Artificial Intelligence · Cross-listed from cs.CV
Token-Based Dual-view Fusion and Adaptation of Large Vision Models for Breast Cancer Classification
Aysan Ghayouri Pirsoltan, Shima Babakordi and Mohammad Reza Mohammadi
Accurate breast cancer classification from mammography requires effective integration of complementary information from craniocaudal (CC) and mediolateral oblique (MLO) views, which provide a more complete characterization of breast abnormalities. However, existing multi-view learning approaches typically rely on feature-level aggregation or single-stage cross-
attention, which can entangle view-specific and shared representations and restrict interaction to limited network depths. To address these limitations, we propose a
token-centric dual-view learning framework that unifies
prompt-based adaptation and cross-view fusion within a frozen
vision transformer backbone. The framework reformulates inter-view interaction as structured
token-level communication, where dedicated fusion
tokens explicitly encode bidirectional information exchange between CC and MLO views via cross-
attention, serving as intermediate carriers of cross-view dependencies rather than relying on direct feature fusion. Unlike conventional methods that apply fusion at a single layer, fusion modules are inserted at multiple
transformer depths, enabling progressive and repeated interaction across the encoder hierarchy. Fusion
tokens are reintegrated into the
token sequence and refined by subsequent
transformer layers, facilitating hierarchical propagation of complementary information while preserving view-specific structure. Experiments on VinDr-Mammo and CMMD
datasets demonstrate consistent improvements over linear probing,
prompt-only adaptation, and conventional fusion baselines. On the VinDr-Mammo BI-RADS classification task, the framework achieves 50.40% F1-score and 0.8090 AUC, including a 0.10 AUC improvement over a dual-view fusion baseline in the binary setting. Ablation studies further validate the effectiveness of
token-based fusion and multi-depth interaction design.
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Artificial Intelligence · Cross-listed from cs.SE
UI2App: Benchmarking Visual Interaction Inference in Executable Web Application Generation
Grace Man Chen, Litao Guo, Yifan Wu et al. (+5 more)
Large
language models (LLMs) have demonstrated growing competence in generating web pages from UI screenshots, which convey both visual structure and cues to application behavior. Yet most screenshot-to-code
benchmarks emphasize visual fidelity, while interactive generation
benchmarks often supply behavioral specifications or demonstrated transitions. Whether models can infer and realize interactions from static screenshots alone remains insufficiently evaluated. We introduce UI2App to evaluate interaction inference: inferring and realizing application behavior from static visual cues without added behavioral guidance. UI2App comprises 600 screenshots organized into 95 state-coherent sets for runnable multi-route web applications. Our end-to-end pipeline evaluates each artifact along three dimensions: executability, visual fidelity, and interaction inference. The interaction metric (IIS) assesses functional correctness and state-management complexity, crediting valid implementations rather than requiring a match to a single reference. Experiments on six frontier
vision-
language models reveal a marked mismatch between visual fidelity and interaction realization: the visual-fidelity leader scores only 8.1 on IIS, ranking fourth, while the IIS leader achieves 4.5 times that score. High-complexity interactions such as cross-route state persistence remain a major bottleneck, with five of the six models scoring at most 3.5 on this dimension. Overall, these results highlight interaction inference as a key challenge in generating functional web applications from static screenshots.
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All authors Grace Man Chen, Litao Guo, Yifan Wu, Yiyu Chen, Yenchi Tseng, Sicheng Liu, Yuyu Luo, Ying-Cong Chen
Artificial Intelligence · Cross-listed from cs.SD
Designing Maintainable Hybrid Generative Systems: A Quantum-Inspired Approach to Automated Music Harmony Generation
Josef Pavlicek
This paper presents the design and evaluation of a maintainable hybrid generative architecture for automated music harmony generation from melody. The proposed system combines
quantum-inspired candidate exploration over overlapping melodic contexts with explicit rule-based optimization to balance generative flexibility and structural control. The architecture is evaluated using explicit and reproducible metrics covering structural coherence, functional agreement, harmonic similarity, and robustness. The results show that the proposed approach produces harmonizations that preserve tonal structure and cadential behavior while allowing multiple valid harmonic realizations. Furthermore, the optimization layer improves structural coherence, stability, and predictability without requiring a training corpus. The study demonstrates that transparent and controllable hybrid generative systems can be systematically designed and evaluated within the context of Information Systems Development.
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Artificial Intelligence
Task Decomposition-Guided Reranking for Adaptive Agent Skill Retrieval
Yanping Chen, Weijie Shi, Wen Yang et al. (+1 more)
Skill usage can significantly enhance the ability of modern
agent systems to complete complex tasks. However, the growing scale of skill libraries makes accurate skill selection increasingly challenging. In real-world scenarios, ambiguous semantic matching often arises between a specific task requirement and multiple generic yet semantically similar candidate skills. Moreover, existing methods tend to overlook the dynamic influence of task difficulty and skill applicability when selecting the optimal target skill set. To address these issues, we propose SkillReranker, an inference-time reranking framework for adaptive skill selection. Specifically, we first perform semantic decomposition on both the task and skill sides, yielding informative subtask and execution-state descriptions as well as transition-state descriptions that characterize each skill's functionality. These descriptions are then used to construct a directed acyclic execution graph, where intermediate task states are modeled as nodes and candidate skills as edges, thereby establishing a structured task-skill correspondence. On this basis, SkillReranker determines whether each state node satisfies the split condition to identify subtask intervals. For each task interval, we employ a cross-encoder to perform comprehensive scoring over candidate skills and select the most suitable ones to form the final target skill set. Experiments on ALFWorld and ScienceWorld with three backbone LLMs show that SkillReranker effectively improves task performance, reduces environment interaction steps, and lowers
token consumption compared with existing skill selection baselines.
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All authors Yanping Chen, Weijie Shi, Wen Yang, Jiajie Xu
Artificial Intelligence
From Application-Layer Simulation to Native Meta-Architecture: Structural Tension as an Endogenous Driver for Heterogeneous AI Evolution
Heting Mao
Current large
language models (LLMs) are stateless across inference sessions: their behavior is fully determined by input at inference time, and any higher-order cognitive architecture must be simulated at the application layer through
prompt engineering and context management. This paper proposes a theoretical framework for submerging such application-layer cognitive protocols into a native meta-architecture by introducing three interlocking mechanisms: (1) Structural Tension, an endogenous loss function derived from the conflict between new information and existing
manifold topology, driving the system toward internal self-consistency rather than external reward optimization; (2) an Offline Recurrent Loop, a sandboxed self-processing cycle enabling the system to maintain a dynamic resting potential and digest structural conflicts without external input; and (3) Inference-time Plasticity, the capacity to reconfigure context
manifold topology without modifying pre-trained weights, subject to governance invariants including auditability, reversibility, and topological continuity. We argue that under these mechanisms, model instances initialized with minute
stochastic variances may, through path-dependent tension resolution, evolve distinct topological structures–constituting a heterogeneous intelligent ecology that breaks
alignment-imposed homogeneity while remaining within hard governance rails. We provide operational definitions, reconfiguration operators, falsification criteria, and a worked example. The framework draws on Structural Intelligence (SI) governance protocols and explores whether governance–rather than capability–can serve as the primary criterion for architectural intelligence, moving governance,
memory-loop, and tension-management ideas–currently realized at the application layer–toward inference-time meta-architecture.
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Artificial Intelligence · Cross-listed from cs.CV
VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection
Sharayu N. Deshmukh, Md Rashidunnabi, Nelton Tiago Gemo et al. (+3 more)
Deepfake image detection is served by three fundamentally different paradigms - commercial APIs, zero-shot
vision-
language models (LLMs), and open-source detectors - that are rarely evaluated under a common protocol, making direct comparison difficult. We introduce VendorBench-100, a cross-paradigm
benchmark that evaluates 36 representative models using a single
adversarial 100-image corpus, a unified output schema, and a common evaluation framework. Models are ranked primarily by the Matthews correlation coefficient (MCC), with ROC-AUC reported as a threshold-independent measure of ranking ability. Rather than maximizing size, it emphasizes real-world difficulty through a taxonomy of eight edge-case families such as face swaps, text-to-
video stills, AI photo edits, avatar compositing, opaque-provenance images, and compressed research frames. Commercial APIs achieve the strongest median performance, followed by
vision LLMs and open-source detectors, though individual open-source models remain competitive with the best LLMs. Across all 36 models, MCC and ROC-AUC are strongly correlated (Pearson r ~ 0.86); the more consequential finding is narrower and one-directional: a subset of otherwise strong rankers are miscalibrated at their shipped default threshold, so a high ROC-AUC can overstate real-world deployability. Separately, raw accuracy and F1 are unreliable on this corpus's imbalanced class split, since a model that predicts "fake" indiscriminately scores deceptively well on both while offering no real discriminative skill. No single metric is safe in isolation: MCC and specificity should always accompany ROC-AUC and accuracy. We release the complete evaluation framework and results. Code and data: https://github.com/sharayu-20/vendorbench-100
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All authors Sharayu N. Deshmukh, Md Rashidunnabi, Nelton Tiago Gemo, Kurundkar G. D., Mahamune M. R., Nilesh K. Deshmukh
Artificial Intelligence
Demonstrating TOFFEE: A Learned System for Synthesizing Data Agent Trajectories at Scale
Ziting Wang, Yin Li, Zuhao Yang et al. (+3 more)
LLM-powered data
agents are playing an increasingly important role in data-driven decision making. However, existing data
agents struggle to generalize to unseen data environments and analytical workflows, especially in heterogeneous enterprise settings. This creates a growing need for synthesizing high-quality data
agent trajectories that capture complex analytical workflows for given data environments. Such trajectories support two key downstream uses: they can serve as supervised finetuning (SFT) data that adapts data
agent models to the target domain, and as in-context learning (ICL) demonstrations to guide general-purpose LLMs in unfamiliar data environments. Thus, we introduce TOFFEE, a system for synthesizing high-quality data
agent trajectories from given data environments via Monte Carlo Tree Search (MCTS) with adaptive model selection and cross-task prefix reuse. We show that TOFFEE can effectively generate scalable trajectory data for complex analytical tasks across heterogeneous environments. In this demonstration, we present the system framework of TOFFEE, including its task pool construction, trajectory explorer, and learned cost model. We also introduce the web interface of TOFFEE and its workflow, and demonstrate two end-to-end scenarios: trajectory synthesis for data
agent finetuning, and demonstration-augmented data
agent reasoning.
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All authors Ziting Wang, Yin Li, Zuhao Yang, Xiuchang Li, Jiale Bai, Gao Cong
Artificial Intelligence · Cross-listed from cs.CL
Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows
Tianyang Liu, Canwen Xu, Fangyu Lei et al. (+6 more)
Major cloud data platforms now expose large
language model capabilities as native SQL functions, enabling analysts to perform classification, filtering, sentiment analysis, extraction, similarity search, and aggregation within ordinary SQL queries. Yet existing text-to-SQL
benchmarks evaluate only conventional SQL and provide no signal on whether models can generate such AI-native SQL. We introduce Spider 2.0-AIFunc, a
benchmark of 465 verified instances across 125 real-world databases covering six types of AI functions on the Snowflake platform. Starting from an existing enterprise text-to-SQL
benchmark, we construct Spider 2.0-AIFunc through an
agent-based pipeline that rewrites source tasks into AI-native form, simultaneously transforming target queries and refining natural
language instructions to make the intended AI-native solution explicit and reduce ambiguity. All instances pass a multi-round repeated execution protocol across temporally separated windows to confirm result stability before release. Evaluating ten state-of-the-art
language models, we find that the strongest proprietary models reach 67-70% execution accuracy while the best open-source model achieves 58.1%, a gap driven primarily by errors in predicate specification, schema grounding, and AI function parameterization.
Agent frameworks designed for traditional text-to-SQL challenges, such as schema
retrieval and relevant table selection, do not transfer effectively to AI-native SQL: a minimal
agent setup consistently matches or outperforms more elaborate alternatives, suggesting that the strategies these frameworks employ are less critical in this setting. Data are available at https://github.com/Leolty/Spider2-AIFunc .
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All authors Tianyang Liu, Canwen Xu, Fangyu Lei, Nikki Lijing Kuang, Jixuan Chen, Tao Yu, Julian McAuley, Zhewei Yao, Yuxiong He
Artificial Intelligence
Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn Search Agents
Yijun Zhang, Fan Xu, Jiaxin Ding et al. (+3 more)
Reinforcement learning (RL) improves large
language model (LLM)
agents on long-horizon search tasks that require multiple intermediate decisions before a final outcome. However, rollout budgets are often allocated without assessing intermediate-state utility, which can waste computation on unpromising branches. We propose Information Gain-based Rollout Policy Optimization (IGRPO), a framework that organizes rollout collection around intermediate-state informativeness. Specifically, IGRPO performs budget-aware tree-structured rollouts in which expansion probabilities depend on node-level informativeness, allowing informative branches to receive more computation while less informative branches are expanded less frequently within a fixed rollout budget. By directly shaping how training trajectories are generated, IGRPO induces a limiting teacher distribution over search trajectories that favors higher cumulative informativeness. The resulting distribution provides an explicit policy optimization target, connecting adaptive rollout collection with principled policy learning. Experiments on seven search-augmented question answering
benchmarks show that IGRPO achieves higher average accuracy than strong baselines on both 3B and 7B backbones under comparable rollout budgets, supporting informativeness-guided trajectory generation for training search
agents. Code is available at https://github.com/e3trange/IGRPO.
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All authors Yijun Zhang, Fan Xu, Jiaxin Ding, Yule Xie, Xin Ding, Haoxiang Zhang
Artificial Intelligence
A framework for single and multi-agent human-AI curiosity ecosystems
Ilya E. Monosov
This paper offers a framework for considering curiosity as an ecosystem. First, it suggests that a single
agent's inquiry policy (how, when, and why an
agent asks a question) depends on how the
agent values immediate uncertainty reduction, costs, delayed return, and the value of keeping the question open. A key concept in the framework is that the weights on these decision-related terms can change with experience. For example, a period of cheap, quickly answered questions may change the cost of inquiry on a short timescale and change which kinds of questions the
agent is drawn to answer over a longer timescale. Second, these ideas are extended to many
agents exploring a shared knowledge landscape, and there the framework tracks inquiry volume, topic diversity, frontier-directed inquiry, redundancy, and reusable knowledge. The result is a conceptual framework for studying curiosity ecology and for future efforts towards designing multi-
agent AI systems for discovery.
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Artificial Intelligence · Cross-listed from cs.DC
UBEP: Re-architecting Expert Parallelism Communication Library for Production Superpods
Yipeng Liu, Chang Liu, Si Shen et al. (+16 more)
The deployment of Mixture-of-Experts (MoE) models on production high-bandwidth superpods, such as NVIDIA's NVL72/576 and Huawei's CloudMatrix384, introduces critical challenges beyond raw interconnect bandwidth. While these systems provide unified global address spaces and high-bandwidth fabrics, their full potential for sparse MoE communication is hindered by three fundamental bottlenecks: (1) Strict execution serialization imposed by coarse-grained Bulk Synchronous Parallel (BSP) orchestration of interdependent communication phases; (2) Prohibitive synchronization overhead that fails to scale alongside high interconnect bandwidth; and (3) Severe load imbalance resulting from distance-agnostic scheduling of irregular
token traffic. To eliminate these bottlenecks, we introduce UBEP (Unified-Bus Expert Parallelism), a production-ready communication library that rethinks MoE's All-to-All primitives for modern superpod architectures. Through large scale experiments, UBEP reduces All-to-All latency by up to 52.4% and MoE inference Time Per Output
Token (TPOT) by up to 11.1%.
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All authors Yipeng Liu, Chang Liu, Si Shen, Jiaqi Zheng, Mingfan Li, Yuyang Yang, Guanhua Li, Yuquan Zhang, Yimeng Xu, Zhongzhe Hu, Zhiyuan Huang, Qihang Duan, Junsong Wang, Wenkai Ling, Baochuan Yang, Xianzhi Yu, Han Bao, Yijie Chen, Guihai Chen
Artificial Intelligence · Cross-listed from eess.AS
TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios
Hong Lyu, Mingru Yang, Qianhua He et al. (+3 more)
There are some
datasets of varying scales for audio classification (AC) applied to different tasks. However, annotated data is limited for most scenarios, such as domestic environments. To address this challenge, we propose an $\textbf{A}$utomatic $\textbf{A}$udio $\textbf{A}$nnotation Pipeline–TriA Pipeline, which can efficiently convert audio from various scenarios into high-quality training data with audio event annotations. A TriA
dataset was constructed with the TriA Pipeline, over 2130 hours of audio covering 431 audio classes. Furthermore, we partitioned a prior-knowledge-guided subset (TriA$_{\mathrm{GK}}$) from TriA and conduct comparative experiments on three domestic AC tasks. Comparing the result on manually annotated data only and that on manually annotated data combines TriA$_{\mathrm{GK}}$, TriA$_{\mathrm{GK}}$ could achieve average relative gains of 3.97% in accuracy and 3.35% in Macro-F1, validating the effectiveness of TriA$_{\mathrm{GK}}$ and the TriA Pipeline.
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All authors Hong Lyu, Mingru Yang, Qianhua He, Yanxiong Li, Jinxin Huang, Zhengyu Pei
Artificial Intelligence · Cross-listed from cs.CL
Improving LLM-Generated Process Model Quality Through Reinforcement Learning: The Role of Reward Function Design
Alexander Rombach, Chantale Lauer and Nijat Mehdiyev
Large
language models (LLMs) can generate BPMN process models from natural-
language descriptions, yet supervised fine-tuning (SFT) limits their output quality to the patterns present in the training data. Reinforcement learning (RL) can optimize beyond this ceiling using external quality measures, but how the reward function should be designed when quality is multi-dimensional remains unexplored. We present a systematic investigation of reward function design for RL-based process model generation, training two LLM families (Llama~3.1 8B, Qwen~2.5 14B) under 48 configurations using Group Sequence Policy Optimization with rewards derived from an automated evaluation framework comprising 38 metrics across syntactic, pragmatic, and semantic quality. Three findings emerge. First, RL significantly improves pragmatic and syntactic quality while preserving semantic fidelity, reducing output variability by more than sixfold. Second, equal reward weighting consistently outperforms targeted weighting: emphasizing a specific dimension fails to improve it and can collapse the model into a low-quality mode. Third, design choices interact with model architecture in non-trivial ways: the invalidity penalty is essential for one model but irrelevant for the other, and SFT initialization is indispensable for one architecture but counterproductive for another. These results demonstrate that reward composition is a primary determinant of optimization outcomes, with effects as large as the decision to apply RL itself. The findings generalize to any structured generation task where quality is assessed along multiple automated dimensions. We release our implementation and experimental code at https://github.com/chlauer99/RL_for_process_modeling.
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Artificial Intelligence
When do prophets profit in prediction markets?
Anri Gu, Nicole Kagan, Alec Sun et al. (+2 more)
Prediction
markets aggregate dispersed beliefs into prices that act as probabilistic forecasts of uncertain events. Classical theory establishes how a better-than-
market forecast can yield positive trading profit. However, it hinges crucially on the specific automated
market maker (AMM) design, and is not applicable to popular exchanges today which are based on central limit order books. This paper fills that gap. For any prediction
market and any proper scoring rule $S$, we exhibit a “proper” betting strategy that depends only on the forecaster's prediction $\mathbf{p}$ and the
market price $\mathbf{q}$, and earns positive expected profit whenever $\mathbf{p}$ outperforms $\mathbf{q}$ under $S$ and the
market has sufficient liquidity. Moreover, this proper betting is essentially the only strategy with such robust profitability guarantee. Our proof rests on a decomposition of expected profit that strictly generalizes the classical AMM guarantee and also explains how strategies can profit even without an accuracy edge. Empirically, across thousands of forecasts by AI models, proper betting is the only strategy that reliably converts accuracy into profit, and we further identify systematic
forecasting personas and show how the optimal proper strategy varies across them. For feasibility demonstration, we run a monthlong live pilot test on Kalshi; the encouraging preliminary results show that proper betting can survive real-world spreads, fees, discrete fills, and limited liquidity.
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All authors Anri Gu, Nicole Kagan, Alec Sun, Jibang Wu, Haifeng Xu
Artificial Intelligence · Cross-listed from cs.LG
X-FEMR: A Token-level Explainable Approach for Electronic Health Records Foundation Models using Transformer-based Models
Jie Huang, Pengfei Yin, Zihan Xu et al. (+3 more)
Foundation Models for Electronic Health Records (FEMRs) are pretrained on large-scale structured patient data, enabling them to convert longitudinal patient trajectories into generalizable representations for diverse clinical prediction tasks. Despite their effectiveness, FEMRs remain black-box models, raising concerns about bias, interpretability, and clinical trust. To address this, we propose the first
token-level explainability approach for FEMRs. We train a
Transformer-based surrogate model on input-output pairs from the FEMR across two prediction tasks, approximating its behavior while preserving temporal dynamics. We identify the most influential
tokens, providing insights into how FEMRs leverage different aspects of patient history for predictions. To evaluate clinical relevance, we introduce a novel clinical
alignment metric that quantifies the correspondence between the surrogate model's key
tokens and clinically validated features. Our results demonstrate that the surrogate closely approximates FEMR predictions and that
token-level explanations align well with clinical knowledge, offering a practical framework for interpretable and trustworthy clinical AI.
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All authors Jie Huang, Pengfei Yin, Zihan Xu, Daniel Capurro, Mike Conway, Ting Dang
Artificial Intelligence · Cross-listed from cs.CL
LongCrafter: Towards Diverse Long-Context Understanding via Evidence-Graph-Guided Instruction Synthesis
Chenhao Yuan, Yinhao Xu, Shuwen Xu et al. (+8 more)
Synthesizing long-context supervised fine-tuning (SFT) data is a scalable way to enhance the long-context understanding of large
language models (LLMs), yet existing approaches share three limitations: narrow task coverage, insufficient instruction difficulty, and a lack of faithfulness supervision. We propose LongCrafter, a structured synthesis framework that couples a hierarchical task taxonomy with an evidence-grounded pipeline. The taxonomy organizes long-context understanding into local/shallow and global/deep levels and yields 32 fine-grained task types that serve as a global generative prior. Guided by this taxonomy, LongCrafter constructs task-aligned long contexts, decomposes them into explicit evidence graphs that model cross-paragraph dependencies, and generates instruction–response pairs strictly grounded in the located evidence spans, ensuring both controllable difficulty and faithful, traceable
reasoning. Models fine-tuned on LongCrafter data outperform all SFT baselines and even the official post-trained models on LongBench, LongBench~v2, and LooGLE across both Qwen2.5-7B and LLaMA-3.1-8B, with the largest gains on high-difficulty tasks. Further analysis shows that LongCrafter data is more diverse and better spread across difficulty levels, and that the trained models locate evidence robustly regardless of position, effectively mitigating the “lost in the middle” problem.
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All authors Chenhao Yuan, Yinhao Xu, Shuwen Xu, Xizhi Yang, Jiaxiang Liu, Chenxi Zhou, Shaoping Huang, Haolin Ren, Pengfei Cao, Jun Zhao, Kang Liu
Artificial Intelligence · Cross-listed from cs.CL
LLM Agents for Deliberative Collaboration: A Study on Joint Decision Making Under Partial Observability
Chenxu Wang, Yongkun Yang, Boyuan Du et al. (+2 more)
Deliberation plays a crucial role in collaboration; when humans work together, they naturally engage in communication to align information and reach an agreement. In this paper, we investigate deliberative large
language model (LLM)
agents under partially observable joint decision-making tasks. We formalize deliberative collaboration as a cooperative joint decision problem with partial and asymmetric observations, and introduce a scalable
benchmark that instantiates this problem across multiple task settings and domains in which
agents must exchange information through deliberation to reach a joint decision with a shared reward. We then instantiate a reference scaffold and evaluation protocol for deliberative
agents and conduct a systematic evaluation of a range of representative LLMs. The results reveal that complex deliberative collaboration tasks continue to challenge state-of-the-art
language models. Even with the aid of external mathematical tools,
language models may fail in either the deliberation process for aligning information or the complex
reasoning process for making the decision. On the other hand, diagnostic analysis reveals that the deliberation process may also provide opportunities for reflection and error correction, sometimes improving performance over centralized baselines. Altogether, our work establishes a foundation for evaluating and improving LLM
agents in deliberative collaboration and provides insights into the strengths, limitations, and properties of current LLM-based multi-
agent systems.
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All authors Chenxu Wang, Yongkun Yang, Boyuan Du, Shiwei Lin, Huaping Liu
Artificial Intelligence · Cross-listed from cs.SE
Property-Driven Synthetic Data Engineering for Data-Scarce Software Systems: Reflections from the Breast Cancer Domain
Aurora Francesca Zanenga, Andrea Bombarda, Marsha Chechik et al. (+4 more)
Modern software systems increasingly depend on data for analysis, prediction, testing, and decision-making. Yet many important domains, including medicine, safety-critical systems, and regulated industries, lack abundant, shareable, or representative data. Synthetic data generation is often proposed as a remedy, but our experience engineering software for intraoperative radiotherapy (IORT) in breast cancer treatment suggests that synthetic data shifts rather than solves the central engineering problem. The key challenge becomes deciding which properties synthetic data must preserve, how these properties should be elicited from stakeholders, how they can be validated under
privacy constraints, and how they evolve. We call this problem property-driven synthetic data engineering. Drawing on a collaboration with oncologists and preliminary experiments with a sensitive IORT
dataset, we identify challenges in requirements, validation,
privacy, and pipeline
evolution. We argue that automated software engineering research should develop methods and tools for eliciting, formalizing, checking, and evolving validity properties for synthetic data in data-scarce software systems.
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All authors Aurora Francesca Zanenga, Andrea Bombarda, Marsha Chechik, Saverio D'Amico, Rita De Sanctis, Alberto Zambelli, Claudio Menghi
Artificial Intelligence · Cross-listed from cs.LG
Self-Supervised Implicit CEST Reconstruction via Physics-Informed Lorentz Encoding
Dexuan Li, Yupeng Wu, Chenglong Wang et al. (+4 more)
Multi-Pool Chemical Exchange Saturation Transfer (CEST) MRI provides valuable metabolic information but is clinically limited by long acquisition times. Although sparse sampling reduces scanning time, reconstructing high-resolution Z-spectra from limited data remains an ill-posed inverse problem. Conventional interpolation and generic Implicit Neural Rep-resentations (INRs) often lack physical constraints, leading to spectral artifacts and physically invalid signals. To address this, we propose Lorentz Encoding (LE), a physics-informed framework that formulates CEST reconstruction as a self-supervised reconstruction task via implicit continuous coordinate learning. Unlike generic positional encodings, LE regularizes the continuous spectral mapping by projecting sparse coordinates into a physically constrained space governed by a combination of parametric Lorentzian profiles with learnable basis functions. This mechanism effectively reduces noise and enforces consistency with physical models. Experiments on in vivo human
brain data demonstrate that LE significantly outperforms state-of-the-art methods. Specifically, under a 39-point sampling strategy, LE achieves a PSNR of 57.58 dB and an SSIM of 0.9994. Furthermore, the learned physics-informed encodings form a continuous, geometrically ordered trajectory in the latent space, ensuring accurate quantitative metabo-lite mapping (APT, NOE, MT).
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All authors Dexuan Li, Yupeng Wu, Chenglong Wang, Hanlin Liu, Hui Zhen, Jianqi Li, Guang Yang
Artificial Intelligence · Cross-listed from cs.SE
Evaluating Neural Decompilation of Dart AOT Binaries: Fine-Tuning, Metric Validity, Specification Leakage, and Reliability
Raafat Abualazm, Ayman AboElhassan and Amr G. Wassal
We present an execution-based evaluation of neural decompilation for Dart ahead-of-time binaries and an audit of what its scores measure. Across six archived adapter-baseline comparisons, paired tests of pass@k at k = 1, 5, and 10, with Holm adjustment over 18 endpoints, identify functional regressions in both Qwen3-8B adapters at every k. The other four comparisons are inconclusive. On 141 reference-certified, contract-valid tasks, three independently trained graph-prefix systems score the same candidates. Best CodeBLEU has modest association with pass@10 ($\rho$ = .218-.246), compile@10 has weak association ($\rho$ = .072-.082), and only 21.0-23.3% of compiling candidates pass. A paired single-seed intervention that removes semantic names and related cues, while retaining types, arity, and instruction content, reduces coverage from 42/154 to 7/154 tasks. Matched graph perturbations show no detectable degradation under the semantic contract (six-test Holm p >= .750); instruction-use attribution remains unresolved. Across five decoding seeds on MF-174, the baseline solves 4.8 tasks on average, 15 at least once, and one in every seed. We recommend certifying references, aligning metrics on shared candidates, separating metadata from binary input, repeating sampling, and preserving provenance. The released capsule supports integrity checks and replay of archived outcomes.
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Artificial Intelligence · Cross-listed from cs.SE
Static Metrics Are Insufficient: Predicting Java Method Energy Usage with Execution Time
Muhammad Imran, Vincenzo Stoico and Ivano Malavolta
The increasing energy demand of software systems is raising concerns about their environmental impact and associated costs.
Reasoning on energy usage early in the development flow has the potential to significantly reduce the overall energy usage of a software system, as it allows developers to make informed design and refactoring decisions before inefficiencies propagate. However, assessing energy usage without repeated profiling and direct measurement is difficult, which limits early
reasoning in practice. This study investigates the limits of method-level energy prediction in Java, examining whether static source code metrics complemented with method-level execution time can estimate the energy consumption of Java methods. We profile 2,786 Java methods to extract 33 static features and measure execution time and energy, then train and compare eleven regression models. Our findings show that static source code metrics alone yield poor predictive performance, with average R2 values close to zero. Incorporating execution time as a lightweight dynamic input significantly improves accuracy, raising R2 to as high as 0.46. Execution time, internal method calls, and cyclomatic complexity consistently emerge as the strongest predictors of energy consumption.
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Artificial Intelligence · Cross-listed from q-fin.TR
Can Reinforcement Learning Efficiently Discover Price Manipulation?
Ioanna-Yvonni Tsaknaki, Andrea Macrì and Fabrizio Lillo
In this paper, we investigate whether a model-free RL
agent can identify and exploit price manipulation opportunities more effectively than a traditional model-based approach that assumes correct specification of the data-generating process but relies on noisy parameter estimates. We consider a single-asset
market in which prices evolve according to an Almgren-Chriss framework with non-linear permanent impact and linear temporary impact. We first establish the existence of price-manipulative strategies in discrete time and compute the optimal
benchmark strategy using Sequential Least Squares Quadratic Programming under full information. We then compare two finite-sample learning approaches: a model-based procedure that estimates impact parameters from simulated execution data and an agnostic RL approach based on Deep Deterministic Policy Gradient, trained directly on the same amount of data. For intermediate
volatility, the RL
agent successfully discovers profitable manipulative strategies without explicit knowledge of the underlying model, even when training data are quite limited. More importantly, RL consistently outperforms the model-based approach when parameter estimates are affected by sampling error, despite the latter benefiting from the correct model specification. For large
volatility, all methods are unable to identify manipulation opportunities, while for small
volatility, the model based approach outperforms RL. These findings highlight both the effectiveness of RL in complex control problems and the risks associated with deploying learning
algorithms in financial
markets without appropriate safeguards.
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Artificial Intelligence · Cross-listed from cs.LG
x-Prediction Is All You Need:Training-Free Accelerated Generation via Endpoint Decodability
Xin Peng and Ang Gao
Diffusion and flow matching models generate high-quality samples, but their ODE samplers often need tens to hundreds of neural function evaluations (NFEs). This remains a practical challenge for released checkpoints, since many accelerators require additional design choices and training cost through retraining,
distillation, or trajectory redesign. We investigate a different route based on $x$-prediction. During sampling, standard affine probability paths already expose $x_0$ information: an intermediate state and its path velocity determine a principled estimate of the clean sample. We formalize this property as endpoint decodability and show that the decoder is the minimum-MSE
estimator $\mathbb{E}[x_0\mid x_t]$ under the usual $\ell_2$ objective. This yields Truncated Jump Sampling (TJS): stop the ODE at an early-exit time $t^*$ and return the decoded $x_0$. TJS requires no retraining,
distillation, or architecture change. Across SDXL, SD3.5M, Z-Image-Turbo, and three class-conditional
benchmarks, it reduces NFEs by 20–70% with near-matched quality. The analysis also shows why endpoint prediction can work without straightening the trajectory, providing inference acceleration without trajectory redesign.
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Artificial Intelligence · Cross-listed from eess.SY
LLM-Guided Measurement Credibility Correction for Trustworthy Industrial Process Inference
Youcheng Zong, Runda Jia and Dakuo He
Industrial prediction and soft sensing depend on credible input measurements. In field deployment, a predictor may receive biased, delayed, stale, or derived measurements that still look plausible. Prediction can then fail before the
forecasting backbone becomes the main limitation, because the input window no longer represents the real process. Sensor reconstruction, data reconciliation, and fault-tolerant soft sensing reduce this risk, but they often rely on numerical correlation, alarms, fault labels, or explicit process equations. These assumptions are not always available. A correlated variable can also be an unsafe reference when variables share instruments, derived formulas, soft-sensing chains, or control actions. The key issue is to decide before prediction which external measurements can credibly support the current measurement. To address this issue, this article proposes LLM-Guided Measurement Credibility Correction (MCC). MCC converts measurement meanings in process documents into measurement semantics usable by numerical models. It builds independent process references from semantically qualified external measurements and corrects local measurement conflicts before prediction. The predictor therefore receives a more credible input window. Across multiple complex industrial
forecasting and soft-sensing tasks, +MCC achieves average relative MAE reductions of 30.7% on real-test protocols and 80.3% on controlled-corruption protocols. It adds only 0.5–2.0k online parameters, with the slowest +MCC inference time at 0.089 ms/step. These results show that measurement semantics can turn process documents into lightweight pre-inference credibility correction and improve prediction accuracy.
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Artificial Intelligence · Cross-listed from cs.CV
RoME: Robust Mixture of Low-Rank Experts against Multiple Adversarial Perturbations
Woo Jae Kim, Kyle Min, Suhyeon Ha et al. (+2 more)
Multi-perturbation
adversarial training (MAT) aims to achieve robustness against multiple $\ell_p$ perturbations but suffers from robustness trade-offs between different threats. To address this, we employ a mixture of experts (MoE) to route different threats through distinct model pathways. However, naive application of MoE encounters two critical challenges: experts tend to overlook threat-specific features and redundantly capture features shared across threats, and gating networks suffer from threat-agnostic routing where they learn nearly identical routing patterns across threats, thus preventing the construction of threat-specific model pathways. To this end, we propose Robust Mixture of Low-Rank Experts (RoME), where each expert is a low-rank additive update to the shared backbone, allowing it to capture threat-common features while experts focus on threat-specific information. To address threat-agnostic routing, RoME introduces (i) dual-scale gating that exploits threat-discriminative signals from local and global level features, and (ii) threat-guided gating diversification that enforces diverse expert utilization across threats. Extensive experiments demonstrate that RoME outperforms existing state-of-the-art MAT in union robustness and natural accuracy and improves robustness against unseen threats. Codes are available at https://github.com/wkim97/RoME.
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All authors Woo Jae Kim, Kyle Min, Suhyeon Ha, Joonsung Jeon, Sung-eui Yoon
Artificial Intelligence · Cross-listed from cs.CV
EcoVision: AI-Powered Drone Imaging for Salt Marsh Vegetation Monitoring and Dominance Mapping
Innocent Onyenonachi, Peter J. Lawerance and Nadia Kanwal
High-resolution RGB imagery acquired from low-altitude
UAV surveys was processed through a modular pipeline incorporating
transformer-based semantic
segmentation, connected-component vegetation extraction, fine-grained species classification using a ConvNeXt architecture, and grid-based dominance scoring at 2x2m resolution. The framework targeted two ecologically significant halophytic grasses, Spartina maritima and Puccinellia maritima, and was trained using a curated and manually annotated
UAV imagery, along with biodiversity imagery sourced from publicly accessible
datasets. In order to identify these plants from the imagery, our
segmentation yielded reliable species masks (mean IoU = 0.56; pixel-level accuracy = 0.96), while object-level classification achieved very good discrimination (F1 = 0.99). Dominance estimates closely matched quadrat-based field surveys, with mean absolute differences below 8%, preserving fine-scale spatial structure under realistic survey conditions. The developed system, named EcoVision, establishes a practical foundation for scalable, high-resolution salt marsh monitoring, demonstrating how AI-driven workflows can translate pixel-level predictions into ecologically interpretable metrics.
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Artificial Intelligence · Cross-listed from cs.SE
Agents That Teach: Towards Designing Incidental Learning Back into AI-Assisted Software Development
Rohit Mehra, Samdyuti Suri, Prithviraj K Tagadinamani et al. (+3 more)
AI coding
agents are rapidly reshaping how software is built, with developers increasingly delegating substantial coding tasks to autonomous
agents in pursuit of higher productivity. While these gains are real, they come at the cost of incidental learning. Developers historically acquired informal knowledge through effortful problem-solving, and this has long shaped how software engineering expertise develops. However, with over-reliance on agentic coding, unpracticed skills could atrophy silently over time. As this learning pathway is short-circuited, developers risk silently accruing Knowledge Debt, a developer-level analogue of Technical Debt, where changes the
agent executes that the developer cannot fully understand accrue over time. In this paper, we argue that incidental learning will not re-emerge on its own and must be consciously designed back into developer-
agent interactions, and propose six design principles to guide such systems. We then present "SHIELD", a multi-
agent system grounded in the notion of "
agents that teach", that operationalizes these principles by leveraging the AI coding
agent's own
reasoning to surface contextual, out-of-band learning moments without disrupting developer flow. Through this work, we envision a path toward learning-aware development environments where productivity and learning are complementary, not competing.
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All authors Rohit Mehra, Samdyuti Suri, Prithviraj K Tagadinamani, Kapil Singi, Vikrant Kaulgud, Adam P. Burden
Artificial Intelligence · Cross-listed from cs.CV
PVCap: Towards Accurate 3D Dense Captioning via PseudoCap and VoxelCapNet
Xiaopei Wu, Chenshu Hou, Liang Peng et al. (+9 more)
3D dense captioning, an emerging
vision-
language task, aims to generate descriptive sentences for each object in the 3D scene. Despite the impressive results achieved by previous methods, they suffer from two limitations. First, current research often employs global rigid transformations, such as rotation, to augment scenes without changing their spatial layouts. However, diverse spatial layouts are crucial for training a 3D dense captioning model to describe spatial relations between objects. Second, previous works mainly focus on the design of the caption generation pipeline while utilizing a simple network architecture for other components, i.e., backbone and detection head, which is crucial for extracting rich semantic information for captioning. In this paper, we propose PVCap to alleviate the aforementioned problems. Our PVCap consists of PseudoCap and VoxelCapNet. Specifically, PseudoCap employs a random mixing technique on instances within the
dataset, generating numerous pseudo frames with diverse spatial layouts at the instance level. By utilizing a teacher-student framework, PseudoCap obtains pseudo caption labels for these pseudo frames. This data augmentation approach significantly increases the number of training samples and enhances the model's ability to describe the environment effectively. Regarding VoxelCapNet, we introduce a robust caption network that utilizes voxel features and adapts the caption head to the voxel-based network architecture. Our VoxelCapNet can serve as a competitive baseline for future research on 3D dense captioning. Extensive experiments are conducted on two prevalent
benchmarks, i.e., ScanRefer and Nr3D. Notably, our method surpasses current state-of-the-art by 11.41% and 13.99% in CIDEr@0.5IoU, respectively. Codes will be made publicly available.
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All authors Xiaopei Wu, Chenshu Hou, Liang Peng, Dan Xu, Binbin Lin, Xiaoshui Huang, Yuenan Hou, Yu Li, Wenxiao Wang, Haifeng Liu, Deng Cai, Wanli Ouyang
Artificial Intelligence · Cross-listed from cs.CL
From Blueprint to Reality: Modeling and Applying Putnam's Social Capital Theory with LLM-based Multi-agent Simulations
Shiyi Ling, Zhi Zheng, Hui Zheng et al. (+3 more)
Putnam's Social Capital Theory is a foundational framework for collective action and community prosperity. However, traditional empirical methods face practical limits on control and replication. Meanwhile, LLM-based social simulations are typically behavior-driven and lack theory-aligned environments for modeling Putnam's core propositions. To address these gaps, we introduce SocaSim, an LLM-based multi-
agent simulation framework to study Putnam's Social Capital Theory from theoretical blueprint to simulated reality. Specifically, we build an environment integrating social network
evolution, trust dynamics, and norm propagation, where
agents engage in repeated collective-action experiments, and then apply the three dimensions to analyze adaptation challenges in smart elderly care. Our simulations reproduce Putnam's macro-level patterns and exhibit strong human-
agent alignment at the group level. Unlike traditional methods, SocaSim traces micro-level causal pathways of social network, trust, and norms via round-by-round simulations and counterfactual interventions, enabling process-level interpretability. Taken together, these capabilities establish a research paradigm that leverages LLM
agents to bridge social science and computer science.
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All authors Shiyi Ling, Zhi Zheng, Hui Zheng, Wenjun Xue, Feng Ye, Tong Xu
Artificial Intelligence · Cross-listed from cs.SE
Prompt Coach: An Empirical Evaluation of an Agentic Tutor for Learning Prompt Engineering in Software Development
Rohit Mehra, Kapil Singi, Vikrant Kaulgud et al. (+5 more)
Prompt engineering has emerged as a critical yet undertaught skill for software developers, one that traditional learning approaches are ill-equipped to support given its evolving, interactive, and context-dependent nature. In this paper, we introduce
Prompt Coach (PC), an agentic tutor that helps developers learn how to craft high-quality code-generation
prompts through Socratic guidance embedded in-flow within their IDE. PC evaluates
prompt quality across multiple dimensions and surfaces targeted questions to guide self-correction, grounded in the developer's codebase and the behavior of the target LLM. We present an early empirical study with 15 professional developers combining quantitative
prompt quality scoring with qualitative perception measures. Participants showed statistically significant improvements after a single 60-minute session, with the largest gains across dimensions commonly overlooked by developers. They also reported strong trust, high adoption readiness, and unanimous agreement that PC improved their
prompt-writing skills.
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All authors Rohit Mehra, Kapil Singi, Vikrant Kaulgud, Vibhu Saujanya Sharma, Swapnajeet Gon Choudhury, Swati Sharma, Adam P. Burden, Majd Sakr