.09235v1

Statistical data anonymization increasingly relies on fully synthetic microdata, for which classical identity disclosure measures are less informative than an adversary's ability to infer sensitive attributes from released data. We introduce RAPID (Risk of Attribute Prediction--Induced Disclosure), a disclosure risk measure that directly quantifies inferential vulnerability under a realistic attack model. An adversary trains a predictive model solely on the released synthetic data and applies it to real individuals' quasi-identifiers. For continuous sensitive attributes, RAPID reports the proportion of records whose predicted values fall within a specified relative error tolerance. For categorical attributes, we propose a baseline-normalized confidence score that measures how much more confident the attacker is about the true class than would be expected from class prevalence alone, and we summarize risk as the fraction of records exceeding a policy-defined threshold. This construction yields an interpretable, bounded risk metric that is robust to class imbalance, independent of any specific synthesizer, and applicable with arbitrary learning algorithms. We illustrate threshold calibration, uncertainty quantification, and comparative evaluation of synthetic data generators using simulations and real data. Our results show that RAPID provides a practical, attacker-realistic upper bound on attribute-inference disclosure risk that complements existing utility diagnostics and disclosure control frameworks.

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### Do Neural Networks Lose Plasticity in a Gradually Changing World?

Authors: Tianhui Liu, Lili Mou
Published: 2026-02-09
Categories: cs.LG, cs.AI
arXiv: https://arxiv.org/abs/2602.09234v2

Continual learning has become a trending topic in machine learning. Recent studies have discovered an interesting phenomenon called loss of plasticity, referring to neural networks gradually losing the ability to learn new tasks. However, existing plasticity research largely relies on benchmarks with abrupt task transitions, without examining whether the abruptness itself contributes to the observed plasticity loss. In this paper, we investigate the role of transition abruptness by simulating gradually changing environments through input/output interpolation and task sampling. We perform theoretical and empirical analysis, showing that the severity of plasticity loss is closely tied to the abruptness of task transitions, and can be substantially reduced when the environment changes gradually.

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### When Does Embedding Magnitude Matter? A Cross-Task Functional-Symmetry Framework

Authors: Xincan Feng, Taro Watanabe
Published: 2026-02-09
Categories: cs.LG, cs.IR
arXiv: https://arxiv.org/abs/2602.09229v3

Cosine similarity normalizes both sides; dot product normalizes neither. We propose a 2x2 framework that independently controls query-side and document-side normalization, exposing two intermediate variants (QNorm, DNorm) that have not been previously studied. On retrieval with four encoders, evaluated in-domain on MS MARCO and out-of-domain on BEIR, BRIGHT, and multi-hop QA, the unilateral variants outperform both cosine and dot product, with relative gains of up to +72% out-of-domain and +24% on downstream RAG. Cross-evaluation reveals the mechanism: document magnitude scales inference scores while query magnitude modulates training gradients, and the Fisher Information Matrix condition number predicts which side to normalize. We then classify tasks by functional symmetry, defined as whether the aggregate scoring procedure treats Q and C as interchangeable, and test whether the mechanism extends beyond retrieval. On five additional task families (semantic textual similarity, CLIP, knowledge graph completion, few-shot classification, recommender systems), the coarse prediction (cosine for symmetric, magnitude-preserving for asymmetric) holds in every case examined; the unilateral variants beat Cosine on recommendation, and on few-shot classification DNorm beats both Cosine and the standard Euclidean default of Prototypical Networks.

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### Barycentric alignment for instance-level comparison of neural representations

Authors: Shreya Saha, Zoe Wanying He, Meenakshi Khosla
Published: 2026-02-09
Categories: cs.LG
arXiv: https://arxiv.org/abs/2602.09225v1

Comparing representations across neural networks is challenging because representations admit symmetries, such as arbitrary reordering of units or rotations of activation space, that obscure underlying equivalence between models. We introduce a barycentric alignment framework that quotients out these nuisance symmetries to construct a universal embedding space across many models. Unlike existing similarity measures, which summarize relationships over entire stimulus sets, this framework enables similarity to be defined at the level of individual stimuli, revealing inputs that elicit convergent versus divergent representations across models. Using this instance-level notion of similarity, we identify systematic input properties that predict representational convergence versus divergence across vision and language model families. We also construct universal embedding spaces for brain representations across individuals and cortical regions, enabling instance-level comparison of representational agreement across stages of the human visual hierarchy. Finally, we apply the same barycentric alignment framework to purely unimodal vision and language models and find that post-hoc alignment into a shared space yields image text similarity scores that closely track human cross-modal judgments and approach the performance of contrastively trained vision-language models. This strikingly suggests that independently learned representations already share sufficient geometric structure for human-aligned cross-modal comparison. Together, these results show that resolving representational similarity at the level of individual stimuli reveals phenomena that cannot be detected by set-level comparison metrics.

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### MUZZLE: Adaptive Agentic Red-Teaming of Web Agents Against Indirect Prompt Injection Attacks

Authors: Georgios Syros, Evan Rose, Brian Grinstead, Christoph Kerschbaumer, William Robertson, Cristina Nita-Rotaru, Alina Oprea
Published: 2026-02-09
Categories: cs.CR, cs.AI
arXiv: https://arxiv.org/abs/2602.09222v2

Large language model (LLM) based web agents are increasingly deployed to automate complex online tasks by directly interacting with web sites and performing actions on users' behalf. While these agents offer powerful capabilities, their design exposes them to indirect prompt injection attacks embedded in untrusted web content, enabling adversaries to hijack agent behavior and violate user intent. Despite growing awareness of this threat, existing evaluations rely on fixed attack templates, manually selected injection surfaces, or narrowly scoped scenarios, limiting their ability to capture realistic, adaptive attacks encountered in practice. We present MUZZLE, an automated agentic framework for evaluating the security of web agents against indirect prompt injection attacks. MUZZLE utilizes the agent's trajectories to automatically identify high-salience injection surfaces, and adaptively generate context-aware malicious instructions that target violations of confidentiality, integrity, and availability. Unlike prior approaches, MUZZLE adapts its attack strategy based on the agent's observed execution trajectory and iteratively refines attacks using feedback from failed executions. We evaluate MUZZLE across diverse web applications, user tasks, and agent configurations, demonstrating its ability to automatically and adaptively assess the security of web agents with minimal human intervention. Our results show that MUZZLE effectively discovers 44 new attacks on 4 web applications with 10 adversarial objectives that violate confidentiality, availability, or privacy properties across different LLMs and agent scaffolds. MUZZLE also identifies novel attack strategies, including 3 cross-application prompt injection attacks and an agent-tailored phishing scenario.

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### A Lightweight Multi-View Approach to Short-Term Load Forecasting

Authors: Julien Guité-Vinet, Alexandre Blondin Massé, Éric Beaudry
Published: 2026-02-09
Categories: cs.LG, cs.AI
arXiv: https://arxiv.org/abs/2602.09220v1

Time series forecasting is a critical task across domains such as energy, finance, and meteorology, where accurate predictions enable informed decision-making. While transformer-based and large-parameter models have recently achieved state-of-the-art results, their complexity can lead to overfitting and unstable forecasts, especially when older data points become less relevant. In this paper, we propose a lightweight multi-view approach to short-term load forecasting that leverages single-value embeddings and a scaled time-range input to capture temporally relevant features efficiently. We introduce an embedding dropout mechanism to prevent over-reliance on specific features and enhance interpretability. Our method achieves competitive performance with significantly fewer parameters, demonstrating robustness across multiple datasets, including scenarios with noisy or sparse data, and provides insights into the contributions of individual features to the forecast.

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### Towards Human-AI Accessibility Mapping in India: VLM-Guided Annotations and POI-Centric Analysis in Chandigarh

Authors: Varchita Lalwani, Utkarsh Agarwal, Michael Saugstad, Manish Kumar, Jon E. Froehlich, Anupam Sobti
Published: 2026-02-09
Categories: cs.HC, cs.CV, cs.CY
arXiv: https://arxiv.org/abs/2602.09216v2

Project Sidewalk is a web-based platform that enables crowdsourcing accessibility of sidewalks at city-scale by virtually walking through city streets using Google Street View. The tool has been used in 40 cities across the world, including the US, Mexico, Chile, and Europe. In this paper, we describe adaptation efforts to enable deployment in Chandigarh, India, including modifying annotation types, provided examples, and integrating VLM-based mission guidance, which adapts instructions based on a street scene and metadata analysis. Our evaluation with 3 annotators indicates the utility of AI-mission guidance with an average score of 4.66. Using this adapted Project Sidewalk tool, we conduct a Points of Interest (POI)-centric accessibility analysis for three sectors in Chandigarh with very different land uses, residential, commercial and institutional covering about 40 km of sidewalks. Across 40 km of roads audited in three sectors and around 230 POIs, we identified 1,644 of 2,913 locations where infrastructure improvements could enhance accessibility.

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### VLM-UQBench: A Benchmark for Modality-Specific and Cross-Modality Uncertainties in Vision Language Models

Authors: Chenyu Wang, Tianle Chen, H. M. Sabbir Ahmad, Kayhan Batmanghelich, Wenchao Li
Published: 2026-02-09
Categories: cs.CV
arXiv: https://arxiv.org/abs/2602.09214v1

Uncertainty quantification (UQ) is vital for ensuring that vision-language models (VLMs) behave safely and reliably. A central challenge is to localize uncertainty to its source, determining whether it arises from the image, the text, or misalignment between the two. We introduce VLM-UQBench, a benchmark for modality-specific and cross-modal data uncertainty in VLMs, It consists of 600 real-world samples drawn from the VizWiz dataset, curated into clean, image-, text-, and cross-modal uncertainty subsets, and a scalable perturbation pipeline with 8 visual, 5 textual, and 3 cross-modal perturbations. We further propose two simple metrics that quantify the sensitivity of UQ scores to these perturbations and their correlation with hallucinations, and use them to evaluate a range of UQ methods across four VLMs and three datasets. Empirically, we find that: (i) existing UQ methods exhibit strong modality-specific specialization and substantial dependence on the underlying VLM, (ii) modality-specific uncertainty frequently co-occurs with hallucinations while current UQ scores provide only weak and inconsistent risk signals, and (iii) although UQ methods can rival reasoning-based chain-of-thought baselines on overt, group-level ambiguity, they largely fail to detect the subtle, instance-level ambiguity introduced by our perturbation pipeline. These results highlight a significant gap between current UQ practices and the fine-grained, modality-aware uncertainty required for reliable VLM deployment.

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### Wearable environmental sensing to forecast how legged systems will interact with upcoming terrain

Authors: Michael D. Murray, James Tung, Richard W. Nuckols
Published: 2026-02-09
Categories: cs.CV
arXiv: https://arxiv.org/abs/2602.09209v1

Computer-vision (CV) has been used for environmental classification during gait and is often used to inform control in assistive systems; however, the ability to predict how the foot will contact a changing environment is underexplored. We evaluated the feasibility of forecasting the anterior-posterior (AP) foot center-of-pressure (COP) and time-of-impact (TOI) prior to foot-strike on a level-ground to stair-ascent transition. Eight subjects wore an RGB-D camera on their right shank and instrumented insoles while performing the task of stepping onto the stairs. We trained a CNN-RNN to forecast the COP and TOI continuously within a 250ms window prior to foot-strike, termed the forecast horizon (FH). The COP mean-absolute-error (MAE) at 150, 100, and 50ms FH was 29.42mm, 26.82, and 23.72mm respectively. The TOI MAE was 21.14, 20.08, and 17.73ms for 150, 100, and 50ms respectively. While torso velocity had no effect on the error in either task, faster toe-swing speeds prior to foot-strike were found to improve the prediction accuracy in the COP case, however, was insignificant in the TOI case. Further, more anterior foot-strikes were found to reduce COP prediction accuracy but did not affect the TOI prediction accuracy. We also found that our lightweight model was capable at running at 60 FPS on either a consumer grade laptop or an edge computing device. This study demonstrates that forecasting COP and TOI from visual data was feasible using a lightweight model, which may have important implications for anticipatory control in assistive systems.

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### CausalGDP: Causality-Guided Diffusion Policies for Reinforcement Learning

Authors: Xiaofeng Xiao, Xiao Hu, Yang Ye, Xubo Yue
Published: 2026-02-09
Categories: cs.LG, cs.AI
arXiv: https://arxiv.org/abs/2602.09207v1

Reinforcement learning (RL) has achieved remarkable success in a wide range of sequential decision-making problems. Recent diffusion-based policies further improve RL by modeling complex, high-dimensional action distributions. However, existing diffusion policies primarily rely on statistical associations and fail to explicitly account for causal relationships among states, actions, and rewards, limiting their ability to identify which action components truly cause high returns. In this paper, we propose Causality-guided Diffusion Policy (CausalGDP), a unified framework that integrates causal reasoning into diffusion-based RL. CausalGDP first learns a base diffusion policy and an initial causal dynamical model from offline data, capturing causal dependencies among states, actions, and rewards. During real-time interaction, the causal information is continuously updated and incorporated as a guidance signal to steer the diffusion process toward actions that causally influence future states and rewards. By explicitly considering causality beyond association, CausalGDP focuses policy optimization on action components that genuinely drive performance improvements. Experimental results demonstrate that CausalGDP consistently achieves competitive or superior performance over state-of-the-art diffusion-based and offline RL methods, especially in complex, high-dimensional control tasks.

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### EExApp: GNN-Based Reinforcement Learning for Radio Unit Energy Optimization in 5G O-RAN

Authors: Jie Lu, Peihao Yan, Huacheng Zeng
Published: 2026-02-09
Categories: eess.SY, cs.LG
arXiv: https://arxiv.org/abs/2602.09206v2

With over 3.5 million 5G base stations deployed globally, their collective energy consumption (projected to exceed 131 TWh annually) raises significant concerns over both operational costs and environmental impacts. In this paper, we present EExAPP, a deep reinforcement learning (DRL)-based xApp for 5G Open Radio Access Network (O-RAN) that jointly optimizes radio unit (RU) sleep scheduling and distributed unit (DU) resource slicing. EExAPP uses a dual-actor-dual-critic Proximal Policy Optimization (PPO) architecture, with dedicated actor-critic pairs targeting energy efficiency and quality-of-service (QoS) compliance. A transformer-based encoder enables scalable handling of variable user equipment (UE) populations by encoding all-UE observations into fixed-dimensional representations. To coordinate the two optimization objectives, a bipartite Graph Attention Network (GAT) is used to modulate actor updates based on both critic outputs, enabling adaptive trade-offs between power savings and QoS. We have implemented EExAPP and deployed it on a real-world 5G O-RAN testbed with live traffic, commercial RU and smartphones. Extensive over-the-air experiments and ablation studies confirm that EExAPP significantly outperforms existing methods in reducing the energy consumption of RU while maintaining QoS. The source code is available at https://github.com/EExApp/EExApp.

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### Genocide by Algorithm in Gaza: Artificial Intelligence, Countervailing Responsibility, and the Corruption of Public Discourse

Authors: Branislav Radeljic
Published: 2026-02-09
Categories: cs.CY, cs.AI, cs.HC, eess.SY
arXiv: https://arxiv.org/abs/2602.09202v1

The accelerating militarization of artificial intelligence has transformed the ethics, politics, and governance of warfare. This article interrogates how AI-driven targeting systems function as epistemic infrastructures that classify, legitimize, and execute violence, using Israel's conduct in Gaza as a paradigmatic case. Through the lens of responsibility, the article examines three interrelated dimensions: (a) political responsibility, exploring how states exploit AI to accelerate warfare while evading accountability; (b) professional responsibility, addressing the complicity of technologists, engineers, and defense contractors in the weaponization of data; and (c) personal responsibility, probing the moral agency of individuals who participate in or resist algorithmic governance. This is complemented by an examination of the position and influence of those participating in public discourse, whose narratives often obscure or normalize AI-enabled violence. The Gaza case reveals AI not as a neutral instrument but as an active participant in the reproduction of colonial hierarchies and the normalization of atrocity. Ultimately, the paper calls for a reframing of technological agency and accountability in the age of automated warfare. It concludes that confronting algorithmic violence demands a democratization of AI ethics, one that resists technocratic fatalism and centers the lived realities of those most affected by high-tech militarism.

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### Fair Feature Importance Scores via Feature Occlusion and Permutation

Authors: Camille Little, Madeline Navarro, Santiago Segarra, Genevera Allen
Published: 2026-02-09
Categories: cs.LG, stat.ML
arXiv: https://arxiv.org/abs/2602.09196v1

As machine learning models increasingly impact society, their opaque nature poses challenges to trust and accountability, particularly in fairness contexts. Understanding how individual features influence model outcomes is crucial for building interpretable and equitable models. While feature importance metrics for accuracy are well-established, methods for assessing feature contributions to fairness remain underexplored. We propose two model-agnostic approaches to measure fair feature importance. First, we propose to compare model fairness before and after permuting feature values. This simple intervention-based approach decouples a feature and model predictions to measure its contribution to training. Second, we evaluate the fairness of models trained with and without a given feature. This occlusion-based score enjoys dramatic computational simplification via minipatch learning. Our empirical results reflect the simplicity and effectiveness of our proposed metrics for multiple predictive tasks. Both methods offer simple, scalable, and interpretable solutions to quantify the influence of features on fairness, providing new tools for responsible machine learning development.

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### ML-DCN: Masked Low-Rank Deep Crossing Network Towards Scalable Ads Click-through Rate Prediction at Pinterest

Authors: Jiacheng Li, Yixiong Meng, Yi wu, Yun Zhao, Sharare Zehtabian, Jiayin Jin, Degao Peng, Jinfeng Zhuang, Qifei Shen, Kungang Li
Published: 2026-02-09
Categories: cs.LG
arXiv: https://arxiv.org/abs/2602.09194v1

Deep learning recommendation systems rely on feature interaction modules to model complex user-item relationships across sparse categorical and dense features. In large-scale ad ranking, increasing model capacity is a promising path to improving both predictive performance and business outcomes, yet production serving budgets impose strict constraints on latency and FLOPs. This creates a central tension: we want interaction modules that both scale effectively with additional compute and remain compute-efficient at serving time. In this work, we study how to scale feature interaction modules under a fixed serving budget. We find that naively scaling DCNv2 and MaskNet, despite their widespread adoption in industry, yields rapidly diminishing offline gains in the Pinterest ads ranking system. To overcome aforementioned limitations, we propose ML-DCN, an interaction module that integrates an instance-conditioned mask into a low-rank crossing layer, enabling per-example selection and amplification of salient interaction directions while maintaining efficient computation. This novel architecture combines the strengths of DCNv2 and MaskNet, scales efficiently with increased compute, and achieves state-of-the-art performance. Experiments on a large internal Pinterest ads dataset show that ML-DCN achieves higher AUC than DCNv2, MaskNet, and recent scaling-oriented alternatives at matched FLOPs, and it scales more favorably overall as compute increases, exhibiting a stronger AUC-FLOPs trade-off. Finally, online A/B tests demonstrate statistically significant improvements in key ads metrics (including CTR and click-quality measures) and ML-DCN has been deployed in the production system with neutral serving cost.

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### Gradient Residual Connections

Authors: Yangchen Pan, Qizhen Ying, Philip Torr, Bo Liu
Published: 2026-02-09
Categories: cs.LG, cs.AI
arXiv: https://arxiv.org/abs/2602.09190v1

Existing work has linked properties of a function's gradient to the difficulty of function approximation. Motivated by these insights, we study how gradient information can be leveraged to improve neural network's ability to approximate high-frequency functions, and we propose a gradient-based residual connection as a complement to the standard identity skip connection used in residual networks. We provide simple theoretical intuition for why gradient information can help distinguish inputs and improve the approximation of functions with rapidly varying behaviour. On a synthetic regression task with a high-frequency sinusoidal ground truth, we show that conventional residual connections struggle to capture high-frequency patterns. In contrast, our gradient residual substantially improves approximation quality. We then introduce a convex combination of the standard and gradient residuals, allowing the network to flexibly control how strongly it relies on gradient information. After validating the design choices of our proposed method through an ablation study, we further validate our approach's utility on the single-image super-resolution task, where the underlying function may be high-frequency. Finally, on standard tasks such as image classification and segmentation, our method achieves performance comparable to standard residual networks, suggesting its broad utility.

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### AIDev: Studying AI Coding Agents on GitHub

Authors: Hao Li, Haoxiang Zhang, Ahmed E. Hassan
Published: 2026-02-09
Categories: cs.SE, cs.AI
arXiv: https://arxiv.org/abs/2602.09185v1

AI coding agents are rapidly transforming software engineering by performing tasks such as feature development, debugging, and testing. Despite their growing impact, the research community lacks a comprehensive dataset capturing how these agents are used in real-world projects. To address this gap, we introduce AIDev, a large-scale dataset focused on agent-authored pull requests (Agentic-PRs) in real-world GitHub repositories. AIDev aggregates 932,791 Agentic-PRs produced by five agents: OpenAI Codex, Devin, GitHub Copilot, Cursor, and Claude Code. These PRs span 116,211 repositories and involve 72,189 developers. In addition, AIDev includes a curated subset of 33,596 Agentic-PRs from 2,807 repositories with over 100 stars, providing further information such as comments, reviews, commits, and related issues. This dataset offers a foundation for future research on AI adoption, developer productivity, and human-AI collaboration in the new era of software engineering. > AI Agent, Agentic AI, Coding Agent, Agentic Coding, Agentic Software Engineering, Agentic Engineering

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### One RNG to Rule Them All: How Randomness Becomes an Attack Vector in Machine Learning

Authors: Kotekar Annapoorna Prabhu, Andrew Gan, Zahra Ghodsi
Published: 2026-02-09
Categories: cs.CR, cs.LG
arXiv: https://arxiv.org/abs/2602.09182v1

Machine learning relies on randomness as a fundamental component in various steps such as data sampling, data augmentation, weight initialization, and optimization. Most machine learning frameworks use pseudorandom number generators as the source of randomness. However, variations in design choices and implementations across different frameworks, software dependencies, and hardware backends along with the lack of statistical validation can lead to previously unexplored attack vectors on machine learning systems. Such attacks on randomness sources can be extremely covert, and have a history of exploitation in real-world systems. In this work, we examine the role of randomness in the machine learning development pipeline from an adversarial point of view, and analyze the implementations of PRNGs in major machine learning frameworks. We present RNGGuard to help machine learning engineers secure their systems with low effort. RNGGuard statically analyzes a target library's source code and identifies instances of random functions and modules that use them. At runtime, RNGGuard enforces secure execution of random functions by replacing insecure function calls with RNGGuard's implementations that meet security specifications. Our evaluations show that RNGGuard presents a practical approach to close existing gaps in securing randomness sources in machine learning systems.

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### Weighted Wasserstein Barycenter of Gaussian Processes for exotic Bayesian Optimization tasks

Authors: Antonio Candelieri, Francesco Archetti
Published: 2026-02-09
Categories: cs.LG
arXiv: https://arxiv.org/abs/2602.09181v1

Exploiting the analogy between Gaussian Distributions and Gaussian Processes' posterior, we present how the weighted Wasserstein Barycenter of Gaussian Processes (W2BGP) can be used to unify, under a common framework, different exotic Bayesian Optimization (BO) tasks. Specifically, collaborative/federated BO, (synchronous) batch BO, and multi-fidelity BO are considered in this paper. Our empirical analysis proves that each one of these tasks requires just an appropriate weighting schema for the W2BGP, while the entire framework remains untouched. Moreover, we demonstrate that the most well-known BO acquisition functions can be easily re-interpreted under the proposed framework and also enable a more computationally efficient way to deal with the computation of the Wasserstein Barycenter, compared with state-of-the-art methods from the Machine Learning literature. Finally, research perspectives branching from the proposed approach are presented.

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### $n$-Musketeers: Reinforcement Learning Shapes Collaboration Among Language Models

Authors: Ryozo Masukawa, Sanggeon Yun, Hyunwoo Oh, SuhgHeon Jeong, Raheeb Hassa, Hanning Chen, Wenjun Huang, Mahdi Imani, Pietro Mercati, Nathaniel D. Bastian, Mohsen Imani
Published: 2026-02-09
Categories: cs.LG, cs.AI
arXiv: https://arxiv.org/abs/2602.09173v1

Recent progress in reinforcement learning with verifiable rewards (RLVR) shows that small, specialized language models (SLMs) can exhibit structured reasoning without relying on large monolithic LLMs. We introduce soft hidden-state collaboration, where multiple heterogeneous frozen SLM experts are integrated through their internal representations via a trainable attention interface. Experiments on Reasoning Gym and GSM8K show that this latent integration is competitive with strong single-model RLVR baselines. Ablations further reveal a dual mechanism of expert utilization: for simpler arithmetic domains, performance gains can largely be explained by static expert preferences, whereas more challenging settings induce increasingly concentrated and structured expert attention over training, indicating emergent specialization in how the router connects to relevant experts. Overall, hidden-state collaboration provides a compact mechanism for leveraging frozen experts, while offering an observational window into expert utilization patterns and their evolution under RLVR.

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### Quantifying Epistemic Uncertainty in Diffusion Models

Authors: Aditi Gupta, Raphael A. Meyer, Yotam Yaniv, Elynn Chen, N. Benjamin Erichson
Published: 2026-02-09
Categories: stat.ML, cs.AI, cs.LG
arXiv: https://arxiv.org/abs/2602.09170v1

To ensure high quality outputs, it is important to quantify the epistemic uncertainty of diffusion models. Existing methods are often unreliable because they mix epistemic and aleatoric uncertainty. We introduce a method based on Fisher information that explicitly isolates epistemic variance, producing more reliable plausibility scores for generated data. To make this approach scalable, we propose FLARE (Fisher-Laplace Randomized Estimator), which approximates the Fisher information using a uniformly random subset of model parameters. Empirically, FLARE improves uncertainty estimation in synthetic time-series generation tasks, achieving more accurate and reliable filtering than other methods. Theoretically, we bound the convergence rate of our randomized approximation and provide analytic and empirical evidence that last-layer Laplace approximations are insufficient for this task.

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### Train Less, Infer Faster: Efficient Model Finetuning and Compression via Structured Sparsity

Authors: Jonathan Svirsky, Yehonathan Refael, Ofir Lindenbaum
Published: 2026-02-09
Categories: cs.LG
arXiv: https://arxiv.org/abs/2602.09169v1

Fully finetuning foundation language models (LMs) with billions of parameters is often impractical due to high computational costs, memory requirements, and the risk of overfitting. Although methods like low-rank adapters help address these challenges by adding small trainable modules to the frozen LM, they also increase memory usage and do not reduce inference latency. We uncover an intriguing phenomenon: sparsifying specific model rows and columns enables efficient task adaptation without requiring weight tuning. We propose a scheme for effective finetuning via sparsification using training stochastic gates, which requires minimal trainable parameters, reduces inference time, and removes 20--40\% of model parameters without significant accuracy loss. Empirical results show it outperforms recent finetuning baselines in efficiency and performance. Additionally, we provide theoretical guarantees for the convergence of this stochastic gating process, and show that our method admits a simpler and better-conditioned optimization landscape compared to LoRA. Our results highlight sparsity as a compelling mechanism for task-specific adaptation in LMs.

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### All-in-One Conditioning for Text-to-Image Synthesis

Authors: Hirunima Jayasekara, Chuong Huynh, Yixuan Ren, Christabel Acquaye, Abhinav Shrivastava
Published: 2026-02-09
Categories: cs.CV
arXiv: https://arxiv.org/abs/2602.09165v1

Accurate interpretation and visual representation of complex prompts involving multiple objects, attributes, and spatial relationships is a critical challenge in text-to-image synthesis. Despite recent advancements in generating photorealistic outputs, current models often struggle with maintaining semantic fidelity and structural coherence when processing intricate textual inputs. We propose a novel approach that grounds text-to-image synthesis within the framework of scene graph structures, aiming to enhance the compositional abilities of existing models. Eventhough, prior approaches have attempted to address this by using pre-defined layout maps derived from prompts, such rigid constraints often limit compositional flexibility and diversity. In contrast, we introduce a zero-shot, scene graph-based conditioning mechanism that generates soft visual guidance during inference. At the core of our method is the Attribute-Size-Quantity-Location (ASQL) Conditioner, which produces visual conditions via a lightweight language model and guides diffusion-based generation through inference-time optimization. This enables the model to maintain text-image alignment while supporting lightweight, coherent, and diverse image synthesis.

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### Faster Rates For Federated Variational Inequalities

Authors: Guanghui Wang, Satyen Kale
Published: 2026-02-09
Categories: cs.LG
arXiv: https://arxiv.org/abs/2602.09164v1

In this paper, we study federated optimization for solving stochastic variational inequalities (VIs), a problem that has attracted growing attention in recent years. Despite substantial progress, a significant gap remains between existing convergence rates and the state-of-the-art bounds known for federated convex optimization. In this work, we address this limitation by establishing a series of improved convergence rates. First, we show that, for general smooth and monotone variational inequalities, the classical Local Extra SGD algorithm admits tighter guarantees under a refined analysis. Next, we identify an inherent limitation of Local Extra SGD, which can lead to excessive client drift. Motivated by this observation, we propose a new algorithm, the Local Inexact Proximal Point Algorithm with Extra Step (LIPPAX), and show that it mitigates client drift and achieves improved guarantees in several regimes, including bounded Hessian, bounded operator, and low-variance settings. Finally, we extend our results to federated composite variational inequalities and establish improved convergence guarantees.

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### FlyAOC: Evaluating Agentic Ontology Curation of Drosophila Scientific Knowledge Bases

Authors: Xingjian Zhang, Sophia Moylan, Ziyang Xiong, Qiaozhu Mei, Yichen Luo, Jiaqi W. Ma
Published: 2026-02-09
Categories: cs.AI, cs.CL, cs.IR
arXiv: https://arxiv.org/abs/2602.09163v2

Scientific knowledge bases accelerate discovery by curating findings from primary literature into structured, queryable formats for both human researchers and emerging AI systems. Maintaining these resources requires expert curators to search papers, reconcile evidence across documents, and produce ontology-grounded annotations. Existing benchmarks usually evaluate isolated subtasks, such as named entity recognition or relation extraction, and therefore do not capture this end-to-end workflow. We present FlyAOC to evaluate AI agents on end-to-end agentic ontology curation from scientific literature. Given a gene symbol, a concise FlyBase gene description, access to a 16,898-paper corpus, and ontology resources, agents must search for evidence and recover as many curator-relevant structured annotations as possible. Outputs span standardized function terms, expression patterns, and historical synonyms linking decades of nomenclature. The benchmark includes 7,397 expert-curated annotations across 100 genes drawn from FlyBase, the Drosophila knowledge base. Across four baseline agent harnesses---memorization, fixed pipeline, single-agent, and multi-agent---FlyAOC is sensitive to harness design, model family, and tool-use reliability. These results reveal system-level failure modes that model-only evaluations do not capture. FlyAOC provides a reproducible testbed for retrieval-augmented scientific curation.

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### FlyAOC: Evaluating Agentic Ontology Curation of Drosophila Scientific Knowledge Bases

Authors: Xingjian Zhang, Sophia Moylan, Ziyang Xiong, Qiaozhu Mei, Yichen Luo, Jiaqi W. Ma
Published: 2026-02-09
Categories: cs.AI, cs.CL, cs.IR
arXiv: https://arxiv.org/abs/2602.09163v1

Scientific knowledge bases accelerate discovery by curating findings from primary literature into structured, queryable formats for both human researchers and emerging AI systems. Maintaining these resources requires expert curators to search relevant papers, reconcile evidence across documents, and produce ontology-grounded annotations - a workflow that existing benchmarks, focused on isolated subtasks like named entity recognition or relation extraction, do not capture. We present FlyBench to evaluate AI agents on end-to-end agentic ontology curation from scientific literature. Given only a gene symbol, agents must search and read from a corpus of 16,898 full-text papers to produce structured annotations: Gene Ontology terms describing function, expression patterns, and historical synonyms linking decades of nomenclature. The benchmark includes 7,397 expert-curated annotations across 100 genes drawn from FlyBase, the Drosophila (fruit fly) knowledge base. We evaluate four baseline agent architectures: memorization, fixed pipeline, single-agent, and multi-agent. We find that architectural choices significantly impact performance, with multi-agent designs outperforming simpler alternatives, yet scaling backbone models yields diminishing returns. All baselines leave substantial room for improvement. Our analysis surfaces several findings to guide future development; for example, agents primarily use retrieval to confirm parametric knowledge rather than discover new information. We hope FlyBench will drive progress on retrieval-augmented scientific reasoning, a capability with broad applications across scientific domains.

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### Boltzmann Reinforcement Learning for Noise resilience in Analog Ising Machines

Authors: Aditya Choudhary, Saaketh Desai, Prasad Iyer
Published: 2026-02-09
Categories: cs.LG, cond-mat.mtrl-sci
arXiv: https://arxiv.org/abs/2602.09162v1

Analog Ising machines (AIMs) have emerged as a promising paradigm for combinatorial optimization, utilizing physical dynamics to solve Ising problems with high energy efficiency. However, the performance of traditional optimization and sampling algorithms on these platforms is often limited by inherent measurement noise. We introduce BRAIN (Boltzmann Reinforcement for Analog Ising Networks), a distribution learning framework that utilizes variational reinforcement learning to approximate the Boltzmann distribution. By shifting from state-by-state sampling to aggregating information across multiple noisy measurements, BRAIN is resilient to Gaussian noise characteristic of AIMs. We evaluate BRAIN across diverse combinatorial topologies, including the Curie-Weiss and 2D nearest-neighbor Ising systems. We find that under realistic 3\% Gaussian measurement noise, BRAIN maintains 98\% ground state fidelity, whereas Markov Chain Monte Carlo (MCMC) methods degrade to 51\% fidelity. Furthermore, BRAIN reaches the MCMC-equivalent solution up to 192x faster under these conditions. BRAIN exhibits $\mathcal{O}(N^{1.55})$ scaling up to 65,536 spins and maintains robustness against severe measurement uncertainty up to 40\%. Beyond ground state optimization, BRAIN accurately captures thermodynamic phase transitions and metastable states, providing a scalable and noise-resilient method for utilizing analog computing architectures in complex optimizations.

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### Minimum Distance Summaries for Robust Neural Posterior Estimation

Authors: Sherman Khoo, Dennis Prangle, Song Liu, Mark Beaumont
Published: 2026-02-09
Categories: stat.ML, cs.LG
arXiv: https://arxiv.org/abs/2602.09161v2

Simulation-based inference (SBI) enables amortized Bayesian inference by first training a neural posterior estimator (NPE) on prior-simulator pairs, typically through low-dimensional summary statistics, which can then be cheaply reused for fast inference by querying it on new test observations. Because NPE is estimated under the training data distribution, it is susceptible to misspecification when observations deviate from the training distribution. Many robust SBI approaches address this by modifying NPE training or introducing error models, coupling robustness to the inference network and compromising amortization and modularity. We introduce minimum-distance summaries, a plug-in robust NPE method that adapts queried test-time summaries independently of the pretrained NPE. Leveraging the maximum mean discrepancy (MMD) as a distance between observed data and a summary-conditional predictive distribution, the adapted summary inherits strong robustness properties from the MMD. We demonstrate that the algorithm can be implemented efficiently with random Fourier feature approximations, yielding a lightweight, model-free test-time adaptation procedure. We provide theoretical guarantees for the robustness of our algorithm and empirically evaluate it on a range of synthetic and real-world tasks, demonstrating substantial robustness gains with minimal additional overhead.

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### CoMMa: Contribution-Aware Medical Multi-Agents for Decentralized Oncology Decision Support

Authors: Yichen Wu, Kailong Fan, Sangjoon Park, Yuhan Liu, Zhiyi Shi, Sekeun Kim, Dania Daye, Hana Farzaneh, Xiang Li, Raul Uppot, Yujin Oh, Quanzheng Li
Published: 2026-02-09
Categories: cs.AI, cs.MA
arXiv: https://arxiv.org/abs/2602.09159v2

Recent multi-agent frameworks have shown promise for oncology decision support, yet most assume centralized data access and rely on prompt-based assignment, limiting their applicability in privacy-sensitive clinical settings. We propose Contribution-Aware Medical Multi-Agents (CoMMa), a decentralized LLM-agent framework where specialists operate on partitioned clinical data streams. Unlike prior approaches that share inputs across agents, CoMMa enforces data decentralization to include stronger role specialization and further enhances this via agent-specific finetuning. To enable reliable and interpretable coordination, we introduce a contribution-aware aggregation mechanism that replaces stochastic, narrative-based reasoning with deterministic embedding projections to approximate each agent's marginal utility. This yields explicit credit assignment over agents, providing a stable and interpretable decision pathway aligned with clinical requirements. We evaluate CoMMa on multiple oncology benchmarks, including real-world multidisciplinary tumor board datasets from large academic hospitals in North America and East Asia, as well as public datasets, demonstrating strong performance and generalization across heterogeneous clinical settings.

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### CoMMa: Contribution-Aware Medical Multi-Agents From A Game-Theoretic Perspective

Authors: Yichen Wu, Yujin Oh, Sangjoon Park, Kailong Fan, Dania Daye, Hana Farzaneh, Xiang Li, Raul Uppot, Quanzheng Li
Published: 2026-02-09
Categories: cs.AI, cs.MA
arXiv: https://arxiv.org/abs/2602.09159v1

Recent multi-agent frameworks have broadened the ability to tackle oncology decision support tasks that require reasoning over dynamic, heterogeneous patient data. We propose Contribution-Aware Medical Multi-Agents (CoMMa), a decentralized LLM-agent framework in which specialists operate on partitioned evidence and coordinate through a game-theoretic objective for robust decision-making. In contrast to most agent architectures relying on stochastic narrative-based reasoning, CoMMa utilizes deterministic embedding projections to approximate contribution-aware credit assignment. This yields explicit evidence attribution by estimating each agent's marginal utility, producing interpretable and mathematically grounded decision pathways with improved stability. Evaluated on diverse oncology benchmarks, including a real-world multidisciplinary tumor board dataset, CoMMa achieves higher accuracy and more stable performance than data-centralized and role-based multi-agents baselines.

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### What do Geometric Hallucination Detection Metrics Actually Measure?

Authors: Eric Yeats, John Buckheit, Sarah Scullen, Brendan Kennedy, Loc Truong, Davis Brown, Bill Kay, Cliff Joslyn, Tegan Emerson, Michael J. Henry, John Emanuello, Henry Kvinge
Published: 2026-02-09
Categories: cs.LG, cs.AI
arXiv: https://arxiv.org/abs/2602.09158v1

Hallucination remains a barrier to deploying generative models in high-consequence applications. This is especially true in cases where external ground truth is not readily available to validate model outputs. This situation has motivated the study of geometric signals in the internal state of an LLM that are predictive of hallucination and require limited external knowledge. Given that there are a range of factors that can lead model output to be called a hallucination (e.g., irrelevance vs incoherence), in this paper we ask what specific properties of a hallucination these geometric statistics actually capture. To assess this, we generate a synthetic dataset which varies distinct properties of output associated with hallucination. This includes output correctness, confidence, relevance, coherence, and completeness. We find that different geometric statistics capture different types of hallucinations. Along the way we show that many existing geometric detection methods have substantial sensitivity to shifts in task domain (e.g., math questions vs. history questions). Motivated by this, we introduce a simple normalization method to mitigate the effect of domain shift on geometric statistics, leading to AUROC gains of +34 points in multi-domain settings.

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### Decoding Future Risk: Deep Learning Analysis of Tubular Adenoma Whole-Slide Images

Authors: Ahmed Rahu, Brian Shula, Brandon Combs, Aqsa Sultana, Surendra P. Singh, Vijayan K. Asari, Derrick Forchetti
Published: 2026-02-09
Categories: cs.CV, cs.LG
arXiv: https://arxiv.org/abs/2602.09155v1

Colorectal cancer (CRC) remains a significant cause of cancer-related mortality, despite the widespread implementation of prophylactic initiatives aimed at detecting and removing precancerous polyps. Although screening effectively reduces incidence, a notable portion of patients initially diagnosed with low-grade adenomatous polyps will still develop CRC later in life, even without the presence of known high-risk syndromes. Identifying which low-risk patients are at higher risk of progression is a critical unmet need for tailored surveillance and preventative therapeutic strategies. Traditional histological assessment of adenomas, while fundamental, may not fully capture subtle architectural or cytological features indicative of malignant potential. Advancements in digital pathology and machine learning provide an opportunity to analyze whole-slide images (WSIs) comprehensively and objectively. This study investigates whether machine learning algorithms, specifically convolutional neural networks (CNNs), can detect subtle histological features in WSIs of low-grade tubular adenomas that are predictive of a patient's long-term risk of developing colorectal cancer.

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### A Hybrid Deterministic Framework for Named Entity Extraction in Broadcast News Video

Authors: Andrea Filiberto Lucas, Dylan Seychell
Published: 2026-02-09
Categories: cs.CV, cs.AI, cs.MM
arXiv: https://arxiv.org/abs/2602.09154v1

The growing volume of video-based news content has heightened the need for transparent and reliable methods to extract on-screen information. Yet the variability of graphical layouts, typographic conventions, and platform-specific design patterns renders manual indexing impractical. This work presents a comprehensive framework for automatically detecting and extracting personal names from broadcast and social-media-native news videos. It introduces a curated and balanced corpus of annotated frames capturing the diversity of contemporary news graphics and proposes an interpretable, modular extraction pipeline designed to operate under deterministic and auditable conditions. The pipeline is evaluated against a contrasting class of generative multimodal methods, revealing a clear trade-off between deterministic auditability and stochastic inference. The underlying detector achieves 95.8% mAP@0.5, demonstrating operationally robust performance for graphical element localisation. While generative systems achieve marginally higher raw accuracy (F1: 84.18% vs 77.08%), they lack the transparent data lineage required for journalistic and analytical contexts. The proposed pipeline delivers balanced precision (79.9%) and recall (74.4%), avoids hallucination, and provides full traceability across each processing stage. Complementary user findings indicate that 59% of respondents report difficulty reading on-screen names in fast-paced broadcasts, underscoring the practical relevance of the task. The results establish a methodologically rigorous and interpretable baseline for hybrid multimodal information extraction in modern news media.

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### SceneSmith: Agentic Generation of Simulation-Ready Indoor Scenes

Authors: Nicholas Pfaff, Thomas Cohn, Sergey Zakharov, Rick Cory, Russ Tedrake
Published: 2026-02-09
Categories: cs.RO, cs.AI, cs.CV, cs.GR
arXiv: https://arxiv.org/abs/2602.09153v2

Simulation has become a key tool for training and evaluating home robots at scale, yet existing environments fail to capture the diversity and physical complexity of real indoor spaces. Current scene synthesis methods produce sparsely furnished rooms that lack the dense clutter, articulated furniture, and physical properties essential for robotic manipulation. We introduce SceneSmith, a hierarchical agentic framework that generates simulation-ready indoor environments from natural language prompts. SceneSmith constructs scenes through successive stages$\unicode{x2013}$from architectural layout to furniture placement to small object population$\unicode{x2013}$each implemented as an interaction among VLM agents: designer, critic, and orchestrator. The framework tightly integrates asset generation through text-to-3D synthesis for static objects, dataset retrieval for articulated objects, and physical property estimation. SceneSmith generates 3-6x more objects than prior methods, with <2% inter-object collisions and 96% of objects remaining stable under physics simulation. In a user study with 205 participants, it achieves 92% average realism and 91% average prompt faithfulness win rates against baselines. We further demonstrate that these environments can be used in an end-to-end pipeline for automatic robot policy evaluation.

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### Overview of PAN 2026: Voight-Kampff Generative AI Detection, Text Watermarking, Multi-Author Writing Style Analysis, Generative Plagiarism Detection, and Reasoning Trajectory Detection

Authors: Janek Bevendorff, Maik Fröbe, André Greiner-Petter, Andreas Jakoby, Maximilian Mayerl, Preslav Nakov, Henry Plutz, Martin Potthast, Benno Stein, Minh Ngoc Ta, Yuxia Wang, Eva Zangerle
Published: 2026-02-09
Categories: cs.CL
arXiv: https://arxiv.org/abs/2602.09147v1

The goal of the PAN workshop is to advance computational stylometry and text forensics via objective and reproducible evaluation. In 2026, we run the following five tasks: (1) Voight-Kampff Generative AI Detection, particularly in mixed and obfuscated authorship scenarios, (2) Text Watermarking, a new task that aims to find new and benchmark the robustness of existing text watermarking schemes, (3) Multi-author Writing Style Analysis, a continued task that aims to find positions of authorship change, (4) Generative Plagiarism Detection, a continued task that targets source retrieval and text alignment between generated text and source documents, and (5) Reasoning Trajectory Detection, a new task that deals with source detection and safety detection of LLM-generated or human-written reasoning trajectories. As in previous years, PAN invites software submissions as easy-to-reproduce Docker containers for most of the tasks. Since PAN 2012, more than 1,100 submissions have been made this way via the TIRA experimentation platform.

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### SemanticMoments: Training-Free Motion Similarity via Third Moment Features

Authors: Saar Huberman, Kfir Goldberg, Or Patashnik, Sagie Benaim, Ron Mokady
Published: 2026-02-09
Categories: cs.CV
arXiv: https://arxiv.org/abs/2602.09146v1

Retrieving videos based on semantic motion is a fundamental, yet unsolved, problem. Existing video representation approaches overly rely on static appearance and scene context rather than motion dynamics, a bias inherited from their training data and objectives. Conversely, traditional motion-centric inputs like optical flow lack the semantic grounding needed to understand high-level motion. To demonstrate this inherent bias, we introduce the SimMotion benchmarks, combining controlled synthetic data with a new human-annotated real-world dataset. We show that existing models perform poorly on these benchmarks, often failing to disentangle motion from appearance. To address this gap, we propose SemanticMoments, a simple, training-free method that computes temporal statistics (specifically, higher-order moments) over features from pre-trained semantic models. Across our benchmarks, SemanticMoments consistently outperforms existing RGB, flow, and text-supervised methods. This demonstrates that temporal statistics in a semantic feature space provide a scalable and perceptually grounded foundation for motion-centric video understanding.

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### PABU: Progress-Aware Belief Update for Efficient LLM Agents

Authors: Haitao Jiang, Lin Ge, Hengrui Cai, Rui Song
Published: 2026-02-09
Categories: cs.AI, cs.CL
arXiv: https://arxiv.org/abs/2602.09138v1

Large Language Model (LLM) agents commonly condition actions on full action-observation histories, which introduce task-irrelevant information that easily leads to redundant actions and higher inference cost. We propose Progress-Aware Belief Update (PABU), a belief-state framework that compactly represents an agent's state by explicitly modeling task progress and selectively retaining past actions and observations. At each step, the agent predicts its relative progress since the previous round and decides whether the newly encountered interaction should be stored, conditioning future decisions only on the retained subset. Across eight environments in the AgentGym benchmark, and using identical training trajectories, PABU achieves an 81.0% task completion rate, outperforming previous State of the art (SoTA) models with full-history belief by 23.9%. Additionally, PABU's progress-oriented action selection improves efficiency, reducing the average number of interaction steps to 9.5, corresponding to a 26.9% reduction. Ablation studies show that both explicit progress prediction and selective retention are necessary for robust belief learning and performance gains.

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### UniComp: A Unified Evaluation of Large Language Model Compression via Pruning, Quantization, and Distillation

Authors: Jonathan von Rad, Yong Cao, Andreas Geiger
Published: 2026-02-09
Categories: cs.LG
arXiv: https://arxiv.org/abs/2602.09130v6

Model compression is increasingly essential for deploying large language models (LLMs), yet existing comparative studies largely focus on pruning and quantization evaluated primarily on knowledge-centric benchmarks. Thus, we introduce UniComp, a unified evaluation framework for comparing pruning, quantization, and knowledge distillation. UniComp evaluates compressed models along three dimensions: performance, reliability, and efficiency, using a diverse set of capability- and safety-oriented benchmarks together with a hardware-aware efficiency analysis. Through evaluation of seven compression techniques across over 40 datasets, we observe (i) a consistent knowledge bias, where factual recall is largely preserved while multi-step reasoning, multilingual, and instruction-following capabilities degrade; (ii) a deployment-critical performance-reliability decoupling, where retained performance does not indicate preserved safety, fairness and privacy; and (iii) that task-specific calibration can yield up to 50% relative improvement in reasoning performance in pruned models.

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### UniComp: A Unified Evaluation of Large Language Model Compression via Pruning, Quantization and Distillation

Authors: Jonathan von Rad, Yong Cao, Andreas Geiger
Published: 2026-02-09
Categories: cs.LG
arXiv: https://arxiv.org/abs/2602.09130v5

Model compression is increasingly essential for deploying large language models (LLMs), yet existing comparative studies largely focus on pruning and quantization evaluated primarily on knowledge-centric benchmarks. Thus, we introduce UniComp, a unified evaluation framework for comparing pruning, quantization, and knowledge distillation. UniComp evaluates compressed models along three dimensions: performance, reliability, and efficiency, using a diverse set of capability- and safety-oriented benchmarks together with a hardware-aware efficiency analysis. Through evaluation of six compression techniques across 40 datasets, we observe (i) a consistent knowledge bias, where factual recall is largely preserved while multi-step reasoning, multilingual, and instruction-following capabilities degrade; (ii) a decoupling between performance and reliability, indicating that retained performance does not consistently imply preserved reliability; and (iii) that task-specific calibration can yield up to 50% relative improvement of reasoning performance in pruned models.

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### Counterfactual Maps: What They Are and How to Find Them

Authors: Awa Khouna, Julien Ferry, Thibaut Vidal
Published: 2026-02-09
Categories: cs.LG
arXiv: https://arxiv.org/abs/2602.09128v2

Counterfactual explanations are a central tool in interpretable machine learning, yet computing them exactly for complex models remains challenging. For tree ensembles, predictions are piecewise constant over a large collection of axis-aligned hyperrectangles, implying that an optimal counterfactual for a point corresponds to its projection onto the nearest rectangle with an alternative label under a chosen metric. Existing methods largely overlook this geometric structure, relying either on heuristics with no optimality guarantees or on mixed-integer programming formulations that do not scale to interactive use. In this work, we revisit counterfactual generation through the lens of nearest-region search and introduce counterfactual maps, a global representation of recourse for tree ensembles. Leveraging the fact that any tree ensemble can be compressed into an equivalent partition of labeled hyperrectangles, we cast counterfactual search as the problem of identifying the generalized Voronoi cell associated with the nearest rectangle of an alternative label. This leads to an exact, amortized algorithm based on volumetric k-dimensional (KD) trees, which performs branch-and-bound nearest-region queries with explicit optimality certificates and sublinear average query time after a one-time preprocessing phase. Our experimental analyses on several real datasets drawn from high-stakes application domains show that this approach delivers globally optimal counterfactual explanations with millisecond-level latency, achieving query times that are orders of magnitude faster than existing exact, cold-start optimization methods.

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### Epistemic Throughput: Fundamental Limits of Attention-Constrained Inference

Authors: Lei You
Published: 2026-02-09
Categories: cs.LG, cs.IT
arXiv: https://arxiv.org/abs/2602.09127v2

Recent generative and tool-using AI systems can surface a large volume of candidates at low marginal cost, yet only a small fraction can be checked carefully. This creates a decoder-side bottleneck: downstream decision-makers must form reliable posteriors from many public records under scarce attention. We formalize this regime via Attention-Constrained Inference (ACI), in which a cheap screening stage processes $K$ records and an expensive verification stage can follow up on at most $B$ of them. Under Bayes log-loss, we study the maximum achievable reduction in posterior uncertainty per window, which we call \emph{epistemic throughput}. Our main result is a ``JaKoB'' scaling law showing that epistemic throughput has a baseline term that grows linearly with verification and prevalence, and an additional \emph{information-leverage} term that scales as $\sqrt{JKB}$, where $J$ summarizes screening quality. Thus, expanding cheap screening can nonlinearly amplify scarce verification, even when informative records are rare. We further show that this scaling is tight in a weak-screening limit, and that in the sparse-verification regime ($B \ll K$), substantial leverage requires heavy-tailed score distributions; for light-tailed scores the amplification is only logarithmic.

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### Uncertainty-Aware Multimodal Emotion Recognition through Dirichlet Parameterization

Authors: Rémi Grzeczkowicz, Eric Soriano, Ali Janati, Miyu Zhang, Gerard Comas-Quiles, Victor Carballo Araruna, Aneesh Jonelagadda
Published: 2026-02-09
Categories: cs.AI
arXiv: https://arxiv.org/abs/2602.09121v1

In this work, we present a lightweight and privacy-preserving Multimodal Emotion Recognition (MER) framework designed for deployment on edge devices. To demonstrate framework's versatility, our implementation uses three modalities - speech, text and facial imagery. However, the system is fully modular, and can be extended to support other modalities or tasks. Each modality is processed through a dedicated backbone optimized for inference efficiency: Emotion2Vec for speech, a ResNet-based model for facial expressions, and DistilRoBERTa for text. To reconcile uncertainty across modalities, we introduce a model- and task-agnostic fusion mechanism grounded in Dempster-Shafer theory and Dirichlet evidence. Operating directly on model logits, this approach captures predictive uncertainty without requiring additional training or joint distribution estimation, making it broadly applicable beyond emotion recognition. Validation on five benchmark datasets (eNTERFACE05, MEAD, MELD, RAVDESS and CREMA-D) show that our method achieves competitive accuracy while remaining computationally efficient and robust to ambiguous or missing inputs. Overall, the proposed framework emphasizes modularity, scalability, and real-world feasibility, paving the way toward uncertainty-aware multimodal systems for healthcare, human-computer interaction, and other emotion-informed applications.

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### SpinCastML an Open Decision-Making Application for Inverse Design of Electrospinning Manufacturing: A Machine Learning, Optimal Sampling and Inverse Monte Carlo Approach

Authors: Elisa Roldan, Tasneem Sabir
Published: 2026-02-09
Categories: cs.LG
arXiv: https://arxiv.org/abs/2602.09120v2

Electrospinning is a powerful technique for producing micro to nanoscale fibers with application specific architectures. Small variations in solution or operating conditions can shift the jet regime, generating non Gaussian fiber diameter distributions. Despite substantial progress, no existing framework enables inverse design toward desired fiber outcomes while integrating polymer solvent chemical constraints or predicting full distributions. SpinCastML is an open source, distribution aware, chemically informed machine learning and Inverse Monte Carlo (IMC) software for inverse electrospinning design. Built on a rigorously curated dataset of 68,480 fiber diameters from 1,778 datasets across 16 polymers, SpinCastML integrates three structured sampling methods, a suite of 11 high-performance learners, and chemistry aware constraints to predict not only mean diameter but the entire distribution. Cubist model with a polymer balanced Sobol D optimal sampling provides the highest global performance (R2 > 0.92). IMC accurately captures the fiber distributions, achieving R2 > 0.90 and <1% error between predicted and experimental success rates. The IMC engine supports both retrospective analysis and forward-looking inverse design, generating physically and chemically feasible polymer solvent parameter combinations with quantified success probabilities for user-defined targets. SpinCastML reframes electrospinning from trial and error to a reproducible, data driven design process. As an open source executable, it enables laboratories to analyze their own datasets and co create an expanding community software. SpinCastML reduces experimental waste, accelerates discovery, and democratizes access to advanced modeling, establishing distribution aware inverse design as a new standard for sustainable nanofiber manufacturing across biomedical, filtration, and energy applications.

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