Artificial Intelligence
CRAFT: Clustering Rubrics to Diagnose Weak LLM Capabilities and Generate Targeted Fine-Tuning Data
Vipul Gupta, Zihao Wang, Razvan-Gabriel Dumitru et al. (+3 more)
Evaluations should do more than measure a models current performance. They should tell us what to fix for the next model iteration and provide a way to generate targeted post training data. Most evaluation pipelines identify weak examples, topics, or categories, but they leave the underlying capability failure implicit: they say where a model fails, not why. We introduce CRAFT, a method that converts any rubric based evaluation
dataset into a model specific diagnosis of weak capabilities. CRAFT treats each grading criterion as a capability probe: it extracts a capability description from every
prompt rubric pair, clusters these descriptions into a hierarchical capability tree, scores the target model at every node, and selects low performing nodes dynamically across tree levels, at the granularity where each failure is clearest. The selected weak capabilities then direct the generation of targeted supervised finetuning data. Holding the data generation, finetuning, and evaluation setup fixed, we compare CRAFT against
prompt level EvalTree clustering and untargeted random generation on four open source models, two professional domains (finance and legal), and 13 held out
benchmarks disjoint from the diagnostic data. CRAFT achieves the strongest finance domain average for all four models under repeated temperature decoding; on legal domain, it is strongest for three of four models and remains within the decoding variance bands of the best baseline on the fourth. Diagnosing weaknesses at the level of rubric criteria, rather than
prompts or categories, thus yields both a sharper picture of what a model cannot do and measurably better models after finetuning on that diagnosis.
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All authors Vipul Gupta, Zihao Wang, Razvan-Gabriel Dumitru, MohammadHossein Rezaei, Aakash Sabharwal, Yunzhong He
Artificial Intelligence
Harmonizing AI Safety Thresholds
Wilber Sean Anterola, Matthew Ball, Luis F. Lafuerza et al. (+1 more)
Frontier AI companies have published capability thresholds that differ substantially, making it difficult for third parties to verify whether a threshold has been crossed or to compare requirements across companies. Moreover, without common minimum thresholds, risk mitigation may be inconsistent, creating a potential race to the bottom in safety standards. We develop a methodology for deriving harmonized thresholds across three risk domains. For misuse risks (cyber and biological), we take expected harm as the key primitive and use an explicit risk-modeling approach that accounts for risk channels and model release conditions. For automated AI R&D, we base our proposed threshold on the observed rate of AI progress rather than expected harm. Our analysis expands upon prior work and highlights existing empirical gaps and limitations.
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All authors Wilber Sean Anterola, Matthew Ball, Luis F. Lafuerza, Markov Grey
Artificial Intelligence · Cross-listed from cs.LG
Understanding Reasoning from Pretraining to Post-Training
Jingyan Shen, Ang Li, Salman Rahman et al. (+4 more)
Reinforcement learning (RL) has become central to improving large
language models (LLMs) on complex
reasoning tasks, yet RL post-training is largely studied in isolation from the pretraining that precedes it. As a result, two basic questions remain open: (1) how do pretraining choices (model size, data) shape the returns to RL compute, and (2) what does RL actually do to the model? These questions are difficult to study in the standard LLM setting: pretraining corpora are vast and uncontrolled, making it hard to attribute behaviors to pretraining versus RL, and systematic compute sweeps across both stages are prohibitively expensive. To address these challenges, we use chess as a controlled testbed for studying
reasoning across the full pretraining-to-post-training pipeline. We follow the standard LLM training pipeline by pretraining
language models from 5M to 1B parameters on human chess games, supervised fine-tuning on synthetic
reasoning traces, and running RL on chess puzzles with verifiable rewards. Using this framework, we find that the post-RL performance at given RL compute level is well-predicted from the pretraining loss, and slope of the RL reward curves improves approximately linearly with the pretraining
tokens. Beyond scaling, we find that RL does not simply sharpen the SFT policy: on easy puzzles it amplifies correct moves the SFT policy already preferred, while on hard puzzles it surfaces correct moves that were nearly absent under SFT. We further test whether our findings transfer beyond chess by training a 1B
language model on math-domain text, where the same predictive pattern emerges: longer-pretrained checkpoints reach higher post-RL performance and improve faster under RL. In sum, we provide a quantitative account of the pretraining-to-RL interface and a controlled testbed for studying the science of
reasoning across the full pretraining-to-post-training pipeline.
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All authors Jingyan Shen, Ang Li, Salman Rahman, Yifan Sun, Micah Goldblum, Matus Telgarsky, Pavel Izmailov
Artificial Intelligence · Cross-listed from cs.LG
DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning
Hanyang Chen, Anirudh Satheesh, Longchao Da et al. (+1 more)
Transferring policies across domains poses a vital challenge in reinforcement learning, due to the dynamics mismatch between the source and target domains. In this paper, we consider the setting of online dynamics adaptation, where policies are trained in the source domain with sufficient data, while only limited interactions with the target domain are allowed. There are a few existing works that address the dynamics mismatch by employing domain classifiers, value-guided data filtering, or representation learning. Instead, we study the domain adaptation problem from a generative modeling perspective. Specifically, we introduce DADiff, a
diffusion-based framework that leverages the discrepancy between source and target domain generative trajectories in the generation process of the next state to estimate the dynamics mismatch. Both reward modification and data selection variants are developed to adapt the policy to the target domain. We also provide a theoretical analysis to show that the performance difference of a given policy between the two domains is bounded by the generative trajectory deviation. More discussions on the applicability of the variants and the connection between our theoretical analysis and the prior work are further provided. We conduct extensive experiments in environments with various shifts to validate the effectiveness of our method. The results demonstrate that our method provides superior performance compared to existing approaches, effectively addressing the dynamics mismatch. We provide the code of our method at https://github.com/hanyang-chen/DADiff-release
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All authors Hanyang Chen, Anirudh Satheesh, Longchao Da, Hua Wei
Artificial Intelligence · Cross-listed from cs.CV
HCIG: A Hierarchical Cross-Modal Incongruity Graph Network for Multimodal Sarcasm and Cyberbullying Detection
Bhavana Verma, Priyanka Meel and Dinesh Kumar Vishwakarma
Multimodal sarcasm and cyberbullying detection remain challenging because the intended meaning often emerges from incongruity between textual and visual information rather than from either modality alone. Existing
multimodal approaches primarily rely on feature fusion or cross-modal
attention, which may not effectively capture hierarchical semantic inconsistencies across different levels of representation. To address this limitation, this paper proposes HCIG (Hierarchical Cross-modal Incongruity Graph Network), a novel framework that models cross-modal incongruity at
token, phrase, and global levels using graph
attention networks and adaptively integrates these representations through a learned hierarchical
attention mechanism. As a complementary architecture, we also introduce GCCN (Graph-based Cross-modal Contradiction Network), which performs graph-based
reasoning using contradiction-aware pooling for efficient
multimodal interaction learning. The proposed models are evaluated on the MMSD sarcasm
benchmark and the MultiBully cyberbullying
dataset, together with comprehensive ablation studies and cross-task transfer experiments. Experimental results demonstrate that HCIG achieves the best performance on MMSD with 85.74% accuracy and 85.29% macro-F1, while GCCN attains the highest macro-F1 (68.66%) on MultiBully and HCIG achieves the highest accuracy (69.62%) and bullying-class F1 (74.90%). The findings demonstrate that hierarchical multi-granularity incongruity modeling provides more effective
multimodal reasoning than conventional fusion strategies, offering a robust framework for sarcasm and cyberbullying detection in social media.
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Artificial Intelligence · Cross-listed from cs.DC
JoyNexus: Service-Oriented Multi-Tenant Post-Training for VLA Models
Haoran Sun, Wentao Zhang, Junyang Hua et al. (+18 more)
The post-training of
Vision-
Language-Action (VLA) models is essential due to the diversity of simulators,
robot embodiments, and task objectives. Existing compute services, whether offered as direct accelerator rental or batch-workload submission, typically allocate an exclusive set of GPU and CPU resources to a single tenant. While this paradigm maximizes client flexibility, it burdens users with infrastructure adaptation, and the fixed card-hour accounting model renders short or bursty workloads both expensive for tenants and inefficient for the service provider. To address these challenges, we present JoyNexus, a unified service for multi-tenant VLA supervised fine-tuning, reinforcement learning, and evaluation. JoyNexus decouples the Training Model Service, Inference Model Service, and Environment Service, each accessed through APIs and backed by resident shared base models with tenant-specific slots. Tenants can directly invoke high-level semantic APIs for training, rollout, and evaluation, or compose custom
algorithms using lower-level APIs and their assigned endpoints. Multiple tenants submit workloads concurrently; their action modules, optimizers, rollout records, and policy versions remain isolated, and the service is scheduled by the global Training Queue and Inference Queue. To further improve multi-tenant training efficiency, JoyNexus introduces group batching for heterogeneous VLA data schemas that share a compatible model-facing prefix, enabling a single shared backbone forward pass over grouped samples. Finally, we evaluate JoyNexus through workload simulation and a group-batching pipeline in a realistic
embodied scenario. Results show that, compared with isolated single-tenant execution, JoyNexus reduces aggregate GPU time and improves service utilization via cross-tenant scheduling on shared resources.
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All authors Haoran Sun, Wentao Zhang, Junyang Hua, Hedan Yang, Yongjian Guo, Yifei Zhang, Xiaolong Xiang, Mingxi Luo, Jing Long, Chen Zhao, Chen Zhou, Wanting Xu, Qiming Yang, Hui Zhang, Song Wang, Xiaodong Bai, Shuai Di, Xu Chu, Xiaotie Deng, Yicheng Gong, Junwu Xiong
Artificial Intelligence · Cross-listed from cs.NI
LLM-Powered Agentic AI for 5G/6G Networks: A Tutorial and Survey on Architectures, Protocols, and Standardization
Mazene Ameur, Abdelkader Mekrache, Bouziane Brik et al. (+1 more)
Agentic Artificial Intelligence (AI), enabled by Large
Language Models, marks a shift from rule-based automation toward autonomous, goal-driven control of Next-Generation Networks (NGNs). Existing surveys treat the two domains in isolation, leaving protocol integration, evaluation, and standardization
alignment underexplored. To address this gap, a two-part tutorial-and-survey is presented. Part I formalises the control, management, and AI-native planes of 5G and 6G. It then covers the foundations of agentic systems:
reasoning, planning, tool use, multi-
agent coordination, and evaluation. Part II maps agentic capabilities onto 5G/6G control surfaces, standardization, and major 6G initiatives. Finally, it identifies open challenges shaping autonomous telecommunications.
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All authors Mazene Ameur, Abdelkader Mekrache, Bouziane Brik, Adlen Ksentini
Artificial Intelligence · Cross-listed from cs.CV
Spatial Normalization for Cross-Domain Retinal Layer Segmentation in Optical Coherence Tomography
Iker Moran-Cavero, Monica Hernandez, Elvira Mayordomo et al. (+4 more)
Retinal layer
segmentation in Optical Coherence
Tomography (OCT) is a fundamental step for extracting quantitative biomarkers of retinal structure. Indeed, there is a growing interest in the analysis of OCTs in the context of neurodegenerative diseases. However,
segmentation remains challenging due to speckle noise, shadowing artifacts, low contrast between adjacent layers, anatomical variability across subjects, and domain shifts arising from different acquisition protocols and clinical populations. While deep learning methods have achieved remarkable performance, their robustness and generalization across heterogeneous
datasets remain limited. In this work, we investigate the role of spatial normalization as a preprocessing strategy to mitigate
geometric domain shifts and improve the consistency of retinal layer
segmentation. Inspired by standard practices in neuroimaging, we introduce a fovea-centered normalization framework that aligns OCT volumes into a common anatomical reference. We perform a comprehensive evaluation of state-of-the-art deep learning architectures. To provide a comprehensive assessment of
segmentation quality, we combine conventional overlap-based metrics at B-scan level with
topology-aware metrics at A-scan level and thickness-based measures at the en-face level. In cases where a ground truth is not available, we propose
topology violation quantitative metrics that do not require ground truth annotations and a thickness-based qualitative assessment that captures structural consistency and clinically relevant patterns at the en-face level. The results demonstrate the importance of spatial normalization in OCT
segmentation pipelines toward the development of robust and clinically meaningful retinal analysis tools, enabling reliable biomarker extraction and downstream computational analysis in neurodegenerative research.
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All authors Iker Moran-Cavero, Monica Hernandez, Elvira Mayordomo, Naiara Artiaga, Beatriz Pardiñas, Beatriz Cordon, Elena Garcia-Martin
Artificial Intelligence · Cross-listed from cs.LG
When Model Merging Rivals Joint Multi-Task Reinforcement Learning: A Task-Vector Geometry Analysis
S. Aaron McClendon
Model merging is promoted as a substitute for joint multi-task training, yet in the reinforcement-learning setting this substitution is essentially never tested against the baseline it claims to replace: methods merge independently released
agents precisely because a joint model is unavailable. We build the missing comparison. Training difficulty-1 and difficulty-2 Qwen3-8B specialists on the AppWorld
agent benchmark with LOOP, we merge them (TIES, RAM+) and pit the result against a jointly trained model on the same data. On task-goal completion, merging matches joint RL – and every merge variant is statistically indistinguishable. To explain why merge method does not matter here, we measure the geometry of the specialists' task vectors, which carries no task-sampling noise: they are near-orthogonal (cosine 0.06 - 0.10) despite ~65% support overlap, a small, shared direction that grows over training and that we calibrate against a random-init floor and a same-run ceiling to confirm it reflects learning, not the low-rank parameterization. Because direction and support are decoupled, support and sign-based merging (RAM, TIES) collapse to near-uniform averaging. We release all code and statistics.
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Artificial Intelligence · Cross-listed from cs.CL
Frontier AI performance across the business disciplines: a case-grounded benchmark of knowledge work and analytical reasoning
Ajay Patel, Kartik Hosanagar, Ramayya Krishnan et al. (+2 more)
Large
language models (LLMs) are improving rapidly as reflected in
benchmark scores, yet these AI
benchmarks largely test capabilities such as factual recall, narrow question answering, mathematical problem-solving, and coding and agentic tool-use. What remains poorly measured is AI progress on the analytical knowledge work white-collar professionals perform daily, including synthesizing complex information, exercising judgment under uncertainty and incomplete information, applying strategic and
adversarial thinking in multi-stakeholder settings, weighing trade-offs, and producing defensible, structured analyses. This gap is even more pronounced for subjective components of such work, where success can be challenging to define. The "case method" form of education practiced by top business schools provides a natural foundation for addressing this measurement gap, and we construct BusinessCaseBench, a
benchmark spanning hundreds of questions drawn from business cases across eighteen disciplines, each paired with a grading rubric derived from the expert-written instructor case solution. On BusinessCaseBench, frontier AI models already score highly against instructor rubrics, and capability within one model family improves substantially over two years. These results provide strong evidence that AI performance on this class of work is already high and rapidly improving, with implications for business schools, where case pedagogy trains undergraduates and MBAs in this kind of analytical
reasoning, and for entry-level professional roles, where such skills have historically anchored early-career work.
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All authors Ajay Patel, Kartik Hosanagar, Ramayya Krishnan, Chris Callison-Burch, Karim Lakhani
Artificial Intelligence · Cross-listed from cs.CL
Loop the Loopies!
Zitian Gao, Yilong Chen, Yihao Xiao et al. (+4 more)
We present Loopie, the most powerful open-source looped
Transformer to date. The Loopie series consists of two Mixture-of-Experts (MoE) models: a 20B-parameter model with 2B active parameters and a 6B parameter model with 0.6B active parameters. Looped
Transformers have long faced a challenge: given an N-fold pre-training compute, increasing the parameter count by a factor of N usually outperforms looping N times. Loopie addresses this challenge. Extensive ablation studies, including comparisons with a vanilla 30B-A3B model, show that Loopie substantially outperforms vanilla
Transformer baselines trained with the same compute budget. With a novel post-training method, Loopie develops strong
reasoning abilities and achieves frontier-level
reasoning performance.
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All authors Zitian Gao, Yilong Chen, Yihao Xiao, Xinyu Yang, Ran Tao, Joey Zhou, Bryan Dai
Artificial Intelligence
SciForge: An AI-Native, Multimodal Workbench for Scientific Discovery
SciForge Team: Zhangyang Gao, Minghao Fang, Yifei Liu et al. (+9 more)
Scientific work increasingly spans heterogeneous artifacts – papers, code,
datasets, scientific file formats, model outputs, figures, manuscripts, and team decisions – yet general-purpose AI assistants rarely preserve these objects as a coherent, auditable research state. We present SciForge, a
multimodal research-native AI workbench that reserves the graphical interface for human judgment while search, parsing, model routing, workflow execution, plotting, writing, and presentation generation run as modular
agent-accessible services. SciForge is built around five pillars: (i) goal-scoped scientific decision governance for goal-oriented research, with review gates and shared review surfaces; (ii) translate-then-reason for
multimodal input, routing scientific objects through domain translators before the
agent reasons; (iii) evidence governance for auditable traceability, linking claims to provenance chains and audit findings; (iv) collaborative team science for collaborative research, enabling multi-role decision governance, with shared team workspaces planned for future releases; and (v) real-world application scenarios for practical impact, demonstrated through eight end-to-end user cases, with flagship demonstrations including multi-day agentic research sprints for gene discovery, AI-guided de novo
protein design, molecular optimization, and genome-to-BGC discovery. The system combines a thin interaction layer, contextual research capability patterns, an
Agent Runtime and Workflow Engine, an Evidence-DAG audit sidecar and a Scientific Model Router. SciForge currently runs as a desktop application, with mobile supervision support; future releases will deepen team collaboration. The system is open-source and available at https://github.com/AGI4Sci/SciForge
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All authors SciForge Team: Zhangyang Gao, Minghao Fang, Yifei Liu, Hanhui Yang, Xinyu Gu, Shixiang Tang, Siqi Sun, Lei Bai, Cheng Tan, Mengdi Liu, Hao Wu, Shuizhou Chen
Artificial Intelligence · Cross-listed from cs.LG
Revisiting data-driven dynamic security assessment with a tabular foundation model
Olayiwola Arowolo, Maosheng Yang and Jochen Cremer
Data-driven pre-fault dynamic security assessment (DSA) rapidly evaluates the dynamic risk of credible contingencies on a power system using machine learning (ML). Existing approaches face two limitations. First, they require a large labelled database for training, with a separate model trained, tuned, and maintained for each contingency in a potentially long list of credible contingencies. Second, the trained models generalize poorly to unseen contingencies. This work addresses these limitations by using a tabular foundation model (TFM) that assesses stability through in-context learning, and requires no retraining or hyperparameter optimization. We use a single TFM to assess many contingencies at once, removing the need to train one classifier per contingency. We also characterize when using electrical distance coordinates (EDC) as continuous features enables the TFM to generalize to unseen contingencies and when it does not, showing how a few labelled samples can reliably improve generalization. Through comprehensive case studies on the IEEE 68-bus system, we show that a single TFM attains an average Macro F1 score of about 90% with only 120 labelled samples per contingency, outperforming both classical ML and deep learning baselines, without any model retraining or hyperparameter tuning. For held-out contingencies, we show that using just 10 labelled samples of the new contingency with a modified EDC encoding matches the balanced accuracy of the best achievable transfer learning oracle model, which requires fully labelled data and is not deployable in practice. Overall, this initial study paves the way for developing and deploying foundation models for power system operations, with possible applications across multiple operational tasks.
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Artificial Intelligence · Cross-listed from quant-ph
Rethinking Quantum Continual Learning with Quantum Fisher Information
Yu-Chao Hsu, Yu-Cheng Lin, Tai-Yue Li et al. (+2 more)
Quantum continual learning aims to train
quantum models on sequential tasks without losing previously learned knowledge. However, variational
quantum classifiers (VQCs) are prone to catastrophic forgetting under nonstationary task distributions. We propose
quantum elastic weight consolidation (QEWC), a
quantum Fisher information (QFI)-informed regularization method for mitigating forgetting. Unlike conventional elastic weight consolidation based on classical Fisher information (CFI), which measures parameter importance through measurement-dependent output statistics, QEWC uses QFI to quantify the intrinsic sensitivity of the parameterized
quantum state. This gives an information-
geometric view in which important parameters are identified by the local response of the
quantum state
manifold. We evaluate QEWC on VQCs trained on sequential binary classification tasks, including classical image-classification and
quantum phase-classification tasks. Simulations show that sequential training without regularization causes severe forgetting, while both CFI-based EWC and QFI-based QEWC improve retention of previous tasks. Mechanistic analyses further show that the two methods impose different regularization geometries: CFI acts selectively on measurement-sensitive directions, whereas QFI imposes a denser state-
geometric constraint over parameter space. Under depolarizing noise, CFI values are strongly suppressed by degraded measurement statistics, while QFI preserves a more stable sensitivity structure of the noisy parameterized
quantum state. These results establish QEWC as a physically motivated approach for studying and mitigating forgetting in
quantum continual learning through
quantum-state geometry.
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All authors Yu-Chao Hsu, Yu-Cheng Lin, Tai-Yue Li, Nan-Yow Chen, En-Jui Kuo
Artificial Intelligence · Cross-listed from cs.CL
Candidate Attended Dialogue State Tracking Using BERT
Junyuan Zheng, Onkar Salvi and John Chan
Dialogue state tracking (DST) is one of the core components in task-oriented dialogue systems. At each turn in a conversation, DST estimates the user belief or dialogue state, which is used as input for downstream modules to predict system actions and generate responses. The increasingly popular dialogue system applications like Google Assistant, Siri and Alexa need to support a large number of services and APIs, resulting in growing
attention to the scalability of such systems. Especially for some domains with little or no training data, the capability of transferring existing knowledge of other domains is highly desired. In this paper, we present a novel scalable framework for multi-domain dialogue state tracking. The proposed system leverages the pretrained BERT model to achieve zero-shot generalization, making it easy to quickly adapt to new domains without additional training. The performance of our model is evaluated on recently released schema-based dialogue (SGD)
dataset, showing significant improvement compared to previous baseline.
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Artificial Intelligence · Cross-listed from cs.CV
DPNeXt: A Lightweight Multi-Scale Feature Fusion Framework for Efficient ViT-Based Multi-Task Dense Prediction
Jehun Kang, Jungha Wang, Youngjun Hwang et al. (+1 more)
Multi-Task Learning (MTL) in robotics perception systems supports comprehensive 3D spatial scene understanding by integrating semantic
segmentation and depth estimation. While
Vision Foundation Models (VFMs) are increasingly adopted as robust feature encoders, existing decoding strategies present a critical bottleneck. To address this, we propose DPNeXt, a streamlined multi-scale feature fusion decoder and efficient alternative to the standard Dense Prediction
Transformer (DPT). DPNeXt uses dual depthwise separable inverted bottlenecks to improve frozen VFM utilization through fusion-centric decoding and independent task modularization. To further mitigate negative inductive transfer between tasks, we introduce the Multi-Task Boundary Guidance (MTBG) strategy. Unlike prior boundary-aware methods that add fusion modules or gating, MTBG applies symmetric boundary-focused supervision to encourage
geometric consistency without extra annotation or inference cost. Experiments on Cityscapes show that DPNeXt-S outperforms prior state-of-the-art (SOTA) MTL models, while DPNeXt-B further improves the overall performance and achieves the best results among the compared methods. On NYUv2, DPNeXt-B also achieves the best semantic
segmentation and depth estimation results among the compared methods while requiring substantially fewer trainable parameters than prior large-scale MTL models. Compared with the standard DPT, DPNeXt-S reduces trainable parameters by 78.6% and achieves the fastest inference speed among the compared models on resource-constrained laptop hardware. The source code, model checkpoints, and a demo
video will be made available at https://github.com/kangjehun/DPNeXt.
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All authors Jehun Kang, Jungha Wang, Youngjun Hwang, David Hyunchul Shim
Artificial Intelligence · Cross-listed from eess.SY
Robustness of Reinforcement Learning-Based Congestion Management in Low-Voltage Grids
Josef Hoppe, Sarra Bouchkati, Farah Nasr et al. (+7 more)
Increases in photovoltaic generation, charging of electric vehicles and heat-pump demand challenge operating limits in low-voltage distribution grids. This requires curative curtailment methods that can operate under sparse observability, noisy measurements, and imperfect grid models. Unlike prior end-to-end reinforcement-learning approaches for partially observable curtailment, this work decouples congestion detection and control by combining a random-forest violation pre-classifier with an actor-critic controller, and evaluates its robustness to measurement noise and grid-parameter mismatch. The framework is tested on a real low-voltage grid using synthetic future operating scenarios with low observability and controllability. With accurate grid parameters, the controller reduces total violation magnitude by 98.9%, and this performance remains nearly unchanged under the tested measurement-noise settings. Grid-model mismatch proves to be more challenging, but the controller still mitigates most violations under the tested mismatch assumptions.
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All authors Josef Hoppe, Sarra Bouchkati, Farah Nasr, Jonathan Krapp, Alexander Och, Maximilian Wirth, Jan Schiefelbein-Lach, Oliver Pohl, Andreas Ulbig, Michael T. Schaub
Artificial Intelligence
Closing the AI Trust Gap: The Case for Independent Certification for Trustworthy AI
Trisevgeni Papakonstantinou, Cansu Canca, Farah Nanji et al. (+14 more)
Over the past decade, responsible AI (RAI) has produced a substantial body of practice for identifying and mitigating the risks AI poses in high-stakes settings. Yet this work has not produced a
market that rewards trustworthiness. Firms that invest seriously in safety,
fairness, and oversight cannot consistently prove to consumers, regulators, and shareholders that their systems go beyond the bare minimum of compliance. What is missing is a way for society to recognize or compare the difference. The result is a trust gap: a structural condition in which responsible development efforts happen inside organizations but produce no external, independently recognized and verifiable signal of trustworthy outcomes. We argue this gap is sustained in part because of a focus on responsible AI (a matter of internal process) as opposed to trustworthy AI (a matter of independently verifiable real-world outcomes), and that it persists because of three compounding failures: (1) the
market cannot distinguish trustworthy systems from their imitations; (2) evaluation targets models and outputs rather than deployed sociotechnical systems and their outcomes; (3) the measurement ecosystem is oriented toward avoiding harm rather than demonstrating benefit. Reviewing existing AI governance instruments and comparing them to certification regimes in healthcare, sustainability, and security, we show that none integrate a governance baseline, independently verified positive-outcome evidence, and
market signaling in a single framework. We propose independent, outcome-oriented certification as the connective layer that can close the trust gap, complementing regulation and internal governance by making trustworthiness measurable, comparable, and commercially rewarded.
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All authors Trisevgeni Papakonstantinou, Cansu Canca, Farah Nanji, Waheedullah Pardess, Jen Weedon, Jasmijn Remmers, Eliza Krigman, Matthew Ball, Yalda Daryani, Kiran Iqbal, Francielle Vargas, María Llorente Sánchez, Joe Humphreys, Fendi Tsim, Kelly Fitzpatrick, Jeff Dunn, Catherine Feldman
Artificial Intelligence
A Formally Grounded ODRL Evaluator: Implementation and Comparison
Jaime Osvaldo Salas, Paolo Pareti, Adeel Aslam et al. (+2 more)
The ODRL policy
language is emerging as the de-facto standard for policy modelling data access and usage preferences, AI governance policies and data workflows in European dataspaces. The current standard has no mathematical formal semantics to describe how a system should implement policy evaluation. This has resulted in a variety of systems and tools that implement their own interpretation of the
language, which limits interoperability and cannot guarantee consistent results. Based on an existing semantic model of ODRL, we formalise the problems of ODRL evaluation for the access control and monitoring scenarios, in both static and streaming settings, and we provide a novel, efficient
algorithm and implementation. We present the first ODRL Evaluator with transparent formal semantics and supporting all rule types. We experimentally measure its performance, analysing different scalability dimensions related to policy complexity and size of the data on which a policy is evaluated. We compare our system with the state-of-the-art by providing a comparative review of existing ODRL evaluators, which highlights the differences in supported ODRL features and evaluation modes.
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All authors Jaime Osvaldo Salas, Paolo Pareti, Adeel Aslam, Christopher Maidens, George Konstantinidis
Artificial Intelligence · Cross-listed from physics.soc-ph
Sociocultural Influences on Opinion Formation: Word of Mouth Dynamics, Mass Media and Behavioural Development
Elpida Tzafestas
We study a society of
agents belonging to a number of occupational or cultural groups that form opinions about others' situation in the same or different group. Opinions develop either by observation within own group or by directly interacting with members of other groups, therefore by word of mouth (WoM). Additionally, global mass media (MM) may be available that inform indirectly about the situation of the various groups. The sociocultural interplay of these processes and the degrees of relative exposure to each of the sources has diversified effects on final opinion and social attitude formation. In large and complex societies and groups where not everyone can physically interact by WoM with everyone about everything, these processes show potential for mass control and social automation engineering. Our model can also represent and be generally informative about segmented societies that consist of groups with different occupational and cultural characteristics and it can offer insights into social issues such as the generation gap, social cleavages and so on.
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Artificial Intelligence · Cross-listed from cs.CL
From Plausible to Actionable: A Position on LLM Self-Explanations
Elize Herrewijnen, Benedetta Muscato, Gizem Gezici et al. (+1 more)
Large
Language Models (LLMs) can generate natural
language explanations that rationalize their own decisions, a phenomenon commonly referred to as self-explanations. Such explanations have emerged as a promising direction for explainable artificial intelligence (XAI), particularly for interpreting LLM behavior. However, while self-explanations often appear plausible, whether they faithfully reflect a model's underlying
reasoning process remains an open question. In this opinion paper, we argue that self-explanations can be highly plausible, questionably faithful, and yet highly actionable. From a traditional XAI perspective, we identify the limitations of standard evaluation protocols for LLM-generated self-explanations and propose practical guidelines for assessing their plausibility and faithfulness. Moreover, we argue that evaluation should extend beyond these criteria to actionability, highlighting applications of LLM rationalization capabilities that support informed decision-making and appropriate action across diverse stakeholders.
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All authors Elize Herrewijnen, Benedetta Muscato, Gizem Gezici, Fosca Giannotti
Artificial Intelligence · Cross-listed from cs.MA
When Not to Automate: A Formal Protocol for Human Preservation in AI-Optimized Organizations
Jose Manuel de la Chica Rodriguez, Jairo Rodriguez Arias and Spyridon Chouliaras
Standard automation ROI misses four categories of systemic risk – tacit knowledge erosion, resilience reduction, regulatory exposure, and socio-institutional capital degradation – that affect long-term organizational performance. PHP-AIO (Protocol for Human Preservation in AI-Optimized Organizations) is a five-gate sequential decision protocol with a final composite check that quantifies these unpriced systemic risks at the role level and produces auditable automation decisions. A closed-form automation-debt measure ($\rho(P)$) formalises how role-level decisions accumulate across multi-step processes; its warning is neutralised only by a regulator-mandated human-in-the-loop anchor. Applied to stylised profiles of representative internal roles, PHP-AIO produces distinct outcomes – automate, augment, hybrid, and preserve – for candidates that standard cost-benefit analysis would uniformly automate. Threshold sensitivity analysis confirms the gate decisions are robust to upward perturbations of at least 14% in three of four representative cases. Keywords: AI governance, automation decision, human oversight, tacit knowledge, organizational resilience, financial services
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Artificial Intelligence · Cross-listed from cs.LG
On the Failure of Boundary-Seeking Distillation in Bottlenecked Generative Architectures
Mohamed Amine Kina
Data-free knowledge
distillation transfers the knowledge encoded in a teacher model to a student model without access to the original training data. Prior work such as Contrastive Abductive Knowledge Extraction (CAKE) achieves this for classifiers by synthesizing samples near the teacher's decision boundary. In this work, we investigate whether this boundary-seeking principle extends to autoencoder
distillation through experiments on the MNIST
dataset . To enable a direct comparison, we reformulate continuous reconstruction as a dense, per-feature classification task, allowing the decoder to output categorical logits. We show that boundary-seeking objectives are fundamentally ill-posed in bottlenecked generative architectures. CAKE operates on a single, instance-level objective, but a decoder acts as an array of tightly coupled, feature-level classifiers constrained by a shared low-dimensional bottleneck. Independently sampling contrastive targets for these coupled outputs violates the geometry of the learned latent
manifold and produces severe gradient conflicts instead of informative boundary samples.
Manifold-aware synthesis bypasses these conflicts entirely and establishes an effective baseline for data-free generative
distillation.
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Artificial Intelligence
DSWorld: A Data Science World Model for Efficient Autonomous Agents
Zherui Yang, Fan Liu and Hao Liu
Despite strong capabilities in data understanding and decision-making, autonomous data science
agents still heavily rely on trial-and-error workflows that involve expensive computation. This bottleneck motivates models that can anticipate the effects of data science operations before real execution. In this paper, we introduce the concept of Data Science World Model, which model the data science execution environment by predicting environment state transitions conditioned on current workflow states and candidate operations. We further propose DSWorld, a practical framework that combines structured state construction, cost-aware routing, lightweight real execution, and an LLM-based simulator for expensive operations. To support training, we construct an 8K-scale transition trajectory
dataset and introduce Reflective World Model Optimization, an error-aware reinforcement learning strategy for improving transition prediction. Experiments show that DSWorld accelerates RL-based
agent training by approximately $14\times$ and search-based inference by approximately $3$-$6\times$ while maintaining competitive performance, and outperforms the strongest LLM baseline by 35.6% on transition prediction tasks. The code is available at https://anonymous.4open.science/r/DSWorld.
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Artificial Intelligence · Cross-listed from cs.CV
Orbis 2: A Hierarchical World Model for Driving
Sudhanshu Mittal, Arian Mousakhan, Silvio Galesso et al. (+4 more)
Current world models operate at a single level of abstraction, with most prioritizing perceptual fidelity while lacking the spatial
reasoning and semantic understanding required for real-world downstream tasks. We present a hierarchical driving world model that factorizes future prediction across two levels operating at distinct temporal and abstraction scales: a high-level predictor that forecasts coarse scene structure over extended temporal horizons, and a low-level generator that produces detailed predictions conditioned on the high-level output. This decomposition yields high perceptual fidelity while also capturing strong spatial and semantic representations. We further show that pretraining with a
diffusion forcing objective yields substantially richer internal representations than the standard teacher forcing objective, while teacher forcing – predicting only the next frame from clean context – produces more stable autoregressive rollouts. We therefore introduce a generic two-stage training paradigm that pretrains the model with
diffusion forcing and fine-tunes with teacher forcing, combining the representational benefits of the former with the rollout stability of the latter. Our approach achieves state-of-the-art results across the standard suite of driving world model evaluations on established
benchmarks, including long-horizon generation fidelity, steering responsiveness evaluated on counterfactual scenarios, and internal representation quality. Project page with code, demo, checkpoints and qualitative results: https://lmb-freiburg.github.io/orbis2.github.io/
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All authors Sudhanshu Mittal, Arian Mousakhan, Silvio Galesso, Karim Farid, Jonannes Dienert, Rajat Sahay, Thomas Brox
Artificial Intelligence · Cross-listed from cs.CL
Induction in Both Directions: A Mechanistic Analysis of In-Context Learning in Masked Diffusion Language Models
Andy Catruna and Emilian Radoi
While the internal mechanisms of autoregressive (AR)
transformers have been studied extensively, much less is known about
diffusion language models (DLMs), an emerging alternative that generates text by iterative denoising. In this work, we study how DLMs implement induction, a mechanism behind in-context learning in which the model finds a repeated context and copies the
token that followed it. Our analysis compares
attention-only AR models and absorbing-mask DLMs with matched architectures. We find that DLMs learn a bidirectional induction
circuit, where previous-
token and next-
token heads write local context into the residual stream and later induction heads use it to find and copy the answer from the matching source position. The
circuit is direction-symmetric, working whether the source appears in the past or in the future. When only left context is visible, matching what an AR model sees, the DLM does not outperform its AR counterpart in induction capabilities. However, we observe it has stronger induction when both sides of the masked
token are visible, pointing to bidirectional context access rather than a stronger one-sided mechanism. Beyond induction, we provide causal evidence that DLMs compute the global fraction of masked
tokens and use it as an implicit timestep, even though they are given no explicit timestep
embedding.
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Artificial Intelligence · Cross-listed from cs.HC
Perceived AGI: Believability as Dimensional Completeness, Not Capability
Sebastian Cochinescu
Large
language models are broadly capable, yet in sustained one-to-one conversation they still read as flat: competent, responsive, and somehow not quite the presence of a mind. We hypothesize that a central missing ingredient is not more capability but dimensional completeness. We propose that the believability of an artificial interlocutor – the degree to which a user attributes an inner life to it, which we call perceived mind – is governed by whether the
agent expresses a small set of first-person stances that humans use as evidence of mind, and that this is separable from task intelligence. We name four such dimensions – time, truth,
entropy, and love – each defined as a behavioral stance rather than a
benchmark competency, each with a human analog and a concrete emulation path; the time dimension already has an author-reported prototype. We identify an observable behavior layer – initiative (unprompted action) and cadence (the shape and timing of turns) – through which the stances surface in conversation, both partially realized as deployed features in a production companion application. We state six falsifiable predictions that a later pre-registered study will test, separating those that are pre-registrable now from those that remain
conjectures pending operationalization. This is a conceptual framework: it reports no human-subjects data, and its central comparative claims are predictions, not findings. Throughout we hold a firm boundary – the object is inferrable interiority, not interiority; this is perception engineering, not a theory of machine consciousness – and we treat the resulting attachment and manipulation risks as load-bearing rather than incidental.
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Artificial Intelligence · Cross-listed from cs.CL
DECODEM: Data Extraction from Corporate Organizational Documents via Enhanced Methods
Jens Frankenreiter
Much empirical legal research depends on translating unstructured text into structured variables. In corporate governance research as elsewhere, this translation has traditionally been performed by humans. Manual coding is time-consuming and costly. In corporate governance research, standardized commercial
datasets offer an alternative, but they cover a limited set of firms and governance variables. This paper introduces the DECODEM
Benchmarks and uses them to evaluate whether large
language models can extract corporate governance variables from charters and bylaws. The
benchmarks combine a sample of corporate charters and bylaws of publicly traded U.S. corporations obtained from the SEC's EDGAR database with hand-coded labels that capture whether a document includes a specific governance arrangement. The paper uses these
benchmarks to evaluate a range of extraction approaches and models. The approaches vary along several dimensions, including the amount of guidance included in the
prompts and the extent to which the model is provided with the full document or with relevant excerpts only. The results show that current models can recover many governance variables with high accuracy across extraction approaches. At the same time, performance varies substantially across documents and variables. In particular, extraction is less accurate for charters than for bylaws, including when the comparison is limited to variables that appear in both
benchmarks. The results also show that frontier models perform comparably on the tasks evaluated here and that more elaborate extraction pipelines do not consistently improve their performance. More elaborate extraction strategies matter more for efficiency-oriented models, where they can substantially narrow the performance gap relative to frontier models.
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Artificial Intelligence · Cross-listed from cs.CV
EgoExoMoCap: Distributed Ego-Exo Human Motion Capture
Jiaxi Jiang, Bharat Lal Bhatnagar, Nan Yang et al. (+6 more)
Human motion capture from head-mounted devices (HMDs) offers a scalable way to acquire real-world human motion and interaction data, which is crucial for applications in
embodied AI and VR/AR. Existing approaches focus on either
egocentric body tracking, estimating the motion of the subject wearing the device, or exocentric tracking, capturing the movements of people in the wearer's surroundings. So far, these two paradigms have largely been explored in isolation. In this paper, we propose a novel distributed framework that jointly leverages ego- and exocentric multi-modal signals for human motion estimation from HMDs. Unlike traditional motion capture systems requiring bulky multi-camera setups or obtrusive mocap suits, our approach, EgoExoMoCap, is as simple as two (or more) people, each wearing a pair of smart glasses. The method leverages head (plus potentially wrist) tracking signals for accurate estimation of global motion in the 3D world and combines context-aware image features based on DINOv3 to achieve robustness in the presence of noise and occlusions. Extensive experiments on two in-the-wild
datasets show that our approach can robustly reconstruct motion even in challenging scenarios.
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All authors Jiaxi Jiang, Bharat Lal Bhatnagar, Nan Yang, Lingni Ma, Sebastian Starke, Robin Kips, Nadine Bertsch, Christian Holz, Federica Bogo
Artificial Intelligence · Cross-listed from cs.CL
Conditional Reliability of Toxicity Signals for Multilingual and Code-Mixed Abuse Detection
Indraveni Chebolu, Rohan Singh, Arnab Mallick et al. (+1 more)
Moderation systems increasingly rely on external toxicity tools, but those tools are unreliable under code-mixing, transliteration, slang, and
language mismatch. We study the conditional reliability of toxicity priors in Indian
multilingual and code-mixed short text: English toxicity, Indic abuse, and rule-based severity cues can be useful evidence, but only in some linguistic and abuse-severity contexts. We propose ToxGate, a trust-fusion head that conditions each auxiliary signal on the encoder representation before adding it to the prediction state. Across three short-text abuse
datasets, four
transformer encoders, and five seeds per setting, ToxGate improves over matched plain encoders in 10 of 12 in-domain settings and 7 of 8 transfer settings. The largest and most interpretable gains occur in high-risk moderation slices, including explicit slurs, violent threats, and cross-
dataset transfer. The broader lesson is that moderation systems should treat external toxicity tools and priors as conditional evidence rather than fixed features or ground truth, in focused ablations, source-specific gating gives the strongest results in transfer, severe-abuse slices, and high-risk triage.
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All authors Indraveni Chebolu, Rohan Singh, Arnab Mallick, Harmesh Rana