Artificial Intelligence · Cross-listed from cs.NI
Trustworthy, Explainable, and Sustainable Decentralized Intelligence for 6G Networks
Giovanni Perin, Michele Rossi, Enrique Tomás Martínez Beltrán et al. (+13 more)
As 6G networks transition from theoretical frameworks into operational realities, artificial intelligence (AI) evolves from an add-on optimization tool into a distributed and interconnected structural layer. Unlike previous network generations that mostly relied on centralized cloud analytics platforms, AI-native 6G networks operate across a dynamic, multi-domain edge-cloud continuum where data originates from heterogeneous sources including user devices,
radio access networks, sensing infrastructures, and vertical applications. Centralizing this massive volume of data creates severe communication overhead, unacceptable latency bottlenecks, single points of failure, and complex cross-domain governance challenges. Consequently, decentralization becomes a fundamental architectural requirement for future 6G network intelligence and zero-touch operations. Security serves as the primary enabler of this decentralized paradigm. Critical security functions, such as real-time threat detection, physical-layer attack mitigation, slice protection, and intrusion detection, require immediate access to local context and telemetry before operational data loses its value. However, moving intelligence to the edge via collaborative paradigms like
federated learning (FL) and decentralized FL (DFL) introduces complex trade-offs. System security cannot be addressed in isolation; it is deeply intertwined with equally important aspects like trustworthiness, explainability, and energy sustainability. Taking these aspects into account, this paper develops a unified perspective on decentralized intelligence for 6G, arguing that decentralization, trustworthiness, explainability, and sustainability must be designed jointly rather than treated as independent requirements
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All authors Giovanni Perin, Michele Rossi, Enrique Tomás Martínez Beltrán, Fernando Torres-Vega, José María Jorquera Valero, Manuel Gil Pérez, Eunjeong Jeong, Nikolaos Pappas, Farah Abed Zadeh, Chamara Sandeepa, Bartlomiej Siniarski, Madhusanka Liyanage, Betül Güvenç Paltun, Leyli Karaçay, Ioannis Pitsiorlas, Marios Kountouris
Artificial Intelligence · Cross-listed from cs.CL
Bangla Sentence Function Classification: Corpus Development, Model Benchmarking, and Interpretability
Swapnil Kundu Argha, Abdullah Al Shafi, Rowzatul Zannat et al. (+4 more)
Automatic sentence function identification is important for many downstream natural
language processing (NLP) applications such as dialogue systems, text-to-
speech synthesis, and machine translation. However,
benchmark resources for Bangla sentence function classification remain limited. To mitigate this gap, this paper introduces a corpus of 10,000 Bangla sentences, manually annotated into four functional categories, namely declarative, interrogative, imperative, and exclamatory. The corpus is nearly balanced across the four classes, with high annotation reliability reflected by a FleissḰappa of 0.82. Furthermore, we evaluate multiple feature representations, including Bag-of-Words (BoW), TF-IDF, and Word2Vec, with several classical machine learning classifiers. In addition, two heterogeneous ensemble models, namely Single-Level Ensemble (SLE) and Double-Level Ensemble (DLE), are utilized to improve classification performance. Experimental results show that TF-IDF consistently outperforms Word2Vec, likely due to its ability to emphasize discriminative lexical cues associated with sentence functions, particularly given the relatively small corpus used to train Word2Vec. The DLE model with TF-IDF features achieves the best performance with accuracy and macro-F1 of 0.95, demonstrating the effectiveness of sparse lexical representations and heterogeneous ensemble learning for this task. Further cross-validation confirms the robustness of the approach, while LIME-based interpretability provides insights into model predictions. The developed corpus and model benchmarking establish strong baselines for Bangla sentence function classification.
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All authors Swapnil Kundu Argha, Abdullah Al Shafi, Rowzatul Zannat, Shoumik Barman Polok, Abdul Muntakim, Jannatul Ferdousi, M.A. Moyeen
Artificial Intelligence
ClinAgent: A ReAct-Based Agent for Conversational Access to Clinical Trial Information
Antonino Vaccarella, Riccardo Cantini, Domenico Talia et al. (+4 more)
Querying clinical trial registries remains a manual and error-prone process, requiring researchers to navigate large volumes of semi-structured data without support for natural
language interaction or cross-source synthesis. To address this, we introduce ClinAgent, a conversational system based on agentic
Retrieval-Augmented Generation (RAG) that enables clinicians and researchers to query clinical trial information in plain
language and receive grounded, up-to-date responses across multi-turn interactions. The system centers on a Large
Language Model (LLM)
agent following the ReAct paradigm, which iteratively reasons over queries, selects among a set of integrated tools, and refines its actions based on intermediate outputs. These tools include a ClinicalTrials.gov search interface, a PubMed module, and a Python-based analyzer operating on a locally cached structured
dataset of clinical trials. We evaluate the system using a three-phase framework assessing operational effectiveness, planning quality, tool-use efficiency, and expert qualitative judgments, comparing three LLM backends: Gemini 3.0 Flash and two variants of DeepSeek V3.2 (thinking and non-thinking). Results reveal complementary strengths, with DeepSeek (thinking mode) excelling in planning quality, while Gemini achieves the highest overall performance and strongest expert ratings. Overall, our findings highlight the potential of agentic AI systems to improve the accessibility and synthesis of clinical trial information, supporting more efficient and user-centered biomedical research workflows.
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All authors Antonino Vaccarella, Riccardo Cantini, Domenico Talia, Paolo Trunfio, Marianna Talia, Rosamaria Lappano, Marcello Maggiolini
Artificial Intelligence · Cross-listed from cs.RO
ReWeight: Leveraging Human Data for VLA Post-Training via Demonstration Retrieval and Sample Weighting
Chenwei Wang, Dianye Huang, Match W.L. Ko et al. (+2 more)
Post-training
vision-
language-action (VLA) models for specific
robots and tasks requires in-domain demonstrations, yet collecting diverse
robot data is costly.
Egocentric human demonstrations provide a scalable alternative, but directly mixing human and
robot data can introduce cross-embodiment discrepancies and degrade policy performance. To address this challenge, we introduce ReWeight, a framework that incorporates human data into VLA post-training through demonstration-level
retrieval and sample-level weighting. ReWeight learns a cross-embodiment visuomotor representation that combines visual observations with future actions to measure behavioral similarity between human and
robot demonstrations. Based on optimal transport, it retrieves human demonstrations relevant to the target
robot data and assigns larger weights to samples with smaller cross-embodiment discrepancies. We evaluate ReWeight using $\pi_{0.5}$ across eight simulation tasks and four real-world tasks under both clean and randomized settings. In simulation, ReWeight improves the average success rate of post-trained $\pi_{0.5}$ from 39% with only
robot data and 44% with randomly mixed human-
robot data to 57%. In the physical experimental setting, it achieves an average success rate of 68.8%, outperforming the baselines by 28.8% and 13.8%, respectively. Overall, ReWeight provides an effective paradigm for transforming abundant
egocentric human experience into transferable supervision for
robot learning. (Project webpage: https://reweight-vla.github.io/)
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All authors Chenwei Wang, Dianye Huang, Match W.L. Ko, Chenjia Bai, Zhongliang Jiang
Artificial Intelligence
UniCAR-RL: Seeing Better before Thinking Deeper in Visual Mathematics
Yuzhe Li, Hao Yan, Hao Wang et al. (+6 more)
Multimodal Large
Language Models (MLLMs) often struggle with complex mathematical visual
reasoning primarily due to a lack of fine-grained perception, causing initial visual
hallucinations to directly trigger cascading
reasoning failures. In traditional end-to-end reinforcement learning (RL), sparse rewards fail to decouple perceptual
hallucinations from logical missteps, hindering targeted perception optimization. Alternatively, fine-tuning with perception-enhanced CoT data incurs high costs and
hallucinations. In this paper, we address these challenges by proposing UniCAR-RL, an annotation-free RL framework. By explicitly decoupling the optimization of perception and
reasoning during the training process, it achieves isolation and optimization of both capabilities. Specifically, UniCAR-RL consists of three synergistic branches: 1) a Caption-RL branch that optimizes perception capabilities through verifier-guided
reasoning validation; 2) a
Reasoning-RL branch that performs logical
reasoning based on a gold image description to halt cascading errors; 3) a QA-RL branch that retains native end-to-end
alignment to ensure robust question-answering performance. Experiments show that UniCAR-RL substantially improves MLLMs' mathematical and visual
reasoning using only raw short-answer data. Furthermore, it demonstrates strong generalization across diverse architectures and scales.
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All authors Yuzhe Li, Hao Yan, Hao Wang, Xingchen Liu, Ya-Qi Yu, Jihao Wu, Minghui Liao, Wei Chen, Yuliang Liu
Artificial Intelligence · Cross-listed from cs.RO
LePlanner: An Iterative Amortized Controller For World Models
Saksham Bansal, Om Naphade, Chayan Aggarwal et al. (+1 more)
World models trained with joint-
embedding predictive architectures learn compact, structured latent representations from physical interaction, yet planning in these latent spaces typically relies on one of two costly approaches. Search-based planners such as CEM, MPPI, and iCEM optimize action sequences through many predictor rollouts, achieving strong performance at the cost of high per-decision compute and latency. Policy-based methods amortize inference into a single forward pass but can degrade on contact-rich tasks where the demonstration distribution is
multimodal. We propose LePlanner, an amortized iterative controller that learns to construct and refine latent action sequences through a frozen world-model predictor. LePlanner is trained with an arrival-and-hold objective that encourages the controller to reach the goal at the earliest feasible horizon and remain there. This addresses horizon-reset procrastination, a failure mode in which repeated receding-horizon replanning continually postpones goal arrival. An additional action-Gaussian loss keeps generated actions near the support of the offline
dataset. Across
navigation, contact-rich manipulation, and continuous-control environments, LePlanner matches or exceeds search-based planners while requiring an order of magnitude fewer predictor evaluations and 3-49x lower wall-clock time per decision. It achieves success rates of 98% on PushT, 100% on Reacher, 100% on TwoRooms, and 92% on the OGBench Cube task. These results show that much of the structure discovered through online search can be amortized into a lightweight iterative policy, enabling fast, horizon-aware, nonlinear physical control without online optimization.
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All authors Saksham Bansal, Om Naphade, Chayan Aggarwal, Vrishin M
Artificial Intelligence · Cross-listed from cs.SD
CRAF: Cross-View Residual-Aware Fusion for Deepfake Speech Detection
Minh-Xuan Phan, Khalid Zaman, Candy Olivia Mawalim et al. (+1 more)
Recent advances in
speech synthesis and voice conversion have made deepfake
speech increasingly realistic, making generalization to unseen spoofing attacks a critical challenge. Pretrained
speech and audio models offer a promising direction for improving robustness to such unseen attacks. Self-supervised learning (SSL) models capture fine-grained, low-level acoustic characteristics, whereas Auditory Large
Language Models (ALLMs) provide higher-level contextual representations. These complementary views can provide useful cues for improving generalization to unseen attacks. However, direct fusion does not explicitly disentangle information shared across the two views from view-specific complementary information, limiting effective cross-view integration. To address this, we propose CRAF, a cross-view residual-aware fusion framework that uses ALLM-guided cross-view
attention to enrich SSL representations and adopts ALLM as a high-level reference to separate ALLM-explainable information from complementary SSL residual information. The residual is selectively refined through adaptive gating and integrated through SSL-primary fusion. Experiments on ASVspoof 5 show that CRAF with Kimi-Audio achieves an EER of 5.96% and a minDCF of 0.1192, demonstrating robustness to unseen spoofing attacks.
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All authors Minh-Xuan Phan, Khalid Zaman, Candy Olivia Mawalim, Masashi Unoki
Artificial Intelligence
ViperQ: Order Flow Pattern Recognition via Auction Market Theory for Reinforcement Learning Trading
Asser Moustafa, Rares-Mihail Neagu and Jugal Kalita
Reinforcement learning trading systems published in the academic literature overwhelmingly rely on price-aggregate state representations (OHLCV bars) or limit-order-book depth features, leaving microstructure pattern theories from the practitioner literature, namely Auction
Market Theory and
Market Profile, without a peer-reviewed computational instantiation. We present ViperQ, a reinforcement learning system whose state representation is built explicitly from Auction
Market Theory primitives: Volume Point of Control, Value Area position, Low Volume Node flags, Cumulative Volume Delta divergence, and tape-velocity signatures, assembled into a 20-dimensional Z-normalised vector. Two Proximal Policy Optimisation
agents are trained with a prospect theory-grounded asymmetric reward function that penalises losing holds at a magnitude consistent with Kahneman and Tversky's loss-aversion coefficient. Evaluated on a held-out twelve-month partition of institutional tick data the
agents have never seen, ViperQ achieves +163.6% ROI on TSLA (-27.5% max drawdown, 27,019 trades) and +116.5% ROI on NVDA (-47.8% max drawdown, 12,892 trades) under zero leverage. The results establish Auction
Market Theory features as a tractable structured input modality for sequential decision-making on financial time series and motivate further work on microstructure-aware policy learning.
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Artificial Intelligence · Cross-listed from cs.CR
Exploring Automated Vulnerability Identification in JavaScript Code Using Large Language Models
Manit Kaushik, Ishir Bhardwaj, Pranav Gupta et al. (+2 more)
JavaScript powers approximately 98.8% of all websites, making vulnerabilities in its code a significant security risk, yet existing detection approaches such as Static Application Security Testing (SAST) tools often fail to identify many real-world vulnerabilities when applied to isolated code snippets. This paper presents an empirical study of Large
Language Model (LLM)-based vulnerability identification for JavaScript programs, evaluating three LLM families (Gemini 1.5 Flash, GPT-4o Mini, DeepSeek-R1-Distill-Llama-8B) across multiple prompting strategies (zero-shot, chain-of-thought, few-shot) and fine-tuning approaches on a
dataset of 1,125 JavaScript code snippets spanning five Common Weakness Enumeration (CWE) categories: Injection (CWE-74), OS Command Injection (CWE-78), Cross-Site Scripting (CWE-79), SQL Injection (CWE-89), and Uncontrolled Resource Consumption (CWE-400). Our experiments show that LLMs substantially outperform traditional SAST tools on snippet-level vulnerability identification, with a fine-tuned Gemini 1.5 Flash model achieving 60% detection accuracy compared to near-zero performance from rule-based analyzers. We find that fine-tuning improves accuracy from 29% to 60%, Chain-of-Thought prompting benefits
reasoning-capable models such as GPT-4o Mini, few-shot prompting is effective for polymorphic vulnerabilities such as Cross-Site Scripting, and performance varies across vulnerability categories, reaching up to 84% accuracy for structured vulnerabilities such as SQL Injection. These results indicate that LLMs provide a practical approach for automated vulnerability identification in JavaScript code, particularly when combined with task-aligned supervision, though they should complement rather than replace existing security analysis workflows due to limited recall and uneven performance across vulnerability types.
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All authors Manit Kaushik, Ishir Bhardwaj, Pranav Gupta, Pankaj Jalote, Arun Balaji Buduru
Artificial Intelligence
Bypass Observation: A Conceptual Design of a Non-Intrusive Layer-Wise Semantic Extraction Architecture
Haibin Tong and Jiang Yu
Large
language models reason in high-dimensional hidden-state spaces, while users observe only final outputs. We introduce Bypass Observation, a non-intrusive layer-wise readout architecture that attaches read-only observation heads to selected
Transformer layers without feeding their outputs back into the backbone. We consider three variants: a shared LM head across layers, layer-specific heads, and a layer- or step-adaptive head. For full-vocabulary readout, we derive a closed-form overhead approximation governed primarily by V/(12d), with representative estimates ranging from about 30% to 240%, and discuss cost reductions via sparse observation, low-rank factorization, reduced vocabularies, top-k readout, and selective positions. We argue that Bypass Observation can make model computation more observable while remaining only a partial, potentially misleading projection of hidden states. We further distinguish bypass chain-of-thought from conventional chain-of-thought: conventional
reasoning tokens enter the autoregressive computation, whereas bypass readouts remain causally external at inference time, although they can still provide training signals in reinforcement learning. Finally, we discuss applications to looped and recurrent-depth
Transformers, where iteration-wise readout may expose convergence, oscillation, and potential halting signals. The proposal is conceptual and analytical; systematic empirical validation remains future work.
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Artificial Intelligence · Cross-listed from cs.CL
Understanding the Limits of Agentic ICD Coding
Chong Yock Eng, Yushi Cao, Yiming Chen et al. (+2 more)
ICD-10-CM codes are alphanumeric codes used in the US to classify diagnoses and injuries for medical billing and epidemiological reporting. Standard ICD-10-CM
benchmarks report aggregate metrics that obscure performance on complex coding scenarios. We evaluate neural, workflow, and agentic systems on a rarity-stratified set of MIMIC-IV discharge summaries and identify two orthogonal failure modes. Neural classifiers exhibit a 0.43 micro-F1 gap between rare and common codes. Workflow systems handle rare codes well but score near zero on injury and external cause codes that require multi-step guideline following. A tool-augmented agentic configuration with structured access to official ICD-10-CM reference materials recovers up to 0.34 micro-F1 on this subset. No single system dominates across all conditions.
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All authors Chong Yock Eng, Yushi Cao, Yiming Chen, Kezhi Mao, Hongchao Jiang
Artificial Intelligence
LLM-Enhanced Multi-Agent Reinforcement Learning for Unified Electric Vehicles-Charging Station-Grid Optimization in Public Charging Systems
Yang Zhang, Lindong Xie, Chongyu Wang et al. (+3 more)
In the era of the Internet of Things (IoT), coordinating connected electric vehicle (EV) charging scheduling to balance EV charging satisfaction, station profitability, and smart grid stability presents a complex multi-objective challenge. Existing Multi-
Agent Reinforcement Learning (MARL) approaches often struggle with high-dimensional state spaces generated by massive IoT sensing data and conflicting stakeholder interests. This paper proposes a novel LLM-enhanced MARL framework that, for the first time, simultaneously optimizes the Grid, EVs, and Stations within a unified loop. By integrating Large
Language Model (LLM), we address two critical bottlenecks: interpretable feature selection and adaptive multi-objective balancing. The LLM analyzes real-time IoT-collected environmental states to extract physically significant features and dynamically assigns weights to conflicting objectives-including profit, user satisfaction, and grid load-using semantic
reasoning instead of complex manual tuning. Extensive experiments demonstrate that our framework significantly outperforms state-of-the-art baselines, achieving superior
market efficiency while reducing training time by over 70%. This approach offers a scalable, transparent solution for efficient and sustainable IoT-enabled urban charging infrastructure management.
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All authors Yang Zhang, Lindong Xie, Chongyu Wang, Gaojunjie Li, Siqi Bu, Edward Chung
Artificial Intelligence
Do Not Restart: Residual Completion for Stateful Agent Handoffs
Runzhi Deng, Yiming Zhong, Fang Zhao et al. (+1 more)
Routing and cascades reduce tool-
agent cost by transferring control across models, but stateful handoffs must preserve accepted choices, realized effects, and unfinished obligations. We formulate this as commitment-constrained residual completion and introduce Commitment-Frontier Residual Completion (CFRC). CFRC enforces target-before-proposal, whole-proposal-before-authority, and live-evidence-before-success: it freezes a residual contract from accepted progress, closes the successor continuation into an evidence-linked graph, and admits execution only when the remainder is covered, with live receipts discharging obligations. We establish contract-relative partial correctness, which extends to the original residual request under complete contract construction. Across five environments and two same-provider model pairs, CFRC achieves comparable macro accuracy to strong full-task
agents at 22.0% to 34.6% mean per-surface cost, with additional cross-provider results demonstrating broader transfer.
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All authors Runzhi Deng, Yiming Zhong, Fang Zhao, Pan Zhou
Artificial Intelligence
Partition Scores Are Not System Scores: Deployment-Fidelity Gaps in Decomposed Algorithm Selection
Jiachen Zhang, Yu Tang and Li Zhu
Oracle-style quantities, including virtual best solvers, selected-portfolio VBS, virtual-best encodings, and best-in-family summaries, are widely reported as upper bounds on what a deployable selector could achieve. In decomposed
algorithm selection, an analogous partition-level score grants an oracle choice of the best
algorithm within the selected family; once the family selector is fixed, the deployable system must replace that within-family oracle with a learned within-family selector. We define the deployment-fidelity gap G(R) as the difference between partition-level and deployable end-to-end utility and derive two accounting consequences: a per-instance margin-regret stability condition that tells us when a partition-time family choice is deployment-optimal, and a sharp partition-only identification interval that, when it strictly crosses zero, prevents the partition-level report from certifying the deployable winner. Across five public
algorithm-selection
benchmarks spanning tabular AutoML and combinatorial CSP/SAT, every decomposed pipeline has positive G(R), ranging from 0.012 on TabZilla to 0.13 on PROTEUS-2014. Four of ten decomposed-versus-flat decisions have sign-changing point estimates; on PROTEUS-2014, a 33-point partition advantage shrinks to a 20-point end-to-end advantage. A training-side validation gap-correction diagnostic recovers the point-estimate deployable sign on all four sign-changing
cells; it is a reporting aid, not a substitute for direct end-to-end evaluation. Partition and end-to-end scores should be reported side by side.
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Artificial Intelligence · Cross-listed from cs.CV
GEAR: From Dynamic Encoding to Dynamic Activation in Social Trajectory Prediction
Jiaheng Chen, Jiaxing Li, Leixia Wang et al. (+2 more)
Human trajectory prediction requires modeling both individual motion patterns and social interactions among
agents. Existing methods have made substantial progress by using
attention mechanisms, graph structures, and temporal encoders to capture dynamic social context. However, most of them primarily focus on how social information is encoded, while paying less explicit
attention to how the encoded social context should take effect during future trajectory generation. In this paper, we argue that dynamic social encoding does not necessarily imply dynamic social activation. The same interaction context may require different activation strengths across future horizons and scene densities: social cues should be strengthened when interaction evidence is strong, but suppressed when they are weak or noisy. To address this issue, we propose GEAR, a generation-aware bias activation model for human trajectory prediction. Built upon a bias-decomposed trajectory generation formulation, GEAR dynamically activates the individual-motion and social-resonance bias terms at each future step before final trajectory composition. This allows the model to explicitly control when and how strongly individual and social bias components participate in generation. Experiments on ETH-UCY, SDD, and NBA show that GEAR consistently improves the resonance-based baseline and achieves competitive state-of-the-art performance. Further analyses of activation patterns and density-grouped errors validate the importance of calibrating encoded social context during trajectory generation. Our code is available at https://github.com/11isnotavailable/GEAR.git.
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All authors Jiaheng Chen, Jiaxing Li, Leixia Wang, Jianan Ju, Tinghe Zhang
Artificial Intelligence
Surprising Effectiveness of Self-Demonstrations in Enhancing Schema-Ontology Mapping with LLMs
Siddhesh Thombre, Manasi Patwardhan and Sunita Sarawagi
Integrating heterogeneous relational databases into a centralized ontology remains a persistent challenge in enterprise knowledge representation, primarily due to semantic heterogeneity, cryptic schema naming, missing metadata, and the abstraction gap between relational schemas and ontological models. Although large
language models (LLMs) offer strong semantic
reasoning capabilities, we show that directly applying them through one-shot prompting or naive multi-stage pipelines leads to poor performance for schema-ontology mapping. This paper presents a self-demonstration-driven approach that combines a neuro-symbolic task decomposition with a novel mechanism for automatically generating pattern-guided, dependency-aware demonstrations to address this integration challenge. Our approach incorporates two key strategies to achieve substantial accuracy gains over existing LLM-based schema integration methods: (i) a neuro-symbolic decomposition of the task into cascaded sub-tasks, where symbolic constraints structure the search space and LLMs perform semantic
reasoning within each focused sub-task, and (ii) self-generated demonstrations guided by domain-agnostic patterns to supervise each sub-task. Experiments on three of the most challenging scenarios from the RODI
benchmark show that our approach achieves state-of-the-art performance, substantially outperforming (25 percentage points F1 improvements) both traditional schema-to-ontology mapping techniques and recent LLM-based schema-to-ontology and schema matching approaches. Ablation studies further reveal the significant benefits of pattern-guided self-demonstrations and the complementary benefits of neuro-symbolic task decomposition.
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Artificial Intelligence
Homeostatic Continual Learning
Yue Jin
In this paper, I formulate a Continual Learning problem and propose a method named "Homeostatic Continual Learning" that enables an AI
agent to learn continuously in a changing environment without catastrophic forgetting. The core of the method is to find outliers in the environment data when the
agent experiences an outlier in its output. Through this method, the
agent gradually completes its model and policy and performs well in more and more contexts. I also suggest that we may use the method to build a world model where the
agent factorizes the objects in the world into features, abstract objects into comparable instances of concepts and map concepts to intents through features. I discuss the works needed to render the method practical, the connections to many fields in Artificial Intelligence and the broader implications of the method.
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Artificial Intelligence
Positioning manuscripts in the scientific landscape with agentic AI
Jiawen Chen, Zichen Zhang, Bingxuan Li et al. (+7 more)
Publishing a research manuscript is a routine yet demanding part of scientific life: time-consuming, stressful, and often uncertain in outcome. Recent advances in large
language model (LLM)-based agentic AI have shown promise across a range of scientific tasks, and here we ask whether agentic AI can help researchers navigate the publication process itself by reliably inferring a manuscript's eventual publication venue from its content and literature context. We introduce PASS (Publication-oriented Agentic Scientific System), an agentic system that understands manuscripts within their domain-specific literature context and predicts top-matched publication venues. PASS positions each manuscript within its surrounding literature landscape by reconstructing its local scientific neighborhood, tracing its topic trajectory, and
reasoning over field-specific journal spaces. Evaluated on a leakage-audited
benchmark of over 2,000 preprints across 16 biomedical fields, PASS achieved Top-1 accuracy of 50.3% and Top-5 accuracy of 86.1%, outperforming state-of-the-art LLM baselines and established journal-selection tools. PASS-produced quality scores, such as impact potential and novelty, aligned with independent measures of publication outcome. We also found that the designed literature
retrieval module is the strongest performance contributor, particularly for positioning manuscripts relative to nearby work, and that PASS maintained near-full performance from the abstract alone, whereas LLM baselines required the full manuscript text. An independent human evaluation found strong researcher agreement with PASS's manuscript understanding and recommendation rationale. PASS has been released as a public platform (https://ratemypaper.ai/) for broad researcher access.
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All authors Jiawen Chen, Zichen Zhang, Bingxuan Li, Quan Sun, Yiyan Zhang, Edric Tam, Jinjie Lin, Didong Li, Yun Li, Bingxin Zhao
Artificial Intelligence · Cross-listed from math.OC
On the Equivalence of Stochastic Control and Path Space Formulations for Schrödinger Bridges over Compact Connected Lie Groups
Hamza Mahmood, Georgiy A. Bondar, Abhishek Halder et al. (+1 more)
We establish the equivalence between the
stochastic optimal control and path space formulations of the Schrödinger bridge problem (SBP) for the kinematic equation on a compact connected Lie group. Using the
geometric concepts of horizontal lift and
stochastic anti-development, we derive a Girsanov-type change-of-measure result, and show that the expected control energy equals the relative
entropy of the controlled path law with respect to the reference Wiener measure. Thus, the SBP is equivalently a path space relative
entropy minimization problem subject to prescribed endpoint marginals.Our result has three useful implications. From an analytic viewpoint, the shown equivalence helps prove the existence and uniqueness of the SB. From a probabilistic viewpoint, it helps interpret the SB as the most probable deviation of the uncontrolled
stochastic dynamics consistent with the endpoint constraints. From a computational viewpoint, it allows using static Sinkhorn recursions to directly solve the relative
entropy minimization problem and compute the optimal path measure. We illustrate the equivalence numerically on the
torus $\mathbb{T}^2$. The code is publicly available at: https://github.com/gradslab/LargeDeviationSBP
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All authors Hamza Mahmood, Georgiy A. Bondar, Abhishek Halder, Adeel Akhtar
Artificial Intelligence
How Should Reasoning Be Organized in a Transformer's Latent Space?
Hongyu Gu, Chang Liu and Jingwen Fu
Continuous
reasoning has emerged as a promising way to improve
reasoning in large
language models (LLMs). Yet we still lack a clear principle for deciding what a latent state should preserve.
Reasoning by superposition shows that a single latent state can encode several search alternatives and expand them in parallel. We ask how those states should be weighted as
reasoning proceeds. A natural choice is to preserve only the states active at the frontier step, since keeping every reached state appears to spread a limited hidden width too thin. We show that the opposite can hold. When later computation draws on several reached states, a cumulative state can guide
attention correctly at a smaller hidden width than a frontier state that stores fewer states. At the same width, the cumulative state therefore keeps more intermediate states available for later
reasoning. More generally, equal cumulative weights are optimal when future queries are unknown and remain close to the best task-specific weights when those queries are known. Experiments with two-layer and GPT-2
Transformers reproduce the predicted width advantage and show that unequal weights fail first on the states that receive the least weight. This suggests a important principle: keep reached states equally weighted, and restore equal weights as computation proceeds.
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Artificial Intelligence · Cross-listed from cs.LG
HarnessBandit: Joint Learnability-Transferability Scheduling for Multi-Harness Agentic Reinforcement Learning
Hongliang Wei, Xiaobing Tu, Yinggui Wang et al. (+6 more)
Language-model
agents are increasingly deployed through diverse harnesses that differ in system
prompts, tool schemas, control loops, and trajectory formats. The same model can perform unevenly across these interfaces, making robustness to harness variation an important objective. A natural approach is to train a shared policy through multiple harnesses, but doing so introduces a scheduling problem: each training step should favor a harness that currently provides a useful learning signal while also producing an update that benefits the other harnesses. We develop HarnessBandit, an online scheduler that selects one harness per optimizer step. After a group-relative policy optimization (GRPO) update, it observes learnability – the mean absolute advantage on the batch – and transferability – the cosine between a low-dimensional gradient sketch of the current harness and exponential moving averages of the remaining harnesses. The two signals are fused after pooled sliding-window min-max normalization and sampled with a visit-dependent bonus and an explicit exploration floor. We train Qwen3.5-2B across six harnesses on ClawGym and evaluate on PinchBench (held-out tasks, in-distribution OpenClaw) and ClawEval (held-out tasks and harness). HarnessBandit improves over mixed-batch multi-harness training on both
benchmarks, while training diagnostics indicate that learnability and transferability provide distinct, evolving signals.
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All authors Hongliang Wei, Xiaobing Tu, Yinggui Wang, Zhengxi Liu, Rongkun Xue, Jinkui Ren, Xiantao Zhang, Debin Zhao, Xiaopeng Fan
Artificial Intelligence · Cross-listed from cs.CL
PolicyMem: Geometric Policy Memory for LLM Governance
Yuanchen Bei, Zhengzhang Chen, Yanjun Zhao et al. (+3 more)
As large
language models (LLMs) are increasingly deployed in real-world high-stakes applications, effective governance has become essential. Existing safeguards largely follow two paradigms: learning-based guards provide strong semantic discrimination but couple policy behavior to trained models and taxonomies, while programmable frameworks offer flexible control but require substantial manual
prompt and workflow engineering. Neither externalizes policies as reusable operational states, making it difficult to consistently reuse policy evidence across detection, intervention, and verification. In this paper, we introduce PolicyMem, a
geometric policy
memory that externalizes natural-
language policies as reusable
geometric memory objects represented by low-rank subspaces in a shared representation space. A
memory writer compiles natural-
language policies into policy
memory slots, and query-response pairs read the policy
memory through projection energy. The resulting policy-evidence profile directly mediates the safety verdict and is reused for policy attribution and post-intervention verification. Coupled with a response rewriter, PolicyMem enables a detect-rewrite-verify loop for LLM governance. Across five widely used
benchmarks, PolicyMem achieves state-of-the-art unsafe behavior detection while enabling effective policy attribution, rewriting, and post-intervention verification through the shared policy
memory.
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All authors Yuanchen Bei, Zhengzhang Chen, Yanjun Zhao, Haoyu Wang, Hanghang Tong, Haifeng Chen
Artificial Intelligence
Trustworthy Agentic AI: A Comprehensive Cybersecurity and Systems Survey on Threat Landscapes, Defense Architectures, and Open Challenges
Seyedakbar Mostafavi
The transition from passive foundation models to autonomous, goal-directed agentic AI systems has introduced unprecedented capabilities by coupling recursive cognitive
reasoning loops, persistent
memory architectures, live tool execution planes, and multi-
agent collaboration
topologies. However, granting probabilistic neural cores execution authority across filesystems, networks, and cloud infrastructure dissolves classical security perimeters: natural
language simultaneously serves as input data, internal control code, and communication protocols, exposing a Turing-complete blast radius where untrusted data represents executable instructions. This survey delivers a comprehensive systems-security reference framework for trustworthy agentic AI, synthesizing 206 foundational studies and regulatory standards. We formalize the general
agent architecture as a stateful 5-tuple and establish a 6-dimensional trustworthiness taxonomy covering security, safety,
privacy, explainability,
fairness, and accountability. We systematically analyze threat surfaces across intra-execution loops and interaction planes, formulate a multi-layered zero-trust defense-in-depth architecture integrating Dual-LLM isolation, Capability-Based Access Control, kernel eBPF probes, and sandboxed runtimes, review standardized evaluation
benchmarks, and map technical controls to international AI governance frameworks.
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Artificial Intelligence · Cross-listed from cs.SE
TyPatch: Transforming Patches into Typestate Rules for Kernel Bug Detection
Ruoyu Wang, Tuo Li and Jia Li
Historical Linux kernel patches capture defect knowledge that applies beyond their original repair sites. Recent work has shown that large
language models (LLMs) can generate static-analysis checkers from historical patches and use them to uncover new kernel bugs. However, complete-checker generation requires the model both to recover the defect semantics expressed by a patch and to implement sophisticated program-analysis machinery, including object tracking, alias analysis, path-state maintenance, and interprocedural propagation. Coupling these responsibilities in a single end-to-end code-generation task can turn a simple defect rule into an unstable and expensive analyzer-implementation problem. To address this problem, we present TyPatch, which decouples patch-specific defect semantics from analyzer implementation. An LLM translates each patch into a typestate rule specifying its tracked object, actions, guards, transitions, and violations. A shared backend then executes these rules, binding their actions to program events, tracking object identity across aliases, propagating typestate along program paths, and producing reports for all rules. On Linux v6.16, TyPatch finds 559 distinct bugs, 121 of which have been confirmed by kernel developers. In a matched 38-patch comparison with the state-of-the-art complete-checker construction workflow, TyPatch uses 88.3-90.1% fewer generation
tokens, while its initial report pools achieve 3.42-14.95$\times$ the precision of those produced by that workflow.
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Artificial Intelligence
IBBench-Light: A Paired Evaluation of Task-Conditioned Responses to External Directives
Kainan Zhou, Zhaoyi Li, Janet Sung et al. (+2 more)
An external record may contain a procedure to apply or text to read, depending on the user's request. IBBench-Light tests both uses against the same record. Twelve semantic bases yield 144 matched pairs per model; four quantized instruction models produced 1,152 archived greedy responses. Paired exact-contract accuracy (PECA) requires both members to satisfy their output contracts. Qwen succeeds on 132 execute and 109 process
prompts, but only 97 complete pairs, showing what marginal averages omit. We audit literal-target exposure and case normalization, then add 1,722 logged CPU generations to test directive-absent controls, twelve additional semantic bases, within-base wording changes, and generation stopping. In the pinned Phi rerun, changing the end-of-sequence (EOS) set changes exact paired success from 0/144 to 62/144. A bounded IHEval comparison uses the same SmolLM2 checkpoint and output budget while preserving its published instruction roles and scorer. The
benchmark measures conditional task and output-contract success. Its task margins and paired count need to be read together with the stopping policy.
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All authors Kainan Zhou, Zhaoyi Li, Janet Sung, Gangzhen Qian, Hang Xiao
Artificial Intelligence · Cross-listed from cs.HC
Oops, Not Now: PEARL, a RAG-Based Support Agent for Gameplay and What Players Want from AI Help
Jiahong Li, Sai Siddartha Maram, Atieh Kashani et al. (+6 more)
AI-powered gameplay support
agents hold promise for game-based learning, yet grounding generative models in structured game data remains an open challenge. We present PEARL (Parallel Education
Agent for Reflection and Learning), a dual-component
Retrieval-Augmented Generation (RAG) system that combines semantic knowledge
retrieval with structural board-state matching to deliver contextualized scaffolding in Parallel, a puzzle game for learning parallel programming. PEARL operates on two input streams (natural
language queries and board
topology), retrieving both conceptual explanations of gameplay moves and peer-generated board states as evidence: capabilities unavailable to a standard Large
Language Model (LLM) with game state access alone. In a qualitative evaluation (N=10) comparing PEARL against an existing community-based Open Player Model (OPM) visualization system, participants preferred the visualization system on perceived usefulness and reported higher frustration with PEARL; five of ten minimized or abandoned the AI tool during play. Proactive delivery, generic responses, and trust deficits drove disengagement, while a subset of four participants found PEARL's grounded explanations complementary to visualization in specific contexts where they initiated the interaction. We position PEARL as a deployed design probe whose failure modes inform a concrete design agenda for AI gameplay support, captured as seven open problems for the community.
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All authors Jiahong Li, Sai Siddartha Maram, Atieh Kashani, Ulia Zaman, Zhiyu Lin, Cameron Marano, Roger Azevedo, Jichen Zhu, Magy Seif El-Nasr
Artificial Intelligence
MANAS-2: Constrained Reconstruction for EEG Foundation Models
Arvasu Kulkarni, Aditya Ray Mishra, Jeet Bandhu Lahiri et al. (+5 more)
Masked reconstruction is widely used for
EEG foundation models, but optimizing reconstruction on low-SNR waveforms does not necessarily produce the most useful latent representation. We introduce MANAS-2, a new
EEG foundation model that combines a Raw-Band Hybrid (RBH) masked autoencoder with Constrained Reconstruction (ConRec), a physics-motivated regularizer. RBH jointly reconstructs temporal waveform patches and compact spectral-band targets, while ConRec acts only on the temporal decoder output, penalizing differences in RMS energy between adjacent short windows of the reconstructed waveform. ConRec is intended to shape the encoder by biasing it toward the organization of oscillatory-envelope information. Across seven held-out
EEG datasets, adding ConRec to an otherwise identical RBH model increases frozen ridge recovery of six-band spectral power from mean R^2=0.860 to 0.906 and recovery of inter-patch band-energy dynamics from R^2=0.283 to 0.354, while temporal waveform information remains highly recoverable from the frozen latents. Applied to a temporal-only masked autoencoder, ConRec also improves frozen downstream transfer and frequency-dependent latent geometry despite receiving no spectral targets: i.e., the effects of ConRec are architecture-independent. MANAS-2 also outperforms leading
EEG Foundation Models on most downstream knowledge-transfer tasks. From the effects of ConRec, we see that a physically motivated constraint imposed through the decoder can make for a more spectrally organized and transferable latent space. MANAS-2 therefore provides a new
EEG foundation model built around constrained reconstruction as a mechanism for shaping representation–rather than reconstruction–quality.
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All authors Arvasu Kulkarni, Aditya Ray Mishra, Jeet Bandhu Lahiri, Mahir Jain, Parshva Runwal, Lakshya Saini, Siddharth Panwar, Sandeep Singh
Artificial Intelligence
JaxAHT: A JAX-Based Library for Ad Hoc Teamwork
Caroline Wang, Rolando Fernandez, Zelal Su Mustafaoglu et al. (+9 more)
As autonomous
agents are increasingly deployed, they must be able to coordinate with previously unseen
agents, such as humans. This challenge has been formalized as Ad Hoc Teamwork (AHT), and has attracted a substantial body of research. However, progress has been hindered by the prohibitive computational cost of the AHT research lifecycle, lack of standardized
benchmark implementations, and the absence of a diverse, validated evaluation teammate suite. This work introduces JaxAHT, the first open-source, JAX-based library designed to accelerate and standardize the AHT research lifecycle. Leveraging JAX's hardware acceleration and massive parallelization capabilities, JaxAHT provides a unified framework for teammate generation, ego
agent training, and evaluation against unseen teammates, achieving approximately 95x wall-clock speedup over existing PyTorch counterparts. Along with the library, we contribute a diverse suite of evaluation teammates across Level-Based Foraging, Overcooked, and Hanabi. Using JaxAHT, we conduct a large-scale, compute-controlled
benchmark study of AHT
algorithms, finding that no
algorithm consistently performs best, that
agent modeling primarily offers benefits in role-based scenarios, and that best response returns are underestimated by reinforcement learning from scratch.
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All authors Caroline Wang, Rolando Fernandez, Zelal Su Mustafaoglu, Montek Singh Kundan, Johnny Liu, Aditya Madhan, Zhihan Wang, Lingyun Xiao, Di Yang Shi, Jiaxun Cui, Arrasy Rahman, Peter Stone
Artificial Intelligence
Degraded but Not Entirely Ineffective: PE-Based Deformable Graph Neural Networks
Jinhua Wu and Xinliang Zhang
Many real-world scenarios can be represented using graph-structured data. However, traditional GNNs that transmit messages based on first-order neighbors have long faced several fundamental contradictions: increasing depth leads to over-smoothing, long-range dependencies cause over-compression, fixed neighborhoods restrict the receptive field, and on heterophilous graphs, topological neighbors become a source of noise. Although many works have addressed these issues individually, few mechanisms can simultaneously alleviate all of these challenges. To address the aforementioned problems, we propose a Position Encoding-Based Deformable Spatial Aggregation Module (PEBDSAM) that solves them all in one step. Specifically, we utilize a deformable mechanism in the position space to identify relevant nodes to supplement the original first-order neighbor information of GNNs, allowing traditional GNNs to adapt to heterophilous scenarios. Through diagnostic experiments, we obtained several major findings: current offsets fail to have any effect; subsequently, we analyzed the causes of offset failure and why model performance still improves even after offset failure, pointing out future research directions. Based on these diagnostic experiments, we streamlined the original PEBDSAM, resulting in a simplified version, which we call the Position Encoding-Based Spatial Aggregation Module (PEBSAM). In addition, we propose a PEBSAM-Speed to adapt to large
datasets. Finally, we designed the module to be plug-and-play and applied it to GCN, GAT, GIN, and GraphSAGE, achieving desirable results on three homophilous
datasets and six heterophilous graph
datasets.
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Artificial Intelligence · Cross-listed from cs.CC
Gap Entropy and Almost Instance-Wise Optimal Best-Arm Identification
Jiarui Yao, Jiaxi Zhao and Xiangxin Zhou
In the best-arm identification problem, we are given $n$
stochastic arms with unknown means and wish to identify the arm with the largest mean with probability at least $1-\delta$, using as few samples as possible. We consider independent Gaussian rewards with unit variance and means in $[0,1]$. Chen and Li [2016] conjectured that the instance-wise sample complexity of this problem is characterized by the gap
entropy, up to an additive term arising from the two-arm problem. In this paper, we resolve their gap-
entropy and almost instance-wise optimality
conjectures. For an instance $I$, let $\Delta_{[i]}$ be the gap between the largest and the $i$-th largest mean, let $H(I)=\sum_{i=2}^{n}\Delta_{[i]}^{-2}$, and let Ent$(I)$ denote the
entropy of the normalized complexities of its dyadic gap groups. For every $0<\delta<0.1$, we show that the order-oblivious instance-wise lower bound is $ {\Theta} (H(I)[\log(1/\delta)+Ent(I)]). $ We also give a single $\delta$-correct
algorithm with expected sample complexity $ O ( H(I)[\log(1/\delta)+Ent(I)] +D\log(e+\log(e+D))),D=\Delta_{[2]}^{-2}, $ without prior knowledge of the gaps. Our lower bound removes the dyadic-gap and monotonicity restrictions of previous work, and our upper bound removes the additional polylogarithmic factor multiplying the two-arm term. Thus, a single
algorithm attains the instance-wise lower bound up to an additive two-arm term. The main
theorems have been formalized and proved in Lean 4.
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