arXiv subjects

Oliver Chen

Publications and source records attributed to Oliver Chen.

4 recordsLinked to original sources

arXiv2026

Monocular Badminton Speed Estimation with Air Drag and Court Geometry

A single camera cannot distinguish between a slowly moving object nearby and a rapidly moving object in the distance without additional information. For a shuttlecock, air drag provides a strong deceleration cue that can be combined with known court geometry. We fit a gravity-and-drag trajectory to smartphone video and compare a free monocular fit with a fit constrained to cross the net plane. Camera calibration uses court lines, and the trajectory is fitted to observed shuttle positions and frame timestamps by robust nonlinear least squares. We evaluated 51 badminton shots from two recording sessions, spanning drop shots, drives, half-smashes and smashes. The net-plane fit reduced the median absolute speed difference from a drag-based stereo reference from 13.3% to 8.6%. A second reference, obtained by differentiating triangulated positions without a drag model, also gave a smaller median difference for the net-plane fit. The results support combining court geometry with flight dynamics for badminton speed estimation from a single camera.

cs.CV↗

arXiv2026

Legal Research Bench: Measuring End-to-End Reliability in Long-Horizon Legal Research Agents

Legal research is a core and time-consuming legal workflow. Lawyers must identify controlling authority, verify that it remains valid, reconcile statutes and cases, and synthesize a grounded answer. Language model agents are a natural fit for this retrieval-intensive workflow, and automating even part of it would be valuable. But that value depends on reliability: a single missing authority, stale citation, or wrong legal conclusion can make an otherwise plausible answer unusable. We introduce \textbf{Legal Research Bench} (LRB), a benchmark of 413 open-ended U.S. legal research questions written by experts, each paired with a gold answer, supporting authorities, and a binary grading rubric. We evaluate thirteen frontier models in a harness with web search, case-law search, page parsing, and retrieval tools. We score agent responses through all-pass grading with source verification, where a response is correct only if every required criterion is satisfied and its cited authorities verify. We also validate the LLM judge against expert attorneys ensuring that benchmark scores track attorney judgment. Agents remain far from reliable: among the models we tested, the strongest, Claude Opus 4.8, is fully correct on 42.9\% of questions. Performance also varies substantially by task setting: all-pass rates differ across areas of law and are lower on questions requiring reconciliation of conflicting authorities. Across models, more turns, tool calls, and inference cost do not predict higher accuracy.

cs.AI↗

arXiv2026

PersistBench: When Should Long-Term Memories Be Forgotten by LLMs?

Conversational assistants are increasingly integrating long-term memory with large language models (LLMs). This persistence of memories, e.g., the user is vegetarian, can enhance personalization in future conversations. However, the same persistence can also introduce safety risks that have been largely overlooked. Hence, we introduce PersistBench to measure the extent of these safety risks. We identify two long-term memory-specific risks: cross-domain leakage, where LLMs inappropriately inject context from the long-term memories; and memory-induced sycophancy, where stored long-term memories insidiously reinforce user biases. We evaluate 18 frontier and open-source LLMs on our benchmark. Our results reveal a surprisingly high failure rate across these LLMs - a median failure rate of 53% on cross-domain samples and 97% on sycophancy samples. To address this, our benchmark encourages the development of more robust and safer long-term memory usage in frontier conversational systems.

cs.AI↗

arXiv2024

HOPPR Medical-Grade Platform for Medical Imaging AI

Technological advances in artificial intelligence (AI) have enabled the development of large vision language models (LVLMs) that are trained on millions of paired image and text samples. Subsequent research efforts have demonstrated great potential of LVLMs to achieve high performance in medical imaging use cases (e.g., radiology report generation), but there remain barriers that hinder the ability to deploy these solutions broadly. These include the cost of extensive computational requirements for developing large scale models, expertise in the development of sophisticated AI models, and the difficulty in accessing substantially large, high-quality datasets that adequately represent the population in which the LVLM solution is to be deployed. The HOPPR Medical-Grade Platform addresses these barriers by providing powerful computational infrastructure, a suite of foundation models on top of which developers can fine-tune for their specific use cases, and a robust quality management system that sets a standard for evaluating fine-tuned models for deployment in clinical settings. The HOPPR Platform has access to millions of imaging studies and text reports sourced from hundreds of imaging centers from diverse populations to pretrain foundation models and enable use case-specific cohorts for fine-tuning. All data are deidentified and securely stored for HIPAA compliance. Additionally, developers can securely host models on the HOPPR platform and access them via an API to make inferences using these models within established clinical workflows. With the Medical-Grade Platform, HOPPR's mission is to expedite the deployment of LVLM solutions for medical imaging and ultimately optimize radiologist's workflows and meet the growing demands of the field.

cs.CL↗