Multi-Agent AI Control: Distributed Attacks Hamper Per-Instance Monitors
Empirical study of multi-agent AI control examining how distributed attacks across shared infrastructure can bypass per-instance safety monitors.
Empirical study of multi-agent AI control examining how distributed attacks across shared infrastructure can bypass per-instance safety monitors.
Technical report on motion controllers for quadruped robots trained from motion-capture data to bridge semantic reasoning and physical execution.
Analysis of adversarial vulnerability in vision-language transformers through spectral structure of intermediate linear transformations.
Graph-based attribute reasoning approach for vision-language models that improves calibration by leveraging relational structure of class attributes.
RL framework for training agents in costly real-world interactions that penalizes constraint violations during paths while rewarding final outcomes.
LLM-based synthetic label generation system for e-commerce attribute extraction with integrated quality control across product categories.
Benchmark study examining fairness-aware learning on differentially private synthetic tabular data in high-stakes ML deployments.
Severity grading framework for agentic red-teaming that replaces binary attack-success rates with ordinal harm scale for tool-using agents.
Reward-adaptive discovery method for automated game testing that reduces re-testing effort by iteratively finding behavioral exploits in AI systems.
In-context learning approach for time series classification that eliminates separate feature encoder training and enables label exploitation at inference.
Study of how hallucinated content propagates through reasoning stages in vision-language models and affects downstream inference.
Asynchronous reinforcement learning system for LLM post-training optimized for long-horizon agentic tasks with improved training stability.
Interactive AI agent framework for structural design that explores alternatives and refines solutions while satisfying spatial, mechanical, and cost constraints.
Federated learning approach using collaborative synthetic data generation for knowledge transfer across distributed clients with divergent data distributions.
Multi-component simulator for autonomous driving that synthesizes corner cases combining visual representation, scene reasoning, and vehicle control.
Systematic review of governance challenges and frameworks for agentic AI systems capable of autonomous planning and task execution.
Method for improving confidence estimation in LLMs by tracking confidence evolution during generation for better deployment in tool use and adaptive systems.
DiaLLM: Method for improving dialectal English generation in open-weight LLMs through continual pretraining and alignment.
Sample-efficient RLHF method for diffusion models using selective timestep weighting and advantage-based replay.
Jailbreak: Agentic approach to bypass database engines by directly reading storage files for high-performance columnar analytics.
Continuous-query limited memory language models that externalize factual knowledge to knowledge bases during pretraining and generation.
Framework for mechanistically explaining structure-property relationships using deep learning with physical constraints and scientific principles.
Method for domain-independent planning that uses LLMs to automatically synthesize heuristics from problem definitions.
Deep reinforcement learning approach for universal robot control using modular recurrence and contextual MDPs across different morphologies.
Study using satellite imagery and LLM-generated text descriptions to investigate socioeconomic indicators in poverty mapping.
LiveOIBench: Large-scale benchmark of competitive programming problems for evaluating LLM coding capabilities with comprehensive test coverage.
AGAPI-Agents: Open-source agentic AI platform integrating LLMs with 28 scientific tools for accelerated materials design.
Multi-agent simulation framework evaluating LLM robustness to adversarial persuasion in simulated clinical emergency medicine scenarios.
Audit of 16 LLMs showing instability in ethical stances when moral dilemmas are reframed as negations versus prescriptions.
VERA-MH benchmark for validating AI chatbot safety in suicide risk detection with human evaluation studies.
Federated learning framework addressing device heterogeneity and non-IID data with adaptive differential privacy mechanisms.
Study showing sensitive attributes emerge in unsupervised embeddings even when withheld from training using self-organizing maps.
Theoretical analysis of aggregating multiple LLM responses in compound AI systems and whether this unlocks new capabilities.
Method for improving emotional reasoning in multimodal LLMs using reflective reinforcement learning for better emotion understanding.
Deep generative models for anomaly detection in multivariate time-series using normalizing flows with inductive biases in latent space.
Methods for measuring metacognitive capabilities of AI systems to assess reliability and manage uncertainty in decision-making workflows.
Research on smaller 4B parameter models for agentic execution tasks using subagent architectural patterns to handle specialized subtasks like debugging and terminal execution.
Study of how LLM-compressed financial summaries can distort investment decisions and information fidelity in agentic systems.
Analysis of how aligned LLMs internally represent safety through harmfulness and refusal directions for robust alignment.
Study of memory failures in LLM agents where conflicting state facts coexist, and methods to decouple them.
Stage-aware agentic framework for physical design optimization avoiding full re-runs after parameter changes.
CAD agent for industrial component design using knowledge distillation to handle ambiguous specifications and parametric modeling.
System for orchestrating mathematical reasoning agents using fact-graph memory for research-level problem solving.
Study of neural plasticity and generalization in deep reinforcement learning for adaptive video streaming.
Framework for Vision-Language-Action models reducing inference latency and improving robotic manipulation through trajectory ensemble voting.
Theoretical analysis of training solutions in two-layer neural networks with smooth activation functions.
Generative model using VAE with Bi-LSTM for time series data augmentation in forecasting and classification.
Study of visual reasoning about object properties and physical attributes in VQA tasks.
Survey on compositional visual reasoning in multimodal AI systems for decomposing scenes and multi-step inference.
Study of gradient-based jailbreak attacks on LLMs using adversarial suffixes without fixed target constraints.