All papers

Introduces a training-free semantic text watermarking method that aligns cumulative sentence embeddings with a secret Gaussian vector.

The paper introduces an interactive web-based tool for analyzing and visualizing execution trajectories of software engineering agents.

Presents a local agentic framework using smaller language models to automatically synthesize edge-case testing scenarios for autonomous systems.

Compares four multi-agent workflow architectures to determine how coordination structure affects filtering of static analysis alerts.

Introduces representation geometry regularization to prevent capability degradation during on-policy self-distillation in language models.

Proposes an agent memory architecture that uses biologically inspired adaptive decay functions to selectively retain and forget context.

Develops a low-rank generalized Laplace approximation over active curvature directions for scalable neural network uncertainty quantification.

This paper introduces a multi-agent system that evaluates code extensibility and detects SOLID design violations through automated code extension.

Presents a stateless service and library providing isolated, semantics-preserving text perturbations for language model robustness testing.

The paper proposes a graph-based framework that integrates multi-agent decisions, rationales, and memory transitions to improve assistant explainability.

Implements encrypted WebAssembly execution on Arm TrustZone to protect proprietary AI models on edge hardware.

The paper presents a multi-agent framework that dynamically generates execution context to enable partial code execution.

The paper presents an adversarial perturbation defense that disrupts the value vector diversity in image-to-video diffusion models to prevent unauthorized generation.

Proposes a conceptual framework to evaluate the resilience of LLM-based multi-agent systems in software engineering tasks.

The paper designs a quantized gradient checksum interrogation scheme for cross-silo federated learning to detect active inference attacks under floating-point variations.

The paper reviews architectures, reasoning methods, and multi-agent collaboration strategies for agentic language models.

The paper proposes embedding multi-scale wavelet-domain watermarks into initial diffusion noise to enable robust provenance tracking under image removal attacks.

Introduces READ-MAS, a multi-agent system using retrieval-augmented generation to decompose requirements engineering and software design.

The paper proposes random allocation batching and GPU-accelerated accounting to improve accuracy and efficiency in differentially private model training.

Presents MOSAICO, an orchestration infrastructure for coordinating specialized collaborative LLM agents across software engineering tasks.

The paper introduces a hypergraph autoencoder framework to filter malicious model updates and preserve Byzantine resilience in federated large language models.

This paper presents a multi-agent framework where a central model coordinates specialized agents and tools for business workflow automation.

Presents SADDER, an inference-time text diffusion safety framework using directional embedding signals to steer unsafe tokens toward benign distributions.

This paper presents a multi-agent architecture using structured states and human checkpoints to manage software requirements engineering workflows.

This paper proposes a preference optimization algorithm grounded in uniform information density theory to align language model outputs.

The paper evaluates five planning algorithms for breaking down complex goals into executable workflows in agentic AI systems.

The paper proposes design requirements and evaluates defenses against model-side protection removal in open-weight image diffusion models.

The paper evaluates token usage, runtime, and energy consumption across four different memory setups for single-model multi-agent educational systems.

The paper surveys security vulnerabilities and defenses in edge-deployed audio language model agents across the sensing-to-action pipeline.

The paper reports experiences using iterative AI agents to discover vulnerabilities across operating system kernels, drivers, and web applications.

Analyzes activation steering interventions in language models by isolating the effects of covariance filtering from target gain and displacement.

This paper introduces a learnable probabilistic instance sampler to handle severe class imbalance and prioritize critical diagnoses in multilabel learning.

The paper presents a feature-proxy method to selectively erase combined object-style concepts from diffusion models without degrading general generative performance.

The paper refines a high-level spatial AI orchestration framework into an implementation-focused low-level architecture for governed agent execution.

The paper evaluates how objective functions and candidate constraints affect decision conversion when generating radiology reports from sampled evidence.

The paper establishes theoretical bounds on interval lengths for split conformal prediction under Huber data contamination.

This paper extends backward conformal prediction to covariate shift settings using importance weighting derived from sorted score vectors.

Presents a taxonomy and research roadmap for measuring algorithmic fairness over time in systems with feedback loops.

This paper proposes a post-hoc framework based on Linearized Laplace approximation to quantify uncertainty across downstream tasks in contrastive learning.

This paper proposes a framework that dynamically steers internal language model neurons based on query-induced activation patterns.

The paper proposes a probabilistic decomposition of neural models that separates predictable features from an input-dependent reliability score.

The paper develops a theoretical Rényi-divergence framework to measure inherent differential privacy guarantees obtained directly from Bayesian posterior sampling.

The paper develops quality functions based on distance metrics to assess the diversity of generated test scenarios for automated driving verification.

The paper applies a lightweight post-hoc classifier to evaluate confidence and filter out incorrect next-location predictions from recurrent models.

The paper introduces a framework providing analytical bounds for counterfactual reasoning in sequential decision processes under statistical and causal uncertainty.

The paper introduces an agentic framework that removes peripheral visual clues to protect geolocation privacy in images without adding visible artifacts.

The paper introduces a compiler framework that transforms defective natural-language protocols into verifiable laboratory execution workflows.

The paper presents a subspace construction method for continual low-rank adaptation to mitigate forgetting under differential privacy constraints.

The paper presents a byte-level transformer model designed to detect malware directly from raw executable byte sequences.

The paper proposes an artificial neural network framework designed to reject uncertain predictions to prevent misclassification errors.

The paper evaluates an eight-qubit variational circuit bottleneck to improve confidence calibration in synthetic speech detection.

The paper presents a method for robots to generate targeted preference queries to learn underspecified rewards from humans.

Applies adversarial training and distillation to remove unwanted gender bias in audio-enabled language models.

The paper proposes SHIP, an architecture for extracting structured differentially private representations from text for analytical queries.

The authors present a graph-based aggregation method to defend federated language model fine-tuning against backdoor attacks by identifying update consistency patterns.

This paper proposes Gaussian process bandit algorithms with selective verification audits to optimize AI system configurations under noisy evaluations.

The paper introduces a dual-stage robust aggregation defense against data and model poisoning attacks in federated financial fraud detection.

Argues that competitive national races toward superintelligent artificial intelligence create risks of conflict and catastrophic loss of control.

The paper introduces an audio deepfake detection framework that dynamically selects latent dimensions and uses spectrogram perturbations to improve generalization across attacks.

The paper fine-tunes a language model on technical telecommunication specifications to automate security incident triage in Open Radio Access Networks.