Artificial Intelligence
Automated Analysis Framework for Multilingual Climate-Health Literature Based on Multi-Agent Large Language Model
Yuze Sun, Shihui Zhang, Jiancheng Pan et al. (+6 more)
The rapid proliferation of interdisciplinary and
multilingual scientific literature has left traditional manual analysis and single-
algorithm methods plagued by low efficiency, poor scalability, and insufficient domain adaptability. Targeting the literature analysis needs of the typical interdisciplinary
climate-health field, this study proposes a multi-
agent large
language model automated analysis framework for
multilingual scientific literature, which realizes full-process automation covering literature screening, structured information extraction, and standardized integration. With a central coordination module as the core, the framework deploys three dedicated
agents for document evaluation, information extraction, and analytical review to mimic the literature analysis thinking of domain experts, and adopts a four-layer
hallucination control strategy together with a manual verification procedure to ensure the accuracy and reliability of analytical outcomes. Validated on a bilingual Chinese-English corpus of 32,642
climate-health papers covering China from 1993 to 2023, the framework achieves an F1 score of 0.92 in core information extraction, and completes the extraction and standardization of 2,012 city-literature association pairs, offering effective technical support for large-scale evidence mining in the
climate-health research domain.
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All authors Yuze Sun, Shihui Zhang, Jiancheng Pan, Yunjia Ye, Wentao Luo, Jiahao Li, Quan Zhang, Wenjia Cai, Xiaomeng Huang
Artificial Intelligence
Should I Use This Synthetic Dataset for Training? How to Test with Minimal Real Data
Zhenyu Tao, Wei Xu, Xiaohu You et al. (+2 more)
Digital twins (DTs) and learned world models are increasingly used to generate synthetic data that augment the scarce real
datasets available for training artificial intelligence (AI) models in engineering systems. Owing to the inevitable simulation-to-reality (sim-to-real) gap, however, augmentation may fail to improve the performance of the trained model on the real data distribution. This paper addresses the resulting decision problem: Given a real
dataset, a candidate synthetic
dataset, and a fixed learning
algorithm, decide whether training on the augmented
dataset improves the true, population-level performance, while consuming as few real test data points as possible. Two formulations are considered: a direct test on the mean loss difference between the two trained models, and a
symmetry-based test on the paired loss difference, which trades a stronger null assumption for faster evidence accumulation. For the latter, we introduce the {adaptive e-process sign-flip test} (aeSFT), a doubly adaptive procedure that adapts both the number of Monte Carlo sign-flip rounds, and hence the computational cost, and the amount of real test data consumed. aeSFT yields anytime-valid Type-I error control, with no need to pre-specify the test-set size. Experiments on a synthetic-data classification task, a DT-aided wireless packet-scheduling task, and a
radio-map prediction task show that aeSFT identifies useful synthetic data using substantially fewer real test samples than mean-based sequential testing, matches the power of fixed-sample sign-flip testing and the paired $t$-test, while keeping the false-positive rate below the target level.
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All authors Zhenyu Tao, Wei Xu, Xiaohu You, Petar Popovski, Osvaldo Simeone
Artificial Intelligence
GOD: Govern, Observe, and Direct - A Real-Time Control Room for Agent Societies
Yige Luo and Ran Guan
Generative-
agent systems are easier to start than to inspect. A run can contain many
agents, locations, messages, commands, and model calls, yet the operator often gets either a finished replay or raw logs. That makes it hard to ask why an
agent moved, test a small intervention, or package a run for another researcher. GOD is a local-first control room for
agent societies. From the same browser workflow, an operator can issue targeted questions or interventions and inspect the resulting replay state. The system combines a setup wizard,
Agent Studio, Map Studio, a spatial replay interface, Ask and Intervene commands, and portable experiment, map, and
agent packs. Its technical contribution is the command and artifact loop: live controls and replay evidence share the same operator command model, while package contracts separate scenario, map, and profile data from local runtime state. The public release includes hosted Smallville-style and PKU replays, the open-source repository, and downloadable packs. We evaluate this path on 15 completed run slots. Across the 14 intervention runs, 78 of 84 target-
agent checks recorded the commanded destination, and 169 of 182 state answers matched a saved location or action string.
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Artificial Intelligence · Cross-listed from cs.CR
CAITLYN: Can LLM Agents Autonomously Synthesize Defenses against Emerging Injection Attacks?
Zi Liang, Xiaoyu Xu, Yanyun Wang et al. (+3 more)
Prompt injection attacks on Large
Language Model (LLM)
agents seek to introduce malicious instructions or content into external text sources retrieved by
agents, forcing the underlying LLMs to execute harmful actions outside their benign scope. While current defenses effectively counter known injection attacks, deploying them in LLM
agent environments remains challenging due to attack variants and emerging threats. Moreover, existing solutions typically suffer from an inherent trilemma, i.e., a constant trade-off among runtime efficiency, contextual precision, and adaptability. To bridge this gap, we propose Continuous
Agents for Injection Threats via Lifelong Yielding Nexus (CAITLYN), an
agent-agnostic defense middleware. CAITLYN integrates two systems. System I focuses on immediate defense against existing attacks using a two-tiered library: Tier-0 for rule-based detection scripts and Tier-1 for optimized LLM-based accurate inference. System II, in contrast, is deployed to monitor potential abnormal signals and attempt to synthesize new defenses. On standard
benchmarks, CAITLYN matches the detection performance of state-of-the-art defenses at lower
token overhead than LLM-as-a-judge baselines. On Emerging, our new delivery-aware
benchmark featuring novel injection techniques, static baselines and the standalone System I configuration remain vulnerable. In contrast, System II autonomously synthesizes verified defense capabilities, substantially lowering the attack success rate across three diverse
agent environments.
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All authors Zi Liang, Xiaoyu Xu, Yanyun Wang, Minxin Du, Qingqing Ye, Haibo Hu
Artificial Intelligence
When Evidence Shapes Collaboration: Knowledge-Conditioned Topology Generation for Multi-Agent Systems
Yangxiao Jiang, Jiarun Fan, Mingcong Xu et al. (+6 more)
Multi-
Agent Systems (MAS) have recently moved from static workflows toward dynamically generated collaboration
topologies. However, existing
topology generation methods rely primarily on the parametric knowledge of large
language models, with external search or
retrieval used only as a reactive tool rather than an explicit determinant of collaboration structure. This leads to structure-knowledge misalignment, where systems exhibit redundant interactions or insufficient verification in knowledge-intensive tasks. We propose K-GAT (Knowledge-Guided
Agent Topology Generator), a neuro-symbolic framework that formulates collaboration
topology design as a knowledge-conditioned structure learning problem, integrating external evidence directly into autoregressive graph generation. Extensive experiments on knowledge-intensive
benchmarks demonstrate K-GAT's efficiency and effectiveness: notably on the expert-level GPQA
dataset, K-GAT outperforms the LLM-Debate baseline by a substantial margin of +15.7% in accuracy, while consuming less than half the computational
tokens.
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All authors Yangxiao Jiang, Jiarun Fan, Mingcong Xu, Yanxi Guo, Jiwen Feng, Shanqing Xu, Mengchen Qian, Wei Chen, Xiaojin Zhang
Artificial Intelligence
Learning from Hard Prompts: Difficulty-aware Advantage Amplification in Dynamic Sampling
Siyuan Gan, Yuhan Li, Xiran Wang et al. (+6 more)
Decoupled Clip and Dynamic Sampling Policy Optimization (DAPO) is a prominent variant of Group Relative Policy Optimization (GRPO). DAPO introduces several improvements over GRPO. Among these, Dynamic Sampling contributes the most to DAPO's accuracy gains relative to GRPO. To improve accuracy, Dynamic Sampling enhances training stability by eliminating zero policy gradients from zero advantages. Specifically, it avoids such zero gradients by filtering out
prompts where sampled responses are either entirely correct or incorrect. However, our theoretical analysis shows that Dynamic Sampling decrease training efficiency as it cannot effectively utilize hard-to-sample correct responses on hard
prompts. Formally, it asymmetrically amplifies the advantages of distinct responses to the same
prompts. On hard
prompts, incorrect responses undergo greater amplification than correct ones. This leads the model to avoid generating the observed incorrect responses rather than capitalizing on the hard-to-sample correct ones on hard
prompts, resulting in low training efficiency. To improve training efficiency, we propose Direct Advantage Amplification (DAA), which amplifies the advantages of hard-to-sample correct responses on hard
prompts, as obtained by Dynamic Sampling. This ensures that, when Dynamic Sampling is used, these hard-to-sample responses can be effectively capitalized on, implying higher training efficiency. By integrating DAA into DAPO, we obtain Difficulty-aware Advantage Amplification Policy Optimization (DA3PO), which is implemented with fewer than 30 lines of code from DAPO. Experiments show that DA3PO significantly outperforms GRPO and other classical GRPO variants.
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All authors Siyuan Gan, Yuhan Li, Xiran Wang, Linjian Meng, Boyan Wang, Zhen Zhao, Jing Huo, Lei Bai, Yang Gao
Artificial Intelligence
openJiuwen: Beyond Static Harnesses for Long-Horizon Coding Agents
openJiuwen Team: Tao Yu, Xinyu Zhang, Qianqian Chen et al. (+16 more)
Long-horizon coding
agents operate over evolving repository states while increasingly relying on heterogeneous capabilities, delegated
agents, and multi-
agent coordination. These trends pose two complementary challenges for the
agent harness. First, developers need to compose capabilities, reconfigure execution logic, and scale increasingly complex
agent systems without repeatedly rebuilding orchestration. Second, complex coding tasks continuously produce new evidence—such as semantic diagnostics, execution outcomes, task progress, and changing context relevance—that should dynamically influence subsequent runtime decisions. We characterize these challenges as Structural Composability and Runtime Adaptivity. We present openJiuwen, an open-source harness designed for both developer composability and adaptive task execution. openJiuwen provides a shared execution substrate and Rail-based capability composition across single
agents, delegated sub-
agents, and Swarm Flow, enabling developers to construct sophisticated
agent harnesses under common execution semantics. It further adapts framework-controlled runtime decisions around a fixed model policy, allowing evolving evidence to dynamically affect context, feedback, and task control toward successful completion. We systematically evaluate openJiuwen on SWE-bench Verified and Terminal-Bench 2.1, where it achieves 82.6% and 87.19%, respectively, exceeding the strongest selected official-leaderboard point estimates by 3.4 and 3.39 percentage points. These results show that openJiuwen achieves strong performance on complex coding tasks while providing a composable and adaptive harness design.
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All authors openJiuwen Team: Tao Yu, Xinyu Zhang, Qianqian Chen, Xiaoneng Xiang, Chia Kwangyang, Xingchen Huang, Ran Chen, Yangkai Ding, Zheng Wang, Yeo Boon Hong, Bingzheng Gan, Enrui Hu, Shuo Cheng, Deyang Li, Ruifeng Shi, Hongbo Wang, Qi Ye, Xuefeng Jin, Zhangchun Zhao
Artificial Intelligence
AERA: Adaptive Evidence Residual Allocation for Efficient Test-Time Reasoning
Ziming Wang, Ivor Tsang and Hangwei Qian
Test-time scaling improves
language-model
reasoning by generating additional candidate solutions, but allocating the same inference budget to every problem is computationally wasteful. Existing adaptive stopping methods commonly rely on confidence, agreement, or answer stability, implicitly assuming that stronger current evidence indicates that further computation is unnecessary. We show that this assumption can fail: checkpoint-level correctness evolves non-monotonically, and observable evidence may strengthen before an answer collapses or weaken before it recovers. Motivated by this mismatch, we introduce Adaptive Evidence Residual Allocation (AERA), a sequential controller that learns whether additional computation is likely to recover a better answer from checkpoint-observable evidence. AERA characterizes cumulative response prefixes using answer-distribution, temporal, re-solving, semantic, and compute features, and repeatedly decides whether to stop or allocate the next response block. Future checkpoint correctness is used only to construct offline supervision and is never available to the controller at inference time. Across GSM8K and GPQA Diamond, AERA identifies question-specific residual opportunities while substantially reducing inference computation. In a frozen-threshold incremental-generation evaluation on 300 untouched GSM8K questions, AERA achieves 92.61% accuracy versus 93.01% with 128 responses while reducing completion
tokens by 95.99%. These results suggest that adaptive
reasoning should estimate the future value of computation rather than equating present confidence with correctness.
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Artificial Intelligence
SABER: Stability-Aware Early Exit for LLM Reasoning via Adversarial Branch Probing
Wanli Cheng, Haiya Xiang, Juntao Li et al. (+2 more)
Large
Reasoning Models (LRMs) achieve strong
reasoning capabilities, yet long-chain
reasoning becomes inefficient once the intermediate answer stabilizes across
reasoning steps: additional
reasoning yields little marginal benefit while incurring substantial inference cost. Existing early-exit methods based on confidence or
entropy poorly capture
reasoning stability, while consistency-based approaches rely on multi-step trajectory agreement, requiring sequential evaluations that delay exit. To better balance efficiency and reliability, we propose SABER, a training-free framework for stability-aware early exit via
adversarial branch probing. SABER constructs simple yet effective semantic perturbations around intermediate
reasoning states to form
adversarial branches, and applies lightweight probing to estimate their likely final outcomes without full trajectory rollouts. When the probed outcomes remain consistent across branches, SABER exits early; otherwise, it continues
reasoning. Experiments across multiple
reasoning benchmarks and model architectures show that SABER reduces
reasoning token consumption by 30.2%–39.8% on average while maintaining competitive accuracy with full-length
reasoning.
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All authors Wanli Cheng, Haiya Xiang, Juntao Li, Hongling Wang, Wenliang Chen
Artificial Intelligence
When Teacher Guidance Misleads: Reward-Aligned On-Policy Distillation
Siyuan Gan, Yuhan Li, Xiran Wang et al. (+5 more)
On-policy
distillation (OPD) has recently emerged as a popular post-training paradigm for large
language models (LLMs), providing an efficient way to transfer the knowledge and capabilities of teacher models into student models. However, teacher guidance on student-generated prefixes is not always reliable. Training should optimize the model to generate responses that are more likely to be correct, or equivalently, to get higher outcome rewards. But during OPD, the teacher model may provide guidance that discourages the student from moving toward correct trajectories or moves the student toward incorrect ones, which is misaligned with outcome reward. Such misaligned guidance is unreliable, as it would mislead the optimization process and ultimately degrade model performance. To mitigate misaligned teacher guidance, we propose Reward-Aligned On-Policy
Distillation (RA-OPD). The key insight is to keep only trajectories whose induced updates move the student toward correct trajectories or discourage the student from moving toward incorrect ones. Specifically, for each sampled trajectory, RA-OPD checks whether its trajectory-level
distillation return is consistent with its outcome reward and then filters out the misaligned trajectories. RA-OPD selects more reliable trajectories to improve student model performance without requiring additional computational cost. We evaluate RA-OPD on math and code
benchmarks using models from the Qwen3 family and the DeepSeek-R1 family. Across seven math
benchmarks and three code
benchmarks, RA-OPD significantly outperforms standard OPD and other tested OPD variants.
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All authors Siyuan Gan, Yuhan Li, Xiran Wang, Linjian Meng, Boyan Wang, Zhen Zhao, Jing Huo, Yang Gao
Artificial Intelligence · Cross-listed from cs.CR
Not to Break, but to Attest: Adversarial Probes for Privacy-Preserving LLM Verification
Cameron Wilding, Mina Shaker and Fatemeh Ganji
Post-deployment changes to large
language models can alter behavior while leaving routine outputs largely unchanged, creating a challenge for AI governance when model weights are proprietary. We present a
privacy-preserving zk-SNARK-based audit framework that searches for probes designed in the spirit of
adversarial examples to amplify logit drift between an approved model and a modified deployment. Our framework explores complementary probe families under different access models.
Token-based probes operate in a black-box setting and require only the input interface, tokenizer, and vocabulary.
Embedding-based probes require gray-box access to the
embedding interface. Stress probes rely on additional interface capabilities but do not require full white-box access to model weights or architecture. This range allows probe selection to balance sensitivity, access requirements, and deployment cost. We evaluate probe constructions across LLM architectures, model-tampering scenarios representative of post-deployment attacks, and GPU platforms. Importantly, our experimental results demonstrate that
token-based probes consistently deliver the strongest mean sensitivity across models and GPU platforms, although operating in a black-box setting. Our Groth16 zk-SNARK workflow remains practical as the probe set scales from 1 to 50, where proving time increases from 1.02 to 1.78 seconds, verification remains near 0.84 seconds, and proof size remains constant.
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Artificial Intelligence
The Illusion of $\textit{What If}$: Evaluating the Breakdown of Counterfactual Reasoning in LLMs
Yucheng Wang, Yuetian Du, Zhengyi Liu et al. (+8 more)
Counterfactual
reasoning requires models to reason beyond the observed world and explain how altered conditions propagate through downstream consequences. Existing
benchmarks largely target bounded settings with fixed variables or single gold outcomes, overlooking open-domain scenarios requiring causal-process evaluation. To this end, we present $\textbf{WhatIfBench}$, a diagnostic
benchmark for open-domain, open-form, long-horizon counterfactual causal
reasoning, containing 220 what-if questions across STEM, HSS, and Hybrid scenarios. To evaluate free-form responses, we further propose $\textbf{PRISM}$, which first converts each natural-
language explanation into a Response-Derived Semantic Causal Graph of events, states, and mechanisms. On top of this graph, PRISM then jointly applies a Process Metric assessing graph-level causal validity and a Rubric Metric assessing answer-level explanatory adequacy. Evaluating six frontier LLMs with this framework, we find that WhatIfBench remains far from saturated: even the strongest model reaches only a 64.62% final score. Further analysis reveals persistent causal gaps, premise drift, and
topology fragmentation, suggesting that fluent counterfactual narratives often mask fragile causal processes. The
benchmark, code, and evaluation scripts are available at $\href{https://github.com/zju-gt/WhatIfBench}{WhatIfBench}$.
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All authors Yucheng Wang, Yuetian Du, Zhengyi Liu, Rongyu Zhang, Bing Zhao, Boyu Yang, Ming Kong, Lin Qu, Hu Wei, Jie Liu, Qiang Zhu
Artificial Intelligence
Cross-Session Decomposition Attacks: Scaling Risk and Intent-Aligned Retrieval Defense
Disen Liao, Yihan Wang, Freda Shi et al. (+1 more)
Scaling laws are usually read as a capability story: lower
language-modeling loss yields more useful models. We study a safety consequence of this mechanism in cross-session decomposition attacks, where benign-looking subqueries are asked across independent interactions and later recomposed toward a forbidden objective. We formalize this setting as compositional safety risk and prove a conditional risk-transfer bound: when the reference environment already contains dispersed evidence for a risky reconstruction, the gap between deployed composed risk and reference composed risk is controlled by the model's excess loss on allowed subqueries. Synthetic withholding experiments show that wider
transformers assign lower loss to held-out instructions that never appear verbatim in training but are recoverable from injected supporting facts. A 600-intent pretrained-LLM evaluation shows that larger Qwen3 and Gemma3 family members can yield greater harmful-capability uplift under a fixed decomposition-composition pipeline. As a defense, IntentAlign-MiniLM, our 22M-parameter intent-aligned retriever, outperforms much larger
embedding models on held-out intent
retrieval and yields the best learned-retriever harmful recall across tested guardrails. Code is available in \href{https://github.com/liaodisen/Cross-Session-Decomposition-Attacks}{our GitHub repository}.
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All authors Disen Liao, Yihan Wang, Freda Shi, Yaoliang Yu
Artificial Intelligence
CASTANET: Causality-Aware Spatio-Temporal Adversarial Network Using Traffic Incident Effects
Toshiya Kitahara, Ryu Shirakami, Koh Takeuchi et al. (+1 more)
Predicting non-periodic traffic congestion caused by sudden incidents (e.g., accidents and road damage) is crucial for advanced intelligent transportation systems. However, incident-driven congestion is difficult to forecast because incidents are extremely sparse, occur at specific times and locations, and have heterogeneous impacts depending on the traffic context. While recent deep learning approaches have significantly improved periodic traffic
forecasting, their performance on non-periodic congestion remains limited, partly because incident records are not explicitly incorporated and their occurrence is strongly biased in space and time. To address these challenges, we propose CASTANET, which integrates spatio-temporal graph neural networks and causal treatment effect estimation to utilize incident records while mitigating selection bias. Experiments on real-world traffic data and accident records from Tokyo, which we treat as incidents, show that CASTANET reduces RMSE by 4.0% overall compared to the best baseline and by 10.1% on incident-conditioned evaluation, with gains reaching 14.55% under severe congestion.
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All authors Toshiya Kitahara, Ryu Shirakami, Koh Takeuchi, Hisashi Kashima
Artificial Intelligence
A Deep Learning-Based Stacking Ensemble Framework for Turbofan Engine Remaining Useful Life Prediction
Limon Bin Hossain, Md. Salehin Seyam, Md Rashedul Islam et al. (+2 more)
This study proposes a two-level stacking ensemble framework for Remaining Useful Life (RUL) prediction of turbofan engines, evaluated on the NASA C-MAPSS
benchmark using the FD001 and FD003 subsets. The framework integrates four heterogeneous deep learning base learners: Long Short-Term
Memory (LSTM), Convolutional Neural Network (CNN), CNN-LSTM, and CNN-GRU, whose out-of-fold predictions are combined by an XGBoost meta-learner to capture complex degradation patterns while mitigating individual model biases. Comprehensive experiments demonstrate that the stacking ensemble achieves superior predictive performance, with Root Mean Square Error (RMSE) of 9.989 and 8.613, Mean Absolute Error (MAE) of 7.081 and 5.195, and R-squared values of 0.899 and 0.906 for FD001 and FD003, respectively. Compared to the best-reported baseline (TCAT: RMSE 11.12 and 11.02), the proposed method achieves RMSE reductions of 10.2 percent and 21.8 percent for FD001 and FD003, respectively. Feature correlation analysis, residual diagnostics, and training convergence curves validate the model's robustness. These findings underscore the efficacy of stacking ensemble methods for prognostics and health management in safety-critical aerospace applications.
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All authors Limon Bin Hossain, Md. Salehin Seyam, Md Rashedul Islam, Abdur Rahman, Md Sharifuzzaman
Artificial Intelligence · Cross-listed from cs.SE
Antipatterns in AI-assisted Qualitative Data Analysis: A Catalog of Temptations and Pitfalls for Software Engineering Researchers
Rashina Hoda, Carolyn Seaman, Victoria Gomes et al. (+1 more)
AI-assisted qualitative data analysis (QDA) offers unprecedented opportunities to streamline software engineering (SE) research, yet uncritical use risks compromising analytical rigor and flooding the field with accelerated production of low-quality research. While tactical best practices will naturally evolve over time, SE researchers currently lack strategic guidance to identify and mitigate methodological risks when attempting AI-assisted QDA. Based on our decades of qualitative SE research expertise and experience combined with an understanding of the emerging landscape of AI-assisted QDA, this paper presents a catalog of antipatterns in AI-assisted QDA - a set of assumptions and practices that initially appear advantageous but ultimately undermine analytical rigor and validity. The antipatterns are grouped into three categories reflecting escalating impact: Dangerous Drivers, Operational Missteps, and Analytical Failures. As more SE researchers attempt AI-assisted QDA, these antipatterns will help them identify and avoid common temptations and pitfalls, while reviewers can be equipped with the vocabulary and criteria to call out problematic and failed practice. Ultimately, this catalog of antipatterns can serve as a stepping stone in our responsible methodological
evolution toward principled and meaningful human-AI collaboration in qualitative research.
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All authors Rashina Hoda, Carolyn Seaman, Victoria Gomes, Rodrigo Spinola
Artificial Intelligence · Cross-listed from cs.CV
PCBnet: A Dataset and Automatic Construction of SPICE Netlists from Schematic Images
Zhen Huang, Yuhao Gao, Yuzhi Liu et al. (+15 more)
Printed
circuit boards (PCBs) are fundamental to modern electronic systems, yet AI-driven PCB design automation remains constrained by the lack of large-scale paired schematic-netlist
datasets. PCB schematics are particularly challenging due to diverse component types, complex wiring
topologies, and noisy textual annotations. To address this gap, we present PCBnet, a large-scale PCB schematic
dataset comprising over 300 real-world designs with annotated pins and paired SPICE netlists. It contains more than 50,000 component instances, 150,000 wires, 100,000 text regions, and 400,000 characters. We further develop an automated schematic-to-netlist pipeline that combines visual recognition,
topology construction, and domain-knowledge-guided multi-
agent correction. The proposed method achieves 94.54% component detection mAP, 98.57% text recognition accuracy, and 84.47% end-to-end connectivity accuracy. PCBnet provides a
benchmark and data foundation for future AI-driven PCB design automation.
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All authors Zhen Huang, Yuhao Gao, Yuzhi Liu, Daian Cheng, Chengyuan Shao, Yucheng Chen, Yongjian Jia, Futing Zhang, Yichen Shi, Wenhao Wang, Zuyan He, Yangbo Wei, Zhanfei Chen, Jinlong Yan, Yu Zhang, Haoying Wu, Ting-Jung Lin, Lei He
Artificial Intelligence
From Documents to Reasoning: A Validated Synthetic Data Pipeline and Semantic-Aware Fine-Tuning for Financial Numerical Reasoning
Lokendra Birla, Milind Savagaonkar, Visnu Srinivasan et al. (+2 more)
Financial question answering (QA) has emerged as a key
benchmark for evaluating the performance of Large
Language Models (LLMs) on domain-specific tasks involving complex data formats such as tables, charts, and rich textual narratives. While recent advancements have enabled models to reason across modalities and perform multi-step arithmetic operations, limitations remain in performance consistency, and evaluation reliability. In particular, standard evaluation metrics like Exact Match (EM) often fail to account for minor variations such as differences in units or formats, misleading performance assessments. In this work, we propose a comprehensive pipeline for improving financial QA systems through high-quality synthetic data generation and fine-tuning of smaller
language models (SLMs) using Quantized Low-Rank Adaptation (QLoRA). Our pipeline includes aggressive data validation for synthetic question answer generation to ensure the relevance and correctness of synthetic question-answer pairs. We introduce a novel evaluation metric that matches answers computed from arithmetic expressions rather than ground-truth answers; providing a more accurate reflection of model
reasoning capability. Furthermore, we propose a modified loss function that aligns predicted and reference expressions using semantic similarity, our novel evaluation metric and standard cross-
entropy, resulting in improved performance. Experimental results on
benchmark datasets, ConvFinQA demonstrate significant gains in QA accuracy after fine-tuning using synthetic
dataset and proposed loss function.
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All authors Lokendra Birla, Milind Savagaonkar, Visnu Srinivasan, Sowmya Rasipuram, Shubhashis Sengupta
Artificial Intelligence
AI Alignment through a Game-theoretic Lens: A Survey
Yanan Cai, Zhongrui Zhao, Zhigang Lu et al. (+6 more)
As large
language models and increasingly capable AI
agents are deployed in high-risk settings, aligning them with complex human values has become a central challenge. Existing
alignment methods, while effective in improving helpfulness, harmlessness, and controllability, often struggle to capture real-world preferences that are context-dependent, non-transitive, and shaped by dynamic multi-party interactions. This survey reviews AI
alignment through a game-theoretic lens. Specifically, it organizes recent progress around key game-theoretic elements and synthesizes the literature along three challenges: preference diversity,
alignment priority, and temporal dynamics. This perspective clarifies where current
alignment methods genuinely benefit from game-theoretic analysis, where the framework is looser, and what challenges remain in building robust, adaptive, and verifiable AI systems.
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All authors Yanan Cai, Zhongrui Zhao, Zhigang Lu, Ickjai Lee, Wei Emma Zhang, Minhui Xue, Yihong Zhang, Shuchao Pang, Wei Xiang
Artificial Intelligence · Cross-listed from cs.IT
Low-Altitude Fluid Antenna Network with Multi-Agent Reinforcement Learning
Tong Zhang, Yanfei Su, Shuai Wang et al. (+3 more)
Low-altitude wireless networks (LAWNs) integrate terrestrial and aerial platforms to provide ubiquitous communication, sensing, and localization services for unmanned aerial vehicles (
UAVs) and electric vertical takeoff and landing (eVTOL) aircraft. However, dynamic air-ground and air-air channels, abrupt blockages, and heterogeneous interference hinder the realization of this goal. Nevertheless, fluid
antenna (FA), a cutting-edge multiple-input multiple-output (
MIMO) technique, overcomes these challenges by reconfiguring
antenna positions to unlock additional spatial degrees-of-freedom. In this paper, towards bringing low-altitude FA networks into reality, we study the fast and high-performance FA reconfiguration for low-altitude FA networks with multi-
agent reinforcement learning (MARL). Specifically, we present an electromagnetic digital twin (EM-DT)-assisted MARL framework. To fill the sim-to-real gap, we introduce a two-stage transfer learning framework. Our case study shows that joint FA positions and beamforming optimization can enhance the system sum-rate by 118.5%, compared to the fixed position baseline. This gain comes from the dynamic millisecond timescale reconfiguration of FA arrays and the adaptive steering of beams toward aerial users with mobility.
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All authors Tong Zhang, Yanfei Su, Shuai Wang, Wanli Ni, Chengzhong Xu, Huseyin Arslan
Artificial Intelligence
Design the Feedback, Not Just the Reward: Environments for Open-Ended Reinforcement Learning
Rui Jin, Jikai Chen, Chengyue Yu et al. (+7 more)
Reinforcement learning has advanced large
language models on tasks with reliable verifiers, where checking an outcome provides a clear basis for reward. Extending this approach to open-ended tasks requires environments that can both evaluate complex, partially successful outputs and turn these judgments into effective learning guidance. We propose a reinforcement learning framework built around a training environment that integrates human domain knowledge with model
reasoning. Human domain knowledge, encoded in rubrics, anchors evaluation to task intent, while model
reasoning combined with hierarchical rewards distinguishes complex open-ended outputs and links diagnosed failures to likely responsible output regions. Furthermore, these diagnostic attributions guide
token-level weights in the GRPO objective, thereby translating the ambiguous notion of success in open-ended tasks into concrete guidance for policy optimization. We instantiate this framework in interactive web application generation, a representative open-ended generation task. The resulting model achieves an average pass rate of 41.25% on MiniAppBench and 76.19% on ArtifactsBench. Ablation studies show the contributions of individual components, while additional out-of-distribution evaluations support transferable gains without degrading performance on other evaluated tasks.
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All authors Rui Jin, Jikai Chen, Chengyue Yu, Yihan Chen, Hao Zhou, Demin Zhu, Kaichen Yang, Dong Wang, Linjian Mo, Chenyi Zhuang
Artificial Intelligence · Cross-listed from cs.CL
LandingAgent: A Reference-Annotated Dataset and Agentic Generation Framework for Landing Pages
Injun Baek, HyeongSeok Lee, Yearim Kim et al. (+2 more)
Landing pages are goal-oriented web interfaces that must communicate a target-specific value proposition while organizing information flow, visual hierarchy, and calls to action (CTA). Although large
language models can generate plausible webpage code from natural-
language prompts, direct generation often yields generic templates and unsupported persuasive claims. We study target-grounded, reference-guided landing-page generation, where a system must create an executable page for a new target by adapting reusable patterns from real pages without copying them. We introduce LandingBench, a reference-profile
dataset that abstracts real landing pages into section sequences, layout patterns, tone descriptors, visual emphasis, and CTA structure. Building on LandingBench, we propose LandingAgent, a three-phase agentic framework that profiles the target, constructs a reference-guided wireframe, and refines the page through critique-guided polishing. We evaluate LandingAgent against direct prompting on faithfulness, conciseness, readability, aesthetics, and structural diversity. Experiments show improved target grounding, presentation quality, and layout diversity. Code is available at https://github.com/IAURAI/LandingAgent.
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All authors Injun Baek, HyeongSeok Lee, Yearim Kim, Junhoo Lee, Nojun Kwak
Artificial Intelligence · Cross-listed from cs.CL
OpenStamp: A Watermark for Open-Source Language Models
Miroojin Bakshi, Saksham Rastogi and Danish Pruthi
With the growing prevalence of large
language model (LLM) generated content, watermarking is considered a promising approach for attributing text to LLMs and distinguishing it from human-written content. A prominent class of techniques embeds subtle but detectable signals in generated text by modifying
token sampling probabilities. However, such methods are unsuitable for open-source models, where users have white-box access and can easily disable watermarking during inference. In this work, we introduce OpenStamp, a watermarking technique that encodes the watermarking logic directly into the model weights by modifying only the final projection, or unembedding, layer. Through experiments across two models, we show that OpenStamp achieves superior detection performance, with minimal degradation in model capabilities compared to prior methods. The implanted watermark is explicitly designed, and empirically confirmed, to be more robust to paraphrasing attacks and harder to scrub off through post-hoc fine-tuning than prior open-source watermarks. To enable developers to watermark their models, we release our code alongside watermarked versions of 4 popular open-source models.
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Artificial Intelligence
Resource Constraints and Performance in Agentic AI Systems
Amaz Salman, Malka Halgamuge and Teo Susnjak
Progress toward more autonomous AI increasingly depends on agentic systems that combine a
language model with tools,
memory, state management, and multi-step execution. These mechanisms shape both task capability and operational burden. We compare OpenClaw and NanoBot as complete agentic systems using a paired primary
benchmark and a more detailed instrumented subset of paired
prompts. In the primary
benchmark, the rate of full task completion was 31% for OpenClaw and 25% for NanoBot, a six-percentage-point difference with a 95% task-bootstrap interval from -3 to 15 percentage points, providing no statistically established full-completion advantage for either system. In the instrumented layer, both systems achieved 26% full completion, while NanoBot reached at least partial completion on 43% of
prompts compared with 26% for OpenClaw. OpenClaw took longer on 83% of
prompts and had a higher recorded peak-
memory value on every
prompt, with
geometric mean ratios of 2.98 for wall time and 19.44 for peak
memory. Among the ten detailed-layer
prompts on which at least one system achieved partial or full completion, NanoBot weakly dominated on eight; across all 23
prompts, however, ten of its eighteen dominance cases were cheaper joint failures. Outcome labels differ across the two evidence layers, showing why
agent-system evaluation should connect capability and resource measurements to attempt-level execution and scoring provenance. These findings show that progress toward more autonomous AI should be evaluated through verified task completion, observed resource use and records linking each result to the execution that produced it.
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Artificial Intelligence · Cross-listed from cs.LG
SOMTab: Set-Order Mamba for Efficient Tabular In-Context Learning
Hao Wang, Siyu Zhang and Wei Ma
Tabular foundation models based on in-context learning have recently emerged as strong alternatives to task-specific model fitting. However, the current performance frontier remains dominated by
attention-heavy architectures, where
attention is used throughout the modeling pipeline. This raises a natural question: is
attention necessary at every stage of tabular in-context learning? We introduce SOMTab, a Set-Order Mamba architecture for efficient tabular in-context learning. SOMTab separates representation construction from query-conditioned
retrieval. For row and column representations, it maps unordered table
tokens into stable latent slots and applies Mamba-based state-space mixing to construct compact representations. For final prediction, it retains
attention-based in-context learning to preserve query-conditioned
retrieval from labeled context examples. We further introduce DCH-TailMix, a synthetic prior that combines degree-corrected graph heterogeneity with mixed heavy-tailed regimes to diversify synthetic dependency structures. Across tabular
benchmarks, SOMTab approaches the performance of strong
Transformer-based tabular foundation models while achieving faster inference and lower GPU
memory usage, yielding a favorable efficiency–accuracy trade-off.
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Artificial Intelligence
HyQuant: Hybrid-Precision Quantization for LLM Attention
Jiatong Ding, Bingxin Xing, Yu Zhang et al. (+9 more)
Quantization has been widely adopted in LLM training and inference to reduce cost and improve efficiency. However, low-bit quantization of the
attention module often introduces large errors at very low bit-widths, causing performance degradation. Existing methods mainly rely on smoothing techniques to handle outliers, while we propose a hybrid quantization design to better balance accuracy and efficiency. Specifically, we propose HyQuant, an efficient hybrid quantization framework for LLM
attention. HyQuant quantizes most
attention states into low-bit formats while retaining a small set of vertical-line
tokens and local-window states in high precision. These accuracy-critical regions are selected using lightweight vertical-line-aware
attention-pattern signals, reducing quantization error with limited overhead. In the Prefill stage, HyQuant uses a hybrid-precision quantized
attention operator that preserves vertical-line
tokens and a local sliding window in full precision while quantizing the remaining context. In the Decode stage, HyQuant applies the same principle to KV-cache compression and fuses KV dequantization with
attention computation to improve
memory and hardware efficiency. Across diverse tasks, models, and
datasets, HyQuant maintains nearly lossless accuracy with an extremely simple design, demonstrating the efficiency and practical feasibility of hybrid quantization for LLM
attention. Code is available at: https://github.com/jerrysfls/HyQuant .
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All authors Jiatong Ding, Bingxin Xing, Yu Zhang, Dian Ding, Xiaodong Yi, Xianbin Ouyang, Feihu Zhou, Kun Zhang, Zhenyu Guo, Hao Pan, Guangtao Xue, Yiming Zhang
Artificial Intelligence
See, Hypothesize, Validate: Multimodal Agentic Framework for Discovering Governing PDEs
Sarang Manoj Pekhale, Amartya Roy, Rajat Sarkar et al. (+1 more)
Discovering governing partial differential equations (PDEs) from observational data remains a core challenge across the sciences. Existing sparse-regression, symbolic-regression, and LLM-based approaches can be constrained by predefined libraries, noise sensitivity,
hallucination, or limited iterative refinement. We introduce MAGE (
Multimodal Agentic Governing Equation Discovery), an agentic framework that organizes PDE discovery as a confidence governed hypothesis validation loop inspired by the scientific cycle of observation, hypothesis, and falsification. Four role-specialized
agents collaborate: a Differential Observer computing derivatives and diagnostic visualizations; a VLM-powered Phenomenology Extractor distilling qualitative cues from
multimodal diagnostics; an LLM-driven Governing Law Synthesizer proposing candidates without a predefined library; and an Equation Arbiter fitting coefficients and assigning confidence scores. Discovery iterates until the top candidate clears a user-specified threshold, providing a structured process with an explicit accept-reject protocol. On the evaluated canonical PDE suite, MAGE obtains 8/8 exact structural recovery and the lowest coefficient error among the compared methods on 7/8 systems, with improvements of up to 4 orders of magnitude and a
geometric-mean improvement of approximately 3 orders of magnitude. The pipeline also recovers the expected operators in two complex geometries and, on one laboratory sensor record, selects a cubic restoring-force model with held-out $R^2=0.98538$. These results support further study of structured agentic
reasoning for library-free governing-law discovery, while broader generalization remains to be evaluated.
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All authors Sarang Manoj Pekhale, Amartya Roy, Rajat Sarkar, Souvik Chakraborty
Artificial Intelligence
CoRe-MoE: Compact Reusable MoE for Continual Multimodal Instruction Tuning
Runze Liu, Naibin Gu, Mingxu Ai et al. (+4 more)
Continual
multimodal instruction tuning requires
multimodal large
language models to acquire new task abilities sequentially while preserving previously learned knowledge. LoRA-MoE provides a promising solution by introducing expert-based capacity, but repeatedly learning and maintaining full LoRA experts leads to substantial parameter overhead. This raises a natural question: is full expert expansion necessary for every new task? To answer it, we analyze the SVD of task-specific LoRA updates and observe substantial overlap in their input- and output-side LoRA direction subspaces, with task-specific adaptation largely captured by lightweight coordinates over these subspaces. Motivated by this observation, we propose CoRe-MoE, a Compact Reusable MoE framework for parameter-efficient continual
multimodal instruction tuning. CoRe-MoE extracts reusable input- and output-side direction bases from an initial expert bank, and for subsequent tasks trains only compact coordinate experts together with task-specific low-rank routers. Experiments on two representative MLLMs show that CoRe-MoE improves final average performance over the strongest competing baseline by up to 5.90 points, while using less than 1% of the trainable parameters required by sequential LoRA for later tasks. The code is publicly available at https://github.com/runzezz/CoRe-MoE.
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All authors Runze Liu, Naibin Gu, Mingxu Ai, Yuqing Li, Peng Fu, Zheng Lin, Weiping Wang
Artificial Intelligence · Cross-listed from cs.CV
From Perspective to Fisheye Depth Estimation and Open-Vocabulary Segmentation
Rit Gangopadhyay and Alex Wong
Vision foundation models are capable of generalizing across 3-dimensional (3D) scenes with high-fidelity estimates; their empirical success can be attributed to training on large-scale
datasets of perspective images. However, when transferred to wide field-of-view (FoV) images, such as those captured by fisheye cameras, they return erroneous outputs due to a
covariate shift stemming from the radial distortion on the image pixels. We propose a method to generalize
vision foundation models to fisheye cameras. The crux of our method lies in a set of learnable parameters, termed Distortion Extenders (DEX), that model the fisheye distortion coefficients and the distributional shift between fisheye and perspective images encoded in the latent space. By minimizing a self-supervised
alignment loss, DEX transforms the latent
embeddings of fisheye images to resemble those of perspective images to recover high-fidelity estimates. DEX is architecture- and task-agnostic: We demonstrate DEX on
monocular depth estimation and open-vocabulary
segmentation for convolution- and
Transformer-based architectures, where we consistently improve over baselines across indoor and outdoor fisheye
datasets. As a byproduct, the activations of DEX can also be decoded to distortion coefficients to support camera calibration. Code available at: https://github.com/Suchisrit/DEX.
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Artificial Intelligence
SpikeOPD: Stable On-Policy Distillation for Autoregressive Spiking Language Models
Enqiao Lu, Xingrui Yu, Yiwei Fu et al. (+7 more)
Spiking neural networks (SNNs) offer a path to energy-efficient
language modeling through sparse encoding and event-driven computation, but training capable spiking
language models from scratch remains difficult. A practical alternative is ANN-to-SNN migration through knowledge
distillation (KD), where a pretrained artificial neural network (ANN) teacher supervises an SNN student. Existing migration approaches distill on fixed corpus prefixes, whereas autoregressive inference conditions on self-generated prefixes, creating prefix-source mismatch. It manifests as output-policy mismatch with the ANN teacher and internal spiking-dynamics drift between self-generated and matched corpus prefixes. On-policy
distillation (OPD) offers a natural way to mitigate both manifestations by continuing teacher supervision on self-generated prefixes. We evaluate a teacher-only full-KL variant, Vanilla OPD, via a controlled stress test and observe it may suffer from delayed rollout-feedback collapse. This result shows that on-policy coverage alone does not ensure stable adaptation. Motivated by these findings, we propose SpikeOPD, a stable on-policy
distillation framework for autoregressive SNNs that learns from self-generated prefixes while maintaining rollout stability. It applies full-KL teacher correction to reduce output-policy mismatch, while matched-prefix policy anchoring constrains policy departure from the frozen reference SNN on the same prefixes. Layerwise spike regularization further limits firing-rate deviations during on-policy adaptation. Across three model scales, SpikeOPD improves average accuracy over the corresponding KD SNNs by 0.8, 1.7, and 2.9 points at 0.125B, 0.35B, and 1.3B, respectively, while preserving their sparse-compute profiles.
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All authors Enqiao Lu, Xingrui Yu, Yiwei Fu, Zhenglin Wan, Pengfei Zhou, Wangbo Zhao, Muqing Jian, Xueyi Zhang, Yang You, Ivor Tsang