Safety, Efficiency
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
SafeSteer: Localized On-Policy Distillation for Efficient Safety Alignment
new Abstract: Aligning Large Language Models (LLMs) with human values often degrades their general capabilities, termed the alignment tax. Existing methods mitigate this by balancing dual objectives, which heavily rely on massive general-purpose data or auxiliary reward models. In this paper, we argue that, because safety features are inherently sparse within the output distribution, alignment requires localized modifications rather than global trade-offs.
Permissive Safety Through Trusted Inference: Verifiable Belief-Space Neural Safety Filters for Assured Interactive Robotics
Announce Type: new Abstract: Autonomous robots that interact with people must make safe and efficient decisions under human-induced uncertainty, such as their preferences, goals, competency, and willingness to cooperate. Safety filters are a popular approach for ensuring safety in interactive robotics, since their modular design separates safety from performance, allowing robots to operate safely around people with minimal impact on task efficiency. While traditional safety filters typically...
Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic
arXiv:2601.18783v2 Announce Type: replace Abstract: Balancing safety, efficiency, and operational costs in highway driving poses a challenging decision-making problem for heavy-duty vehicles. A central difficulty is that conventional scalar reward formulations, obtained by aggregating these competing objectives, often obscure the structure of their trade-offs. We present a Proximal Policy Optimization based multi-objective reinforcement learning framework that learns a set of policies...
Distilling Safe LLM Systems via Soft Prompts for On Device Settings
arXiv:2606.09388v1 Announce Type: new Abstract: Deploying safe large language models (LLMs) on resource-constrained edge devices presents a critical challenge: while dual-model systems combining LLMs with guard models provide effective safety guarantees, their substantial memory and computational demands make them prohibitively expensive for on-device deployment. This paper presents a comprehensive study of parameter-efficient safety alignment methods for resource-constrained settings....
Plan First, Judge Later, Run Better: A DMAIC-Inspired Agentic System for Industrial Anomaly Detection
Announce Type: new Abstract: Large language model (LLM) agents have shown promise in automating complex data-analysis workflows, but their reliable deployment remains challenging in high-stakes industrial scenarios. Industrial anomaly detection (IAD) is essential for manufacturing quality, safety, and efficiency, yet existing LLM-based IAD agents mainly focus on execution while under-exploiting strategy formulation. Consequently, they struggle to handle heterogeneous modalities in a unified...
Transforming Police-Car Swerving for Mitigating Isolated Stop-and-Go Traffic Waves: A Practice-Oriented Jam-Absorption Driving Strategy
arXiv:2602.10234v3 Announce Type: replace-cross Abstract: Stop-and-go traffic waves, a major form of freeway congestion, impose severe and persistent adverse impacts, including reduced traffic efficiency, increased safety risks, and elevated vehicle emissions. Among various freeway traffic management strategies, jam-absorption driving (JAD), in which a dedicated vehicle performs "slow-in" and "fast-out" maneuvers before being captured by a stop-and-go wave, has been proposed as a promising...
Transforming Police-Car Swerving for Mitigating Isolated Stop-and-Go Traffic Waves: A Practice-Oriented Jam-Absorption Driving Strategy
arXiv:2602.10234v3 Announce Type: replace Abstract: Stop-and-go traffic waves, a major form of freeway congestion, impose severe and persistent adverse impacts, including reduced traffic efficiency, increased safety risks, and elevated vehicle emissions. Among various freeway traffic management strategies, jam-absorption driving (JAD), in which a dedicated vehicle performs "slow-in" and "fast-out" maneuvers before being captured by a stop-and-go wave, has been proposed as a promising...
Smart pipelines: Can AI protect the world’s energy lifelines?
As ageing pipelines face growing risks, the energy industry is increasingly turning to AI and smart monitoring systems to improve their safety and efficiency. Around 500,000 kilometres of oil and gas pipelines worldwide need to be renovated, rebuilt or upgraded, while leaks, ruptures and incidents already cost the sector more than $7 billion (€6bn) a year — and roughly 40% of failures go undetected in the first 24 hours, according to industry experts speaking at the Baku Energy Forum. The...
Multi-Agent Next-Best-View Optimization for Risk-Averse Planning
arXiv:2606.04158v1 Announce Type: new Abstract: Multi-agent Next-Best-View (NBV) selection for safe path planning in uncertain and unknown environments requires informative, safety-aware, and efficient coordination. Centralized approaches rely on sharing raw sensor data or significant communication overhead, resulting in limited scalability.
Visibly Transparent, Near-Infrared Absorbing Nanofluids Enable High-Efficiency and Safe Laser Lithotripsy
Laser lithotripsy (LL) is the gold standard for urinary stone management yet maximizing ablation efficiency while maintaining procedural safety remains clinically challenging. Here, we present a visibly transparent, near-infrared (NIR)-absorbing ITO@SiO2 nanofluid irrigation strategy that significantly enhances LL efficiency without compromising endoscopic visibility. By spectrally matching the absorption profile of ITO@SiO2 with the clinical Holmium:YAG laser wavelength, ablation efficiency...