Millimeter-wave (mmWave) links promise multi-gigabit rates but degrade sharply in non-line-of-sight (NLoS) indoor settings. This paper introduces a LiDAR-guided adaptive beamforming framework that dynamically steers the transmit beam to optimize NLoS coverage based on user location. A LiDAR sensor co-located with the transmitter detects the user position in an L-shaped corridor and determines the corresponding angle of departure (AoD) for reflection toward the user. A mirror and glossy metallic reflector placed at the corridor corner provide a common reflection path for both LiDAR and mmWave signals, enabling precise beam alignment without exhaustive angular scanning. Experimental results show that the proposed LiDAR-assisted adaptive beamforming enhances the minimum received signal strength by up to 12 dB compared to fixed-beam transmission. The findings demonstrate that common indoor surfaces capable of reflecting both LiDAR and mmWave signals can be effectively leveraged to extend and optimize coverage.

Passive reflectors mitigate millimeter-wave (mm-wave) link blockages by extending coverage to non-line-of-sight (NLoS) regions. However, their deployment often leads to irregular reflected beam patterns and coverage gaps. This results in rapid channel fluctuations and potential outages. In this paper we propose two LiDAR-aided link enhancement techniques to address these challenges. Leveraging user position information, we introduce a location-dependent link control strategy and a user selection technique to improve NLoS link reliability and coverage. Experimental results demonstrate that silver-coated mirror reflectors achieve comparable performance to silver reflectors and validate the efficacy of the proposed techniques in reducing outages and enhancing NLoS signal strength.

Unlocking deep and interpretable biological reasoning from complex genomic data remains a major AI challenge limiting scientific progress. While current DNA foundation models excel at representing sequences, they struggle with multi-step reasoning and lack transparent, biologically meaningful explanations. BioReason addresses this by tightly integrating a DNA foundation model with a large language model (LLM), enabling the LLM to directly interpret and reason over genomic information. Through supervised fine-tuning and reinforcement learning, BioReason learns to produce logical, biologically coherent deductions. It achieves major performance gains, boosting KEGG-based disease pathway prediction accuracy from 86% to 98% and improving variant effect prediction by an average of 15% over strong baselines. BioReason can reason over unseen biological entities and explain its decisions step by step, offering a transformative framework for interpretable, mechanistic AI in biology. All data, code, and checkpoints are available at [https://github.com/bowang-lab/BioReason](https://github.com/bowang-lab/BioReason).

For computing to serve humanity, all individuals must be able to feel safe within computing. While prior work has surfaced how hegemonic racial and gendered expectations manifest in computing, neurodivergent identities have received far less attention. Existing neurodiversity narratives look to deconstruct mechanisms that privilege certain ways of thinking and being over others, but those narratives are scarce within computing. Critically, narratives are constructed in dialogue with community, and prior SIGCSE conferences have largely lacked official support for this construction, relegating those conversations to corners and edges within the conference space. This BoF continues work from last SIGCSE in supporting the construction of narratives that center neurodivergent identities and creating neurodivergent communities. We have three goals for this space: 1) cultivate community around an "invisible" identity for which public disclosure is often problematic, 2) give an explicit space for folks to "unmask" within a broader conference where masking is typically expected, and 3) utilize existing work to create connections around more specific aspects of neurodivergence.