Artificial Intelligence · Cross-listed from cs.CL
Staying In Character: Perspective-Bounded Memory For Book-Based Role-Playing Agents
Xushuo Tang, Junhe Zhang, Zihan Yang et al. (+4 more)
Recent LLM role-playing systems build character
agents from novels by extracting characters, scenes, and relations. Yet long-narrative role-playing suffers from two failures: Factual Overreach, where shared
retrieval or parametric
memory lets a character use facts outside its perspective, and Stylistic Monotony, where profile descriptions flatten a character into a fixed voice. To address these failures, we propose REVERIEMEM, a three-layer
memory architecture for book-based character
agents. The episodic layer stores first-person scene
memories; the semantic layer stores visibility-tagged facts; and the personality layer stores situation-dependent
speech and behaviour patterns. For evaluation, we construct KBF-QA, a 4,386-question
benchmark over eight novels for testing knowledge boundaries. REVERIEMEM improves Knowledge Boundary Fidelity by 34.6 percentage points over the strongest prior method. On BOOKWORLD's five-dimension pairwise narrative protocol, REVERIEMEM achieves a ~ 79% win rate, suggesting that perspective-bounded
memory improves both boundary fidelity and character-grounded narrative generation.
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All authors Xushuo Tang, Junhe Zhang, Zihan Yang, Yifu Tang, Sichao Li, Longbin Lai, Zhengyi Yang
Artificial Intelligence · Cross-listed from cs.LG
TL++: Accuracy and Privacy Preserving Traversal Learning for Distributed Intelligent Systems
Erdenebileg Batbaatar and Young Yoon
Distributed intelligent systems increasingly need to train across data silos without centralizing raw data.
Federated learning keeps data local but can suffer under heterogeneous partitions and requires repeated full-model exchange. Split learning reduces communication through cut-layer activations, but standard protocols generally do not recover centralized mini-batch gradient behavior and may expose activations and gradients in plaintext. We present TL++, a two-mode traversal-learning framework that constructs virtual batches across nodes to recover centralized mini-batch gradient behavior under explicit synchronization assumptions. Base mode exchanges cut-layer activations and gradients rather than full models. Secure mode secret-shares each cut-layer activation and gradient between an orchestrator and a non-colluding helper, preventing either server from observing plaintext cut-layer tensors. This protection is limited to a semi-honest two-server setting; labels and loss-related outputs remain visible to the orchestrator. In the lightweight secure path evaluated here, exactness requires a linear or affine server path, while nonlinear operations require nonlinear MPC or approximation. We formalize TL++, analyze communication and computation costs, and evaluate it against
federated and split-learning baselines on CIFAR-10 and BioGPT/PubMedQA using full fine-tuning and LoRA. On CIFAR-10, TL++ base cut 1 and exact secure cut 3 achieve accuracies of 91.41% (SD 0.19) and 90.93% (SD 0.17), respectively, exceeding the strongest measured non-TL++ baseline by more than 12 percentage points. TL++ base cut 1 also reduces per-step communication by 13.1-fold relative to full-model synchronization. PubMedQA results similarly favor TL++. Overall, TL++ approaches centralized-training performance while reducing communication and providing activation-level secret sharing.
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Artificial Intelligence
Reasonable Motion: A General ASP Foundation for Environment Constrained Movement Trajectory Computation
Julius Monsen, Jakob Suchan, Mehul Bhatt et al. (+1 more)
We present a general answer set programming based hybrid quantitative-qualitative method for computing constrained branching trajectory modes for moving objects in real-world settings. The method performs constrained traversal of an environment graph, enumerating geometrically admissible motion behaviours as stable models, each constituting a distinct trajectory mode characterised by both domain-dependent and independent factors such as derived event sequence, map
topology, and domain norms. The hybrid trajectory computation method is generally applicable across motion characteristics typically encountered in diverse dynamic domains with moving objects, e.g., autonomous driving. We demonstrate applicability and highlight how computed trajectories are traceable to their underlying stable model, thereby affording verifiable interpretability that purely learned approaches cannot provide. We also perform an empirical evaluation with Argoverse 2, a large-scale real-world autonomous driving
benchmark representative of the class of dynamic domains within the scope of the proposed method.
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All authors Julius Monsen, Jakob Suchan, Mehul Bhatt, Lars Karlsson
Artificial Intelligence · Cross-listed from cs.CR
Probabilistic Agents in Deterministic Audits: Evaluating Multi-Agent Systems for Automated Audits Based on the German IT-Grundschutz
Lea Roxanne Muth and Marian Margraf
The NIS-2 Directive mandates robust Risk Management from thousands of small and medium enterprises. To ensure compliance, companies rely on established standards such as the German IT-Grundschutz (IT-GS) of the Federal Office for Information Security. However, IT-GS certification is resource-intensive and requires a high level of manual effort for documentation, validation, and revision, making scalable implementation difficult and expensive. Building upon our previous conceptual framework, this paper presents the technical implementation and empirical evaluation of a Multi-
Agent System (MAS) architecture combined with Hybrid
Retrieval Augmented Generation (HybridRAG) for the partial automation of IT-GS certification. We introduce two novel technical contributions to the MAS architecture to enforce the compliance rigor. The Hypothesis-Verification Loop in the Structural Analysis (SA) phase that cross-references
agent-inferred dependencies against the Knowledge Graph to reduce
hallucinations, and a Decoupled
Reasoning Pipeline that separates
agent-driven semantic extraction from the deterministic protection need inheritance. We utilize the BSI's "RecPlast GmbH" case study as a human expert-generated reference data set for end-to-end evaluation of the architecture and to quantify Precision, Recall, and F1-scores. The performance of the system is investigated across the phases of SA, Protection Needs Assessment (PNA), Modeling, and IT-GS Check. The empirical results reveal noticeable differences throughout the different steps of IT-GS. While the MAS demonstrates high efficacy in semantic tasks (SA and Modeling), significantly reducing manual effort through automated information extraction, quantitative results reveal limitations in logical
reasoning phases (PNA and IT-GS Check) as the probabilistic nature of current LLMs struggles to meet the deterministic rigor required by IT-GS.
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Artificial Intelligence · Cross-listed from cs.CR
An Approach for a Supporting Multi-LLM System for Automated Certification Based on the German IT-Grundschutz
Lea Roxanne Muth and Marian Margraf
This paper presents a novel approach to perform semi-automated BSI IT-Grundschutz certification using a MultiLarge
Language Model system (MLS) with Hybrid RetrievalAugmented Generation (HybridRAG). Facing the challenges of the Network and Information Security Directive 2 (NIS2) directive, a shortage of specialists, and high implementation costs, our MLS architecture aims to increase efficiency, reduce costs, and support certifiers in maintaining the quality of security concepts while meeting the increased demand for certifications of newly affected companies. The system combines Large
Language Models (LLMs) and Knowledge Graphs (KGs) to support different phases of the certification process, including protection needs assessment, modeling, IT-Grundschutz check, measure consolidation, and subsequent realization. Our architecture addresses the growing demand for security concepts and offers an approach to handle the digital security challenges introduced by NIS2.
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Artificial Intelligence · Cross-listed from cs.CV
Expresso-AI: Explainable Video-Based Deep Learning Models for Depression Diagnosis
Felipe Moreno, Sharifa Alghowinem, Hae Won Park et al. (+1 more)
Given the widespread prevalence of depression and its consequential impact on individuals and society, it is crucial to obtain objective measures for early diagnosis and intervention. As a multidisciplinary topic, these objective measures should be interpretable and accessible to health care professionals, ensuring effective collaboration and treatment planning in the realm of mental health care. Even though current automated depression diagnosis approaches improved over the last decade, a critical gap exists as they often lack affect-specificity and interpretability, limiting their practical application and potential impact on mental health care. In particular, interpretability from temporal activities from
videos when deep models are used is not fully explored. In this study, we present a novel framework for analyzing Deep Neural Networks' decisions when trained on facial
videos, specifically focusing on automatic depression severity diagnosis. By fine-tuning Deep Convolutional Neural Networks (DCNN) pre-trained on Action Recognition
datasets on depression severity facial
videos from AVEC depression
dataset, our framework is able to interpret the model's saliency maps by examining face regions and temporal expression semantics. Our approach generates both visual and quantitative explanations for the model's decisions, providing greater insight into its
reasoning. In addition to this interpretability, our
video-based modeling has improved upon previous single-face
benchmarks for visual depression diagnosis, resulting in enhanced predictive performance. Overall, our work demonstrates the successful development of a framework capable of generating hypotheses from a facial model's decisions while simultaneously improving depression's predictive capabilities.
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All authors Felipe Moreno, Sharifa Alghowinem, Hae Won Park, Cynthia Breazeal
Artificial Intelligence · Cross-listed from cs.LG
Low-Complexity Policy Tessellations in Structured Markov Decision Processes
Fredy Pokou
We study optimal-policy geometry in structured Markov decision processes. While approximate dynamic programming and reinforcement learning typically approximate high-dimensional value functions, we show that optimal policies induce simpler decision tessellations. We propose boundary-based policy approximations that learn policy regions directly. A policy-loss decomposition links performance degradation to action margins and explains why errors concentrate near indifference boundaries. Inventory control and queue admission experiments show lower policy error, smaller value gaps, faster error decay, and stability than reinforcement learning baselines.
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Artificial Intelligence · Cross-listed from cs.CL
BiPACE: Bisimulation-Guided Policy Optimization with Action Counterfactual Estimation for LLM Agents
Hanyang Wang, Weijieying Ren, Yuxiang Zhang et al. (+4 more)
Stepwise group-based RL is an attractive way to train long-horizon LLM
agents without a learned critic: it reuses multiple sampled rollouts to estimate local advantages. Its weakness is less visible but more fundamental: every group-relative
estimator assumes that the steps it compares are equivalent for credit assignment. We show that current agentic variants violate this assumption through a state-action credit mismatch. The observation-hash partition is overly fine on the state side, creating singleton groups with zero step-level signal, while a single within-group mean is too coarse on the action side, mixing state-value estimation with action-specific credit. We introduce BiPACE (Bisimulation-Guided Policy Optimization with Action Counterfactual Estimation), a drop-in advantage
estimator that fixes both sides without adding a critic, auxiliary loss, or extra rollouts. BiGPO clusters steps by cosine distance in the actor's own hidden-state geometry, an empirical policy-induced proxy for bisimulation that substantially lowers the singleton rate left by observation hashing. PACE then recenters returns within each behavioral cluster using action-conditioned peer baselines; its Q-style instance estimates a local Q(s,a)-V(s) nonparametrically. On ALFWorld/Qwen2.5-7B, BiPACE_Q raises overall validation success from GiGPO's 90.8 to $97.1\pm0.9$ over three seeds, and crosses the 95% threshold on every seed, which GiGPO never does within the same budget. On Qwen2.5-1.5B it reaches $93.5\pm1.2$ versus GiGPO's 86.7, and on WebShop and TextCraft it improves over GRPO and GiGPO at both model scales. The measured BiPACE-specific overhead is 11.3% of a single training-step wall time. Yet it changes the
estimator's comparison unit from surface identity to approximate behavioral equivalence plus action-side counterfactuals. The code is available at https://github.com/TianxiangZhao/BiPACE.
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All authors Hanyang Wang, Weijieying Ren, Yuxiang Zhang, Ding Cao, Zhizhao Zeng, Ke Zeng, Tianxiang Zhao
Artificial Intelligence · Cross-listed from cs.CL
SFL-MTSC: Leveraging Semantic Frame-Level Multi-Task Self-Consistency for Robust Multi-Intent Spoken Language Understanding
Po-Yen Chen and Berlin Chen
Prompt-based spoken
language understanding (SLU) with large
language models (LLMs) often suffers from inconsistent intent–slot structures due to decoding stochasticity, particularly in multi-intent scenarios. In view of this, we propose Semantic Frame-Level Multi-Task Self-Consistency (SFL-MTSC), a novel structured aggregation framework operating at the semantic frame level. Instead of output-level majority voting, SFL-MTSC decomposes predictions into intent-specific frames, applies domain–intent grouping and slot-level clustering, and evaluates cluster reliability using path support scoring. Reliable frames are retained and re-integrated to form the final prediction. Zero-shot experiments on the MAC-SLU
benchmark dataset show improved slot F1 and overall accuracy over single-path inference, while intent accuracy remains largely stable across most settings.
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Artificial Intelligence
Agentic evolution of physically constrained foundation models
Jiangwei Zhang, Wen Sun, Chong Wang et al. (+7 more)
Artificial intelligence increasingly drives automated scientific discovery, yet contemporary generalist
agents lack physical grounding, frequently hallucinating hardware-incompatible designs. Here, we present a physically grounded, multi-
agent discovery engine that autonomously architects hardware-compliant computing systems. Anchored by an Evolutionary Knowledge Graph structuring past scientific innovations, the framework extracts an "algorithmic Chain-of-Thought" to transform blind
stochastic search into directed structural
evolution. Applied to the extreme testbed of foundation model deployment, the engine evolved two hardware-aware compression methodologies surpassing human-engineered heuristics: Q-Enhance mitigates long-context accuracy loss in dense models, and MoE-Salient-AQ outperforms state-of-the-art manual sparse Mixture-of-Experts designs by 3.7% at sub-3-bit regimes. Utilizing a bandwidth-efficient Sensitivity Profile, we successfully deployed a massive 235-billion-parameter model onto a constrained dual-A100 server, reducing
memory requirements by 75% with a marginal 0.64% accuracy degradation. By transforming unconstrained combinatorial search into knowledge-driven autonomy, this establishes a scalable hardware-software co-design paradigm for machine-driven discovery within strict physical boundaries.
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All authors Jiangwei Zhang, Wen Sun, Chong Wang, Shiyao Li, Cheng Che, Chunjing Han, Dan Meng, Jian Yang, Yu Wang, Rui Hou
Artificial Intelligence · Cross-listed from cs.SE
SWE-Opt: A Benchmark for Evaluating LLMs on Performance-Oriented Repository-Level Optimization
Ezgi Sar{\i}kayak, Wenchao Gu, Hesham Ghonim et al. (+1 more)
Software performance optimization is a notoriously complex and manual task. Despite the growing use of Large
Language Models (LLMs) for code refinement, we still lack
benchmarks that capture how optimization actually happens in real-world codebases. Existing frameworks often oversimplify the problem by focusing on isolated functions or a single performance metric, missing the critical trade-offs between execution time and
memory footprint, the inherent noise of the measurement environment, and the variability introduced by different input data and execution conditions. We address this by introducing SWE-Opt, a repository-level
benchmark derived from 102 expert-written optimizations from open-source projects. Unlike previous
benchmarks, SWE-Opt pairs each task with parameterized tests to evaluate runtime, peak
memory, and Time-Weighted
Memory Usage (TWMU) across varying input data and execution conditions under noise-aware measurement conditions. Our evaluation shows that current LLMs struggle significantly: runtime gains are sparse and inconsistent, and
memory optimizations are nearly non-existent. An agentic setting with iterative repository exploration improves patch correctness substantially but does not translate into performance gains. This stands in sharp contrast to expert implementations, which achieve an aggregate speedup of 15.5x and peak
memory reduction of 171.3x over
benchmark tasks. Expert-written improvements are observed in 93.1% of tasks for runtime and 70.6% for peak
memory. Our findings expose a substantial gap between current LLM capabilities and the demands of expert-level engineering.
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All authors Ezgi Sar{\i}kayak, Wenchao Gu, Hesham Ghonim, Chunyang Chen
Artificial Intelligence · Cross-listed from cs.SD
STEB: A Speech-to-Speech Translation Expressiveness Benchmark for Evaluating Beyond Translation Fidelity
Sitong Cheng, Weizhen Bian, Songjun Cao et al. (+9 more)
Speech-to-
speech translation (S2ST) should preserve not only lexical meaning, but also expressive attributes:
emotion, scenario style (e.g., news reporting vs. dramatic dialogue), and nonverbal vocalizations (NVs). Moreover, collecting cross-lingual target
speech that is both translation-faithful and expressively aligned with the source is difficult at scale, making reference-based evaluation impractical. We introduce STEB (
Speech-to-
Speech Translation Expressiveness
Benchmark), a 32.6-hour Chinese–English
benchmark that evaluates both standard dimensions (translation fidelity, speaker similarity, duration
alignment) and expressiveness dimensions (
emotion, scenario style, NV preservation). For expressiveness evaluation, STEB uses a caption-then-summarize framework that converts
speech into structured expressive attributes and compares source and hypothesis attributes with an LLM judge. Human validation shows statistically significant correlations with listener judgments across all expressive dimensions. We evaluate six S2ST systems covering cascaded systems, end-to-end models, and
speech large
language models. Many systems, especially cascaded ones, achieve strong translation fidelity, but they still struggle with
emotion preservation (best: 3.82/5) and NV preservation (best: 2.31/5). These results reveal a gap between semantic transfer and expressive transfer, identifying expressiveness preservation as an open challenge for S2ST. Audio samples are available at https://cmots.github.io/steb.github.io/.
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All authors Sitong Cheng, Weizhen Bian, Songjun Cao, Jin Li, Bei Liu, Chunyang Jiang, Yike Zhang, Weihao Wu, Yiming Li, Chi-Min Chan, Long Ma, Wei Xue
Artificial Intelligence · Cross-listed from cs.SE
The impact of artificial intelligence on enterprise software user roles
Isabel Unger, Elizangela Valarini, Martin Schrepp et al. (+3 more)
Artificial Intelligence (AI) is rapidly reshaping the nature of work in software development, transforming user roles, workflows, and collaboration patterns across enterprise platforms. This qualitative study investigates how AI alters professional responsibilities within the context of SAP's Business Technology Platform (BTP), combining expert interviews (n=20) and a participatory workshop (n=24). The results reveal substantial shifts in day-to-day tasks and roles in the development domain, characterized by increasing automation of operational tasks, expanding human-AI collaboration, and growing reliance on agentic AI systems. The study further identifies significant implications for existing user-role frameworks, such as the BTP User Type
Matrix, which requires adaptation as the workforce is undergoing significant role specific changes. Collectively, these findings highlight a workforce landscape in transition and underscore the need for revised role taxonomies, new governance and oversight functions, and updated design approaches for AI-native enterprise software systems.
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All authors Isabel Unger, Elizangela Valarini, Martin Schrepp, Nina Hollender, Gabriela Rocha, Erik Bertram
Artificial Intelligence
Cliff Tokens: Analyzing Failure Trigger Tokens in LLM Mathematical Reasoning
Jaeyong Ko, Jinu Lee, Pilsung Kang et al. (+1 more)
Large
language models reach high accuracy in mathematical
reasoning, but individual traces on the same problem diverge; some arrive at the correct answer while others fail. Prior work localizes such failures at the step, chunk, or sentence level, or identifies
tokens where failure has already occurred. These approaches leave open which
token triggers failure. We introduce the cliff
token, a
token at which the estimated probability of reaching the correct answer (success probability) drops beyond an adaptive threshold. Across seven models and three mathematical
reasoning benchmarks (GSM1K, MATH500, AIME 2025), cliff
tokens act as failure triggers. For incorrect traces containing cliff
tokens, we compare resampling immediately before and after the first cliff
token. Resampling before it shows higher pass@$k$ at the same sample count. We further introduce a cliff taxonomy of deterministic, uncertain, and sampled-off cliffs, defined by greedy choice and
token entropy. Additionally, we show that the three types differ as training signals. Using single-
token preference optimization at cliff positions (Cliff-DPO), we find that uncertain and sampled-off cliffs show larger accuracy gains than deterministic cliffs on three evaluation
benchmarks. We release
token-level rollout data and source code to enable further analysis without regenerating costly rollouts: https://github.com/beaver-22/Cliff-
token
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All authors Jaeyong Ko, Jinu Lee, Pilsung Kang, Yukyung Lee
Artificial Intelligence
Quantization Inflates Reasoning: Token Inflation as a Hidden Cost of Low-Bit Reasoning Models
Xinyu Lian, Walid Krichene, Beichen Huang et al. (+4 more)
Quantization is widely used to reduce the inference cost of large
language models, but its effect on
reasoning models is not fully captured by final-answer accuracy or per-
token latency. We show that low-bit post-training quantization can introduce a hidden test-time compute cost: quantized
reasoning models often generate longer chains of thought even when they still answer correctly. Across mathematical
reasoning, code generation, scientific question answering, and agentic tool-use
benchmarks, we find that INT4/INT3 quantization can preserve accuracy but increase
reasoning-
token usage, offsetting the expected per-
token speedup. To measure this effect, we introduce the CoT
Token Inflation Ratio, which compares
reasoning length between quantized and full-precision models averaged across all evaluation
benchmarks. We further show that
token inflation is accompanied by behavioral changes in the
reasoning trace, including more intermediate steps and greater semantic repetition. These changes translate into measurable end-to-end real-world serving penalties. Finally, we evaluate mitigation strategies and find that prompting and decoding-time sampling offer inconsistent accuracy-length trade-offs, while quantization-aware training shows more promise in reducing both accuracy degradation and
token inflation. Our results suggest that
reasoning-
token usage should be reported alongside accuracy when evaluating quantized
reasoning models.
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All authors Xinyu Lian, Walid Krichene, Beichen Huang, Masahiro Tanaka, Olatunji Ruwase, Li Zhang, Minjia Zhang
Artificial Intelligence · Cross-listed from cs.CL
Spam and Sentiment Detection in Arabic Tweets Using MARBERT Model
Abrar Alotaibi, Atta-ur Rahman, Raheel Alhaza et al. (+6 more)
Saudi Telecom Company (STC) is among the most popular companies in Saudi Arabia, with many customers. Yet, there is still a big room for improvement in users' satisfaction. Social media is the most robust platform to gauge users' satisfaction and determine their sentiments and critics. Twitter is among the most popular social media platform in this regard. STC customers prefer to use Twitter to write their feedback because it's a fast way to get responses due to the STC customer services account. One way to achieve customer demands and improve customer service is using the Sentiment Analysis tool. Sentiment Analysis on Twitter is highly used because of the significant number of tweets and the different opinions. Likewise, Deep learning is the best existing Sentiment Analysis method, and it has diverse models. Bidirectional Encoder Representations from
Transformers (BERT) model is one of the deep learning models which have achieved excellent results in Sentiment Analysis for Natural
Language Processing (NLP). NLP is mainly investigated in the English
language. However, for Arabic, there is a significant gap to be filled. This study trained the proposed model using MARBERT and measured the performance using f1-score, precision, and recall metrics. We trained the model with an Arabic
dataset of 24,513 tweets, including 1,437 positive, 13,828 negative, 5,694 neutral, 1,221 sarcasm, and 2,297 indeterminate tweets. The main goal is to analyze the tweets and get the sentiment to improve STC customer service. The proposed scheme is promising in terms of accuracy in contrast to existing techniques in the literature.
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All authors Abrar Alotaibi, Atta-ur Rahman, Raheel Alhaza, Wala Alkhalifa, Narjes Alhajjaj, Atheer Alharthi, Dhai Abushoumi, Maryam Alqahtani, Dania Alkhulaifi
Artificial Intelligence · Cross-listed from cs.CV
HG-Bench: A Benchmark for Multi-Page Handwritten Answer-Region Grounding in Automated Homework Assessment
Chuangxin Zhao, Boyan Shi, Yanling Wang et al. (+7 more)
Automated homework assessment depends not only on recognizing student answers, but also on accurately locating where each answer and each intermediate
reasoning step appears in noisy, multi-page handwritten work. This paper addresses the missing evaluation setting of page-aware, two-level answer-region grounding: given a sequence of homework page images, a model must localize complete answer regions and their ordered step-level subregions. We introduce HG-Bench, a
benchmark of 500 human-annotated K-12 homework samples curated from a 1,489,278-image source pool, with question-level and step-level boxes linked by a hierarchical containment constraint. HG-Bench is paired with a page-aware evaluation protocol that separately measures complete-answer localization (FA) and step-level decomposition (FSm), revealing whether models truly ground the spatial structure of student
reasoning rather than merely parse visible text. Across frontier closed-source APIs and competitive open-weight VLMs, no zero-shot system exceeds 55.22% on FA or 48.22% on FSm, while a GLM-4.6V 9B reference model fine-tuned on ~10k in-domain examples reaches 74.97/72.26. These results identify step-level handwritten grounding as a concrete capability gap and provide a reproducible
benchmark, evaluation protocol, and trained reference point for future work on automated homework assessment.
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All authors Chuangxin Zhao, Boyan Shi, Yanling Wang, Yijian LU, Canran Xiao, Jiali Chen, Jun Xia, Yan Wang, Ji Qi, Juanzi Li
Artificial Intelligence · Cross-listed from cs.MA
Rate-Aware Quantum-Inspired Trajectory Learning for Interference-Limited Multi-UAV Networks
Khaoula Khaled, Muhammad Afaq, Ali Arshad Nasir et al. (+1 more)
Unmanned aerial vehicle (
UAV) can provide on-demand, high-capacity connectivity in disaster and normal situation. However, it faces a challenge of curse of dimensionality in trajectory optimization, where interference-limited environments and vast search spaces make real-time coordination computationally expensive. To overcome this challenge, we propose the Rate-Aware
Quantum-Annealed Graph Condensation (RA-QAGC) scheme, which combines rate-aware graph abstraction with decentralized reinforcement learning to enable scalable, interference-aware
UAV coordination. By identifying high throughput locations and guiding
UAV trajectory adaptation toward throughput-optimal regions, RA-QAGC effectively balances network capacity by maintaining quality-of-service (QoS) requirements. Simulation results demonstrate the proposal outperformed over existing schemes by achieving 59.4 Mbps total throughput and 23.9 Mbps priority-user throughput, representing gains of approximately 15% and 34%, respectively, over the baseline schemes.
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All authors Khaoula Khaled, Muhammad Afaq, Ali Arshad Nasir, Zeeshan Kaleem
Artificial Intelligence · Cross-listed from cs.CL
A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation
Abrar Alotaibi, Raed Mughus and Moataz Ahmed
Large
language models (LLMs) have demonstrated remarkable performance across natural
language processing tasks, yet their deployment in high-stakes applications raises critical concerns regarding reliability, safety, and trustworthiness. In this paper, we present a red teaming framework that systematically uncovers vulnerabilities in LLM outputs. Our approach employs a novel multi-role architecture comprising target, attacker, and jury models. The attackers generate increasingly effective
adversarial prompts while the jury rigorously evaluates response accuracy and consistency across tasks. In a case study, our strategy proved particularly effective at exposing unfaithfulness in LLM responses. Exploitative
adversarial prompts increased the attack success rate by up to 7.9% in question-answering tasks, revealing weaknesses in reliability. The approach identifies how structural constraints in summarization can shape vulnerability patterns, with format limitations yielding measurable gains in faithfulness, and shows that architectural design choices typically outweigh parameter scaling in determining model safety. The framework's key strength is its adaptability across evaluation tasks, from English question-answering to Arabic summarization, enabling comprehensive comparison of model vulnerabilities. While it excels at comparing cross-model and cross-linguistic vulnerabilities, it faces challenges in fully automating
adversarial prompt generation across
languages. Our experiments also reveal limitations in detecting subtle forms of unfaithfulness that do not manifest as explicit factual contradictions, particularly across linguistic contexts. Overall, this architecture provides both actionable insights into current LLM vulnerabilities and a scalable methodology for ongoing safety evaluation as models evolve.
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Artificial Intelligence · Cross-listed from cs.CV
EchoStyle: Unlocking High-Fidelity Video Stylization with Reverse Data Synthesis
Huaqiu Li, Jiahao Wang, Sijia Cai et al. (+4 more)
While image stylization has been studied extensively,
video stylization remains a critical and largely unsolved challenge in the field of intelligent content creation. Existing methods, usually utilizing a reference image as the style prior, suffer from content leakage, data scarcity and limited adaptability to long
videos, leading to suboptimal results with severe style drift and motion distortion. For these issues, we present EchoStyle, a scalable text-driven framework to achieve high-quality stylization of
videos with arbitrary lengths. To start with, we construct a
video-to-
video architecture to appropriately re-fuse the
video content and the text style. To address data scarcity, we pioneer an automatic reverse-synthesis pipeline to establish V-Style20k, a large-scale stylization
dataset of 20k high-quality
video pairs. To facilitate long
video stylization, we devise an init-follow-mode mechanism along with a sliding-window inference strategy. Extensive experiments demonstrate EchoStyle's excellent performance across a wide range of artistic styles, even comparable to leading closed-source solutions.
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All authors Huaqiu Li, Jiahao Wang, Sijia Cai, Hualian Sheng, Bing Deng, Jieping Ye, Wenhan Luo
Artificial Intelligence · Cross-listed from cs.LG
Learning with a Single Rollout via Pass@k Policy Optimization
Fengdi Che, Yang Liu, Lei Yu et al. (+4 more)
Group Relative Policy Optimization (GRPO) typically requires multiple rollouts per
prompt to estimate relative credit, incurring substantial sampling cost and exacerbating training asynchrony. This limitation is particularly severe for scientific
reasoning agents, where each trajectory may involve long-horizon
reasoning, multiple turns, and parallel sub-runs. We study single-rollout reinforcement learning, where only one rollout is collected per
prompt, and find that naive training can stagnate or become severely unstable. We introduce Single-Rollout Pass@k Policy Optimization (SR-PPO), which combines Monte Carlo
token-level advantage estimation with Pass@k gradient reweighting. An online critic without large-scale pretraining is used both as a prefix-dependent baseline for credit assignment and to estimate the Pass@k gradient weight. Using only one rollout per
prompt, SR-PPO achieves stable learning and performance comparable to GRPO across
reasoning and logic
benchmarks, even under off-policy drift that degrades the performance of standard Pass@1 PPO. A key factor in this stability is Pass@k reweighting, which emphasizes harder
prompts while downweighting high-success
prompts, for which imperfect credit estimates tend to produce noisier policy updates. Through analyses of critic quality, gradient noise, and update behavior, we investigate how Pass@k reweighting stabilizes single-rollout learning and why residual critic errors may limit SR-PPO's ability to outperform GRPO.
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All authors Fengdi Che, Yang Liu, Lei Yu, Meng Cao, Tong Che, Rupam Mahmood, Dale Schuurmans
Artificial Intelligence · Cross-listed from cs.CL
Reclaim Evaluation: A Lossy Memory Is Worse Than an Empty One
Alex Kwon
A
language model's
memory can be worse than no
memory at all when the model or its interface is disposed to act on it: a
memory that keeps a wrong conclusion but drops the work behind it leads a model to re-emit the stale value as a confident answer, where an empty
memory leads it to abstain. We call this brittle
memory. The information loss is definitional; the finding is behavioral, and it turns on one thing, whether the
memory kept a re-derivation basis (the source) rather than the answer. We measure it with reclaim evaluation: induce a known drift, compress at a fixed budget, deliver a correction that names the error, and score exact recovery, judge-free. Holding the budget fixed and varying only what the compression keeps isolates correctability from capability and from size; an 8B model and a frontier one wall in the same place. A one-line source-first policy, keep the recomputable source, drop the re-derivable conclusion, restores correctability at equal budget where the source is compact and identifiable, with a length-matched control ruling out "more text." We map where the fix fails, show the failure compounds through
memory loops, and replicate across three deployed
memory systems, real dialogue (MultiWOZ), and tau-bench, a deployed-
agent benchmark where whether a lossy
memory becomes a harmful action is a joint model-and-interface property. We release the harness, the paired
memory conditions, and validators built to come out false.
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Artificial Intelligence · Cross-listed from cs.CV
C3-Bench: A Context-Aware Change Captioning Benchmark
Jae-Woo Kim, Hyeongbeom Kim and Ue-Hwan Kim
While Change Captioning systems have garnered substantial
attention to respond to our evolving world, their true performance on diverse real-world change contexts remains largely unexplored due to the lack of comprehensive evaluation frameworks. To fill this gap, we propose C3-Bench, a comprehensive
benchmark for evaluating Context-aware Change Captioning. C3-Bench features: (1) 4,996 human-labeled image pairs of 51 real-world change contexts across four domains (e.g., natural scenes, remote sensing imagery, image editing, and anomalies), each with diverse, carefully curated scenarios derived from multiple change-centric communities; and (2) the first LLM-as-Judge evaluation framework in the change captioning task that measure fine-grained dimensions (e.g., correctness, specificity, fluency, and relevance), along with a novel reversibility metric exploring whether models understand changes with symmetric consistency. Based on C3-Bench, we
benchmark 32 models – including conventional change captioning models, proprietary Large
Multimodal Models (LMMs), and 2B-90B open-source LMMs. We reveal a fundamental blind spot in the prevailing change captioning paradigm: Once the change context departs from training-style regimes, conventional models collapse, and even state-of-the-art LMMs such as GPT-5.2 exhibit systematic domain- and position-dependent errors that distort reliable change understanding. By making these hidden failure modes explicit and measurable, we delineate the next frontier for building generalizable and trustworthy change captioning systems. All codes and
datasets are publicly available on the project page.
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Artificial Intelligence · Cross-listed from cs.LG
TopoCast: A Topological Fidelity Framework for Evaluating Transformer-Based Time Series Forecasting
Sandeepa Weerasekara and Sandareka Wickramanayake
Deep learning-based models have achieved state-of-the-art performance in Time Series
Forecasting (TSF), yet their evaluation remains dominated by pointwise error metrics such as Mean Squared Error (MSE), which quantify numerical accuracy but overlook structural properties of the forecast signal, including recurrent dynamics, oscillatory behavior, and phase
alignment. As a result, forecasts exhibiting over-smoothing, phase shifts, or frequency distortions may achieve favorable error scores despite substantial structural degradation. To address this limitation, we propose TopoCast, a
topology-driven framework for evaluating structural fidelity in TSF. TopoCast reconstructs phase-space representations of forecast and ground-truth sequences using Takens delay
embedding and applies persistent homology to characterize their intrinsic dynamics. We derive four complementary topological fidelity measures from persistence diagrams and aggregate them into a Topological Fidelity Score (TFS). We further introduce dominant cycle overlap, a novel metric that maps persistent topological features to the temporal domain to assess whether dominant oscillatory patterns occur at the correct time points. Combined with TFS, this yields the Localized Topological Fidelity Score (LTFS), a phase-aware measure that captures temporal localization errors invisible to existing evaluation metrics. Experiments on five
Transformer architectures across three real-world
benchmark datasets demonstrate that models with similar
forecasting errors can exhibit markedly different structural fidelity profiles, revealing failure modes overlooked by conventional evaluation and highlighting the value of
topology-aware forecast assessment.
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Artificial Intelligence · Cross-listed from cs.LG
Interpretable Concept-Guided Polynomial Tabular Kolmogorov-Arnold Network for EEG-Based Mild Cognitive Impairment Detection
Yosef Bernardus Wirian and Qiang Cheng
Early and scalable detection of mild cognitive impairment (MCI) remains an unresolved clinical challenge. Existing
EEG-based screening approaches are constrained by handcrafted feature pipelines that discard neurophysiologically meaningful domain structure and deep learning classifiers that sacrifice interpretability for performance. No existing work unifies physiologically organized concept encoders, cross-concept interaction modeling, and nonlinear tabular classification in a sleep
EEG-based MCI detection framework. This study proposes Concept-guided
Polynomial-transformed Tabular learning using Kolmogorov-Arnold Network (CPTabKAN), which maps heterogeneous
EEG-derived features into domain-informed concept representations, expands them via degree-2
polynomial transformation to expose first- and second-order interactions, and applies a
Fourier-parameterized TabKAN classifier to learn nonlinear decision boundaries. CPTabKAN was evaluated on the Study of Osteoporotic Fractures cohort (372 subjects, overnight polysomnography), using 1,379 features organized into ten physiologically motivated concept groups. Under 10-fold cross-validation, CPTabKAN-Second Order achieved a weighted F1-score of 0.9038 (SD 0.034), outperforming GradientBoosting by 5.65 percentage points (t(9)=1.934,p=0.043, one-sided paired test), with advantages persisting under SMOTE-based balancing. Ablation analysis confirmed independent contributions from each component. Concept importance analysis revealed that power spectral density, multi-scale
entropy, and Hjorth parameters dominated first-order weights, while cross-concept interactions involving Lempel-Ziv-Welch complexity, statistics, demographics, and slow oscillations exceeded all first-order scores. These results demonstrate that concept-structured, interaction-aware tabular learning surfaces physiologically coherent
reasoning, supporting clinical trust.
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Artificial Intelligence · Cross-listed from cs.LG
Brevity is the Soul of Inference Efficiency: Inducing Concision in VLMs via Data Curation
DatologyAI: Matthew L. Leavitt, Siddharth Joshi, Haoli Yin et al. (+6 more)
Inference efficiency is typically pursued by shrinking the model:
distillation,
pruning, quantization, and sparse routing each lower per-
token cost while treating
token count as fixed. But output length has been inflating, and it is precisely the component the standard toolkit leaves untouched. Here, we argue that brevity is the missing inference-efficiency lever, and that pretraining data curation is a practical way to pull it: a model trained on concise, correct data learns to answer in fewer
tokens; i.e. it has a lower Cost-of-Pass. We apply our VLM curation pipeline to the MAmmoTH-VL single-image subset, and compare models trained on our curated data, the standard MAmmoTH-VL data, and external open-weight frontier VLMs. On a controlled 20-evaluation set and 14 VLMs at 1B-4B activated parameters, we hold output length fixed with a per-model regression, separating brevity from quality, and price models in FLOPs per correct answer. Curation buys a 35x Cost-of-Pass advantage over the most verbose 4B comparator (Qwen3.5-4B) within $\sim$1 pp of accuracy (0.41 vs 14.58 TFLOPs per correct answer; 0.691 vs 0.704 mean accuracy). Curation also buys a +17.55-percentage-point matched-length accuracy gain over the uncurated baseline that grows with model scale (from +16.7 pp at 1B to +21.2 pp at 4B). This brevity improvement concedes no quality: generic verbosity buys no accuracy at any capability or scale, and the window where
reasoning-structured verbosity still earns its
tokens shrinks from 4 of 8 capability groups at 2B to 1 of 8 at 4B. Per example, the concise model even reaches correct answers the verbose
reasoning model misses, marking
reasoning as a distinct curation target rather than something brevity gives up. Inference efficiency in this regime is a
tokens-per-correct problem, and brevity is the lever that targets it directly.
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All authors DatologyAI: Matthew L. Leavitt, Siddharth Joshi, Haoli Yin, Rishabh Adiga, Haakon Mongstad, Alvin Deng, David Schwab, Bogdan Gaza, Ari Morcos
Artificial Intelligence · Cross-listed from eess.AS
Adaptive Oscillatory Inductive Bias for Modeling Sharp Prosodic Dynamics in Diffusion-Based TTS
Sandipan Dhar, Nirmesh J. Shah, Ashishkumar P. Gudmalwar et al. (+1 more)
Diffusion-based text-to-
speech (TTS) models have achieved significant improvements in
speech quality. However, modeling sharp prosodic transitions and rapid pitch variations in expressive
speech remains challenging. Existing
diffusion-based TTS decoders commonly utilize periodic nonlinearities such as Snake activation function to capture harmonic structures, but this activation funcation provides limited adaptability when modeling abrupt amplitude and frequency variations. In this paper, we investigate the role of oscillatory inductive bias in
diffusion-based TTS decoders and introduce an adaptive oscillatory nonlinearity that enables controllable periodic modulation while maintaining signal stability through a linear bypass component. We refer the resulting TTS system as OscillaTTS. Experiments on the LJSpeech and Emotional
Speech Dataset show consistent improvements across objective and subjective evaluations, indicating improved modeling of expressive prosodic dynamics.
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All authors Sandipan Dhar, Nirmesh J. Shah, Ashishkumar P. Gudmalwar, Pankaj Wasnik
Artificial Intelligence · Cross-listed from eess.AS
CrossAccent-TTS: Cross-Lingual Accent-Intensity Controllable Text-to-Speech via Disentangled Speaker and Accent Representations
Ram Annamdevula, Ankit Tatawat, Ashishkumar P. Gudmalwar et al. (+2 more)
Accent conversion and controllability remain fundamental challenges in cross-lingual text-to-
speech (TTS), particularly for low-resource and phonetically diverse Indic
languages. While recent large
language model (LLM)-based TTS systems exhibit strong cross-lingual generalization, they provide limited explicit control over accent characteristics and intensity. In this paper, we propose CrossAccentTTS, a framework that enables both accent control and conversion while preserving speaker identity. Specifically, we introduce an Accent Intensity Controller (AIC) that injects weighted
language embeddings into the accent subspace, allowing smooth interpolation between accents and fine-grained modulation of accent strength at inference time. Experiments on the Indic
Multilingual and L2-arctic
datasets shows that CrossAccent-TTS achieves precise control of accent intensity, outperforming strong baselines in accent similarity and controllability by maintaining speaker similarity and naturalness.
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All authors Ram Annamdevula, Ankit Tatawat, Ashishkumar P. Gudmalwar, Nirmesh J. Shah, Pankaj Wasnik
Artificial Intelligence · Cross-listed from cs.SE
LibEvoBench: Probing Temporal Knowledge Stratification in Code Generation Models
Daniele Cipollone, Sergey Titov, Maliheh Izadi et al. (+2 more)
Large software projects often depend on older versions of libraries, even as APIs continue to evolve across releases. This creates a challenge for LLMs: they must maintain knowledge of multiple API versions, not merely the latest or most common one. However, current LLMs are trained on temporally mixed corpora and lack explicit mechanisms for such version-specific
reasoning, leading to anachronistic errors - calling APIs as they exist in a different library version. To systematically evaluate this phenomenon, we introduce LibEvoBench, a multi-task
benchmark spanning multiple versions of widely used Python libraries, along with a new metric, the Software
Evolution Understanding Score (SEUS), to measure models' consistency when working with evolving APIs. Our results show that state-of-the-art models are largely version-oblivious: performance degrades for evolving APIs, while for stable APIs it remains the same across versions. Moreover, simply specifying the target version provides no benefit, while relevant documentation significantly boosts models' accuracy. These findings highlight a systematic limitation of current training paradigms and motivate new approaches for temporally grounded knowledge in code generation.
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All authors Daniele Cipollone, Sergey Titov, Maliheh Izadi, Egor Bogomolov, Arie van Deursen
Artificial Intelligence · Cross-listed from cs.NI
Lightweight PCGAE-Net: Parallel CrossGate Attention and Bottleneck AutoEncoder for Efficient 5G Channel Prediction
Uma Kishore Godavarti, K. Giridhar, Vanani Prince Dharmendrabhai et al. (+2 more)
Accurate channel state information (CSI) prediction is essential for proactive beamforming and resource management in 5G massive
MIMO systems, yet the deployment of high-accuracy
transformer-based predictors on base-station hardware remains challenging because the most capable models carry upwards of 30\,M parameters. This paper introduces Lightweight PCGAE-Net, which addresses the efficiency problem not by post-hoc compression but by correcting two architectural flaws in the current state of the art. The first is a sequential
attention ordering bias: in CS3T-UNet, group-wise temporal
attention (GTA) always operates on features that have already been transformed by cross-shaped spatial
attention (CSA), distorting what temporal information GTA can capture. We remove this dependency by routing both
attention modules to the same layer-normalized input and combining their independent outputs through a learned per-channel sigmoid CrossGate. The second flaw is an uncompressed bottleneck: applying full self-
attention at the deepest encoder stage, where channel depth reaches $4C$, is quadratically expensive and carries redundant features. A Bottleneck AutoEncoder (BAE) with $1\times1$ convolutions halves this depth and uses an auxiliary reconstruction loss to prevent information collapse. Wrapping these components inside a shallower encoder-decoder with frequency-domain dimensionality reduction ($N_f\!=\!32$, $C\!=\!48$) produces a model with just 8.54\,M parameters – 58% fewer than the CS3T-UNet baseline – that outperforms it by up to 3.26\,dB at 5\,km/h and 6.0\,dB at 9\,km/h in single-step prediction on QuaDriGa
dataset.
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All authors Uma Kishore Godavarti, K. Giridhar, Vanani Prince Dharmendrabhai, Anchit Panday, Madhan Raj Kanagarathinam