Artificial Intelligence
Common-Witness Certificates and Sharp Feature Bounds for Counterfactual Image Auditing
Usef Faghihi and Amir Saki
An image editor may satisfy every regional plausibility constraint separately even when no single latent explanation fits the complete output. We formalize this local-to-global failure using a common witness grade and witness nerve. The framework separates
auditing from causal identification: shared exogeneity alone allows every coupling of the regime marginals, whereas an externally justified witness relation yields sharp partial-identification bounds for prespecified image features. Helly-type arguments provide short incompatibility certificates for quasiconvex losses, heterogeneous action strata, and finite witness atlases; a blocker-hypergraph formula gives exact repair counts. Simultaneous confidence regions for the regime marginals give finite-sample outer coverage of the complete identified interval. Controlled MNIST, Morpho-MNIST, and smallNORB studies demonstrate the predicted local-global separation, while synthetic experiments test sharp bounds, certificate recovery, and structured computation. The method audits a declared feature relation and does not identify unrestricted pixel-level counterfactuals.
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Machine Learning
OSR: Output Space Redistribution for Adaptive Label Removal in Classification Models
Minyi Peng, Darian Gunamardi, Ivan Tjuawinata et al. (+2 more)
Label removal occurs frequently in classification systems with evolving taxonomies, where categories must be dynamically updated or eliminated. To accommodate such changes, classification models must adapt accordingly. Existing solutions, broadly categorized as retraining-based and feature-space-adjustment-based, share common limitations despite their variations, including reliance on access to original data, substantial computational and storage costs, inconsistent results, poor scalability, and degradation of model utility. To address this, we propose a novel approach that leverages statistical redistribution in the output space to approximate the post-removal confidence vectors of a retrained model. Applicable as a modular output filter, our method bypasses the burden of feature-space adjustments or loss-function convergence, alleviating scalability limitations. Furthermore, by requiring only existing labels and prior output confidences, the method potentially mitigates
privacy concerns inherent to data-dependent solutions. Extensive experiments demonstrate competitive performance against full retraining, with improvements in computational efficiency and
privacy preservation across several classification tasks.
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All authors Minyi Peng, Darian Gunamardi, Ivan Tjuawinata, Yongsen Zheng, Kwok-Yan Lam
Information Retrieval
The WebKurator.de Platform: Combined Regional and Topical Web Curation
Michael Dinzinger, Natanael Arndt, Ben Böck et al. (+2 more)
The systematic curation of the Web remains a central challenge for national libraries and
memory institutions that aim to preserve culturally and regionally relevant content. Existing directory-based approaches such as Curlie implement a predominantly topic-centric, one-dimensional hierarchy, where geographic aspects are intertwined with topical and linguistic categories. To address this limitation, we present WebKurator.de, a collaborative platform for combined regional and topical web curation, initially focused on the German web. WebKurator introduces a two-dimensional curation model that explicitly separates topical categorization and geographic annotation. The system integrates LLM-based topic classification and imprint-based address extraction with geocoding, and supports user suggestions together with moderated review. The platform is bootstrapped from the German Imprints
Dataset, a large-scale collection of 5.54 million websites. Among them, 3.14 million contain imprint pages, for which we successfully extracted and geocoded postal addresses. Of these, 2.58 million (85.17%) are located in Germany and also have an assigned topic label. These websites form the initial foundation of WebKurator.de and can be continuously extended through user suggestions.
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All authors Michael Dinzinger, Natanael Arndt, Ben Böck, Jelena Mitrović, Michael Granitzer
Robotics
Automated Weld Seam Recognition and 3D Mapping for Robotic Post Processing Using Photogrammetry and Semantic Segmentation
Augustin Raju, Abilash Madavath, Chandra Yuvesh Aubeeluck et al. (+3 more)
Accurate identification of weld seam geometries is essential for automated robotic post processing operations such as grinding, finishing, and inspection. For large workpieces, complete surface scanning using high precision
laser scanners or structured light sensors can be time consuming and often generates substantial amount of data that are not relevant. This paper presents an experimental
vision based pipeline for the approximate localization of weld seams. This serves as a preliminary stage before high precision measurement. The proposed approach aims to reduce the overall scanning effort and data acquisition efficiency. The proposed method includes capturing images of the workpiece from multiple viewpoints, identifying weld seams from the images using semantic
segmentation, reconstructing the workpiece using photogrammetry, and projection of identified weld seams into the reconstructed model.
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All authors Augustin Raju, Abilash Madavath, Chandra Yuvesh Aubeeluck, Nicolas Pyschny, Felix Hackelöer, Florian Zwanzig
Computation and Language
Investigating the Ability of Large Language Models to Analyze Recipes for Diabetes
Revathy Venkataramanan, Aditya Luthra, Venkatesan Nadimuthu et al. (+1 more)
Several studies have evaluated the ability of Large
Language Models (LLMs) for meal planning, yielding positive outcomes. These models can process natural
language inputs and leverage learned knowledge from their pretraining to generate meal plans. In this work, we investigate the ability of LLMs to analyze the suitability of given recipes for diabetes. The primary challenge for LLMs is to retrieve relevant dietary guidelines for diabetes, decompose recipes into ingredients and cooking methods, and apply these guidelines to determine the recipe's suitability. To study these challenges, we employ three kinds of
prompts namely, (i) Direct Query
Prompt (ii) Context-Guided
Prompt, and (iii) Exemplary Context
Prompt that incorporate different levels of diabetes dietary guidelines from medical sources. We introduce a
benchmark dataset curated for this investigation consisting of 7607 recipes that include 3807 recipes suitable for diabetes and 3800 recipes not suitable for diabetes. Our results demonstrate that most LLMs are cautious in predicting recipes as suitable to prevent detrimental outcomes. Further, the models that can reason using the dietary guidelines performed better in predicting the suitability of recipes for diabetes. Overall, Mistral-7B and Llama 70B showed superior performance to their counterparts.
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All authors Revathy Venkataramanan, Aditya Luthra, Venkatesan Nadimuthu, Amit Sheth
Artificial Intelligence
Interface-Induced Trajectory Censoring
Wenbo Wang and Di Sang
Tool-use evaluations typically measure calls through a serving stack, which can report zero calls even when model outputs contain call-shaped payloads. We study interface-induced trajectory censoring: a mismatch between model emissions and the interface that converts them into actions. On selected BFCL v4 subsets, changing only the serving adapter while holding model weights and other evaluation settings fixed moves scores under the official scorer from 0.00 to 0.96 on simple_python and from 0.00 to 0.19 on multi_turn_base. A template-parser factorial experiment shows strong complementarity: neither component replacement alone restores parsing from the baseline, whereas their joint replacement recovers 196/200 first-request parses. Across five Qwen2.5-Coder checkpoints, classifier-positive emissions increase with model size while the mismatched interface reports no calls; a separate matched-interface Qwen3 ladder exhibits few silent emissions through 8B. Experiments on 115 tau-bench retail tasks extend the mechanism to a stateful, non-code environment. In a preregistered, single-seed, 150-step 7B LoRA experiment, changing only the parser registered in verl increases tool executions from 10 to 16,844. However, neither a shared programmatic-feedback evaluation nor a post-hoc common native function-calling evaluation detects a held-out success gain from repair; rescue counts do not increase. Interface repair thus restores tool-mediated experience without a detected performance gain in this setting. These findings show that tool-use measurements depend on the model-interface configuration and motivate layer-wise observability and conformance checks before attributing evaluation or training outcomes to the model alone.
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Artificial Intelligence
FiMI Banking: A Sovereign Model for Indian Retail Banking
NPCI AI Research Team: Aman Kumar, Asit Desai, Chandra Bhushan et al. (+14 more)
Banks need conversational systems that can answer product questions, assist customers with account-related requests, and operate safely within strict operational and regulatory constraints. General-purpose
language models do not reliably meet these requirements. They fall short when a task requires grounded information, correct tool use, or cautious handling of bank-specific sensitive situations. We introduce FiMI Banking, a controlled Indian retail-banking setting. We build it from vetted banking documents, structured ground truth, synthetic customer backgrounds, and banking tools. We evaluate two post-training approaches: preference optimization for response-level behavior, and reinforcement learning with verifiable rewards for multi-turn tool-use tasks. Preference optimization improves safe behavior substantially: out-of-scope refusal rises from 52% to 80%. Reinforcement learning improves edge-case performance from 0.509 to 0.718 and order-sensitive task performance from 0.590 to 0.679, while using 29% fewer generated
tokens. These results show that preference optimization and verifiable-reward reinforcement learning address complementary requirements for reliable banking
agents.
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All authors NPCI AI Research Team: Aman Kumar, Asit Desai, Chandra Bhushan, Harsh Sharma, Harshit Bhushan, Hrithik Kadam, Keyur Doshi, Kolisetty Sai Kapardheeswar, Krishanu Adhikary, Nadeem Shaik, Navya Prakash, Nitin Kukreja, Prashant Devadiga, Shamanth MH, Shantanu Pandey, Suvradip Paul, Yatharth Dedhia
Computer Vision
RARF: Region-Aware Rectified Flows for 3D Brain MRI Inpainting
Tomas Guija-Valiente, Blanca Rodriguez-Gonzalez, Norberto Malpica et al. (+1 more)
Medical image inpainting has the potential to improve automated
brain MRI analysis by reconstructing healthy tissue within pathological regions. We introduce RARF, a task-agnostic region-aware rectified flow framework for masked data generation. We instantiate the framework for 3D
brain MRI inpainting as our submission to the BraTS Inpainting Challenge 2026. RARF restricts the
stochastic interpolation process to the inpainting region, while the observed voxels remain fixed and provide patient-specific anatomical context. A three-dimensional neural network receives the partially voided image, with Gaussian noise filling the missing region, together with the inpainting mask and the corresponding timestep. The model is trained using masked flow-matching and reconstruction-consistency objectives, combined with mask-aware preprocessing and data augmentation. During inference, the learned velocity field transports the initial noise toward a plausible reconstruction of the missing tissue, which is then combined with the unchanged observed anatomy. Experiments under the BraTS evaluation protocol show that the proposed approach produces competitive reconstructions while maintaining anatomical consistency. Source code is available at: https://github.com/TomasGuija/rarf.
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All authors Tomas Guija-Valiente, Blanca Rodriguez-Gonzalez, Norberto Malpica, Angel Torrado-Carvajal
Computation and Language
Two-Stage Reinforcement Learning for Sound and Adversarial Test Generation in Code LLMs
Jiacheng Xu, Wentao Zhang, Zhiyi Lyu et al. (+4 more)
Reinforcement learning (RL) has substantially advanced code generation with large
language models (LLMs) through executable feedback. The feedback for coding problems mainly comes from specific test cases, where high-quality test cases are often scarce since they should be both sound and discriminative. We thus turn to study the auto-generation of test cases using the learned model. We find this is naturally an
adversarial RL problem: the model is expected to generate effective test cases as counterexamples, depending on the solver's current failure modes. We propose Test Cases Scaling (TCS), a two-stage RL framework for effective test generation. Both stages train a test generator from a rolling policy-aligned buffer: Stage 1 generates tests consistent with the reference solution, and Stage 2 restricts the buffer to current failure modes and learns counterexample tests. Across TACO and LiveCodeBench, TCS improves both pass@1 and inference-time answer selection according to generated tests. We find the learned test generator also enables effective selection among other LLM outputs.
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All authors Jiacheng Xu, Wentao Zhang, Zhiyi Lyu, Fuxiang Zhang, Chaojie Wang, Yang Liu, Bo An
Computation and Language
Beyond Majority Vote: Multi-Perspective Adjudication for Medical Hallucination Detection
Joe Cecil and Marjorie Freedman
Understanding the frequency of factual errors in chatbot-generated text and evaluating systems that detect these errors is critical for determining chatbot safety. Yet factual-error detection is often treated as a single-pass, single-annotator labeling problem. In long-form chatbot responses, factual errors can be subtle and embedded within mostly correct text. We develop a multi-perspective annotation study of medically relevant chatbot responses, combining first-pass annotation, LLM-as-a-Judge (LaJ) candidate discovery, and two forms of adjudication: medical-expert and evidence-based fact-checking. First-pass annotators frequently miss factual errors later validated by adjudicators. LaJ improves candidate discovery, but is insufficient on its own: It misses factual errors that annotators catch. We also find disagreement among adjudicators, suggesting that adjudication over multiple candidate sources can improve
benchmark completeness, but does not eliminate the need to apply judgment and expertise. Applied to an existing
benchmark, this technique reveals a similar pattern of missing annotations. Together, these results suggest that in the settings examined here, single-pass
hallucination benchmarks may achieve scale at the cost of undercounting factual errors. Multi-pass adjudication can improve coverage, but inferences drawn from the
benchmarks are still sensitive to the judgment, expertise, and evidence used to determine error presence.
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Computer Vision
WorldReward: Reward Modeling for Camera-Conditioned World Models
Yibin Wang, Zehan Wang, Junshu Tang et al. (+13 more)
Camera-conditioned world models generate interactive
videos in which commanded actions should induce the expected scene changes while appearance, geometry, and temporal dynamics remain coherent. Existing rewards assess these requirements separately: geometry-based rewards estimate trajectory execution but cannot judge the visual quality of the executed motion, whereas image-based rewards measure frame quality without capturing action execution or temporal dynamics. We posit that a
vision-
language model (VLM) offers a shared
reasoning space for relating actions to their visual outcomes. However, judging a complete long
video against its full action sequence creates a lengthy, noisy context in which short-lived local action evidence can be missed or diluted. We present WorldReward, a VLM-based pairwise preference reward model that unifies action-consistency and visual-quality evaluation for camera-conditioned world models. WorldReward decomposes paired
videos into action-aligned chunks, organizes each chunk into structured visual evidence, and aggregates chunk-level decisions by voting into separate
video-level action and visual-quality preferences. To train it, we construct a large-scale
reasoning-augmented preference
dataset using structured judgments generated by a frontier VLM and refined through tool-based
agent auditing and targeted human review. We further introduce WorldReward-Bench, a human-annotated
benchmark measuring reward-model agreement with human preferences across action consistency, appearance quality, and motion quality. WorldReward achieves the highest agreement on all three dimensions, exceeding GPT-5.5 by 3.42, 1.45, and 3.56 percentage points, respectively. When used for RL post-training of HY-WorldPlay 1.5, it consistently improves both action execution and visual quality across short- to long-term horizons.
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All authors Yibin Wang, Zehan Wang, Junshu Tang, Zhimin Li, Yujie Zhou, Jiazi Bu, Pengyang Ling, Feng Han, Zhixiong Zhang, Long Xing, Shengyuan Ding, Ziang Li, Cheng Jin, Yuhang Zang, Jiaqi Wang, Tianyu Pang
Machine Learning
VestigeKV: The NoPE-MLA KV Cache Carries Its Own Sparse-Attention Signal in a Vestigial Branch
WenJie Fan
A long-lived KV cache must be compressed before the queries that will read it exist. Selection by observed
attention collapses there: on a NoPE-MLA model, H2O and SnapKV retrieve 0.00 and 0.33 of needles at 8x compression, because a
token's importance has not yet been observed. VestigeKV instead derives a sparse
attention pattern from a signal the cache already carries, occupying the sparse-
attention literature's one unoccupied quadrant: training-free and query-independent. In NoPE-MLA the 64-dimensional decoupled branch is a vestige of RoPE that training repurposes into a salience channel; reading 11% of each row, it partitions the cache into an attended tier and a GPU-resident archive that no row ever leaves, reachable each step by a certified, query-adaptive trigger. Nothing is trained and cache rows are never quantized, so every quality effect attributes to selection and scheduling. On Kimi Linear 48B,
retrieval holds at 1.00 under 8x and 0.96 under 32x from 8k to 65k context, with zero gap to full-row selection, and the recall tier holds 128x at 1.00 (8k). Both tiers stay on the GPU, so the win is speed, not
memory: the per-step scan reads ~26% of the bytes dense
attention would, and on a two-node sglang deployment the crossover sits at ~40k context, reaching 1.18x at 256k and 1.39x at 496k. The mechanism is exclusive to NoPE: the identical operator on a RoPE MLA collapses to 0.08, query-independent salience exists only without rotation, and query-universal exact merging is provably impossible under RoPE. All thresholds were frozen before their data; 20 archived verdicts and 8 closed routes accompany the paper.
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Artificial Intelligence
More Criticism Does Not Make a Better Review: EquiReview-R
Zexing Zhang, Jichao Li, Tianyang Lei et al. (+2 more)
AI reviewers can now produce many specific criticisms, but more criticism is not necessarily a better review. A review may miss a consequential weakness or retain an allegation that available evidence does not support. These failures require opposite corrections, yet generation-oriented systems and aggregate measures obscure the distinction. We therefore recast AI-assisted review as evidence-guided refinement of a structured concern set, with omission and overcritique treated as separate risks. Building on this formulation, we introduce EquiReview-R, which resolves existing concerns against localized evidence, searches for missing issues from independent and review-conditioned perspectives, and returns stop, continue, or defer. To expose the failure mode that motivates this design, we construct an evidence-linked trajectory corpus. Its retrospective analysis shows why revision must precede further search: nearly all concerns in a high-recall review lack a definitive evidential disposition, while an earlier refinement mechanism cannot revise them. On a frozen cohort of previously unseen papers, EquiReview-R satisfies the prespecified non-inferiority criterion for major omission, reduces major overcritique from 15.5% to 8.1%, and attains a one-sided omission upper bound of 9.9% while stopping on 52.4% of papers. Computation-matched controls, controlled pairs, and ablations show that the gain comes from revision rather than extra inference or shorter output. We release the corpus as ReviewTrace, an evidence-linked resource for studying review revision, disagreement, and provenance.
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All authors Zexing Zhang, Jichao Li, Tianyang Lei, Yude Fu, Yang Kewei
Machine Learning
Headroom-Drift Replay: A Primitive for Principled Replay Control in GRPO
Hyun Bin Park and Du-Seong Chang
RL-based post-training for
reasoning models is increasingly bottlenecked by repeated fresh rollout generation, particularly in agentic settings where environment interaction dominates wall-clock cost. Replay can reduce this burden by reusing past trajectories, but existing methods typically embed it within larger training pipelines involving exploration, experience restructuring, or mixed-policy optimization. This makes replay's own contribution difficult to isolate. We ask a focused question: how far can principled replay selection alone go? We introduce Headroom-Drift Replay, a group-level replay control primitive for GRPO that separates reuse into two decisions. Headroom ranks stored groups by remaining learning value, while Drift gates them by compatibility with the current policy. The fresh on-policy stream remains unchanged, and the method adds no auxiliary generation or training machinery. Across mathematical
reasoning,
multimodal reasoning, and Agentic Search
benchmarks, this single intervention outperforms naive replay and matches or exceeds broader replay methods on Avg Mean@32. In Agentic Search, where environment interaction dominates cost, it delivers comparable quality at materially lower wall-clock time.
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Sound
Masked Autoregressive Speech Enhancement with Continuous Neural Audio Codec Representations
Yoto Fujita, Simon Leglaive and Laurent Girin
Most previous work on
speech enhancement (SE) based on masked generative modeling relied on discrete
token representations of audio signals, obtained using neural audio codecs (NACs). However, a recent study has shown that continuous latent representations of NACs can be advantageous for SE in terms of
speech quality and intelligibility. In this work, we propose masked autoregressive SE (MARSE), a method for SE based on iterative decoding of masked clean
speech frames using continuous NAC representations of
speech. In particular, we investigate a set of different decoding policies, ceteris paribus, that is, using the same DNN (a Conformer model), the same NAC (the DAC codec) and the same training setup. The results show that MARSE enables a flexible trade-off between SE performance and computational cost. Audio examples and code are available online.
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Artificial Intelligence
Towards Numerical TOHTN Planning with SMT-based HTN-SAT Encoding
Gaspard Quenard, Takudzwa Togarepi, Damien Pellier et al. (+1 more)
While HTN planning has received significant
attention in recent years, support for numerical
reasoning remains very limited. In this paper, we investigate numerical Totally-Ordered HTN (TOHTN) planning and show how standard SAT-based encodings can be naturally extended with SMT to handle numeric fluents. In addition, we introduce a
benchmark suite for numerical TOHTN planning, providing a first common basis for evaluation in this setting. Experimental results show that this simple encoding already constitutes a competitive baseline. This work opens the way to more expressive approaches to HTN planning.
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All authors Gaspard Quenard, Takudzwa Togarepi, Damien Pellier, Humbert Fiorino
Machine Learning
RATL: Learning from Retrieved Residuals for Robust Multivariate Time-Series Forecasting
Yuchen He, Yueyang Cang, Zhiyuan Ning et al. (+2 more)
Retrieval-augmented generation (RAG) complements parametric models with retrieved external evidence. The same idea is attractive for continuous-output regression, but directly reusing retrieved target values is often not robust when samples differ in output level, numerical scale, or local dynamics. Moreover, conventional
forecasting pipelines generally use residuals for model optimization and error diagnosis, but do not retain individual historical residual examples as
memory that can be accessed at inference time.For multivariate time-series
forecasting, we propose RATL, a plug-in residual-
retrieval and feedback-correction method. RATL freezes a base forecaster to construct
retrieval keys and turns its historical forecast residuals into a train-only
memory specific to that base model. At inference time, RATL retrieves residual trajectories from similar historical contexts subject to causal availability constraints, then uses a set-aware router operating over forecast blocks and variables to select and combine these trajectories. Experiments show that historical residuals matched to the current context contain reusable
forecasting information and that RATL improves frozen base forecasters in most experimental settings. Ablations further show that learned routing strengthens raw residual feedback, while validation-based correction-strength selection limits residual over-injection.On real-world
benchmarks, we use iTransformer as the primary frozen base forecaster, compare against multiple strong
forecasting baselines, and test transferability across backbones. The results show that RATL can further improve base-forecaster performance in most settings.Overall, RATL shifts the retrieved object from historical target values to base-model-specific historical forecast errors, providing a plug-in, residual-
memory-based paradigm for learned feedback correction in continuous-output
forecasting.
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All authors Yuchen He, Yueyang Cang, Zhiyuan Ning, Ningyu Wang, Li Shi
Computers and Society
Making Gender-Inclusive Practices Actionable: Evaluating a Research-Informed Computing Education Toolkit
Alina Berry, Susan McKeever, Brenda Murphy et al. (+1 more)
The persistent gender imbalance in computing remains a global concern, and universities offer a key part of the pipeline to address it. Although research has identified practices that support under-represented student groups, translating this evidence into actionable guidance remains challenging. This paper first presents a novel web- based toolkit (TechMate) designed to address this gap by helping computing educators implement gender- inclusive initiatives through practical research-informed guidance. The toolkit defines over 25 actions, ranging from operational to strategic, and provides case studies and implementation resources. Second, this work reports on the evaluation of TechMate, capturing educators first impressions of its usefulness and usability through authentic tasks, and eliciting unanticipated insights about structural barriers to gender-inclusive practice in computing higher education. Eighteen computing educators of varying seniority from eight universities in Ireland assessed the toolkit using nine research-driven attributes: four usefulness measures (novelty, relevance, trustworthiness, actionability) and five usability measures (effectiveness, aesthetics,
navigation, terminology, user satisfaction). Data was analysed using mixed methods: deductive coding, quantitative sentiment scoring, and inductive thematic analysis. Educators rated TechMate highly for user satisfaction and praised its novelty and trustworthiness, with over 60% of participants reporting positively on all attributes, suggesting that research -informed tools can be designed for real-world use. Thematic analysis highlighted other challenges, including women students isolation, institutional resistance to change, and the scarcity of women role models, while also surfacing proposed solutions such as leveraging student ambassadors and fostering supportive learning environments.
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All authors Alina Berry, Susan McKeever, Brenda Murphy, Sarah Jane Delany
Computer Vision
Sparse auto-regressive modeling for scene generation from multi-view images
Thomas Lucas, Maxime Pietrantoni, Philippe Weinzaepfel et al. (+4 more)
Generating complete 3D scenes from sparse, unconstrained views is a fundamental challenge in 3D
vision which requires
reasoning beyond observed content while remaining computationally tractable. Existing feed-forward reconstruction methods are inherently limited to content visible in the input images, while 3D generative modeling is hindered by the high computational cost of dense volumetric representations and the scarcity of large-scale 3D supervision. We introduce SPAR3S, a sparse voxel-aligned 3D latent generative model for conditional scene completion without requiring ground-truth 3D data for supervision. Our key insight is to formulate 3D scene generation in a structured, compact, voxel-aligned 3D latent space where only occupied voxels are represented. We learn this sparse latent space directly from multi-view images using photometric supervision via differentiable 3D Gaussian Splatting. Given a partial set of observed voxels encoded from sparse input views, scene completion reduces to predicting the missing latent
tokens and their spatial support within the voxel grid. To this end, we train a masked autoregressive
transformer that jointly models voxel occupancy and latent
token values, enabling efficient and spatially consistent generation of unseen regions. We demonstrate the effectiveness of our method on synthetic indoor scenes, achieving higher novel-view quality than prior work. We further validate its generalization on RealEstate10k, highlighting its applicability to real-world data.
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All authors Thomas Lucas, Maxime Pietrantoni, Philippe Weinzaepfel, Wonjune Cho, Bardienus Pieter Duisterhof, Vincent Leroy, Jerome Revaud
Computation and Language
Fixed Suffix Dependency Ratio: Quantifying the Dual-Track Mechanism of Gender Assignment in Latvian Loanwords
Yelingyun Zhang
Existing research has repeatedly observed the tendency for English loanwords to cluster in the masculine gender across different recipient
languages, yet the origin of this pattern remains difficult to determine, as fixed morphological rules and default assignments are frequently analysed together. This study proposes the Fixed Suffix Dependency Ratio (FSDR) to quantify the degree of reliance on fixed derivational suffixes across different genders, and to distinguish between morphological anchoring and free-choice in distribution. By examining 1,832 Latvian noun lemma types, the results reveal a significant FSDR asymmetry within the loanword system: feminine loanwords rely significantly more on fixed derivational suffixes, while masculine loanwords are more concentrated in the free-choice zone. This pattern exhibits loanword specificity and has become more pronounced in contemporary usage. FSDR therefore provides a quantitative framework for testing default gender and shows how masculine default can be activated and reinforced under
language contact.
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Systems and Control
Quantifying Power to Voltage and Frequency Dynamics for Oscillation Propagation Assessment
Onur Alican, Dionysios Moutevelis, Marc Cheah-Mañe et al. (+2 more)
The integration of Inverter-Based Resources (IBRs) into power systems introduces multi-timescale dynamics and oscillations which may propagate to distant areas and endanger the safe system operation. These oscillations pose a significant challenge as their source, frequency, and propagation pathways are often challenging to identify in large interconnected systems, comprising numerous synchronous machines and IBRs. For the above reasons, analytical tools that identify the sensitivity of the system to oscillations within a large frequency spectrum, affecting both voltage and frequency variables across various network locations, are of interest. This paper addresses this topic by introducing a frequency-domain framework based on linear analysis, which characterizes the sensitivity to oscillations of each network bus voltage and of each generation unit frequency. Within this framework, two quantitative indicators are proposed, namely the Frequency Sensitivity Index (FSI) and Voltage Sensitivity Index (VSI). These indexes are derived analytically from the transfer functions which relate the active and reactive power injections to each bus with the voltage and frequency variables across the network, derived from the linear Electromagnetic Transient (EMT) power system model. The proposed indices quantify the sensitivity of the system to oscillations of different type and frequency, providing insights for both oscillation detection and propagation analysis. The methodology is applied to a case study based on the modified IEEE 68-bus
benchmark system under partial and full IBR penetration, while its accuracy is validated through EMT time-domain simulations using linear and nonlinear models developed in Matlab/Simulink environment.
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All authors Onur Alican, Dionysios Moutevelis, Marc Cheah-Mañe, Oriol Gomis-Bellmunt, Eduardo Prieto-Araujo
Robotics
Toward Unified Robot Learning: Bridging Representation, Vision-Language-Action, and World Models
Shaunak A. Mehta, Ananya Hazarika, Haochen Zhang et al. (+5 more)
For
robots to operate reliably in real-world environments, they need to perceive their surroundings, act, and reason about the consequences of those actions. Rapid progress in the domains of representation learning, VLA models, and world models has significantly enhanced the capabilities of
robot learning systems, enabling
robots to work in increasingly complex environments. However, these paradigms are typically developed in isolation, resulting in fragmented systems that struggle with generalization, long-horizon temporal
reasoning and planning, and deployment in unstructured environments. In this survey, we present a unified perspective on
robot learning by organizing the existing methods along three complementary axes: understanding through representation learning, acting through VLA models, and
reasoning through world models. We introduce a structured taxonomy that captures key design choices in environment representation, policy learning, and predictive modeling, and summarize the recent progress in these domains. Beyond classifying the existing works, we analyze how these components interact, discuss common limitations, and highlight emerging trends towards more integrated systems. Through this lens, we identify the challenges in the domain of
robot learning, including uncertainty quantification, out-of-distribution generalization, cross-embodiment transfer, long-context understanding, and long-horizon planning. We argue that these challenges arise not only from limitations within individual components but also from the lack of integration across perception, action, and
reasoning. Building on this analysis, we outline future directions towards unified, physically grounded, and probabilistic
robot learning to develop robust robotic systems that maintain consistent internal representations and support decision making over extended interactions in real-world environments.
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All authors Shaunak A. Mehta, Ananya Hazarika, Haochen Zhang, Fan Yang, Ryo Moriyama, Wenkai Li, Yash Patel, Kanata Suzuki
Artificial Intelligence
Speak for Me: Giving LLMs the Situational Awareness to Participate in a Meeting
Muneeb Khan, Frederic Kirstein, Terry Ruas et al. (+1 more)
In online meeting delegation, LLM
agents fail to recognize when to speak. With no structured way to track stances, coverage, and floor, they miss the moments where they should contribute.
Prompt-only delegates stay silent on 51.4% of the absent participant's talking opportunities on the AMI corpus. We present CAPA (Collaborative
Agent Predictive Architecture), an architecture for online meeting delegation. A Perceiver updates the meeting state from each observed turn. A Predictor forecasts how the conversation will continue. A Controller decides whether to speak and which proposition to surface. A Generator phrases the chosen contribution in the participant's style. Two judges score the forecast and the action against the next observed turn. A Recalibrator updates the meeting state from those verdicts for future decisions. To evaluate online delegation, we introduce an episode-level protocol that scores whether, when, and what a delegate contributes around the participant's actual idea units. The protocol's schema-constrained LLM judges align with human annotations at Cohen's kappa = 0.71. On 137 AMI meetings, CAPA reduces the silence rate from 51.4% to 2.5%, doubles credited recovery (26.1 –> 52.2), and keeps
hallucination at 0.6%. The failure mode shifts from omission to selection, with each residual near-miss attributable to a specific module of the architecture. Mechanism ablations identify the meeting state as the lever that closes the recognition gap, where raw-context scaling alone does not.
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All authors Muneeb Khan, Frederic Kirstein, Terry Ruas, Bela Gipp
Computational Engineering
sp-DBA: a general framework for adaptive transform-domain computation
Jingkun Jiang, Pingchuan Deng and Yang Xia
Transform-domain methods simplify analysis and computation, making them central to scientific computing and signal processing. However, existing adaptive strategies often introduce new data structures or require substantial workflow redesign, limiting efficient execution on massively parallel hardware. Here we present spectral dynamic block activation (sp-DBA), an adaptive acceleration framework for transform-domain workflows. sp-DBA dynamically activates transform-domain computation blocks as a calculation evolves, concentrating computation where it is needed while retaining numerical accuracy and optimized transform operations. Across representative workflows in materials science, biology, and optical communications, the demonstrated implementations achieve speedups of up to 28.1-fold for transform-domain updates and up to 8.4-fold overall acceleration; strong- and weak-scaling tests on up to eight GPUs show that the local adaptive update remains effective within distributed FFT workflows. By introducing execution-level adaptivity into mature transform-domain workflows without rebuilding existing solvers, sp-DBA extends the scale, duration, and complexity of scientific simulations and signal-processing calculations on modern parallel systems.
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Systems and Control
Data-Based Clustering and Control of Similar Biological Systems
Peilin Zhang, Antonis Papachristodoulou and Idris Kempf
Cybergenetic control of gene expression enables applications in synthetic biology, drug development, and biomanufacturing. Microfluidic platforms allow the parallel control of large
cell populations. However, the resulting computational burden and intrinsic biological heterogeneity limit the scalability of conventional control strategies. In this work, we propose a similarity-based framework to reduce the computational requirement of controlling large numbers of dynamical systems. Building on existing data-driven methods for quantifying control-relevant similarity from input-output data, we cluster systems with similar dynamics without requiring explicit system identification. Based on this grouping, we develop a hierarchical leader-follower control architecture, where a single controller is designed for each cluster and applied to all members. This significantly reduces the number of control problems that need to be solved. Furthermore, we analyse the closed-loop behaviour within clusters and develop data-driven conditions under which the clustered closed-loop systems remain well-posed. The proposed approach is demonstrated in simulations of gene expression dynamics, showing that similarity-based grouping enables scalable and reliable control of heterogeneous biological systems.
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Artificial Intelligence
Value-Preserving Architectures for Agentic AI Systems
Alessandro Pesare, Tommaso Dolci, Katja Hose et al. (+1 more)
The emergence of agentic AI and LLM-based multi-
agent systems (MAS) presents unprecedented opportunities for automating complex tasks, while simultaneously raising critical concerns about the preservation of fundamental human-centered values, such as
privacy,
fairness, and safety. Although software engineering has traditionally focused on functional correctness, the adoption of LLMs and AI
agents into complex socio-technical systems has intensified the need for responsible software engineering and robust value
alignment. In MAS, architectural design decisions, such as coordination mechanisms, communication protocols, and system
topologies, play a central role in shaping system behavior and the outcomes they produce. This paper argues that architectural choices influence not only the functionality and performance of MAS but can also promote value-oriented system behavior. Therefore, we investigate how different architectural designs support different human-centered values, discussing the following value-preserving architectural patterns: (i) a
privacy-aware architecture with a
federated topology, (ii) a distributed architecture to promote pluralism and diversity, and (iii) a guard-
agent architecture to detect and mitigate unfairness. Finally, we introduce representative use cases to illustrate the proposed architectures in real-world scenarios. By linking architectural design with human-centered values, this work lays the foundation for a unified set of architectural patterns and guidelines towards the design of trustworthy MAS.
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All authors Alessandro Pesare, Tommaso Dolci, Katja Hose, Emanuel Sallinger
Computer Vision
OctWorld: Long-Range World-Consistent Video Generation with Octree-Based 3D Mapping
Zelong Lv, Sicheng Xu, Jianfeng Xiang et al. (+5 more)
We present OctWorld, a
video diffusion framework with persistent 3D
memory for generating explorable, world-consistent, and high-fidelity visual scenes. Given a single image, OctWorld performs stable autoregressive world generation along user-specified camera trajectories. We focus on long-range generation, characterized by extended camera paths and wide viewpoint coverage, where preserving spatial consistency is particularly challenging when previously generated regions are revisited. To address this problem, we introduce OctMap, an extensible and spatially adaptive 3D
memory that progressively fuses generated visual observations and their corresponding depth maps into a global representation. OctMap employs TSDF fusion within a dynamic sparse octree whose spatial resolution adapts to image evidence. This design preserves
geometric and appearance details across diverse scene scales while maintaining low
memory overhead. Experiments demonstrate that OctWorld generates long-range, spatially consistent
videos and outperforms prior methods on both existing
benchmarks and challenging long-range generation settings. OctMap also provides clear advantages over point-based caches and fixed-resolution TSDF volumes. Project page: https://github.com/microsoft/OctWorld
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All authors Zelong Lv, Sicheng Xu, Jianfeng Xiang, Ruicheng Wang, Yue Dong, Yu Deng, Guangzhong Sun, Jiaolong Yang
Human-Computer Interaction
Grounding GUI Design in Computational Psychology
Xianni Wang, Javier Romero Davila, Saku Sourulahti et al. (+2 more)
Creating visually appealing user interfaces often requires extensive manual iteration. We propose an approach that applies answer set programming (ASP) to automatically generate and optimize UI layouts while satisfying design objectives such as grid
alignment, grouping, color harmony, and whitespace, along with designer-specified preferences. Our method encodes constraints on element properties and relative positioning, producing layouts that balance functional and aesthetic goals. We evaluate this approach in three studies. Across two user studies, participants rated layouts generated with the full ASP model higher than both random designs and those based on simple heuristics. Designers reported that ASP-generated layouts supported early-stage sketching and exploration.
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All authors Xianni Wang, Javier Romero Davila, Saku Sourulahti, Torsten Schaub, Jussi P. P. Jokinen
Human-Computer Interaction
From Misconceptions to Evidence: What Science Teachers Make Visible When Co-Designing Agentic Learning Apps
Nizam Kadir, Wei Ting Liow, Sumbul Khan et al. (+1 more)
Science educators increasingly encounter AI tools that generate content, yet disciplinary teaching depends on eliciting learners' models, diagnosing misconceptions, interpreting evidence, and preserving professional judgment. This study asks how science teachers translate such epistemic work into specifications for agentic learning applications. It contributes to the conference theme, "Innovating Pedagogies, Inspiring Minds: Transforming Science Learning," and the Teachers' Professional Learning strand by examining app co-design as a form of pedagogical
reasoning. We conducted a bounded qualitative cross-case analysis of four de-identified artifacts produced in a teacher professional-learning workshop: an experimental-design diagnostic, a Kinetic Particle Theory dialogue guide, a chemistry prior-knowledge checker, and a physics application/scaffolding tool. Each artifact was coded for the disciplinary problem, learner interaction, evidence made visible, teacher authority, and safeguard. All four connected a science-learning problem to an interaction and pedagogically interpretable evidence: misconceptions and gaps, explanations-in-progress, class-level readiness patterns, or investigation performance. However, only two made teacher control or evaluation explicit, and only two named a safeguard. The proposals therefore positioned AI less as an answer generator than as an elicitor, scaffold, and evidence-return mechanism, while leaving decision rights and protections unevenly specified. We argue that teacher professional learning should treat AI app ideation as epistemic specification work. A five-question design protocol–problem, learner interaction, evidence, teacher authority, and safeguard–can help teachers transform science-learning needs into accountable human-AI arrangements before building or adopting a tool.
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All authors Nizam Kadir, Wei Ting Liow, Sumbul Khan, Lay Kee Ang
Computation and Language
RuleMem: Active Rule Memory for Long-Term Conversational Agents
Xingyuan Zeng, Zuohan Wu, Quanming Yao et al. (+5 more)
Question answering
agents in long-term conversations must reason over massive, temporally dispersed dialogue histories. However, existing
memory mechanisms primarily treat past information as passively stored facts, leading to semantic gaps and unreliable
reasoning. To address this limitation, we propose RuleMem, a rule-based
memory framework that induces reusable logical rules from historical interactions to actively guide both evidence
retrieval and
reasoning. Specifically, RuleMem constructs natural-
language Horn clauses from conversations and validates them via a Rule Perplexity Consistency (RPC) mechanism. These induced rules enable the
retrieval of semantically distant evidence while providing an explicit logical structure for answer generation. We conducted a comprehensive evaluation of RuleMem on two long-term conversational
benchmarks, LoCoMo and LongMemEval_s*. In a rigorous comparison against 14 baselines on LoCoMo, RuleMem achieved the highest accuracy, exceeding the baseline average by 27.47 points (a 54.3% relative improvement).
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All authors Xingyuan Zeng, Zuohan Wu, Quanming Yao, Yue Wang, Wei Liu, Libin Zheng, Jiuke Wang, Jian Yin