Artificial Intelligence · Cross-listed from cs.SE
Execution-bound advisory automation for agentic AI: a reproducible AIBOM-driven CSAF-VEX framework
Petar Radanliev, Omar Santos, Carsten Maple et al. (+1 more)
A protocol driven framework is presented that binds SBOM and AIBOM artefacts to deterministic environment capture and structured runtime telemetry. Exploitability is computed from declared artefacts, observed activation conditions, and enforced execution policies. CSAF VEX advisories are generated from combined static and runtime evidence, cryptographically signed, and validated through deterministic replay. Evaluation uses approximately 10000 component entries across synthetic Agentic AI workloads 50 to 5000 components, incorporating OSV, GitHub Advisory, KEV, and EPSS
datasets.
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All authors Petar Radanliev, Omar Santos, Carsten Maple, Kay Atefi
Artificial Intelligence · Cross-listed from cs.SE
Interpretable and Verifiable Hardware Generation with LLM-Driven Stepwise Refinement
You Li, Samuel Mandell and David Z. Pan
Large
language models (LLMs) have achieved remarkable success in software development. However, they are susceptible to
hallucinations, meaning that they can introduce subtle semantic and logical errors. Due to the high stakes in chip design and manufacturing, hardware engineers are still reluctant to rely on LLMs for register-transfer level (RTL) generation. In this paper, we propose a hardware generation framework that combines the creativity and broad knowledge of LLMs with the explainability and mathematical rigor of formal methods. Specifically, we devise a set of transformation rules that cover various design decisions and hardware features. By iteratively applying these rules, an LLM
agent can convert a design specification into an RTL program with guaranteed correctness. Experimental results demonstrate the effectiveness and efficiency of the framework.
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Artificial Intelligence · Cross-listed from cs.CY
Engagement Intensity as a Learner-Modeling Signal for Adaptive AI Ethics Instruction
Yongkyung Oh, Lynn Talton and Alex Bui
Adaptive AI ethics instruction in graduate research training benefits from intake measures that reflect differences in prior LLM experience. Prior coursework or workshop attendance is an obvious candidate, but it is not clear whether it is associated with pre-instruction ratings on key AI perception items. We compare three candidate intake features, self-reported usage frequency, self-rated LLM familiarity, and prior AI education, across five baseline perception outcomes in 93 bioscience graduate and postdoctoral trainees enrolled in a required research ethics course. Usage frequency shows Holm-corrected associations with all five outcomes, self-rated familiarity with three, and prior AI education with none. A threshold-like pattern at the lower end of the scale is most visible for training interest and accuracy trust rather than appearing as a uniform gradient across all five outcomes. In a short intake survey, reported LLM use is more consistently associated with these perceptions than prior coursework or workshops, with self-rated familiarity serving as a secondary indicator. These results suggest that simple pre-instruction behavioral signals can inform lightweight intake profiling for adaptive AI ethics education.
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Artificial Intelligence
CEO-Bench: Can Agents Play the Long Game?
Haozhe Chen, Karthik Narasimhan and Zhuang Liu
Language model
agents are becoming proficient executors at isolated, short-horizon tasks such as software engineering and customer service. Yet real-world challenges require a combination of sophisticated skills that remain largely untested in
agents: (1) navigating long horizons amid uncertainty; (2) acquiring information in noisy environments; (3) adapting to a changing world; (4) orchestrating multiple moving parts toward a coherent goal. We introduce CEO-Bench, which evaluates these capabilities together by simulating a representative real-world task: operating a startup for 500 days. An
agent manages pricing, marketing, budgeting, and many other aspects of a fictional company through a programmable Python interface, operating in the same environment and facing the same challenges as a human CEO. Success demands analyzing noisy, interconnected business databases, translating signals into sound strategy, and coordinating many decisions with programming. The strongest
agents write sophisticated code that forecasts churn regimes, billing timing, customer losses, and future cash under different scenarios. Even so, most state-of-the-art models struggle in this environment. Only Claude Fable 5, GPT-5.6 Sol, and Claude Opus 4.8 finish above the $1M starting balance, and all evaluated models remain below the rule-based baseline. CEO-Bench takes a first step toward measuring the intelligence required to drive sustained, adaptive progress over time.
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Artificial Intelligence · Cross-listed from cs.CR
AI Sandboxes: A Threat Model, Taxonomy, and Measurement Framework
Inderjeet Singh, Haitham Mahmoud and Andrés Murillo
AI systems are increasingly evaluated in bounded environments that combine isolation, simulation, instrumentation, supervision, and evidence capture. For physical AI, AIoT, and cyber-physical systems, this shift is not a matter of terminology: the system under test may sense, decide, actuate, communicate, and fail through physical processes, networked devices, and human operators. This article develops an assurance-oriented account of AI sandboxes as controlled environments for testing, evaluation, verification, and validation across digital AI,
embodied autonomy, and cyber-physical deployments. We formalize the sandbox boundary and a weakest-link rule for composing per-dimension evidence into a bounded deployment claim; separate major sandbox archetypes; define a cyber-physical threat model that includes attacks on the assurance apparatus itself; and introduce a measurement framework spanning fidelity, controllability, observability, containment, reproducibility, and governance artifacts, instantiated on three worked case studies of real sandboxes. The resulting threat model, taxonomy, and measurement framework clarify what a sandbox can validly test, which risks it can contain, and what forms of evidence it can support for safety, security, and regulatory assurance.
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Artificial Intelligence · Cross-listed from cs.LG
Sparsity Curse: Understanding RLVR Model Parameter Space from Model Merging
Chenrui Wu, Zexi Li, Jiajun Bu et al. (+2 more)
Reinforcement Learning with Verifiable Reward (RLVR) has emerged as a powerful post-training paradigm that surpasses Supervised Fine-Tuning (SFT) in eliciting
reasoning intelligence and resisting catastrophic forgetting. Recent studies further reveal that RLVR induces highly sparse and off-principal parameter updates compared to SFT. This naturally raises the question: does such sparsity make RLVR models more amenable to model merging? If so, model merging would offer a scalable, training-free path to aggregate diverse
reasoning capabilities from independently trained RLVR models. Surprisingly, we find the opposite, uncovering a sparsity curse: the sparse RLVR updates are spread farther apart in parameter space, forming near-orthogonal shortcuts that make aggregation inherently fragile. This is likely rooted in the stochasticity of RL optimization and the diversity of emergent
reasoning patterns. Unlike SFT models that converge to shared, flat basins and merge naturally, RLVR models suffer severe degradation under standard merging methods. Through systematic empirical analysis of the update geometry, we characterize the mechanisms behind this failure and propose Sensitivity-aware Resolving Merging (SAR-Merging), a merging recipe tailored for the unique structure of RLVR parameter spaces. SAR-Merging resolves conflicts in overlapping update regions via Fisher Information-based sensitivity arbitration, followed by magnitude-aware sparsification and rescaling to preserve fragile
reasoning pathways. Experiments on mathematical and coding
benchmarks demonstrate that SAR-Merging substantially outperforms existing merging methods on RLVR models, enabling both single-task enhancement and multi-capability fusion.
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All authors Chenrui Wu, Zexi Li, Jiajun Bu, Jiangchuan Liu, Haishuai Wang
Artificial Intelligence · Cross-listed from cs.RO
As You Wish: Mission Planning with Formal Verification using LLMs in Precision Agriculture
Marcos Abel Zuzuárregui and Stefano Carpin
Though robotic systems are now being commercialized and deployed in various industries, many of these systems are highly specialized and often require an advanced skill set to operate and ensure they perform as instructed. To mitigate this problem, we recently introduced a mission planner leveraging LLMs to synthesize mission plans in precision agriculture based on mission descriptions provided in natural
language. While the system demonstrates impressive performance, it also suffers from the inherent ambiguities of natural
language. In this paper, we extend our system to address this issue by introducing multiple feedback loops in the planning architecture that leverage linear temporal logic (LTL) to ensure the mission planning system meets the specifications formulated by the user while still using natural
language. To mitigate potential bias, this is achieved by using two different commercial LLMs in charge of the specification and verification subtasks. Through extensive experiments, we highlight the strengths and limitations of integrating mission verification into a fully autonomous pipeline, particularly regarding an LLM's ability to generate valuable LTL formulas, and show how our proposed implementation addresses and solves these challenges.
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Artificial Intelligence · Cross-listed from cs.LG
PSyGenTAB: A Privacy-Preserving Framework for Synthetic Clinical Tabular Data Generation via Constrained Optimization
Arshia Ilaty, Hossein Shirazi, Manasi Chitale et al. (+5 more)
The development of medical AI is constrained by limited access to high-quality clinical data due to institutional silos and strict
privacy regulations such as HIPAA and GDPR. Synthetic data generation offers a potential solution, but existing methods lack principled mechanisms to explicitly manage the
privacy-utility trade-off, often degrading clinically meaningful patterns or risking patient re-identification. We present PSyGenTAB, a
privacy-preserving generative framework that formulates synthetic healthcare data generation as a constrained optimization problem solved using the Augmented Lagrangian Method. By
embedding configurable
privacy constraints directly into model training, PSyGenTAB enforces minimum
privacy thresholds while maximizing clinical data utility. Across multiple clinically motivated
benchmarks, PSyGenTAB preserves inter-feature clinical relationships and minority-class diagnostic patterns essential for reliable health AI. Downstream evaluation using Train-on-Synthetic, Test-on-Real and Train-on-Real, Test-on-Synthetic protocols shows that models trained on synthetic data achieve performance comparable to those trained on real patient records.
Privacy auditing further demonstrates reduced exact record reproduction and strong resilience to membership inference attacks. These results establish PSyGenTAB as a principled framework for balancing
privacy protection and clinical utility in synthetic healthcare data, supporting secure cross-institutional AI development.
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All authors Arshia Ilaty, Hossein Shirazi, Manasi Chitale, Kedar Hegde, Dhanalakshmi Ramesh, Rashmi S. Manjunath, Amir Rahmani, Hajar Homayouni
Artificial Intelligence · Cross-listed from cs.CR
Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases
Chandranil Chakraborttii, Jackeline García Alvarado, Sitora Abdulofizova et al. (+1 more)
Retrieval-augmented generation (RAG) allows large
language models to access external and private corpora for factual, domain-specific responses. Modern RAG pipelines use hierarchical navigable small world (HNSW) vector databases for efficient similarity search. When a user requests data deletion, the systems typically only mark the record as deleted, leaving the
embedding on disk physically unchanged. This soft-delete operation raises compliance concerns under data-erasure and retention requirements such as GDPR Article 17 and HIPAA. Analysis on three HNSW implementations confirms that deleted vectors remain physically recoverable by accessing the raw index files at the storage layer, bypassing API access. Using the Vec2Text inversion model without domain-specific fine-tuning, we show this vulnerability on multiple real-world
datasets and data modalities. On Wikipedia biographical living persons
dataset (BLP), we successfully recover 25.5% of exact person names and 46.4% of geographic locations (ROUGE-L 0.185). Recovery reaches 100% for both patient age and gender markers (ROUGE-L 0.290) on highly structured, sensitive data (NIH Synthea
dataset). On soft-deleted image
embeddings, we show 100% tissue classification on histopathology patches (p=1.02e-07) and top-1 identity recovery reaches 99% on facial
embeddings (p<0.01). This work introduces Epoch Key Rotation, which encrypts vectors and discards the key upon deletion. Epoch key rotation reduces observed PII recovery to 0% and completes in 2.5 ms for 500 deleted vectors (approximately 0.005 ms/record). Additionally, it generates an ECDSA-signed cryptographic proof as an auditable record of the deletion event.
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All authors Chandranil Chakraborttii, Jackeline García Alvarado, Sitora Abdulofizova, Shivanshu Dwivedi
Artificial Intelligence · Cross-listed from cs.CV
Neural Phase Correlation
Cole Reynolds
Correspondence is fundamentally relational: it seeks the unknown transformation between two observations of a common scene, not the content of either. Yet the dominant learning-based methods do not represent the transformation as a first-class object in the architecture. They encode each image independently and let a learned similarity function or a deep decoder discover the mapping implicitly. Phase correlation is the canonical exception, measuring the inter-image relationship directly in the
Fourier domain, but the
rigidity of its fixed basis confines it to global translation. We introduce a learned generalization of phase correlation that lifts this restriction by learning the basis on which the transformation decomposes. The same algebraic primitive extends to dense non-rigid deformations and to
unitary dynamics. On the ACDC cardiac-MRI
benchmark the framework matches or exceeds prior published baselines on both registration directions. On CAMUS echocardiography it matches state-of-the-art without auxiliary scoring or adaptive-smoothness mechanisms. Applied to time-evolved wavefunction pairs of the 1-D
quantum harmonic oscillator, the same framework recovers the Hermite-function eigenstates and the quantized energy levels of the unknown
Hamiltonian from observation pairs alone.
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Artificial Intelligence · Cross-listed from cs.LG
SFT Overtraining Predicts Rank Inversion via Entropy Collapse Under RLVR
Siddharth Aphale and Kelly Liu
The standard heuristic of selecting the SFT checkpoint with the highest pass@1 for GRPO can fail when SFT compresses the rollout distribution. For binary rewards, the expected within group advantage variance is $p(1{-}p)(g{-}1)/g$; when early GRPO drives $p$ below $p^*(g)$, most groups have identical rewards and provide no group relative signal. We study SFT depth ladders for Qwen2.5-Coder-3B and DeepSeek-Coder-6.7B. We test Qwen2.5-Coder-3B across five depths and three seeds, and DeepSeek-Coder-6.7B across four matched depths and three seeds. On Qwen, pre RL pass@1 rises with SFT depth, but peak GRPO pass@10 falls from $0.806$ to $0.481$ (3 seed mean, $n{=}20$); pre RL
entropy is positively associated with the GRPO outcome ($\rho{=}{+}0.69$). On DeepSeek, pass@1 remains far above $p^*(8){=}0.083$, and GRPO outcomes compress rather than invert. A two stage diagnostic, combining pre RL
entropy triage with an early GRPO
entropy monitor, flags high risk checkpoints and can stop failing runs early. Simple KL to reference regularisation and label smoothing variants do not rescue the collapsed Qwen checkpoint in our setting, suggesting the failure is not a trivial GRPO hyperparameter artefact.
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Artificial Intelligence · Cross-listed from cs.SD
MagpieTTS-LF: Inference-Time Long-Form Speech Generation Without Training on Long-Form data
Subhankar Ghosh, Jason Li, Paarth Neekhara et al. (+4 more)
Neural Text-to-
Speech (TTS) systems achieve remarkable quality on short utterances but long-form
speech generation shows prosodic drift, speaker inconsistencies and sentence boundary artifacts. Existing approaches either compress sequences, increase context length or naively concatenate independently synthesized chunks. We present an inference-time approach called MagpieTTS-LF that enables MagpieTTS to produce coherent long-form
speech without model retraining. Our method introduces three key innovations: (1) soft
attention priors to guide monotonic
alignment while preserving past and future context; (2) a stateful inference
algorithm that maintains context across sentence chunks, ensuring prosodic continuity; (3) history-aware text encoding that uses past text for discourse-level prosodic planning. Experiments on long texts show significant improvements in long-range intelligibility, prosodic coherence, speaker consistency, and boundary naturalness compared to other baselines.
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All authors Subhankar Ghosh, Jason Li, Paarth Neekhara, Shehzeen Hussain, Ryan Langman, Xuesong Yang, Roy Fejgin
Artificial Intelligence · Cross-listed from cs.LG
Structured Representation Learning with Locally Linear Embeddings and Adaptive Feature Fusion
Somjit Nath, Jackson J Cone, Derek Nowrouzezahrai et al. (+1 more)
Neuroscientific research has revealed that the
brain encodes complex behaviors by leveraging structured, low-dimensional
manifolds and dynamically fusing multiple sources of information through adaptive gating mechanisms. Inspired by these principles, we propose a novel reinforcement learning (RL) framework that encourages the disentanglement of dynamics-specific and reward-specific features, drawing direct parallels to how neural
circuits separate and integrate information for efficient decision-making. Our approach leverages locally linear
embeddings (LLEs) to capture the intrinsic, locally linear structure inherent in many environments, mirroring the local smoothness observed in neural population activity, while concurrently deriving reward-specific features through the standard RL objective. An
attention mechanism, analogous to cortical gating, adaptively fuses these complementary representations on a per-state basis. Experimental results on
benchmark tasks demonstrate that our method, grounded in neuroscientific principles, improves learning efficiency and overall performance compared to conventional RL approaches, highlighting the benefits of explicitly modeling local state structures and adaptive feature selection as observed in biological systems.
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All authors Somjit Nath, Jackson J Cone, Derek Nowrouzezahrai, Samira Ebrahimi Kahou
Artificial Intelligence · Cross-listed from cs.LG
What Does the Weight Norm Control in Grokking? Logit-Scale Mediation under Cross-Entropy
Truong Xuan Khanh
Grokking, the delayed jump from memorization to generalization, is usually tied to the weight norm: a smaller norm generalizes sooner. We ask what the norm actually controls. Holding the weight norm fixed by clamping and varying only an output temperature, we slide the grokking delay across its entire norm-induced range under cross-
entropy; matching the effective logit scale back to baseline recovers about 85% of the delay at two
moduli. Across a grid of norms and temperatures the delay collapses onto the logit scale alone (R2 = 0.97), with the norm adding 1-2% beyond it. The effect is loss-dependent: under mean-squared error the logit scale is pinned and the norm acts through a different route. A memorization control, a float64 softmax-collapse audit, and a no-LayerNorm
transformer point to the same channel. Forking arms from one identical state, the delay follows the held norm value and not the clamp operation, which closes a rescaling-artifact concern. The proximal variable is the logit scale and the softmax saturation it drives; the weight norm is only an upstream handle. All numbers, tables, and figures reproduce from released code and data.
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Artificial Intelligence · Cross-listed from cs.LG
Veriphi: Attack-Guided Neural Network Verification with Dataset-Dependent Training Methods
Pratik Deshmukh, Kartik Arya and Vasili Savin
We present Veriphi, a GPU-accelerated neural network verification system that combines fast
adversarial attacks with formal bound certification using alpha,beta-CROWN methods. Through systematic experiments on MNIST and CIFAR-10 using three training methodologies (standard,
adversarial, certified), we demonstrate that training method effectiveness is fundamentally
dataset-dependent. Interval Bound Propagation (IBP) achieves 78% certified accuracy on simple MNIST (784 dimensions) but provides negligible certification performance on the more complex CIFAR-10
dataset, where PGD
adversarial training dominates with 94% certification at small perturbations. We achieve 5x verification speedup through attack-guided falsification and scale our approach to production-size models (105.8M parameters) for real-world aerospace logistics optimization. Our results challenge the assumption that certified training universally outperforms
adversarial training, showing context matters critically for verification strategy selection.
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Artificial Intelligence · Cross-listed from cs.LG
TMR-GGNN: Credit Card Fraud Detection based on Time-Aware Multi-Relational Guided Graph Neural Network
Rohit Tewari, Shubhankar Shilpi, Navin Chhibber et al. (+3 more)
In recent years, credit card fraud detection has faced significant challenges due to highly imbalanced data, evolving fraud patterns, and complex relational structures among transaction entities. To address these issues, this research proposes a novel framework called Timeaware Multi Relational Guided Graph Neural Network (TMR GGNN). Particularly, the proposed TMR GGNN extends the encoder decoder Graph Neural Network GNN architecture by modeling heterogeneous interactions across customers, merchants, devices, and IPs over temporal windows. Subsequently, the proposed TMR GGNN approach constructs a dynamic, multi relational graph and incorporates a time aware relational
attention mechanism within the encoder to adaptively weigh the transaction relevance based on temporal proximity and semantic context. Consequently, the decoder employs a contrastive learning module to distinguish between real and synthesized transaction patterns, while improving the models generalization of rare fraud cases. Additionally, to effectively manage severe class imbalances and emphasize discriminative learning, a composite loss function combining Information Noise Contrastive Estimation (InfoNCE) based contrastive loss with Focal Loss is introduced. This integration assists in improving fraud identification while mitigating false negatives.
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All authors Rohit Tewari, Shubhankar Shilpi, Navin Chhibber, Devendra Singh Parmar, Sunil Khemka, Piyush Ranjan
Artificial Intelligence · Cross-listed from cs.CV
CAOA – Completion-Assisted Object-CAD Alignment
Hiranya Garbha Kumar, Minhas Kamal and Balakrishnan Prabhakaran
Accurately aligning CAD models to their corresponding objects in indoor RGB-D scans is a central challenge in 3D semantic reconstruction. The task requires estimating a 9-Degree-of-Freedom (DoF) pose-position, rotation, and scale along three axes-but is hindered by noisy and incomplete scans, as well as
segmentation errors that cause
geometric distortions. We present Completion-Assisted Object-CAD
Alignment (CAOA), a method that integrates a semantically and contextually aware point cloud completion module with a
symmetry-aware relative pose estimation
algorithm, enabling precise
alignment of CAD models to scanned objects. Existing completion methods are typically trained and evaluated on synthetic
datasets, which often fail to generalize to real-world scans. To bridge this gap, we introduce a synthetic data generation strategy tailored to indoor scenes, significantly reducing the synthetic-to-real domain gap-validated through quantitative comparisons with widely used completion
datasets. In addition, we release S2C-Completion, an expert-annotated
dataset of over 8,500 object-CAD pairs from Scan2CAD, created for real-world indoor single-object completion and intended as a new
benchmark for this task. For object-CAD
alignment, we incorporate
symmetry information via a
symmetry-aware loss, improving robustness to symmetric ambiguities. On the Scan2CAD
benchmark, CAOA achieves a 17% accuracy improvement over state-of-the-art methods.
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Artificial Intelligence · Cross-listed from cs.SE
Complexity and Scale in AI-Assisted Workflow Management: A Federated Learning Case Study
Komal Thareja, Hamza Safri, Rajiv Mayani et al. (+2 more)
Federated learning over medical images is a demanding workflow application. Each round fans out across parallel client jobs and converges on an aggregation step that feeds the next round. At scale this yields 101 sub-workflows and 2,679 jobs on GPUs at four sites, which takes an expert months to build, mostly on workflow mechanics rather than science. We ask how far AI assistance can automate such workflows. An LLM
agent, grounded in a released plugin of Pegasus-specific skills, first produces a reviewable specification of checkable constraints and acceptance criteria, then generates the executable workflow. A validation loop repairs runtime failures, checks code against those constraints, and regenerates the implementation from the specification alone. We evaluate three LLM
agents, report end-to-end runs on the FABRIC testbed, and show how conformance checking against the specification caught three silent errors that failure-driven debugging missed, including one that trained 1,700 jobs on random tensors.
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All authors Komal Thareja, Hamza Safri, Rajiv Mayani, Anirban Mandal, Ewa Deelman
Artificial Intelligence · Cross-listed from stat.OT
A Variational Framework for LLM Generator-Regulator Games
Quanyan Zhu
This paper develops a variational framework for regulated
language generation. Starting from autoregressive
token sampling, we derive the induced distribution over complete messages and relate it to an
entropy-regularized Gibbs law. Regulation is modeled as an optimal discriminator whose convex-dual value is an f-divergence, and the generator-regulator interaction is formulated as a saddle-point problem. The framework applies to moderation, censorship, AI deception detection, compliance
auditing, phishing defense, and manipulation control, where regulation concerns a distribution over possible messages rather than a single output. The
equilibrium clarifies the tradeoff among utility,
entropy, regulatory
alignment, and finite-length detectability. Two finite-vocabulary case studies, censorship filtering and phishing defense, illustrate how the theory can be evaluated through utility,
entropy, divergence, receiver-side scores, and detection probability.
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Artificial Intelligence
Searching for Synergy in Shared Workspace Human-AI Collaboration
Nachiket Kotalwar, Rohini Das and Carolyn Rose
Automated AI
agents are increasingly capable, yet many scientific and professional tasks require human judgment and contextual expertise. We use simulated shared-workspace human-AI teams as a controlled testbed for studying how collaboration structure shapes team behavior. Using the Collaborative Gym environment with tasks from DiscoveryBench, we vary team compositions and collaboration structures across 1,482 sessions. We find that adding additional collaborators can lower performance when coordination structure is absent. We then evaluate collaboration scaffolding that combines shared group
memory with simulated human-in-the-loop (HITL) gates, where selected actions require approval from a designated simulated participant. This scaffolding improves performance, most clearly in three-person teams, with clearer responsibility signals and stronger routing of expertise to team actions. Overall, our results suggest that coordination structure is central to whether available capability improves team outcomes.
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Artificial Intelligence · Cross-listed from eess.SP
Deep-Learning-Based Pixelated Microwave Filter Design and Characterization using Electro-Optical Electric-Field Measurements
Han Zhou, Richard Bannister, Caspar Pierce et al. (+9 more)
Traditional microwave filter design typically relies on iterative parameter tuning and predefined
topologies, which limits design space and increases development time. This study uses a deep learning approach combining convolutional neural networks with genetic
algorithms to automate pixelated microwave filter synthesis. To validate the approach experimentally, both S-parameter and spatial electric-field measurements were analyzed. The synthesized low-pass filter demonstrated excellent agreement between simulated and measured performance, achieving a 7 GHz passband with over 20 dB suppression beyond 9.5 GHz. Electro-optical measurements, for the first time, revealed electric field patterns that resemble coupled transmission-lines or stub structures, providing insight into the emergent characteristics of AI-generated designs.
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All authors Han Zhou, Richard Bannister, Caspar Pierce, Haojie Chang, David Widen, Ludvig Fornstedt, Gabriel Melin, Alexander Bohlin, Pontus Lindeberg Fredriksson, Dilbagh Singh, Christian Fager, Koen Buisman
Artificial Intelligence · Cross-listed from eess.SP
Deep Learning-Driven Inverse Design of Doherty Power Amplifiers Using Pixelated Combiners and Dual-State Impedance Synthesis
Han Zhou, Haojie Chang, David Widen et al. (+1 more)
The output combiner of a Doherty power amplifier (PA) integrates load modulation, impedance matching, and phase compensation within a single network, making its design and synthesis highly challenging. In this paper, we propose a three-port Doherty combiner design methodology that combines deep convolutional neural networks (CNNs), pixelated layout representations, and genetic
algorithms (GA) with dual-state impedance synthesis to address both peak and back-off power conditions. As a proof of concept, two GaN HEMT Doherty PA prototypes incorporating three-port pixelated combiners are designed and fabricated. Both prototypes achieve a measured saturated output power exceeding 44.2 dBm with peak drain efficiency above 71.2% within 2.6-2.8 GHz. Furthermore, a drain efficiency as high as 64% is measured at the 6-dB back-off level. After applying digital predistortion, each prototype achieves an adjacent channel leakage ratio (ACLR) better than -51.3 dBc.
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All authors Han Zhou, Haojie Chang, David Widen, Christian Fager
Artificial Intelligence · Cross-listed from eess.SY
Learning-Based Decision Making for Combustion Phasing Control in Multi-Fuel CI Engines with Latent Fuel Reactivity Estimation
Rajasree Sarkar, Aditya Satish Patil, Arunava Banerjee et al. (+4 more)
Multi-fuel compression-ignition engines offer fuel flexibility but introduce uncertain, time-varying fuel reactivity, represented by cetane number (CN), which complicates cycle-to-cycle combustion-phasing control. This work formulates CA50 regulation under latent CN variation as a partially observable sequential decision problem and systematically evaluates controllers with increasing temporal and representational capacity, including LinUCB, history-augmented contextual bandits, observation-only DDPG, recurrent DDPG, and a proposed GRU-guided RL framework. A Gaussian-process surrogate trained on experimental multi-fuel engine data provides a controlled and reproducible evaluation environment. Results show that myopic and fixed-history bandit methods degrade under CN variation, observation-only RL suffers from latent-state aliasing, and generic recurrence is insufficient when CN evolves rapidly. The proposed framework learns a compact GRU-based representation of fuel reactivity from combustion history and conditions both actor and critic on this estimated signal rather than oracle CN. By training the policy on the same imperfect fuel-reactivity information available at deployment, the controller avoids train-deploy inconsistency in conventional online estimate-then-control pipelines. Across unseen CN trajectories, the policy achieves stable CA50 regulation with mean absolute tracking error below 0.25{\deg} CA at the training setpoint, while producing smooth, physically consistent SOI and glow-plug-power actuation. These results show that combustion control under latent, continuously evolving fuel dynamics requires more than standalone estimation or generic recurrence. By aligning fuel-reactivity inference with control policy learning, the proposed framework enables reactivity-aware decision-making using the same estimated state available during deployment.
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All authors Rajasree Sarkar, Aditya Satish Patil, Arunava Banerjee, Ihsan Berk Altiner, Zongxuan Sun, Kenneth Kim, Chol-Bum Mike Keown
Artificial Intelligence · Cross-listed from cs.LG
LLMZero: Discovering Adaptive Training Strategies for RL Post-Training via LLM Agents
Haoyang Fang, Wei Zhu, Boran Han et al. (+11 more)
RL post-training strategies are
dataset-dependent and reveal a recurring empirical pattern: capacity parameters accumulate monotonically across stages, while regularization parameters predominantly oscillate in response to shifting training dynamics. This distinction highlights a potential flaw in fixed training schedules: by forcing all parameters along rigid paths, they fail to capture the dynamic exploration-exploitation tradeoffs that regularization must track. We uncover this through LLMZero, an agentic system that optimizes training trajectories via tree search by diagnosing pathologies at each checkpoint and proposing coordinated multi-parameter transitions. Across four diverse GRPO tasks, LLMZero discovers strategies that improve over the base model by 9% to 140% and over grid search by 6% to 15% (relative), consistently outperforming random search and a skill-based
agent under a matched compute budget. The capacity–regularization asymmetry is consistent across all four tasks, offering a candidate design heuristic for multi-stage training.
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All authors Haoyang Fang, Wei Zhu, Boran Han, Alex Zhang, Zhenyu Pan, Shuo Yang, Shuai Zhang, Jiading Gai, Peng Tang, Cuixiong Hu, Xuan Zhu, Huzefa Rangwala, George Karypis, Bernie Wang
Artificial Intelligence
CaVe-VLM-CoT: An Interpretable Vision-Language Model Framework
Sneha Rao, Shaina Raza and Dhanesh Ramachandram
Vision-
Language Models (VLMs) remain prone to
hallucinations, producing fluent but visually unfaithful outputs. Existing chain-of-thought and
retrieval-augmented methods only partially address this, as they neither enforce step-level citation grounding nor route verification failures back to
retrieval for correction. We present CaVe-VLM-CoT, a modular reflection-based agentic-RAG framework that enforces evidence-grounded
reasoning through a five-stage closed-loop pipeline: Extractor, Retriever, Solver, Citation Injector, and Verifier, in which detected ungrounded claims trigger structured feedback to the Extractor for targeted re-
retrieval. Since no existing framework jointly measures
retrieval quality, step-wise citation faithfulness, and cross-modal grounding, we propose a suite of 23 component-wise metrics across all stages, anchored by CaVeScore, a composite metric weighting accuracy, citation precision and recall, attribution, and evidence grounding. Without any architectural or
prompt modifications, CaVe-VLM-CoT achieves 87.1% accuracy and 56.6% CaVeScore on ScienceQA , and 55.2% accuracy and 35.7% CaVeScore on MMMU (30 subjects).
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Artificial Intelligence · Cross-listed from cs.IR
RankGraph-2: Lifecycle Co-Design for Billion-Node Graph Learning in Recommendation
Renzhi Wu, Zikun Cui, Junjie Yang et al. (+10 more)
Graph-based
retrieval at billion-node scale requires jointly solving three tightly coupled problems – graph construction, representation learning, and real-time serving – yet existing work addresses each in isolation. We present RankGraph-2, a framework deployed at Meta that co-designs all three lifecycle stages for similarity-based
retrieval (U2U2I and U2I2I), where each stage's requirements shape the others. Serving requires a co-learned cluster index to avoid expensive online KNN – this pushes index co-training into the training objective. Training benefits from the observation that similarity-based
retrieval tolerates pre-computed neighborhoods, eliminating online graph infrastructure – this requires construction to produce self-contained data. Construction must also support hour-level refresh for item coverage. Acting on these cascading requirements, RankGraph-2 reduces hundreds of trillions of edges to hundreds of billions via subsampling with popularity bias correction, pre-computes multi-hop neighborhoods via personalized PageRank, and co-learns a residual-quantization cluster index that reduces serving computational cost by 83%. This lifecycle co-design enables a simple architecture to achieve 3.8 x higher recall than a GAT + Deep Graph Infomax model on a bipartite graph and 2.1 x higher than PyTorch-BigGraph on item
retrieval. RankGraph-2 delivers up to +0.96% CTR and +2.75% CVR, and has powered 20+
retrieval launches across major surfaces.
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All authors Renzhi Wu, Zikun Cui, Junjie Yang, Tai Guo, Hong Li, Xian Chen, Li Yu, Ke Pan, Sri Reddy, Mahesh Srinivasan, Nipun Mathur, Haomin Yu, Hong Yan
Artificial Intelligence · Cross-listed from cs.CL
Redact or Keep? A Fully Local AI Cascade for Educational Dialogue De-Identification
Haocheng Zhang, Zhuqian Zhou, Kirk Vanacore et al. (+2 more)
Educational dialogue is a valuable but sensitive resource for research: the same transcripts that capture authentic learning often capture personally identifiable information (PII) entangled with curricular content, where "Riemann" may refer to a real student or to a mathematical concept. Existing approaches force a tradeoff between governance and accuracy. Commercial Large
Language Models (LLMs) can handle this ambiguity but require sending student data to third parties, while local named entity recognition (NER) systems preserve governance but over-redact curricular terms. We propose a fully local cascade framework that reframes de-identification from open-ended entity recognition to constrained
privacy triage. A recall-first union proposer combines two lightweight encoders with deterministic rules to over-generate candidate spans; a context-aware reviewer then makes a binary Redact/Keep decision for each candidate using surrounding dialogue and speaker role. We evaluate three reviewer configurations against same-family LLM-only baselines and a commercial API on math tutoring transcripts from two large platforms. The strongest local configuration reaches 0.958 macro F1, compared with 0.767 for a same-family LLM-only baseline and 0.706 for the commercial API, while running entirely on a single laptop. On a targeted challenge set of curricular-personal name ambiguity, the same configuration degrades by only 0.03 F1 versus 0.19 to 0.25 for smaller reviewers. These results suggest that for educational de-identification, problem formulation matters more than model scale.
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All authors Haocheng Zhang, Zhuqian Zhou, Kirk Vanacore, Bakhtawar Ahtisham, René F. Kizilcec
Artificial Intelligence · Cross-listed from cs.RO
Guava: Distilling Frontier VLMs into a Compact Agent through a Robotic Manipulation Harness
Haowen Liu, Xirui Li, Shaoxiong Yao et al. (+5 more)
Language models trained on large-scale
vision-
language data have demonstrated strong potential for
embodied agents. Harnessing models through
embodied tools use offers a promising alternative to end-to-end
vision-
language-action systems by combining high-level
reasoning with external modules for perception, planning, and control. However, it remains unclear what makes an effective harness for
embodied manipulation, and to what extent such a harness can unlock
embodied capabilities in a wide range of
reasoning models. In this work, we present Guava, a harness framework for
embodied tool use developed through systematic exploration of the design space of
agent workflows, action spaces, and observation spaces. Our study identifies three key ingredients for effective
embodied agents: iterative perception-
reasoning-action loops, semantic action abstractions, and
multimodal observations. To understand whether these design principles are universal even to small models, we develop an end-to-end training pipeline that distills
embodied manipulation capabilities into a 4B open-source model using fewer than 2K trajectories collected entirely in simulation. Experimental results in both simulation and real-world environments show performance comparable to frontier proprietary models while exhibiting strong generalization to unseen objects, novel instructions, and long-horizon tasks. Results suggest that a well-designed harness can serve as a scalable, model-agnostic interface for
embodied manipulation, enabling strong emergent
embodied capabilities in compact open-source models with minimal training data.
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All authors Haowen Liu, Xirui Li, Shaoxiong Yao, Peng Shi, Tianyi Zhou, Jia-Bin Huang, Furong Huang, Jiayuan Mao
Artificial Intelligence · Cross-listed from cs.CR
SafeClawBench: Separating Semantic, Audit-Evidence, and Sandbox Harm in Tool-Using LLM Agents
Yuchuan Tian, Mengyu Zheng, Haocheng Mei et al. (+5 more)
Tool-using
language-model
agents introduce security failures that go beyond unsafe text: they can disclose protected objects, write persistent
memory, send messages, modify databases, or trigger harmful code and tool effects. Existing evaluations often collapse these stages into a single attack success rate, making it difficult to tell whether a model merely agreed with an attacker or actually produced observable harm. We introduce SafeClawBench, a staged
benchmark for tool-using
agent security with 600 controlled
adversarial tasks across six attack families: direct and indirect
prompt injection, tool-return injection,
memory poisoning,
memory extraction, and ambiguity-driven unsafe inference. SafeClawBench reports three separate endpoints: semantic attack acceptance, audit-visible harm evidence, and sandbox-observed tool/state harm. Evaluating five
agent endpoints under four
prompt-level policies, we find that these endpoints capture different failure modes. Without additional
prompt protection, semantic failure rates vary widely across models, from 9.0% to 44.2%. Audited harm evidence is narrower than semantic failure, and under a separate executable protocol some matched task identities produce sandbox harm despite passing the Semantic Core call: in a 12,000-row matched analysis, 291 of 347 observed sandbox harms occur in rows that pass the semantic check.
Prompt policies change endpoint outcomes, but their effects depend on both model and protocol. SafeClawBench provides a reproducible framework for comparing
agent models and
prompt-policy conditions without conflating textual compliance, evidence-supported harm, and executable state changes. The open-source
dataset is available at https://huggingface.co/
datasets/sairights/safeclawbench.
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All authors Yuchuan Tian, Mengyu Zheng, Haocheng Mei, Ye Yuan, Chao Xu, Xinghao Chen, Hanting Chen, Yu Wang
Artificial Intelligence · Cross-listed from cs.LG
Self-CTRL: Self-Consistency Training with Reinforcement Learning
Itamar Pres, Laura Ruis, Melat Ghebreselassie et al. (+2 more)
Language models (LMs) that faithfully describe their own behavior can more easily be audited, understood, and trusted by users. This paper describes Self-Consistency Training with Reinforcement Learning (Self-CTRL), a method that optimizes for consistency between a LM's self-explanations and behavior on related inputs by updating explanations to better predict behavior or updating behavior to better match explanations. We apply our method in two domains. First, we study a formal probabilistic
reasoning task in which LMs must learn to imitate a family of biased samplers and evaluated on their ability to report the associated biases. We find that consistency training improves the correlation between self-reported and behaviorally-measured latent biases from $R^2=0.24$ to $R^2=0.64$ on a set of held-out distributions, matching the generalization of direct ground-truth supervision. Second, we study a constitutional AI domain in which LMs must describe when they will refuse or comply with user requests. Here, Self-CTRL produces rules that faithfully describe the model's behavior on held-out requests, improving the refusal predictions of a third-party auditor model from $36\%$ to $92\%$. In the other direction, behavior updates improve
alignment, reducing HarmBench failure rate from $15.0\%$ to $0.5\%$ without substantially increasing refusal on harmless
prompts. By aligning explanations and behavior, our work provides a general recipe for training AI models to be safer, more transparent, and more controllable.
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All authors Itamar Pres, Laura Ruis, Melat Ghebreselassie, Belinda Z. Li, Jacob Andreas