Alexander Spangher, Michael Lu, Sriya Kalyan, Hyundong Justin Cho, Tenghao Huang, Weiyan Shi, Jonathan May. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Current large language models (LLMs) struggle to answer questions that span tens of thousands of tokens, especially when multi-hop reasoning is involved.While prior benchmarks explore long-context comprehension or multihop reasoning in isolation, none jointly vary context length and reasoning depth in natural narrative settings.We introduce NOVEL-HOPQA, the first benchmark to evaluate 1-4 hop QA over 64k-128k-token excerpts from 83 full-length public-domain novels.A keywordguided pipeline builds hop-separated chains grounded in coherent storylines.We evaluate seven state-of-the-art (SOTA) models and apply oracle-context filtering to ensure all questions are genuinely answerable.Human annotators validate both alignment and hop depth.We additionally present retrieval-augmented generation (RAG) evaluations to test model performance when only selected passages are provided instead of the full context.We noticed consistent accuracy drops with increased hops and context length, even in frontier models-revealing that sheer scale does not guarantee robust reasoning.Our failure mode analysis highlights common breakdowns, such as missed final-hop integration and long-range drift.NOVELHOPQA offers a controlled diagnostic setting to test multi-hop reasoning at scale.All code and datasets are available at: https://novelhopqa.github.io.

Existing approaches for embedding unobtrusive tags inside 3D objects require either complex fabrication or high-cost imaging equipment. We present InfraredTags, which are 2D markers and barcodes imperceptible to the naked eye that can be 3D printed as part of objects, and detected rapidly by low-cost near-infrared cameras. We achieve this by printing objects from an infrared-transmitting filament, which infrared cameras can see through, and by having air gaps inside for the tag’s bits, which appear at a different intensity in the infrared image.

Hamilton-Jacobi (HJ) reachability analysis is a powerful technique used to verify the safety of autonomous systems. HJ reachability is ideal for analysing nonlinear systems with disturbances and flexible set representations. A drawback to this approach is that it suffers from the curse of dimensionality, which prevents real-time deployment on safety-critical systems. In this paper, we show that a customized hardware design on an Field Programmable Gate Array (FPGA) could accelerate 4D grid-based HJ reachability analysis up to 14 times compared to an optimized implementation and 103 times compared to state-of-the-art MATLAB toolboxes on a 16-thread CPU. Because of this, we are able to achieve guaranteed real- time collision avoidance in dynamic environments that abruptly change with a 4D car model by re-solving the HJ partial differential equation (PDE) at a frequency of 4Hz on an FPGA. Our design can overcome the complex data access pattern while taking advantage of the parallel nature of the computations for solving the HJ PDE. The low latency of our computation is consistent, which is crucial for safety-critical systems. The methodology presented here is without loss of generality: it can potentially be applied to different systems dynamics, and more- over, leveraged for higher dimensional systems. We validate our approach in real world collision avoidance experiments with a robot car in a changing environment. We also provide the code of our hardware design and an AWS AFI image.