5 accepted papers
Obtaining high-quality generations in modern LLMs has largely been framed as a selection problem: identifying a single winning generation from a diverse pool of $N$ samples, the Best-of-$N$ (BoN). Yet, this approach is inherently zero-sum, discarding diverse and potentially useful information from t…
Measuring progress is fundamental to the advancement of any scientific field. As benchmarks play an increasingly central role, they also grow more susceptible to distortion. Chatbot Arena has emerged as the go-to leaderboard for ranking the most capable AI systems. Yet, in this work we identify syst…
One of the most profound challenges of modern machine learning is performing well on the long-tail of rare and underrepresented features. Large general-purpose models are trained for many tasks, but work best on high-frequency use cases. After training, it is hard to adapt a model to perform well on…
In machine learning, a question of great interest is understanding what examples are challenging for a model to classify. Identifying atypical examples ensures the safe deployment of models, isolates samples that require further human inspection, and provides interpretability into model behavior. In…
We present the Intelligent Robotic IoT System (IRIS), a modular, portable, scalable, and open-source testbed for robotic wireless network research. There are two key features that separate IRIS from most of the state-of-the-art multi-robot testbeds. (1)Portability: IRIS does not require a costly sta…