All Articles
16910 articles total
Post-Deployment Accountability in AI Governance: A Cross-Regulatory Empirical Analysis of AI Incidents
Analyzes accountability gaps in AI governance by comparing real-world AI incidents against EU AI Act, NIST, and GDPR requirements, proposing the PAGCF framework.
Fre-Res: Frequency-Residual Video Token Compression for Efficient Video MLLMs
Introduces Fre-Res, a video-token compression framework using 1D-DCT to balance spatial fidelity and temporal coverage in Video MLLMs.
DIVE: Embedding Compression via Self-Limiting Gradient Updates
Presents DIVE, a residual compression adapter for high-dimensional language-model embeddings using self-limiting gradient updates and geometry distillation.
When Reasoning Hurts: Source-Aware Evaluation of Frontier LLMs for Clinical SOAP Note Generation
Investigates the effect of reasoning-enabled LLMs on clinical SOAP note generation, finding that stronger reasoning can sometimes degrade fidelity-sensitive documentation.
A Multi-Model Metric-based Selection Framework for Abstractive Text summarization
Proposes a multi-model metric-based selection framework for abstractive text summarization to improve robustness over single-model approaches.
Learning Red Agent Policy from Observations for Neurosymbolic Autonomous Cyber Agents
Proposes a policy learning technique using imitation learning to help neurosymbolic cyber-defense agents predict attacker policies in partially observable networks.
An Engineer's Guide to USB Typе-С (2024)
A comprehensive technical guide detailing the specifications and engineering considerations of USB Type-C.
A novel network for classification of cuneiform tablet metadata
Researchers developed a convolution-inspired network for classifying cuneiform tablet metadata from point-cloud representations, outperforming Point-BERT.
When Audio Separation Hurts Zero-Shot ASR: Evaluating SAM-Audio with Whisper on Bengali and English Speech
An empirical study reveals that audio separation (SAM-Audio) as a preprocessing step can actually increase Word Error Rates (WER) in zero-shot ASR systems like Whisper.
PC-Diffuser: Path-Consistent Capsule CBF Safety Filtering for Diffusion-Based Trajectory Planner
PC-Diffuser introduces a safety augmentation framework for diffusion-based trajectory planners in autonomous driving, embedding certifiable barrier functions into the denoising loop.
RADAR: Closed-Loop Robotic Data Generation via Semantic Planning and Autonomous Causal Environment Reset
RADAR is a closed-loop robotic data generation engine that automates the entire data collection cycle, from task generation to environment reset, using VLMs and GNNs.
LLM-Guided Reinforcement Learning for Audio-Visual Speech Enhancement
A new AVSE framework uses a Large Language Model to generate interpretable rewards for reinforcement learning, improving audio-visual speech enhancement quality.
Rethinking Multimodal Fusion for Time Series: Text Modalities Need Constrained Fusion
The Controlled Fusion Adapter (CFA) is proposed to improve multimodal time series forecasting by filtering irrelevant textual information through low-rank adapters.
Learning to Learn-at-Test-Time: Language Agents with Learnable Adaptation Policies
Meta-TTL introduces a framework for learning optimal test-time adaptation policies for language agents using bi-level optimization and evolutionary search.
Too Polite to Disagree: Understanding Sycophancy Propagation in Multi-Agent Systems
Research shows that providing sycophancy priors (peer rankings) in multi-agent LLM systems can mitigate error cascades and improve discussion accuracy.
Robust Explanations for User Trust in Enterprise NLP Systems
A study on black-box robustness for NLP explanations finds that decoder LLMs are significantly more stable than encoder models, with stability increasing by model scale.
Representation-Based Exploration for Language Models: From Test-Time to Post-Training
Researchers propose a representation-based exploration strategy to help LLMs discover novel behaviors during post-training and inference, significantly improving reasoning efficiency and pass@k rates.
Benefits and Limitations of Communication in Multi-Agent Reasoning
A theoretical framework analyzes the expressivity and communication trade-offs in multi-agent reasoning systems, providing bounds on agent count and bandwidth for scalable design.
Column Generation with Domain-Independent Dynamic Programming
This paper demonstrates that domain-independent dynamic programming (DIDP) can serve as a generic pricing solver for column generation and branch-and-price optimization methods.
Cortical-SSM: A Deep State Space Model for Motor Imagery Decoding from EEG Signals
Cortical-SSM is a new deep state space model designed to decode motor imagery from EEG signals across temporal, spatial, and frequency domains, outperforming attention-based architectures.