Artificial Intelligence · Cross-listed from cs.CV
FlashDecoder: Real-Time Latent-to-Pixel Streaming Decoder with Transformers
Minguk Kang and Suha Kwak
Real-time
video generation demands fast decoding as much as fast denoising, yet current latent
video diffusion models rely on 3D convolutional decoders that are slow and
memory-intensive at high resolutions or for long
video. We introduce FlashDecoder, a fast,
memory-efficient pure-
Transformer video decoder that decodes latents to pixels frame by frame. At each step, the current frame attends only to a fixed-size window of past frames through a rolling KV cache. The fixed temporal window keeps decoding fast and
memory bounded regardless of
video length, enabling constant-latency streaming. Because frames are processed sequentially, temporal causality is enforced without explicit
attention masks, enabling training at resolutions up to 1080p and matching the reconstruction quality of convolutional decoders. On the Wan2.1 and Wan2.2 latent spaces, FlashDecoder matches each convolutional decoder in reconstruction quality (e.g., 41.55dB vs. 41.49dB PSNR at 1080p) while decoding 3.6x-4.7x faster with up to 11x less
memory on a single H100 GPU. With architecture-aware inference optimizations, the speedup widens to 12x.
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Artificial Intelligence · Cross-listed from cs.SE
StructureClaw: Traceable LLM Agents and an Executable Benchmark for Structural Engineering Workflows
Sizhong Qin, Yi Gu, Yao Jiang et al. (+13 more)
Addressing a structural-engineering request requires more than a single answer; it requires a chain of interdependent artifacts: interpreted requirements, a computable model, validation records, solver outputs, applicable engineering checks, and a final report. Evaluations centered on question answering or script generation may therefore reward fluent outputs even when the underlying workflow is incomplete, inconsistent, or non-executable. We present StructureClaw, an artifact-centered workbench in which LLM
agents operate through governed engineering skills, typed tools, shared artifact state, and local analysis backends, together with StructureClaw-Bench, an executable
benchmark of 150 controlled scenarios spanning standard workflows, interactive robustness, and
multimodal structural-model reconstruction. Its analyzable standard and
multimodal cases require both strict one-to-one structural-model matching and numerical-response agreement with frozen reference responses from the selected analysis engine; interactive cases instead require positive clarification or recovery evidence together with safe non-execution when appropriate. A trial succeeds only when every fixture-required assertion passes. Across nine text-
agent configurations, generic-only execution passed the model-artifact check in 87.0% of retained outcomes but achieved only 22.0% E2E Success, whereas automatic StructureClaw reached 82.9%. Interactive and
multimodal evaluations further identify semantic state consistency and executable model reconstruction as the dominant remaining bottlenecks. The code and
benchmark are available at https://github.com/structureclaw/structureclaw.
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All authors Sizhong Qin, Yi Gu, Yao Jiang, Ao Cai, Changjian Zhou, Shaoxuan Shuai, Jiachang Wang, Tianhao Shen, Yueqiang Li, Xinhao Li, Li Zeng, Yueshi Chen, Dachen Gao, Genrong Xu, Wenjie Liao, Xinzheng Lu
Artificial Intelligence
Proof-or-Stop: Don't Trust the Agent, Trust the Evidence – Loop Engineering for Verifiable Evidence-Gated Lifecycle Control
Jek Huang, Jeffery Hsia, Jiayi Sun et al. (+3 more)
Autonomous coding
agents increasingly execute multi-step software work, but lifecycle states such as reviewed, tested, DONE, and ready-to-merge remain claims unless supported by current evidence. We present Proof-or-Stop Lifecycle Control, a method that permits lifecycle transitions only when fresh, tracked-source-state-bound, mechanically verifiable evidence satisfies the relevant gate. The method treats
agent outputs as claims rather than lifecycle state, and uses proof operationally to mean gate-admissible evidence under a stated trust model, not semantic program correctness. We evaluate an open-source implementation through mechanism tests, a powered control-policy ablation, and operated self-application evidence. The unattended-loop engine passed 10 of 10 scenarios with zero false-DONE, and local-key receipt bundles rejected 18 tamper classes with zero false accepts. In a 9,240-
cell ablation, the pre-registered A4 versus A2-
prime comparison reduced visible-pass/hidden-fail amplification from 31 of 1,800 injected
cells under a compute-budgeted naive loop to 2 of 1,800 under the gated loop, a 1.6 percentage-point improvement in not-amplified rate with a 95 percent confidence interval of [0.8, 2.5]. A near-compute A3 versus A4 comparison, 14 of 1,800 versus 2 of 1,800, indicates that the gain is associated with enforcing review as a lifecycle gate rather than merely adding a reviewer. The self-application corpus contains 565 stories and 1,007 review findings, with 94.8 percent resolved, plus a 68-row high/critical cross-vendor exhibit. These results support Proof-or-Stop as a model-agnostic, host-neutral control layer for deciding which autonomous-
agent claims a lifecycle may act on. The evaluation is limited to one model family, 24 ablation tasks, and a self-hosted corpus.
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All authors Jek Huang, Jeffery Hsia, Jiayi Sun, Freddie Shi, Wei Huang, Ian H. White
Artificial Intelligence · Cross-listed from cs.LG
Innocuous-Seeming Data, Latent Ideology: Ideological Generalisation in Finetuned LLMs
Robert Graham, Edward Stevinson and Yariv Barsheshat
Finetuning
language models on small, curated
datasets is standard practice for adapting them to specific policies or domains. We show that finetuning on narrow, factually-defensible, moderation-passing data can cause broad ideological shifts across unrelated domains, while preserving general capabilities. Training GPT-4.1 on right- or left-leaning economics Q&A yields matched ideological shifts on topics such as criminal justice, the environment, and cultural taste. The same effect appears with plausibly-deployed
datasets such as workplace HR policy and practical finance queries, as well as on a science-pseudoscience axis where food-safety finetuning increases sycophantic agreement with users expressing false health beliefs. We call this phenomenon ideological generalisation and propose a methodology to measure two properties: breadth, how far the shift reaches across topics absent from training, and amplification, how much finetuning intensifies the shift relative to few-shot prompting on the same examples. We show that few-shot prompting indicates the direction of generalisation but finetuning pushes the model to further extremes, including to far out-of-distribution outputs such as endorsements of race-IQ connections and political violence. The effect replicates on Gemma-3, holds under judge-free evaluations and external
benchmarks, survives mixing with generic data, and leaves GSM8K accuracy within $\pm 1$pp of the baseline.
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Artificial Intelligence
Reachability-Aware Pretraining for Efficient Target-Oriented Path Exploration in Temporal Knowledge Graph Reasoning
Chien-Liang Liu and Tsao-Lun Chen
Temporal Knowledge Graph (TKG)
reasoning under the extrapolation setting focuses on
forecasting future time-stamped events (facts) from historical data in a temporal knowledge graph. Existing approaches, reinforcement learning (RL)-based multi-hop
reasoning methods are prominent for TKG
reasoning because they produce human-interpretable predictions via explicit multi-hop path tracing. However, during RL training, rewards are typically sparse, and exploration is highly inefficient due to the vast, time-evolving action space. These issues hinder efficient training and often limit overall performance. To address these challenges, we propose RAPTOR (Reachability-Aware Pretraining for Efficient Target-Oriented Path Exploration), a self-supervised pretraining method that injects a reachability-aware inductive bias to the
agent. By learning to estimate the reachability of candidate actions to the target entity, RAPTOR reduces exploration over unpromising paths and provides a strong initialization for downstream RL fine-tuning. Experimental results on the ICEWS14, ICEWS05-15, and ICEWS18
datasets demonstrate that RAPTOR pretraining markedly improves the training efficiency and consistently outperforms conventional baselines, establishing it as an effective approach for enhancing RL-based multi-hop
reasoning methods for TKG
reasoning.
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Artificial Intelligence · Cross-listed from cs.IR
Does generative AI supersede supervised XMLC? A Benchmark Study on Automated Subject Indexing with German Scientific Literature
Maximilian Kähler, Katja Konermann, Lisa Kluge et al. (+1 more)
With a large controlled vocabulary as the label set, the task of automated subject indexing in a library can be understood as a multi-label classification task. If the set of subject terms is large, the problem fits the Extreme Multi-Label Classification (XMLC) objective. In this study, we apply a selection of specialised supervised XMLC methods to the test case of subject indexing contemporary German scientific literature, collected at the German National Library (DNB). We contrast these results by including a classical lexical matching baseline and three of our own recently developed LLM-based methods into the
benchmark.
Algorithms are evaluated and compared in several metrics. This includes binary relevance comparisons with previously indexed material, as well as graded relevance ratings by professional subject librarians. A challenge for all methods is to reliably make suggestions from the long tail of the subject vocabulary. We find that supervised XMLC
algorithms relying on
transformer-based dense features give best results in terms of overall binary relevance metrics. However, focusing on graded relevance and performance in the long tail of our subject vocabulary, the LLM-based generative methods give better results, making them a promising alternative for future productive use.
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All authors Maximilian Kähler, Katja Konermann, Lisa Kluge, Markus Schumacher
Artificial Intelligence · Cross-listed from cs.LG
Asymmetric Peak-Aware Loss for Peak-Critical Time Series Forecasting
Theivaprakasham Hari, Yanan Xin, Winnie Daamen et al. (+2 more)
In many operational time-series
forecasting applications, such as crowd demand
forecasting, the risk related to under-prediction is substantially higher than that of over-prediction. Accurate prediction of rare demand spikes plays a critical role in downstream tasks. Yet most time-series forecasters are trained with symmetric objectives (e.g., MSE, MAE) and evaluated primarily on aggregate error, which can mask failures in extreme-values and peak-timing predictions. We introduce Asymmetric Peak-Aware Loss (APAL), a simple, model-agnostic objective that (i) penalizes under-predictions more heavily and (ii) increases the training weight of peak regions within each forecast window. We further propose a peak-critical evaluation protocol that complements MAE/MSE with channel-wise tail error (Top-10% and Top-1%) and peak metrics (precision, recall, F1 under timing tolerance, and peak timing error). We evaluate APAL on long-horizon multivariate
forecasting across five state-of-the-art backbones, with a focus on pedestrian demand
forecasting using (i) a production-ready subset of the City of Melbourne pedestrian hourly count
dataset and (ii) a beach visitor count
dataset. The generality of the loss function for time-series
forecasting is tested on additional
benchmarks. Across peak-critical
datasets and settings, APAL improves tail accuracy and peak-prediction quality while exposing a controllable trade-off with aggregate error, making it a practical solution when peak-prediction failures are the dominant operational concern.
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All authors Theivaprakasham Hari, Yanan Xin, Winnie Daamen, Serge Paul Hoogendoorn, Sascha Hoogendoorn-Lanser
Artificial Intelligence · Cross-listed from cs.SD
RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems
David Ayllon, Alice Baird, Jeffrey Brooks et al. (+11 more)
Current voice AI
benchmarks typically evaluate isolated capabilities such as
speech intelligibility, word error rate, or text-based dialogue quality, but they rarely test whether systems harness the acoustic information that distinguishes spoken
language from its textual representation. To this end, we introduce the Real World Voice EQ Bench, a multidimensional
benchmark for evaluating voice AI across text-to-
speech (TTS),
speech-to-
speech (STS),
speech understanding (SU), and automatic
speech recognition (ASR). Our evaluations indicate that performance is highly dimension-specific. For TTS, naturalness, expressiveness, identity stability, and reliability are largely independent evaluation dimensions. For STS, access to audio does not guarantee use of vocal affect, and some
agents remain largely transcript-driven. For SU, models perform unevenly across paralinguistic tasks. For ASR, real world accent,
emotion, noise, and conversational conditions expose failures that are not captured by established clean-
speech benchmarks. Together, these results show that voice AI should be evaluated as a profile of acoustic, expressive, interactional, and robustness capabilities rather than by a single aggregate score.
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All authors David Ayllon, Alice Baird, Jeffrey Brooks, Franc Camps-Febrer, Jakub Piotr Cłapa, Theo Lebryk, Jens Madsen, Olya Ossipova, Sharath Rao, Hoon Shin, Tigran Soghbatyan, Georg Streich, Rashish Tandon, Panagiotis Tzirakis
Artificial Intelligence · Cross-listed from cs.RO
Interventional Causal Circuits for Safe Robot Action Testing and Failure Recovery
Naren Vasantakumaar, Tom Schierenbeck and Michael Beetz
Safe physical AI for
robot actions are required not only likely to succeed but tested to be safe before execution. In practice, however, formal testing of motion parameters is computationally expensive, and the cost scales poorly with the dimensionality of the action space. When a proposed action is rejected by a tester, the naive response is to resample blindly until a passing candidate is found. This is wasteful, uninformative, and offers no convergence. We argue that rejection should instead trigger causal diagnosis: a principled identification of which action parameter caused the failure and what corrective value maximises the probability of passing testing under the interventional probability distribution. We propose a closed-loop framework that couples a Joint Probability Tree (JPT) with a Causal
Circuit derived from a Marginal-Deterministic Variable Tree, enabling exact polytime computation without retraining, or additional data collection. The framework validates tractability of all interventional queries before the
robot begins operating, and out-of-support candidates are detected and excluded from correction automatically. We perform experiments in a ROS2 simulation environment, and the framework demonstrates complementary roles across quality of distribution: under a high-quality JPT, the Causal
Circuit reduces failed attempts by 10.3% and under a degraded JPT, it reduces total failed attempts by 37%. Every rejected plan produces a structured, interpretable causal report naming the primary cause variable, its observed value, and the recommended corrective region, supporting operator oversight and autonomous recovery without a separately trained failure model.
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Artificial Intelligence · Cross-listed from cs.LO
Can LLMs Build a MaxSAT Solver from Papers? The CoreForge Experience
Ruben Martins
We report on CoreForge, an experience in using large
language models (LLMs) to build an unweighted MaxSAT solver from research papers rather than from an existing solver codebase. The project focuses on unsatisfiability-based MaxSAT
algorithms and follows an iterative workflow that combines paper discussions with ChatGPT, implementation through Codex
prompts, and repeated LLM-assisted code audits and revisions. Although the codebase implements several
algorithms and solver components, our evaluation focuses on configurations that combine core-guided optimization, lightweight preprocessing, core minimization, integration with integer linear optimization backends, and a new core-sequence lookahead approach. Our experience suggests that LLMs can support solver implementation from papers, while requiring external validation, benchmarking, and human guidance. In our experiments, fuzzing and MaxSAT Evaluation instances did not reveal wrong answers in the tested configurations, although performance remains below the best hand-engineered MaxSAT solvers. We summarize what worked, what remained difficult, and the lessons for future LLM-assisted solver development.
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Artificial Intelligence · Cross-listed from cs.LG
Evaluating Epistemic Uncertainty: Beyond OOD Detection and Active Learning
Jakub Paplhám, Willem Waegeman, Eyke Hüllermeier et al. (+1 more)
Current evaluation of epistemic uncertainty relies on tasks such as out-ofdistribution detection and active learning. However, the Bayes-optimal decision strategies for these tasks do not coincide with the scores commonly used to quantify epistemic uncertainty. Building on the epistemic reject-option framework, we evaluate epistemic uncertainty using its ability to identify regret, the reducible error. Formulating selective prediction as a constrained optimization over coverage, expected risk, and regret, we prove the optimal selector is a thresholded convex combination of the ground-truth aleatoric and epistemic uncertainties. This theoretical unification exposes a weakness in recent uncertainty disentanglement literature: we demonstrate that standard correlation metrics between learned components do not necessarily predict their actual operational utility. We instead propose to evaluate the achievable risk, regret, coverage surface of the decomposition as a diagnostic for joint disentanglement and utility. Benchmarking standard methods on
datasets with dense human annotations reveals that decision-theoretic rankings can disagree substantially with proxy-task rankings, including pairwise rank inversions between methods that are top-ranked on one criterion and bottom-ranked on other.
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All authors Jakub Paplhám, Willem Waegeman, Eyke Hüllermeier, Vojtěch Franc
Artificial Intelligence · Cross-listed from cs.SE
Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality
Saima Afrin, Alessandro Midolo, Camilo Escobar-Velásquez et al. (+5 more)
Large
Language Models (LLMs) perform differently on identical programming tasks when prompted in different natural
languages, a phenomenon known as
language bias. While this behavior has been widely studied for general text generation, its impact on code generation quality and programming conventions remains largely unexplored. We investigate how the
language used to describe programming tasks affects the source code generated by GPT-4o mini, DeepSeek, and Claude. Our study comprises 460 coding tasks spanning Python (230) and Java (230). We translate and manually curate the original English
prompts into Chinese, Hindi, Spanish, and Italian while preserving their technical meaning. We evaluate the generated code using multiple dimensions, including functional correctness through test pass rates, structural quality using established code metrics, issues detected by static analysis tools, and lexical characteristics such as the
language used in identifiers and comments. Our results show that (i) English
prompts do not consistently produce the best functional correctness or code quality, (ii) the impact of
prompt language depends on both the programming
language and the LLM, and (iii) generated code frequently mixes English with the
prompt language in comments and string literals. These findings provide the first curated
multilingual benchmark for studying
language bias in code generation and offer insights for developing more robust
multilingual code generation systems.
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All authors Saima Afrin, Alessandro Midolo, Camilo Escobar-Velásquez, Mario Linares-Vásquez, Weiyuan Ding, Bowen Xu, Massimiliano Di Penta, Antonio Mastropaolo
Artificial Intelligence
CrimeNER Demo: Named-Entity Recognition in the Crime Domain
Miguel Lopez-Duran, Julian Fierrez, Aythami Morales et al. (+7 more)
We present CrimeNER Demo, an AI-powered platform that enables us to extract general crime-related information from documents and classify them into entity types with two levels of granularity. We provide pretrained NER models on the CrimeNER database, and we give the possibility to users to provide their own annotated data to train models for their own specific cases. This demonstrator aims to promote crime-related NER research and provides a practical tool to automatically extract crime information for researchers and law enforcement agencies. The demonstrator includes: i) Pretrained NER models on the crime domain; ii) Possibility to finetune the models on specific data annotated by the user; and iii) An automatic pipeline to extract and annotate crime entities from documents. The demo platform, a tutorial to run the demo, and a
video demonstration are publicly available on GitHub.
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All authors Miguel Lopez-Duran, Julian Fierrez, Aythami Morales, Daniel DeAlcala, Gonzalo Mancera, Javier Irigoyen, Ruben Tolosana, Oscar Delgado, Francisco Jurado, Alvaro Ortigosa
Artificial Intelligence
Transcoders for Investigating Deception in Language Models
Darius Lim, Nathan Leow and Xin Wei Chia
Transcoders have recently emerged as a promising approach for mechanistic interpretability (MI), enabling
circuit-level analysis of model behaviour. In this paper, we investigate the use of transcoders to analyse deceptive behaviour in
language models, a behaviour that poses a safety and security risk. Using a Qwen3-4B model with pre-trained transcoders, specifically per-layer transcoders (PLTs), we construct attribution graphs that capture feature activations and inter-feature dependencies, allowing
circuit-level analysis of deception. Through feature steering and
circuit analysis, we identified a dictionary of deception-related features and show that these features exert a stronger influence on deceptive outputs, as they produce predictable shifts between deceptive and non-deceptive responses. These findings suggest that deception emerges from internal model mechanisms and highlight the potential of transcoders for behavioural monitoring and early detection of security vulnerabilities related to malicious behaviours in
language models.
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Artificial Intelligence
Global Index on Responsible AI: 2026 Report
Rachel Adams, Fola Adeleke, Ayantola Alayande et al. (+4 more)
Grounded in human rights-based frameworks such as the UNESCO Recommendation on the Ethics of AI, the Global Index on Responsible AI (GIRAI) examines how countries translate responsible AI commitments into enforceable protections, institutional capacity, and redress mechanisms. GIRAI 2026 assesses these across five dimensions: Inclusion and Diversity, Ethics and Sustainability, Labour and Skills, Trust and Safety, and AI Use in Public Service. A global network of 135 country-level researchers collected and assessed 68,138 data points on 38 indicators organised across three pillars: government AI policy and implementation (17 indicators), civil society engagement (5), enabling conditions (15), and documented cases of government deployment of unacceptable-risk AI systems. The data covers November 2023 to September 2025. A score of 100 is derived from the indicators to rank all countries. Findings show that while responsible AI governance is expanding, with 126 of 135 countries having at least one government policy or initiative across the 17 AI Policy indicators, this does not often translate into meaningful protection. For instance, Global South countries account for 203 of 306 new cases of indicators with frameworks since the first edition, yet 78% of their frameworks remain non-binding compared with 42% in the Global North. Government commitment to AI governance also does not extend to their own
algorithms: whereas Transparency and Explainability is one of the strongest performing indicators, with 58% of countries having some framework, only 18% require Public Disclosure of Government
Algorithms. Credible evidence of government deployment of unacceptable-risk AI systems was also found in 35 countries. These findings show that responsible AI governance must move beyond framework adoption toward enforceable rights-based protections, resourced oversight institutions, and accessible redress.
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All authors Rachel Adams, Fola Adeleke, Ayantola Alayande, Selamawit Engida Abdella, Ana Florido, Nicolás Grossman, Leah Junck
Artificial Intelligence · Cross-listed from cs.CL
Dialogue Summarization with Emotion Dynamics Using Topic- and Participant-Centric Decomposition
Linyun Xiang, Mark Neerincx and Stephanie Tan
Existing text summarization research has focused much on monologic information (e.g., newspaper articles, reports) without accounting for the interaction between speakers or authors. In contrast, dialogues are a rich communication channel where multiple participants conduct back and forth exchanges to construct meaning. We propose a dialogue summarization framework that explicitly models both semantic and
emotion dynamics using
multimodal dialogue inputs, built on an adapted hierarchical Chain-of-
Agents approach. We decompose dialogues from two perspectives: (1) topic segments based on the utterances of all participants, and (2) participant-specific utterance segments. These are used to generate corresponding summaries while incorporating automatically inferred
emotions. Topic- and participant-level summaries are aggregated into a dialogue summary capturing semantic content and
emotion trajectories. To evaluate beyond content accuracy, we introduce
emotion trajectory metrics measuring how well summaries preserve emotional flow. Experiments with small
language models on
multimodal dialogue
datasets show that our framework produces summaries with both semantic and
emotion content. Further experiments on explicit
emotion label availability highlight the efficacy of our proposed methodology and the opportunities in dialogue analysis using
language models.
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Artificial Intelligence
AI vs Human Expert Reasoning: Assessing Agreements in Building Typology Predictions based on Street View Imagery
Zahratu Shabrina, Muhammad Asa, Jin Rui et al. (+2 more)
This research investigates the potential of
Vision-
Language Models (VLMs) to infer building typologies: Construction, Current Use, and Storeys from Google Street View (GSV) images. Predictions generated by VLMs are compared with inference by human experts (civil engineers and architects) as a source of manually labelled ground-truth data. We evaluate several state-of-the-art VLMs, including GPT-4o, Claude 3.5 Sonnet, and Gemini 2.0 Flash. By applying different scaling strategies and prompting techniques, we found that Chain-of-Thought
prompts provide an overall more stable model performance. We also investigate the
reasoning behind VLMs' building-typology predictions by examining the probabilities of keywords appearing in AI explanations. This enabled us to analyse patterns in these reasonings and identify key themes driving both agreements and disagreements between VLM and expert labels. We find that AI tends to focus on visual indicators, whereas human experts place greater emphasis on broader contextual cues and domain knowledge, in addition to visual cues. Overall, VLM can approximate experts' capability in building-typology classification at scale, with an average accuracy of approximately 70%. The study demonstrates the VLM's potential for AI automation in tasks that require pattern recognition and object identification in an urban context. AI have the potential to serve as complementary and collaborative tools for urban analysis, leveraging their strengths in understanding visual patterns. This study contributes to the exploration of the efficiency and scalability of AI visual prediction and provides insights into the
reasoning processes that could support automation processes in urban analysis and prediction.
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All authors Zahratu Shabrina, Muhammad Asa, Jin Rui, Lu Yin, Stephen Law
Artificial Intelligence · Cross-listed from cs.SD
Large Audio Language Models for Spoofing-Aware Speaker Verification
Sofya Savelyeva, Mariia Perunova, Evgeny Kushnir et al. (+3 more)
Recent advances in text-to-
speech and voice cloning make high-quality spoofing inexpensive and scalable, threatening voice authentication systems, especially automatic speaker verification (ASV). Existing defenses mainly address this threat through binary countermeasures (CMs) for deepfake detection or spoofing-aware speaker verification (SASV), where current systems are dominated by modular ASV-CM fusion and cascaded pipelines. Although large audio
language models (LALMs) have shown promise on related audio tasks, including CM and ASV, their use for SASV remains unexplored, despite their capacity to produce natural-
language rationales for
auditing and robustness beyond discriminative predictions. This work systematically evaluates LALMs for SASV against conventional pipelines under zero-shot prompting, supervised adaptation,
reasoning-oriented training, and reinforcement-learning-based optimization. Our results show that pretrained LALMs are near chance in the zero-shot setting, confirming that they are not natively suited to SASV, but that task-specific adaptation closes this gap. We further find that competitive SASV performance can be achieved through several distinct routes. These findings position LALMs as a promising and auditable foundation for unified SASV, while clarifying where conventional cascade systems still lead.
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All authors Sofya Savelyeva, Mariia Perunova, Evgeny Kushnir, Artem Dvirniak, Dmitrii Korzh, Oleg Y. Rogov
Artificial Intelligence · Cross-listed from cs.CV
FoMoVLA: Bridging Visual Foresight and Motion Guidance for Vision-Language-Action Models
Wei Li, Peijin Jia, Yuan Ma et al. (+9 more)
Vision-
Language-Action (VLA) models have achieved impressive results in visuomotor policy learning, yet remain fundamentally reactive, mapping current observations and
language to actions without explicit forward prediction of world dynamics. Existing visual foresight methods predict future visual states but lack explicit motion guidance: they show where to go but not how to get there. We argue that future feature prediction and sparse point tracking are naturally complementary: the former provides the goal state, while the latter captures the continuous motion path toward it. We propose FoMoVLA, a framework that augments VLA representations with explicit spatio-temporal supervision by jointly learning future feature foresight and sparse 2D point tracking, enhancing the continuous action policy. FoMoVLA introduces compact foresight
tokens to decode future feature states, decodes sparse temporal 2D point trajectories to model compact
geometric motion, and couples both through a lightweight future-conditioned cross-
attention module that enables consistent
reasoning between anticipated states and point dynamics. Extensive experiments on LIBERO, RoboCasa GR-1 Tabletop, and LIBERO-Plus demonstrate state-of-the-art performance and strong zero-shot generalization. Project page is available at https://liauto-research.github.io/FoMoVLA.
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All authors Wei Li, Peijin Jia, Yuan Ma, Xuefeng Jiang, Titong Jiang, Sheng Sun, Yujian Li, Xin Wen, Han Hong, Zhikang Liu, Bailin Li, Kun Zhan
Artificial Intelligence · Cross-listed from cs.CL
The Misclassification of Autistic Writing as AI-Generated
Summer Chambers and Matthew C. Kelley
Recent findings suggest that detection models for artificial intelligence (AI) cannot accurately identify AI-generated text and may exhibit bias against certain minority groups. In the present study, anecdotal claims that autistic writers more often have their work flagged as AI-generated are examined empirically. A corpus of approximately 60,000 Reddit posts split into "likely-autistic" and "general-Reddit" subcorpora is used to compare the distribution of probabilities output by the OpenAI GPT-2 detection model. Differences in textual features between subcorpora are observed and compared to reported features of AI-generated text. Results showed that while less than two-percent of either subcorpus was flagged as AI-generated by the model, significantly more texts from the likely-autistic subcorpus were flagged. Connections between features of text with likely-autistic authors and AI-generated text were not straightforward. The widespread use of AI-detection models with a potential bias against autistic writers in their output
prompts ethical scrutiny, and the authors recommend further critical examination of the models themselves as well as their use in academic contexts.
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Artificial Intelligence · Cross-listed from cs.CV
VideoSEMA: a scalable and efficient Mamba-like attention for video understanding
Nhat Thanh Tran, Fanghui Xue, Shuai Zhang et al. (+4 more)
We present for
video understanding (classification) a split space-time
attention model, VideoSEMA, consisting of a scalable and efficient Mamba-like
attention (SEMA) block in space and a softmax temporal
attention in time. In each frame, SEMA
attention applies a local window
attention in parallel with a global averaging in a Mamba macro-architecture, which is called Mamba-like. Under certain rank conditions, we prove that the computationally cheaper split space-time
attention is equivalent to full space-time
attention. On
benchmark K400 data sets, VideoSEMA out-performs heavier
vision transformer and Mamba models. On
benchmark SSv2 data, VideoSEMA leads in top-1 accuracy among models of similar parameter sizes. As image resolution scales up from standard $224^2$ to $1024^2$ on K400 and without fine-tuning, VideoSEMA degrades much more gracefully than VideoMamba in accuracy. It is promising to extend VideoSEMA to longer
videos with a dilated/sparse temporal
attention.
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All authors Nhat Thanh Tran, Fanghui Xue, Shuai Zhang, Jiancheng Lyu, Yunling Zheng, Yingyong Qi, Jack Xin
Artificial Intelligence · Cross-listed from cs.CL
Harnessing LLMs for Reliable Academic Supervision: A Comparative Study
Akash Raj
Large
language models routinely produce fluent answers to single-shot
prompts, yet deploying them as reliable components of a domain decision system is substantially harder. Closing this gap is the work of harness engineering: the deliberate composition of deterministic scaffolding (symbolic filters,
retrieval, schema-typed I/O, LLM-as-judge loops, HITL gates, persistent state, audit trails) around an LLM core. We present a case study in academic supervision, a domain combining high-stakes recommendation, longitudinal accountability, and structured operational workflows. We compare a baseline Academic Supervision Assistant (ASA), a GPT-5 chatbot with no scaffolding, against a multi-module system, Academic Supervision System (ASuS) that wraps the much smaller GPT-4o-mini in a LangGraph harness with symbolic semantic
retrieval, schema-validated outputs, LLM-as-judge with bounded retry, HITL gates, deterministic weighted risk scoring with LLM narration, and a per-node SQLite audit trail. The evaluation rubric is retargeted at six harness-mechanism dimensions (grounding, explainability, consistency, process integrity, cognitive load, constraint adherence). A blind ten-rater hybrid evaluation, supplemented by a 2 x 2 model-harness ablation, finds that ASuS, despite using a much smaller base model, outscores ASA on every dimension. Across ten raters the pooled mean for ASuS is 4.08 versus 1.23 for ASA, and 8 of 10 raters reject the null at alpha = 0.05 on a paired Wilcoxon test; full numbers are in Sections 6.4 and 6.7. The ablation confirms that the structural contributions of the harness are largely model-invariant. We extract seven recurring harness-engineering patterns and argue that where reliability, traceability, and institutional consistency matter more than open-ended fluency, harness engineering challenges the prevailing 'bigger model is better' intuition.
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Artificial Intelligence · Cross-listed from cs.CV
Pretraining Multiple Instance Learning Networks with Multi-Teacher Distillation from Pathology Slide Foundation Models
Mingxi Fu, Jiawen Li, Renao Yan et al. (+4 more)
Multiple instance learning (MIL) has become the main paradigm for whole-slide image (WSI) analysis in computational pathology. However, existing MIL aggregators are still typically trained from scratch for each downstream task, relying on limited slide-level labels to learn both aggregation mechanisms and downstream discriminative representations simultaneously. As a result, they often suffer from unstable optimization, overfitting, and limited transferability. Similar to pretrained ResNet and
Vision Transformer models in natural image learning, MIL also requires reusable pretrained initialization. However, high-quality slide-level pretraining data remain scarce, and MIL models are usually lightweight and weakly supervised, making large-scale pretraining difficult in practice. To address this challenge, we propose a
distillation-based pretraining framework for MIL, which leverages two slide-level foundation models, TITAN and CARE, as teachers to transfer their representational knowledge into a diverse set of MIL architectures. To effectively balance supervision from different teachers, we further introduce an angular dispersion normalized
distillation loss. The distilled weights are then used as initialization for downstream adaptation. We conduct systematic evaluations on 15
benchmark datasets under both linear probing and full-parameter fine-tuning, and further validate its advantages in few-shot scenarios. Experimental results show that pretraining generally improves MIL aggregators over from scratch training, especially in linear-probing and few-shot settings, while maintaining the computational efficiency of lightweight MIL models. Code is available at https://github.com/fu0201/MIL_Pretrained.
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All authors Mingxi Fu, Jiawen Li, Renao Yan, Jiali Hu, Qiehe Sun, Tian Guan, Yonghong He
Artificial Intelligence · Cross-listed from cs.CV
Team RAS in 11th ABAW Competition: Multimodal Ambivalence Recognition Approach
Elena Ryumina, Maxim Markitantov, Alexandr Axyonov et al. (+4 more)
Automatic recognition of ambivalence and hesitancy is challenging because these states may be expressed through inconsistent linguistic, acoustic, facial, and contextual patterns, while top-performing systems often rely on computationally expensive ensembles. We present a single text-centered
multimodal approach for
video-level ambivalence and hesitancy recognition for the 11th Affective & Behavior Analysis in-the-Wild (ABAW) Challenge. The proposed approach combines linguistic, acoustic, facial, and scene features using text-centered
multimodal fusion model. Text Residual Fusion treats text as the anchor modality and applies gated residual adjustments based on the other modalities. Experiments on the Behavioural Ambivalence/Hesitancy (BAH) corpus confirm that text is the strongest unimodal modality. The Text Residual Fusion model achieves an average Macro F1-score (MF1) of 75.14% across the Development and Public Test subsets. On the Private Test subset, it reaches an MF1 of 78.24%, outperforming the text model by 4.03%. These results demonstrate that complementary
multimodal information can improve recognition performance without requiring a large model ensemble.
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All authors Elena Ryumina, Maxim Markitantov, Alexandr Axyonov, Fedor Shchetinin, Timur Abdulkadirov, Dmitry Ryumin, Alexey Karpov
Artificial Intelligence
InCarEmo: A Multimodal Dataset for In-Cabin Emotion Recognition and Driver State Monitoring
Hao Yang, Yanyan Zhao, Kewei Zhao et al. (+11 more)
Understanding driver
emotion and state is critical for the next generation of intelligent in-cabin systems that ensure safety and enhance human-vehicle interaction. However, existing public
datasets for in-cabin affective computing are largely limited to visual modalities and rarely include conversational information, making it difficult to capture the linguistic and interactive cues underlying driver
emotion. To address these gaps, we introduce InCarEmo, a
multimodal dataset for in-cabin
emotion recognition and driver state monitoring. InCarEmo integrates RGB and infrared
video, in-cabin audio, and dialogue text collected from scripted in-cabin scenarios designed to simulate realistic driver behaviors, covering diverse lighting conditions and driving contexts. The
dataset supports three primary tasks: 1)
multimodal emotion recognition, 2) fatigue detection, and 3) distraction monitoring. In addition to the original Chinese data, we construct an auxiliary English
benchmark to support preliminary cross-lingual evaluation. We provide a unified
benchmark with extensive baseline results across unimodal and
multimodal methods, including analyses under modality-missing and noise conditions. Experimental results demonstrate the benefits of
multimodal fusion and reveal remaining challenges under real-world noise and low-light conditions. By releasing InCarEmo, we aim to establish a comprehensive foundation for robust, interpretable, and human-centric in-cabin affective understanding, promoting safer and more empathetic driver-vehicle interaction.
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All authors Hao Yang, Yanyan Zhao, Kewei Zhao, Hongbo Zhang, Tian Zheng, Yusheng Liu, Xing Fu, Bichen Wang, Yu Zhang, Hao He, Zhen Wu, Xuda Zhi, Yongbo Huang, Bing Qin
Artificial Intelligence
Stop Thinking, Start Looking: Efficient Post-Training for Multimodal Document Question Answering via Reasoning-Free Alignment
Harikrishnan P M, Goutham Vignesh, Ganesh Parab et al. (+4 more)
Efficient
multimodal document question answering with explicit visual grounding, locating the precise document region that supports each answer remains an open challenge. Current approaches bifurcate into Supervised Fine-Tuning (SFT), which requires large annotated
datasets and reaches optimization plateaus, and
reasoning-centric Reinforcement Learning (RL), which depends on verbose intermediate traces that inflate inference
token cost without clear benefit. We introduce Perception-RFT, a training framework that applies Group Relative Policy Optimization (GRPO) to
multimodal document QA, bypassing intermediate
reasoning tokens to directly align visual features with structured grounding outputs. To rigorously evaluate the necessity of
reasoning, we construct a
reasoning variant under identical reward settings. We find that
reasoning-enabled models suppress their
reasoning traces during training, converging to direct perception-based policies at the 4B parameter scale, reducing per-query inference
token length by more than 60%, while
reasoning-enabled RL underperforms perception-only training. Through a fine-grained analysis of Qwen3-VL-4B optimization dynamics, we confirm that SFT saturation and cold-start RL instability established in text-domain post-training extend to
multimodal, and identify a previously uncharacterized Grounding Divergence: a selective trade-off between semantic robustness and
geometric precision on two out of distribution (OOD)
benchmarks (4,828 samples) under joint RL optimization. We further show that an early SFT$\rightarrow$RL transition achieves comparable precision with 65% less training data.
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All authors Harikrishnan P M, Goutham Vignesh, Ganesh Parab, Saisubramaniam Gopalakrishnan, Vishal Vaddina, Varun V, Rohit Agrawal
Artificial Intelligence · Cross-listed from cs.RO
An Intelligent-Cloud Edge Multimodal Interaction System for Robots
Zihan Guo and Xiaoqi Li
Robust human-
robot interaction in complex environments requires accurate gesture perception, semantic scene understanding, and reliable task planning under limited onboard computing resources. This paper presents a cloud-edge
multimodal interaction framework that integrates an enhanced YOLO-based gesture detector with coordinated large
language model (LLM) and
vision-
language model (VLM)
agents. The proposed detector, incorporates the Convolutional Block
Attention Module (CBAM) into the neck and replaces the baseline bounding-box regression objective with Distance-IoU (DIoU) loss. These modifications improve feature discrimination and localization for small or partially occluded gestures in complex backgrounds. The cloud layer performs gesture detection, scene understanding,
multimodal fusion, and action planning, whereas the TonyPi
robot locally handles data acquisition, communication, action execution, and feedback. Experiments on a public gesture
dataset and a custom
dataset show that YOLO-DC achieves precision values of 98.9% and 95.0%, with mAP@0.5 values of 90.7% and 92.7%, respectively. System-level evaluation yields success rates of 95%, 88%, and 82% for single-action, composite-action, and
vision-dependent tasks. A 30 participant evaluation yields an overall mean satisfaction score of 3.69 out of 5. These results demonstrate the feasibility of combining refined gesture detection with
multimodal agents for resource-constrained robotic interaction.
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Artificial Intelligence
Project Kaleidoscope: Contextual, Human-Aligned Evaluation for Real-World AI Applications
Leanne Tan, Rohan Jaggi, Shaun Khoo et al. (+1 more)
Evaluations (Evals) are a deployment bottleneck for real-world AI applications: public
benchmarks rarely match a team's users, context, or policies, and human review is often tedious to scale. Motivated by our work with AI applications in the public sector, this project addresses recurring evaluation challenges encountered when applications must satisfy local policy and governance requirements. We present Kaleidoscope, an integrated workflow for contextual functional evaluation that links persona-based test generation, contextualized rubrics, and human review for reliability-gated automated scoring. Generated test cases are scored against application-specific rubrics; human annotations provide reviewable labels; and LLM judges automate scoring only when their agreement with those labels meets a configured threshold. Kaleidoscope is therefore a practical, inspectable, iterative workflow for product teams. We report early evidence from a three-week pilot across four organizational use cases and custom-rubric judge experiments on 108 annotated Q&A pairs spanning four domains and 14 evaluation dimensions. The results highlight useful features for end-to-end reliable, automated scoring.
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All authors Leanne Tan, Rohan Jaggi, Shaun Khoo, Roy Ka-Wei Lee
Artificial Intelligence
SmartRAG: Native Graph-Based RAG for Mobile Device
Zhihan Jiang, Meng Li, Shenghao Liu et al. (+6 more)
Deploying large
language models (LLMs) as personal assistants on mobile devices demands
privacy, low latency, and offline availability, yet the computational cost of
giant models clashes with strict edge-hardware budgets. We argue that this tension cannot be resolved by model compression alone; it requires decomposing on-device intelligence into complementary functional roles. We present SmartRAG, a fully on-device framework that organizes an intelligent assistant around four coordinated modules – Perception,
Memory, Focus, and Thinking. At the core of SmartRAG is EvoNER, a continually learnable named-entity recognizer that incrementally expands its label inventory through teacher-distilled updates, enabling the system to absorb previously unseen entity types without retraining the backbone LLM. Extracted knowledge is stored in MRGraph, a three-layer provenance-preserving knowledge graph, and retrieved at query time through a hybrid pipeline combining graph traversal, lexical matching, and dense semantic search. The on-device LLM is invoked only for high-value semantic operations – labeling, planning, and answer synthesis – keeping inference costs bounded. Experiments on four QA
benchmarks (TriviaQA, Natural Questions, HotpotQA, MultiHopQA) show that SmartRAG with a quantized 1.7B-parameter backbone achieves multi-hop
reasoning performance competitive with models up to 18$\times$ larger, while running entirely on commodity smartphones within practical
memory and latency envelopes.
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All authors Zhihan Jiang, Meng Li, Shenghao Liu, Keran Li, Ruiben Zhou, Wei Wang, Xianjun Deng, Shuai Wang, Haipeng Dai
Artificial Intelligence · Cross-listed from cs.SE
LLM-Driven Approach to Modeling Tool Interoperability in Automotive Domain
Nenad Petrovic, Jiajie Zhang, Vahid Zolfaghari et al. (+1 more)
Interoperability between heterogeneous modeling tools remains a significant challenge in Model-Driven Engineering (MDE), particularly in the automotive domain where multiple modeling
languages, as well as defacto standard proprietary and open-source tools coexist. This paper presents an LLM-driven approach for automated model interoperability by considering two relevant aspects: 1) mapping model instances to a target metamodel 2) merging of metamodels. The proposed methodology is demonstrated through transformations involving Ecore and SysML v2 based metamodels and incorporates structural validation of generated model instances against user-defined target models. Automotive case studies illustrate the feasibility of the approach and show that large
language models can significantly reduce manual transformation effort while generating structurally valid target models for cross-tool interoperability.
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All authors Nenad Petrovic, Jiajie Zhang, Vahid Zolfaghari, Alois Knoll