Artificial Intelligence · Cross-listed from cs.CL
ReLTEx: Reliable LLM-based Taxonomy Expansion
Zeinab Ghamlouch and Mehwish Alam
Recent advances in Large
Language Models (LLMs) have demonstrated strong capabilities in generating semantically relevant concepts and relations, making them promising tools for taxonomy enrichment. However, directly relying on LLM-generated expansions often leads to noisy, redundant, or hierarchically inconsistent structures, limiting their reliability for automated taxonomy expansion. In this paper, we present ReLTEx, a framework for reliable LLM-based taxonomy expansion. ReLTEx combines LLM-driven candidate generation with structure-aware validation and recursive expansion control to improve the consistency and quality of generated taxonomies by reducing
hallucinations. We evaluate the proposed framework using
benchmark taxonomies under a masked taxonomy expansion setting and compare multiple validation strategies. Experimental results, supported by both adapted evaluation metrics and human evaluation, demonstrate that ReLTEx produces more reliable and semantically coherent taxonomy expansions.
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Artificial Intelligence · Cross-listed from cs.CV
CARE: Confidence-Aware Reasoning for Reliable Medical VQA
Yuetian Du, Yucheng Wang, Zhenyuan Chen et al. (+9 more)
Reinforcement Fine-Tuning (RFT) has enabled medical
Multimodal Large
Language Models (MLLMs) to produce Chain-of-Thought (CoT)
reasoning for visual question answering, yet these models suffer from $\textit{confidence miscalibration}$—a systematic gap between expressed certainty and actual diagnostic accuracy that undermines clinical trust. We propose $\textbf{CARE}$, a $\textbf{C}$onfidence-$\textbf{A}$ware medical $\textbf{RE}$asoning framework that jointly optimizes accuracy and calibration through a dual-stage pipeline. First, a scalable Medical-CoT synthesis provides structured cold-start data for Supervised Fine-Tuning. Second, Group Relative Policy Optimization (GRPO) with a novel $\textbf{Confidence-Aware Reward (CAR)}$ mechanism ties the model's confidence to diagnostic correctness within the reward signal. Across three Medical VQA
benchmarks, $\textbf{CARE}$ achieves the highest diagnostic accuracy while obtaining the lowest Expected Calibration Error and
Hallucination Rate, establishing a foundation for trustworthy clinical decision support. Our code is available at https://github.com/anotherbricki/CARE.
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All authors Yuetian Du, Yucheng Wang, Zhenyuan Chen, Luyuan Chen, Rongyu Zhang, Jinjian Zhang, Wei Zhou, Zhijie Xu, Ming Kong, Zhan Zhou, Jie Liu, Qiang Zhu
Artificial Intelligence · Cross-listed from cs.CV
Evidence-Grounded Trustworthy Multimodal Reasoning and Evaluation Benchmark in Complex Urban Scenes
Zhaoyang Wei, Bowen Jiang, Xumeng Han et al. (+6 more)
While
Multimodal Large
Language Models (MLLMs) demonstrate impressive performance in benign scenarios, their cognitive reliability deteriorates significantly in complex scenes under adverse conditions. In these settings, models often rely on implicit inference without sufficient visual evidence, leading to a disconnect between perception and
reasoning. Meanwhile, existing outcome-oriented
benchmarks evaluate only final predictions and fail to diagnose failures in the underlying
reasoning process. To address this gap, the authors propose AD2-Bench, which introduces a Hierarchical Visual Diagnosis framework that decomposes
reasoning into a structured Chain of Evidence (CoE). This fine-grained diagnosis reveals that robust
multimodal reasoning fundamentally depends on accurate evidence acquisition. Building on this perspective, the authors formulate
reasoning from a probabilistic viewpoint and identify two primary causes of
reasoning failure: Spatial Ambiguity, where models fail to distinguish target objects from background clutter, resulting in localization errors; and Semantic Uncertainty, where degraded visual features lead to incorrect semantic interpretation, resulting in understanding errors. To overcome these evidence deficiencies, they further propose Evidence-grounded Visual
Reasoning (EGVOR), which replaces implicit
reasoning with the explicit generation of Evidence Atoms - structured spatial-semantic triplets that enforce tight
alignment between localization and semantic understanding. The model is trained through a hierarchical curriculum that progresses from reflective supervision construction to reinforcement learning, where reducing
reasoning variance is explicitly rewarded. Extensive experiments demonstrate that EGVOR substantially improves
reasoning stability under adverse conditions, providing a more robust framework for trustworthy
multimodal cognition.
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All authors Zhaoyang Wei, Bowen Jiang, Xumeng Han, Jiashu Li, Xuehui Yu, Yuling Liu, Guorong Li, Zhenjun Han, Jianbin Jiao
Artificial Intelligence · Cross-listed from cs.CL
A Cost-Efficient Routing Pipeline for Multilingual Short-Text Classification Using Small Language Models
Wajdi Ben Saad and Safa Madiouni
Multilingual short-text classification supports operational systems such as content moderation, customer support routing, and intent recognition, yet aggregate evaluation often hides large differences between high-resource and low-resource
languages. Uniform inference policies are simple to deploy, but they assume that all
languages are equally well served. In this work, we evaluate a fixed-list routing strategy that keeps stronger
languages on a direct
multilingual path and selectively sends weaker
languages through translation into English before zero-shot classification. The pipeline is fully self-hosted, uses pretrained compact sentence encoders, and requires no task-specific fine-tuning. We test the approach on two
benchmarks chosen to differ in scale and label granularity: a 15-
language subset of SIB-200 for seven-way topic classification and a 15-locale subset of MASSIVE for intent classification over an official 60-intent inventory. On SIB-200, the best overall configuration is R1, which translates only the low-resource tier: high-tier and mid-tier Macro-F1 remain unchanged, while low-tier Macro-F1 rises from 0.4632 to 0.6828. On the MASSIVE subset, the same low-tier intervention raises low-tier Macro-F1 from 0.2143 to 0.4417, but the best overall result is obtained by full translation, R3, at Macro-F1 0.4647. Across these two
benchmarks, selective translation is a reliable intervention for weaker
languages, whereas the optimal routing boundary depends on the task. We therefore report routing through tier-level quality gains and tier-level latency rather than a single global efficiency score.
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Artificial Intelligence · Cross-listed from cs.CV
Temporally Grounded Compositional Camera Motion Understanding via Geometric Knowledge Distillation
Dazhao Du, Shiyan Du, Jian Liu et al. (+8 more)
Understanding camera motion is fundamental to
video perception, with applications in spatial intelligence and controllable
video generation.
Multimodal large
language models (MLLMs) provide a natural interface for this task, but existing work typically assigns one or more labels to an entire clip. Such clip-level recognition overlooks two defining properties of real camera motion: it can change within a shot, and multiple movements can occur simultaneously. We therefore formulate camera-motion understanding as temporally grounded, compositional recognition, which requires a model to localize motion-consistent intervals and identify every movement active within each interval. We introduce CamChoreo, a
benchmark of 4,229 real single-shot clips with expert-annotated temporal segments. Its annotations use a compact vocabulary of 20 direction-aware labels, and nearly half of the segments contain compound camera motion, with multiple movement primitives active simultaneously. Recognizing such fine-grained, compositional motion is hard for current MLLMs, whose visual encoders emphasize semantic content rather than the
geometric evidence on which camera motion depends. Directly injecting features from a frozen 3D foundation model addresses this gap, but requires running the expensive geometry model on every input; we refer to this baseline as CamInject. We instead propose CamDistill, which distills the same
geometric knowledge into lightweight camera
tokens during training and removes the 3D model at inference. CamDistill matches the accuracy of direct feature injection without running the 3D teacher at inference. Together, CamChoreo and CamDistill advance camera-motion understanding from clip-level labeling to temporally grounded, compositional recognition. Project page: https://ddz16.github.io/cammotion.github.io/.
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All authors Dazhao Du, Shiyan Du, Jian Liu, Yongjian Yu, Bohai Gu, Tao Han, Hualuo Liu, Eric Liu, Yujia Zhang, Xi Chen, Song Guo
Artificial Intelligence
FedCGR: Federated Cross-Domain Generative Recommendation
Zhuodong Liu, Hugen Lv, Xiangyu Li et al. (+2 more)
Cross-domain recommendation (CDR) transfers preference knowledge across related domains, but
federated deployment makes cross-domain
alignment difficult because the behavioral anchors that align item spaces, such as overlapping users and shared interaction signals, are often sparse, unavailable, or
privacy-sensitive across clients. To address this tension, we revisit
federated CDR as generation over a stable semantic item
language. By representing items as discrete semantic ID (SID) sequences derived from public item-side metadata, cross-domain item
alignment is induced by a shared vocabulary rather than by exchanging private interactions or aligning domain-specific
embeddings. Directly federating SID-based generators, however, introduces two design constraints: the SID tokenizer must remain fixed to preserve cross-client
token consistency, which creates a semantic-only bottleneck because local collaborative filtering (CF) signals cannot be globally shared or aligned; meanwhile, standard
federated averaging can cause negative transfer under domain heterogeneity. To overcome these constraints, we propose FedCGR, a
federated generative CDR framework that keeps the item
language stable and makes adaptation explicit. FedCGR injects local CF evidence through a reliability-aware semantic interface and trains a prototype-personalized generator that selectively aggregates shared parameters according to domain relatedness while keeping domain-specific quantities local. Experiments on six Amazon cross-domain scenarios show that FedCGR consistently outperforms
federated generative baselines and achieves competitive performance against strong sequential and
federated CDR methods under both full-ranking and sampled evaluation protocols.
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All authors Zhuodong Liu, Hugen Lv, Xiangyu Li, Bohan Guo, Peiyu Hu
Artificial Intelligence
ThinkRetrieve: Retrieval-Augmented Reasoning Traces for Test-Time Scaling
Vaibhav Singh, Soumya Suvra Ghosal, Sarvesh Gharat et al. (+3 more)
Large
Reasoning Models (LRMs) improve performance by allocating additional inference-time compute to generate extended chain-of-thought
reasoning. However, recent studies reveal that sequential test-time scaling often yields diminishing or even negative returns, as longer traces exhibit increased uncertainty, error compounding, and drift from the original problem. We propose ThinkRetrieve, a test-time scaling framework that augments the
reasoning traces of LRMs with dynamically retrieved solved examples at each
reasoning step. Given an external corpus of problems paired with step-by-step solutions, ThinkRetrieve retrieves relevant exemplars at each intermediate step and injects them directly into the thinking trace, providing the model with guidance on how to reason rather than merely what facts are relevant. Experiments across five
reasoning models (1.5B–8B parameters) on GSM-8K, MATH-500, AIME 2025, and SciQ demonstrate that ThinkRetrieve consistently improves accuracy over standard test-time scaling, with relative gains of up to $60\%$ on AIME 2025.
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All authors Vaibhav Singh, Soumya Suvra Ghosal, Sarvesh Gharat, Soumyabrata Pal, Ramasuri Narayanam, Dinesh Manocha
Artificial Intelligence
IO Factory: Simulating AI-Enabled Influence Campaigns at Scale
Lukasz Olejnik, Wenchao Dong, Jonas R. Kunst et al. (+4 more)
We introduce IO Factory, an AI-driven framework for simulating information and influence campaigns as fully integrated, traceable processes. The threat of digital manipulation now extends beyond persuasive text from individual
language models to AI swarms, i.e., persistent groups of coordinated
agents that adapt to platform feedback and disguise organized campaigns as ordinary social interaction. Because such campaigns cannot be identified from isolated messages alone, they must be analyzed across a continuous spectrum of planning, platform action, exposure, interpretation, measurement, and adaptation. IO Factory represents this process inside a controlled simulated platform, linking actor roles, platform actions, exposure records, structured model-based evaluations, and configured changes in the simulated population. We implement the architecture and evaluate it across configurations of up to 100,000
agents. The results show that IO Factory executes campaign timelines at scale and produces inspectable evidence of exposure and measured movement in configured belief variables. By recording the actors, objectives, action constraints, exposure paths, and measurement rules used in each run, IO Factory supports reproducible research and red-team analysis of coordinated influence.
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All authors Lukasz Olejnik, Wenchao Dong, Jonas R. Kunst, Signe Riemer-Sørensen, Tobias Herb, Meeyoung Cha, Daniel Thilo Schroeder
Artificial Intelligence · Cross-listed from cs.CL
FaithformBench: Benchmarking Faithfulness of Mathematical Chain-of-Thought Autoformalisation
Rob Cornish, Iacopo Ghinassi, Po-Hung Yeh et al. (+7 more)
Autoformalisation (AF) systems map natural
language reasoning steps into formal statements in a proof assistant such as Lean. We consider how to assess the faithfulness of these systems. Existing approaches require expensive human-annotated ground truth, or rely on LLM judges or
embedding models, which come with limited guarantees of accuracy. In addition, these methods typically only consider inputs that are known to be correct, and therefore do not assess whether the AF translates incorrect inputs faithfully. To address these limitations, we propose a new
benchmark for AF faithfulness that is cheap to apply, sound under weak assumptions, and assesses both positive and negative examples. Our method is based on automatically generating perturbed
reasoning steps that are designed to be invalid, and then measuring validity preservation on unperturbed steps and invalidity preservation on perturbed steps. We apply our method to eight AF systems across four mathematical
datasets, and observe pervasive sycophancy: many AFs "silently correct" invalid inputs into provable statements. The most validity-preserving fine-tuned AFs are also the most sycophantic, suggesting a tension between validity and invalidity preservation in current AF systems.
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All authors Rob Cornish, Iacopo Ghinassi, Po-Hung Yeh, Shuqi Liu, Qiyuan Xu, Haoxuan Yin, Dominik Wagner, Wenda Li, Yee Whye Teh, Luke Ong
Artificial Intelligence
ComBodied Agents: a New Paradigm of Human-Centric Agentic AI
Qianggang Ding, Xingyao Wang, Rui Feng et al. (+19 more)
After an older adult misses a medication dose, a software
agent can send another reminder and an
embodied agent can bring the medication. Yet neither explains whether the person forgot, is confused, has side effects, or deliberately refused, nor what support is appropriate. This reveals a structural gap in Agentic AI: Digital
Agents primarily transform software states, while
Embodied Agents transform physical states; neither makes a person's evolving state and agency the primary object of modeling, intervention, and evaluation. We introduce Combodied
Agents, a human-centered paradigm that perceives, models, predicts, and supports individual human-state trajectories over time, using software tools, sensors, wearables,
robots, and human services as action channels rather than end goals. We unify fragmented capabilities across personal assistants, health
agents, AI companions, and adaptive human–AI systems into a closed loop: event-based
multimodal perception reconstructs meaningful personal events; longitudinal, correctable
memory provides temporal context; Personal World Models estimate future personal states and outcomes under alternative decisions and interventions; and an admissible intervention policy selects proportionate support under consent, uncertainty, safety, reversibility, and user control. Feedback from the person and environment updates the loop. Rather than requiring an exhaustive Human Digital Twin, the framework uses purpose-bounded, uncertainty-aware, user-correctable representations. We organize the design space by human-state targets, relational contexts, and
agent roles, and propose scenario-centered evaluation, agency-preservation metrics,
benchmark requirements, edge-native personal models, and governance directions. Combodied
Agents shift Agentic AI from external task completion toward sustained human benefit.
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All authors Qianggang Ding, Xingyao Wang, Rui Feng, Zhibin Wang, Feixiang Yao, Kelong Mao, Hao Sun, Zhiyao Luo, Jiankai Tang, Lei Li, Jiadong Guo, Minheng Ni, Weicong Lin, Chenxi Yang, Hongxiang Gao, Zhenghua Chen, Yang Bai, Min Wu, Jun Cheng, Huazhu Fu, Dacheng Tao, Bang Liu
Artificial Intelligence · Cross-listed from cs.SE
GitSkills: A Dataset of Agent Skills on GitHub
Giuseppe Destefanis, Daniel Graziotin, Matteo Vaccargiu et al. (+1 more)
An
agent skill is a folder containing a SKILL.md file with instructions for a
language-model
agent, optionally accompanied by scripts and reference files. The
agent loads the skill when it judges that a task matches the skill description. Anthropic introduced the format in October 2025 as an open specification. Nine months later, public GitHub repositories hold millions of skill files. Skills are unlike the artifacts that software engineering researchers usually mine: they are written mainly in natural
language, a model selects them probabilistically at run time, and no compiler or type checker verifies the selection. Skills also have no central registry or package manager; developers reuse them by copying folders between repositories. How developers write, reuse, and maintain skills is therefore an empirical question, and no existing
dataset records this population. We present GitSkills, a
dataset of 3,797,117 SKILL.md files collected from 282,200 public repositories in July 2026. The
dataset retains every file occurrence with its repository, path, and content hash. We group identical files into 1,877,981 distinct contents and enrich one representative per group with the full text, parsed front matter, folder contents, repository metadata, and, for a subset, the commit history of the file. A single self-contained SQLite file supports research on the adoption, reuse, structure, authorship, maintenance, and security of
agent skills.
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All authors Giuseppe Destefanis, Daniel Graziotin, Matteo Vaccargiu, Marco Ortu
Artificial Intelligence
Enhanced Filtering Algorithms for the Euclidean Traveling Salesperson Problem and its variants in Constraint Logic Programming
Alessandro Bertagnon and Marco Gavanelli
The Traveling Salesperson Problem (TSP) is one of the best-known problems in computer science and arises in many engineering applications, such as smart vehicles and intelligent transportation systems. In the "Euclidean" case, each node is defined by its coordinates in the plane and distances are computed using the Euclidean metric. In the Constraint Programming (CP) literature, the Euclidean TSP is typically addressed by computing the full distance
matrix and treating it as a general case; however this approach ignores the
geometric information carried by the points' coordinates. In this work, we propose new filtering
algorithms, implemented in Constraint Logic Programming (CLP), that exploit such
geometric information to achieve stronger constraint propagation than existing approaches. Moreover, we show how this methodology can be extended to other Euclidean variants of the TSP, including the Euclidean Generalized Traveling Salesperson Problem (EGTSP), which is relevant in practical routing and logistics applications. Experimental results demonstrate the computational advantages of the proposed approach.
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Artificial Intelligence · Cross-listed from cs.CL
VibeLifeBench: Can Your Life Agent Be Proactive and Persistent in a Living World?
Xiaohongshu Dots Studio and Evolvent AI
Large
language model (LLM)
agents are increasingly deployed as personal assistants. Existing evaluations, however, mostly use short, self-contained requests in static environments. Everyday life assistance is different. A task runs for weeks rather than minutes. The world keeps changing while the
agent is not being prompted. Many constraints are never stated outright. An
agent that merely answers the request in front of it will fail at such a task. What is needed instead is an
agent that stays proactive and consistent. It decides on its own when to act, when to ask, and when to stay silent. It notices changes that nobody announced. It keeps one plan coherent from the first day to the last. No current
benchmark measures this. We introduce VibeLifeBench, a
benchmark of 200 long-horizon tasks across ten everyday-life domains. Each task is a scripted multi-week timeline in a simulated world of 22 mock services. The world advances on its own clock, and many of its changes are silent, so only an
agent that re-inspects the world discovers them. Every task is graded by fine-grained, weighted checks that read only what the
agent actually left behind, covering the end state, the timeliness of its actions, and whether it upheld the implicit constraints. We evaluate seven frontier models. All of them score low, which shows how far current
agents are from assisting with real life. We will open-source all tasks, environments, and the evaluation framework.
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Artificial Intelligence
Hypothesis Frontier: Verifier Guided LLM and Symbolic Search for First-Order Induction
Serafim Batzoglou
First-order concept synthesis asks a system to infer one formula that classifies labeled objects consistently across several finite relational structures. Every candidate can be evaluated exactly, but quantified first-order formulas form a vast search space, and LLM outputs are often semantically promising without being fully correct. We introduce Hypothesis Frontier, a verifier-guided neurosymbolic framework that evaluates each LLM formula on every training object, retains the strongest verified hypothesis across rounds, and uses its remaining errors to guide subsequent generation. Symbolic processing repairs invalid formulas while remaining anchored to the LLM-generated hypothesis, and simplifies train-valid formulas without changing any training prediction. Under matched models, problem sets, and LLM-round budgets, Hypothesis Frontier solves substantially more problems than repeated original-
prompt generation. After the final formulas are selected, exact simplification shortens many train-valid formulas while preserving every training prediction. Exact symbolic
reasoning therefore helps both to solve more induction problems and to compress many of the resulting formulas.
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Artificial Intelligence · Cross-listed from cs.LG
TACTICL: Task-Aware Compression of Tabular ICL Models
Mykhailo Koshil, Matthias Feurer and Katharina Eggensperger
The strong performance of foundation models for tabular tasks comes at substantial inference costs. Distilling models into task-specific architectures reduces model size and computational demands but also sacrifices in-context adaptability. Here we introduce TACTICL, an automated task-aware compression framework for tabular in-context learning models that jointly prunes
transformer layers and replaces them with lightweight adapters trained on downstream tasks, thus blending in-context with in-weight learning. We study TACTICL on 47
benchmark datasets and show that we can substitute up to 85% of layers without substantial performance drop on a given downstream task. We further show that TACTICL maintains robustness to data shifts, leaving its in-context ability intact. Overall, TACTICL provides a robust framework for exploiting the depth-wise redundancy of tabular foundation models by combining task-specific adaptation and structured compression. We provide the code at: https://github.com/Hebog/tfm_compression
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Artificial Intelligence · Cross-listed from cs.SD
Whisper-Aware LLM: Self-Supervised Uncertainty Learning for Robust Whispered Speech Recognition
Gaopeng Xu, Zhenyu Wang, Zheng Xue et al. (+2 more)
The signal ambiguity of whispered
speech drives ASR systems toward two opposing failure modes: failing to capture whispered
speech or hallucinatory transcription of noise. This paper introduces the Whisper-Aware LLM, a framework that teaches an Audio-LLM to perceive and react to this uncertainty. Our model develops an intrinsic self-awareness by learning to quantify the physical deficiencies of acoustic signals through targeted self-supervised tasks. This learned uncertainty is then operationalized via a novel Confidence-Fused Decoding mechanism, which provides both high-level instructions and frame-level
attention modulation to the LLM decoder. Our experiments confirm the effectiveness of this approach. The model sets a new state-of-the-art on whispered
speech with a 17% relative CER reduction on AISHELL6-Whisper. At the same time, it directly addresses the reliability trade-off, with
hallucination rates dropping from over 25% to 4.5%.
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All authors Gaopeng Xu, Zhenyu Wang, Zheng Xue, Yinfeng Xia, Haitao Yao
Artificial Intelligence · Cross-listed from cs.CV
MIRA: Medical Image Reflection for Agentic Diagnosis
Shengzhi Wang, Jun Yang, Kai Wu et al. (+11 more)
Medical visual
agents can use tools to inspect images and retrieve external knowledge, but indiscriminate tool use may introduce noisy or misleading evidence. Reliable diagnosis therefore requires not only acquiring additional observations, but also verifying whether tool actions are necessary and whether the resulting evidence supports the current hypothesis. We introduce MIRA (Medical Image Reflection for Agentic Diagnosis), a medical visual diagnostic framework for autonomous evidence search and reflective verification. MIRA dynamically invokes image-processing operations, including zooming, grounding, pointing, rotation, and measurement, as well as web search, while evaluating the relevance and consistency of the acquired evidence. We develop MIRA through a two-stage training strategy. First, a tool-augmented Monte Carlo Tree Search data engine explores diverse diagnostic hypotheses and jointly verifies visual grounding accuracy and semantic consistency to construct supervised fine-tuning trajectories. Second, reinforcement learning further improves decision-making through online reflective principle
evolution: failure cases are distilled into candidate principles, and only principles that improve held-out rollout rewards are retained. Across nine medical visual
reasoning benchmarks, MIRA achieves an average score of 64.73, improving its Qwen3-VL-8B backbone by 7.44 points. It also increases useful tool-use judgments from 56.2% to 73.8% and reduces harmful judgments from 8.9% to 1.6%. Qualitative analyses show that MIRA can re-examine evidence, correct premature conclusions, and adapt its tool-use strategy. Project page: https://MIRA-VL.github.io/
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All authors Shengzhi Wang, Jun Yang, Kai Wu, Xiaozhong Ji, Yiwen Ye, Ziyang Chen, Mingliang Xiong, Wen Fang, Mingqing Liu, Mengyuan Xu, Miaoxuan Shan, Caiyan Liu, Bin He, Qingwen Liu
Artificial Intelligence · Cross-listed from cs.CL
Reference-Free Post-Training of Open Large Language Models for Multilingual Machine Translation
Chris Han, Pengzhi Gao, Pei Fu et al. (+1 more)
We study reference-free post-training for
multilingual machine translation with open large
language models. Starting from the supervised-finetuned MiLMMT-46-v0.1 models, we apply Group Relative Policy Optimization (GRPO) with a reward that averages two reference-free quality estimation models and is gated by
language identification. We then linearly interpolate the supervised fine-tuning (SFT) and reinforcement learning (RL) model checkpoints to obtain MiLMMT-46-v1.0. Across 46
languages, the resulting models consistently improve translation quality over their SFT counterparts, outperform strong recent open baselines, including Seed-X, HY-MT2, and TranslateGemma, and achieve leading reference-free scores against evaluated proprietary systems such as Google Translate, Gemini 3 Pro, and GPT-5. We further investigate on-policy
distillation (OPD) and find that it reaches, but does not surpass, the quality frontier achieved by RL with checkpoint interpolation. We release the models and code to facilitate future research.
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All authors Chris Han, Pengzhi Gao, Pei Fu, Jian Luan
Artificial Intelligence · Cross-listed from cs.CL
Surfacing the Unsaid: CUE-Bench for Affective Stance in Chinese Discourse
Zhenyan Zheng, Yunyao Zhang, Junxi Sheng et al. (+2 more)
Emotion understanding in discourse requires
reasoning beyond surface sentiment because speakers often convey affect through indirect, implicit, polite, ironic, or deliberately mismatched expressions. Existing
emotion benchmarks mainly annotate surface polarity or final
emotion categories, while lacking a structured account of how explicit expression, implicit affect, pragmatic intent, and fine grained
emotion interact. This limitation makes current evaluations insensitive to cases where affective meaning is concealed, weakened, inverted, or pragmatically reshaped, thereby obscuring model failures in deeper
emotion understanding. To address this gap, we introduce CUE Bench, a Chinese Unsaid
Emotion benchmark that centers on Affective Stance and covers diverse communicative scenarios. CUE Bench constructs nine human interpretable affective stances from explicit implicit polarity interaction and further provides intent and fine grained
emotion annotations for structured affective inference. Experiments show that incorporating Affective Stance improves fine grained
emotion recognition by 3.1 percentage points and pragmatic intent detection by 8.1 percentage points over strong baselines.
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All authors Zhenyan Zheng, Yunyao Zhang, Junxi Sheng, Junqing Yu, Zikai Song
Artificial Intelligence · Cross-listed from cs.CV
Modelling Geographic Atrophy Progression using Implicit Neural Representations
Simone Sarrocco, Paul Friedrich, Florentin Bieder et al. (+4 more)
Age-related Macular Degeneration (AMD) is the major cause of blindness in the Western world. Its late dry phase is characterised by irreversible atrophic areas, namely Geographic Atrophy (GA). Longitudinal Fundus Autofluorescence (FAF) image acquisitions are currently the main tool for assessing lesion growth over time at the image level. However, due to its highly individualised progression, the
evolution of late AMD remains poorly understood. In this work, we propose using Implicit Neural Representations (INRs) to model GA progression at the individual level in a low-data setting. Our approach generates both FAF and GA
segmentation at both past and future time points. Among the comparison models, our method achieves competitive
segmentation quality across different scenarios, yielding the lowest Mean Absolute Error (MAE) for the GA lesion area and the highest DICE score, without sacrificing FAF image quality. The code is available at https://github.com/SimoneSarrocco/ga-progression-with-inrs.
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All authors Simone Sarrocco, Paul Friedrich, Florentin Bieder, Christina Bornberg, Philippe Valmaggia, Peter Maloca, Philippe Cattin
Artificial Intelligence · Cross-listed from cs.CV
Fast and Memory-Efficient Wavelet Convolutions via I/O-Aware Reformulation
Amit Aflalo, Shahaf E. Finder, Roy Amoyal et al. (+2 more)
Wavelet convolution (WTConv) has emerged as an increasingly popular drop-in replacement for standard convolutions, expanding a network's receptive field exponentially with the number of decomposition levels while keeping the parameter count linear. However, its reference implementation is severely
memory-bound due to excessive data movement through high-bandwidth
memory (HBM). We develop an I/O model of WTConv to characterize this bottleneck and use it to guide three algebraic reformulations: (1) recomputing the inexpensive Haar analysis butterfly on chip, (2) collapsing the multi-level synthesis cascade into a single closed-form pass indexed by output-coordinate bits, and (3) folding learned per-channel scales into the convolution weights. Together, these reformulations enable an I/O-aware fused implementation that substantially reduces HBM traffic. We evaluate the WTConvNeXt configuration across decomposition levels and a broad range of tensor shapes. Despite performing comparable arithmetic, the reference WTConv is substantially slower than the depthwise convolution it replaces. Our reformulation reduces modeled HBM traffic by approximately $2.55\times$, yielding up to a $4.35\times$ training speedup over the reference while roughly halving peak
memory usage. Thus, our reformulation preserves the benefits of WTConv while substantially reducing its execution time and
memory footprint, removing the systems overhead that previously limited its practical efficiency.
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All authors Amit Aflalo, Shahaf E. Finder, Roy Amoyal, Eran Treister, Oren Freifeld
Artificial Intelligence · Cross-listed from cs.CV
BPG: Balancing Plasticity and Generalization for Domain Incremental Learning
Qiang Wang, Songlin Dong, Shaokun Wang et al. (+5 more)
Deep neural networks excel in various tasks but struggle to generalize across evolving data distributions, leading to significant performance degradation under domain shifts. Domain incremental learning (DIL) addresses this challenge by enabling models to continuously adapt while retaining prior knowledge. Among existing DIL approaches, the parameter-isolation paradigm achieves state-of-the-art performance. However, these methods often adopt a one-size-fits-all approach to adapt to new domains, resulting in either insufficient learning capacity or redundant parameters. In this work, we propose BPG, a unified framework that addresses both challenges through two complementary components: BPG-Adapter, which dynamically determines each domain's adapter hidden dimension based on domain-specific feature separability, and BPG-Inference, a soft domain mixture strategy that integrates multiple domain-specific models at test time, mitigating domain ID misselection. Experimental results on DomainNet, CDDB, and CORe50 demonstrate that BPG consistently outperforms uniform adapter-based approaches and hard domain selection strategies, achieving state-of-the-art average accuracy while reducing forgetting to as low as 0.22% on DomainNet.
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All authors Qiang Wang, Songlin Dong, Shaokun Wang, Jizhou Han, Xiang Song, Chenhao Ding, Yuhang He, Yihong Gong
Artificial Intelligence · Cross-listed from cs.CV
Beyond Fixed Luminance: Towards Panchromatic and Orthochromatic Image Colorization
Swarnim Maheshwari, Syed Imam Ali and Vineeth N. Balasubramanian
Most image colorization systems operate in $Lab$ space by predicting chroma ($ab$) while preserving an input-derived luminance channel ($L$). While effective on standard
benchmarks, this fixed-luminance design restricts brightness changes and becomes unreliable when grayscale formation deviates from natural-image luminance, as in historical orthochromatic photography. We propose a luminance-agnostic colorization framework that formulates colorization as full-RGB image editing using a foundation image-editing model. To bridge modern panchromatic and historical orthochromatic conditions, we introduce a mixed grayscale objective that trains the model under both standard luminance grayscale and a red-insensitive grayscale formation. Experiments on COCO, ImageNet, and a multi-instance
benchmark show that our method is competitive on standard grayscale inputs and substantially more robust under orthochromatic inputs, with qualitative comparisons and a human study indicating fewer visible color artifacts.
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Artificial Intelligence
EvoMem: Memory-Augmented Evolution for Code Optimization
Viktor Volkov, Valentin Khrulkov, Andrey V. Galichin et al. (+8 more)
Successful mutation strategies in evolutionary code search may contain reusable knowledge that is useful beyond a single run, and in some cases may transfer across related tasks and domains. However, existing LLM-driven evolutionary frameworks largely discard such knowledge, repeatedly rediscovering similar ideas and limiting opportunities for cross-run and cross-task learning. We introduce EvoMem, a persistent
memory architecture for LLM-based evolutionary program search that captures and reuses candidate mutation knowledge. EvoMem converts successful mutation events into structured, task-aware advice for future runs. It operates in two phases: after each run, it extracts and stores promising ideas with provenance, and during subsequent
evolution, it retrieves a small set of relevant instructions based on the current task and program context to guide mutation. Across
geometric optimization, multi-hop question answering, GPU kernel optimization, and related
benchmarks, our experiments show positive average improvements in target metrics or search speed for most evaluated settings, while also revealing variability across tasks. Overall, EvoMem provides evidence that persistent
memory can reduce some redundant exploration and improve the reuse and adaptation of successful strategies in LLM-driven evolutionary search.
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All authors Viktor Volkov, Valentin Khrulkov, Andrey V. Galichin, Danil Sivtsov, Nikita Glazkov, Olga Volkova, Konstantin Pchelin, Iaroslav Bespalov, Dmitry V. Dylov, Petr Anokhin, Ivan Oseledets
Artificial Intelligence
ChemWorld: Programmable Chemical Worlds for Controlled and Replayable Agent Experimentation
Jiangjie Qiu, Yijun Li and Xiaonan Wang
Autonomous chemistry increasingly depends on environments in which
agents can repeatedly act, observe, and adapt.Physical laboratories provide essential real-material evidence but are costly to repeat and difficult to use for tightly matched interventions, whereas most digital environments keep the underlying experimental world largely fixed. We introduce ChemWorld, a programmable chemical environment in which reusable process and observation components are compiled into executable worlds. ChemWorld separates the public experimental contract available to an
agent from evaluator-owned chemical and material laws. Researchers can therefore vary world composition and operating conditions, or change a single hidden law while holding the public task and interaction conditions fixed. Transactional execution records operations, failures, resource changes, and state transitions, allowing complete environment-action trajectories to be replayed exactly and audited. Full-census qualification covered the reference registry, 52 generated compositions, and module, interface, compilation, and invalid-action tests. Eight deterministic experimental cases demonstrated shared lifecycle semantics, failure recovery, and exact replay, while six parent-child world-fork pairs isolated the effects of single private-law interventions under matched public conditions. An independent
agent also completed a full lifecycle in a non-reference world through the same public interface. Within the declared component and model domain, ChemWorld provides a controlled and replayable substrate for studying experimentation across systematically varied chemical worlds, complementary to physical-laboratory evidence and calibration.
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Artificial Intelligence · Cross-listed from cs.CV
MVTrack: Ultrafast Appearance-Free Moving Object Tracking from Compressed Bitstreams
Iñaki Erregue, Kamal Nasrollahi and Sergio Escalera
Deploying modern
video trackers at scale is bottlenecked by the computational cost of RGB-based object detectors. To this end, we present MVTrack, an ultrafast tracker for moving objects that operates directly on H.264 bitstreams. MVTrack combines MVDet, a lightweight detector for motion vector fields, with MVLink, a minimalist kinematic association module. On VIRAT, MVTrack outperforms YOLO26n while using 60$\times$ fewer parameters, requiring 40$\times$ fewer FLOPs, and reducing CPU latency by 8.6$\times$. These results demonstrate that compressed
video data alone can enable accurate and scalable surveillance tracking, thereby bypassing the need for pixel reconstruction.
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Artificial Intelligence
SkillLens: Visual Skill Cards for Retrieval-Augmented GUI Action Prediction and On-Policy Distillation
Zhou Liu, Ligang Huang, Zeli Su et al. (+5 more)
Computer-using
agents can perceive rich software interfaces, yet their decisions often lack visual procedural
memory: they may recognize individual controls without identifying which familiar workflow is active, which control matters next, or what evidence would confirm progress. Raw interaction traces preserve such information but are long and noisy to condition on, whereas text-only skills often omit the visual state that makes a procedure applicable. We introduce Visual Skill Cards (VSCs), a state-conditioned
memory representation that binds reusable procedures with applicability cues, visual evidence, and verification signals. SkillLens constructs VSCs from heterogeneous interaction experience through Trace-to-Visual-Skill-Card and, at inference time, retrieves relevant cards and selectively expands only the evidence needed by a fixed visual-
language model executor for grounded GUI action prediction. The same representation also supports CardDistill, which uses VSC evidence as privileged teacher context to train a student that acts without runtime card
retrieval. Across
Multimodal-Mind2Web and WebLINX-BrowserGym, SkillLens improves the frozen GPT-5.4-mini executor by +11.6 points in Step SR and +2.9 points in Overall, respectively; CardDistill further improves the corresponding student-only Qwen3-VL-2B metrics by +12.0 and +3.2 points.
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All authors Zhou Liu, Ligang Huang, Zeli Su, Zewei Pan, Zhaoyang Han, Xing Chen, Yuanfeng Song, Wentao Zhang
Artificial Intelligence · Cross-listed from cs.CY
The GenAI Catch-22: Use of Generative Artificial Intelligence in Norwegian Newsrooms During the 2025 Parliamentary Election
Mari Reisjå and Anders Sundnes Løvlie
The increasing use of Generative Artificial Intelligence (GenAI) in journalism raises concerns about possible detrimental effects both on journalism and its democratic function. We explore these risks through a case study of GenAI in Norwegian Newsrooms during the 2025 parliamentary election campaign. Based on interviews with managers and journalists over a ten-month period, we analyse how ambitious
visions fared in the face of technological and practical challenges. We highlight the risk of an internal threat stemming from the journalists' own use of AI, contrasting the dominant focus on external disinformation threats. We show how newsroom managers shared sociotechnical imaginaries resulting in unrealistically optimistic beliefs about the capabilities of the technology and the pace of development, leading to plans for audience-facing GenAI services collapsing and giving way to more mundane uses of GenAI tools internally in the newsrooms. Furthermore, we identify a vulnerability in the newsroom's resilience against GenAI influence: a GenAI Catch-22. In order to monitor the GenAI tools and prevent errors and undue influence, newsrooms rely on human expertise. But by using GenAI extensively, the newsrooms risk a deterioration of human expertise, preventing them from monitoring the GenAI systems adequately.
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Artificial Intelligence
Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information
Kaivalya Rawal, Daria Onitiu, Brent Mittelstadt et al. (+2 more)
Explainable Artificial Intelligence (XAI) seeks to explain how an Artificial Intelligence (AI) system arrived at a particular decision. We propose ”Rule of Thumb” (RoT) explanations, a new approach to XAI based upon a novel formulation that identifies the most relevant features for predicting the behaviour of an AI system, for a particular datapoint. We show how RoT is well-suited to enable XAI in: (a) zero-shot classification using large
language models (LLMs), (b)
auditing of opaque AI systems without model access, and (c) the use of AI in scientific discovery. Additionally, RoT meets specific requirements from leading AI regulations, provides a familiar interface and visualisations for XAI practitioners, is model-agnostic, and is substantially faster than alternatives. Code available at: https://github.com/KaiRawal/Rule-of-Thumb-Explaining-Artificial-Intelligence-Systems-using-Partial-Information
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All authors Kaivalya Rawal, Daria Onitiu, Brent Mittelstadt, Sandra Wachter, Chris Russell
Artificial Intelligence
Compositional Benchmark Synthesis for Hierarchical Human Action Recognition
Farnaz Soleimani, Abdelghani Chibani, Yacine Amirat et al. (+1 more)
Recognizing human behavior across levels of abstraction, from atomic actions to long-horizon intentions, requires data annotated along a semantic hierarchy. Large corpora provide isolated, atomically labeled clips without temporal composition, whereas recorded composite-activity corpora offer shallow, domain-narrow, fixedhierarchies. A
benchmark-generation and evaluation frameworkis proposed that synthesizes a four-level hierarchical-intention
benchmark, spanning actions, activities, low-level intentions (LLIs), and high-level intentions (HLIs), from a flat single-label action corpus while retaining real pre-extracted features at the action level. Episodes are assembled by a transition model under a subject-consistency constraint, and a coverage-aware sampler reduces the subject usage Gini from 0.566 to 0.248. Synthesizing such a
benchmark raises a circular-supervision risk that recorded
datasets avoid: if the rules generating the episodes also govern the evaluation, models can succeed by recovering the generator rather than through genuine
reasoning. Validity is addressed by design, holding sequence-generation rules disjoint from the first-order-logic rules used at evaluation. The instantiation yields 15,002 episodes. Four reference baselines from different model families characterize difficulty, not as recognition methods. A compositional held-out gap of 0.13 to 0.17 macro-F1 appears across all baselines, including a graph-aware model that recognizes best yet does not close the gap, indicating a structural property of the
benchmark rather than a model artifact. A logic-free baseline still violates the held-out semantic rules above their intrinsic data rate, and the order-destroying control changes macro-F1 within seed variation, serving as a generator-consistency check. Theontology, transition model, and generator are released so the
benchmark can beregenerated and extended.
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All authors Farnaz Soleimani, Abdelghani Chibani, Yacine Amirat, Ghazaleh Khodabandelou