FPGAgent is the first task-specification-to-executable HLS generation framework experimentally validated on a well-established benchmark, and the value of end-to-end validation is demonstrated.
Abstract
Large language models (LLMs) have shown substantial promise for high-level synthesis (HLS) code generation, but most existing approaches validate only simulation or synthesis results. Because of timing and place-and-route constraints, \emph{HLS code that passes simulation and synthesis may still fail to produce deployable, runnable designs on real FPGA platforms}. Moreover, the lack of public benchmarks has limited many evaluations to small, self-curated test suites. We propose FPGAgent, a multi-agent framework tailored to real FPGA environments for autonomous HLS coding with end-to-end executability validation. To the best of our knowledge, FPGAgent is \emph{the first task-specification-to-executable HLS generation framework experimentally validated on a well-established benchmark}. Given a natural-language task specification, FPGAgent injects HLS-specific knowledge and employs evolutionary search to iteratively derive reliable HLS kernel implementations. It then generates a C++ validation program to verify functional correctness, diagnoses potential defects, and guides targeted repairs. Finally, it synthesizes host code for compilation and board-level execution on FPGA hardware. We comprehensively evaluate FPGAgent with five established LLMs on HLS-Eval, a benchmark containing 78 tasks across multiple domains, and verify board-level executability on a real FPGA platform. Compared with existing baselines, FPGAgent improves the synthesizable rate by 16.9% on average, executability by 26.7%, and functional correctness by 30.6%. These results show that FPGAgent substantially improves the practical usability of LLM-based HLS generation and demonstrates the value of end-to-end validation.
LifeSciBench is introduced, a benchmark of 750 expert-authored tasks designed to evaluate whether language models can handle realistic life science research work, with each constituent task paired with a human expert-written rubric.
Amelia Liu, Andrew Ho, Anne Marie Droste et al.· bioRxiv· 2 citations
A rapidly advancing precision-therapy pipeline-including antisense oligonucleotides to upregulate the intact allele, AAV-based gene replacement, CRISPR-mediated transcriptional activation, epigenetic modulators, and rational pathway-targeted small molecules-offers realistic prospects for disease modification.
Autistic children experience disproportionately high rates of anxiety, yet common interventions such as cognitive behavioural therapy and traditional mindfulness practices may be less effective due to their cognitive and abstract demands. Yoga nidra, a form of guided meditation using concrete visualisations and breath awareness, may offer a more accessible alternative. This mixed methods study evaluated the feasibility and pilot efficacy of a co-designed six-week online yoga nidra intervention targeting anxiety in autistic children aged 8-14 years. Neurophysiological and psychological data were collected from 13 participants using parent- and self-reported measures of anxiety (ASC-ASD), intolerance of uncertainty (IUSC), and emotion dysregulation (EDI), alongside heart rate variability (HRV). Notably, the cohort included a minimally speaking child, which demonstrates the potential for the intervention to extend to autistic children with language support needs, a group who are often underrepresented in similar research. Results indicated no statistically significant changes in anxiety over time; however, medium effect sizes were observed in self- (Hedge's g = 0.55) and parent-reported anxiety (Hedge's g = 0.45), with three participants moving from clinically significant to non-significant anxiety levels post-intervention. Additionally, intolerance of uncertainty and emotion dysregulation demonstrated small to medium effect size reductions (IUSC, Hedge's g = 0.50; EDI-Reactivity, Hedge's g = 0.55; EDI-Dysphoria, Hedge's g = 0.25), suggesting potential benefits of yoga nidra in these areas. Greater attendance was significantly associated with self-reported reductions in anxiety (ASC-ASD-SR, r = .757, p < .05), but not the other outcome measures. Unexpectedly, HRV outcomes indicated reduced autonomic functioning post-intervention. Additionally, qualitative data from semi-structured interviews with three child participants and their mothers from the study were analysed using reflexive thematic analysis, revealing five key themes of their experience: Thinking About the Body is Hard; We Want More; Homework Sucks; Making Mindfulness Concrete; and On-Screen and At Home is Convenient. While the online format was appreciated for its accessibility, challenges with adherence and scheduling highlighted the need for more flexible delivery models. This study advances upon prior research by being the first to develop a co-designed yoga nidra intervention specifically for autistic children. Findings support the feasibility of yoga nidra as a complementary intervention for autistic children and suggest directions for future research, including larger trials and further co-design with the autistic community.
Tundi Loftus, Shu H Yau, Sophia Soares et al.· Research in Developmental Di...· 1 citation
TestifAI, a deep learning testing framework for efficient and accurate estimation of robustness against combinations of perturbations, is proposed and partial model tomography is introduced, a novel approach to reconstructing model behaviour in a multi-perturbation space from tests that apply only a small number of perturbations.
Arooj Arif, T. Hartung, E. Botoeva et al.· 1 citation
A multilevel conceptual pathway in wearable reflectance PPG is supported, in which mechanical conditions at the sensor-skin interface are associated with changes in PPG signal characteristics, derived features, and, in a smaller body of studies, downstream physiological estimation.
Chenxi Yang, Jiahang Xie, Zifei He et al.· JMIR mHealth and uHealth· 0 citations
Large Language Models show potential in their diagnostic accuracy and consequent ability to reduce clinician burden, and may provide the greatest benefit when used to optimise referral quality at source, improving both clinician and potentially LLM triage downstream.
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A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
