We further design an expert-wise training strategy to alleviate the impact of unhelpful demonstrations when optimizing the retriever model.

Partial observability of the underlying states generally presents significant challenges for reinforcement learning (RL).

History is already being made at the Milano Cortina Games--and they haven't even started. Typically, it's a cool costume at the opening ceremony, or a new cauldron for the Olympic flame, or maybe a new fancy stadium the host city will have no use for in 10 years. Then there are the brand-new records set during each Games, jaw-dropping examples of human strength, talent, and mind-melting perseverance. But for the 2026 Winter Olympics, some of the most notable firsts are coming out of the Olympic Village rather than the individual venues. They are the ones pushing their sports forward and making history in the process.

War of 1812 veteran Shadrack Byfield's second book describes a grittier life story--and a hook for a hand. Breakthroughs, discoveries, and DIY tips sent six days a week. A long-lost second memoir penned by a famed 19th-century British soldier named Shadlock Byfield resurfaced in a rather unexpected place--Cleveland, Ohio. As explained in a study recently published in the, Byfield's second book depicts a very different war veteran than the one described in his first autobiography written 11 years earlier. Although he may not be a household name, many early American history buffs are well acquainted with Shadrack Byfield .

Legacy (unsupported) robotic platforms often lose research utility when manufacturer support ends, preventing integration of modern sensing, speech, and interaction capabilities. We present the Enhanced NAO, a revitalized version of Aldebaran's NAO robot featuring upgraded beamforming microphones, RGB-D and thermal cameras, and additional compute resources in a fully self-contained package. This system combines cloud-based and local models for perception and dialogue, while preserving the NAO's expressive body and behaviors. In a pilot user study validating conversational performance, the Enhanced NAO delivered significantly higher conversational quality and elicited stronger user preference compared to the NAO AI Edition, without increasing response latency. The added visual and thermal sensing modalities established a foundation for future perception-driven interaction. Beyond this implementation, our framework provides a platform-agnostic strategy for extending the lifespan and research utility of legacy robots, ensuring they remain valuable tools for human-robot interaction.

The widespread deployment of Large Language Models (LLMs) has intensified concerns about subtle social biases embedded in their outputs. Existing guardrails often fail when faced with indirect or contextually complex bias-inducing prompts. To address these limitations, we propose a unified framework for both systematic bias evaluation and targeted mitigation. Our approach introduces six novel Metamorphic Relations (MRs) that, based on metamorphic testing principles, transform direct bias-inducing inputs into semantically equivalent yet adversarially challenging variants. These transformations enable an automated method for exposing hidden model biases: when an LLM responds inconsistently or unfairly across MR-generated variants, the underlying bias becomes detectable. We further show that the same MRs can be used to generate diverse bias-inducing samples for fine-tuning, directly linking the testing process to mitigation. Using six state-of-the-art LLMs - spanning open-source and proprietary models - and a representative subset of 385 questions from the 8,978-item BiasAsker benchmark covering seven protected groups, our MRs reveal up to 14% more hidden biases compared to existing tools. Moreover, fine-tuning with both original and MR-mutated samples significantly enhances bias resiliency, increasing safe response rates from 54.7% to over 88.9% across models. These results highlight metamorphic relations as a practical mechanism for improving fairness in conversational AI.

The home space for optimal control is a Sobolev space. The home space for pseudospectral theory is also a Sobolev space. It thus seems natural to combine pseudospectral theory with optimal control theory and construct ``pseudospectral optimal control theory,'' a term coined by Ross. In this paper, we review key theoretical results in pseudospectral optimal control that have proven to be critical for a successful flight. Implementation details of flight demonstrations onboard NASA spacecraft are discussed along with emerging trends and techniques in both theory and practice. The 2011 launch of pseudospectral optimal control in embedded platforms is changing the way in which we see solutions to challenging control problems in aerospace and autonomous systems.