{"id":1048935,"date":"2024-06-26T09:00:00","date_gmt":"2024-06-26T16:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/?p=1048935"},"modified":"2026-02-23T07:47:14","modified_gmt":"2026-02-23T15:47:14","slug":"research-focus-week-of-june-24-2024","status":"publish","type":"post","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/blog\/research-focus-week-of-june-24-2024\/","title":{"rendered":"Research Focus: Week of June 24, 2024"},"content":{"rendered":"\n

Welcome to Research Focus, a series of blog posts that highlights notable publications, events, code\/datasets, new hires and other milestones from across the research community at Microsoft.<\/p><\/blockquote><\/figure>\n\n\n\n

\"Research<\/figure>\n\n\n\n

NEW RESEARCH<\/h3>\n\n\n\n

Towards Energy Efficient 5G vRAN Servers<\/h2>\n\n\n\n

Virtualized radio access networks (vRANs), which run the cellular radio stack on commodity servers instead of specialized hardware, are increasingly used in modern cellular networks (e.g., 5G), owing to advantages such as a multi-vendor ecosystem, easier maintenance, and faster feature upgrades. In a recent paper: Towards Energy Efficient 5G vRAN Servers<\/a>, researchers from Microsoft and external colleagues present RENC, a system that saves energy by adjusting CPU frequency in response to sub-second variations in cellular workloads, using three techniques. First, despite large fluctuations in vRAN CPU load at sub-ms timescales, RENC establishes safe low-load intervals, e.g., by coupling media access control (MAC) layer rate limiting with CPU frequency changes. This prevents high traffic during low-power operation, which would otherwise hurt performance. Second, they design techniques to compute CPU frequencies that are safe for these low-load intervals, achieved by measuring the slack in vRAN threads\u2019 deadlines using Linux eBPF hooks, or minor binary rewriting of the vRAN software. Third, they demonstrate the need to handle CPU load spikes triggered by control operations, such as new users attaching to the network. Their evaluation in a state-of-the-art vRAN testbed shows that their techniques reduce a vRAN server\u2019s CPU power consumption by up to 45% (29% server-wide).<\/p>\n\n\n\n

RENC is purely a research project and there are no current plans to incorporate RENC into a product.<\/p>\n\n\n\n

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Read the paper<\/a><\/div>\n<\/div>\n\n\n\n
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NEW RESEARCH<\/h3>\n\n\n\n

The CoExplorer Technology Probe: A generative AI-powered adaptive interface to support intentionality in planning and running video meetings<\/h2>\n\n\n\n

Video meetings have enabled a new era of distributed work, but running effective meetings can be challenging. Traditional videoconferencing systems offer little support for reducing the effort of planning and conducting a video meeting. Generative AI has the potential to radically redefine meetings by augmenting intentional meeting behaviors.<\/p>\n\n\n\n

In a recent paper: The CoExplorer Technology Probe: A Generative AI-Powered Adaptive Interface to Support Intentionality in Planning and Running Video Meetings<\/a>, researchers from Microsoft present a novel adaptive meeting prototype. It preemptively generates (1) likely phases that meetings would undergo, (2) tools that allow capturing attendees\u2019 thoughts before the meeting, and (3) appropriate files and applications for each phase of the meeting and their window layout. Using CoExplorer as a technology probe in a guided walkthrough, their study findings suggest that generative AI has the potential to keep meetings on track and reduce workload. The researchers present some design implications of their findings, and discuss some concerns, e.g., about users\u2019 agency, trust, and possible disruption to traditional meeting norms.<\/p>\n\n\n\n

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Read the paper<\/a><\/div>\n<\/div>\n\n\n\n\t
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\n\t\tSpotlight: Microsoft research newsletter<\/span>\n\t<\/p>\n\t\n\t

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\n\t\t\t\t\n\t\t\t\t\t\"\"\n\t\t\t\t<\/a>\n\t\t\t<\/div>\n\t\t\t\n\t\t\t
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Microsoft Research Newsletter<\/h2>\n\t\t\t\t\n\t\t\t\t\t\t\t\t

Stay connected to the research community at Microsoft.<\/p>\n\t\t\t\t\n\t\t\t\t\t\t\t\t

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\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSubscribe today\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t<\/div>\n\t<\/div>\n\t\n\n\n

NEW RESEARCH<\/h3>\n\n\n\n

Automatic Bug Detection in LLM-Powered Text-Based Games Using LLMs<\/h2>\n\n\n\n

Advancements in large language models (LLMs) are revolutionizing interactive game design, enabling dynamic plotlines and interactions between players and non-player characters (NPCs). However, LLMs may exhibit flaws such as hallucinations, forgetfulness, or misinterpretations of prompts, causing logical inconsistencies and unexpected deviations from intended designs. Automated techniques for detecting such game bugs are still insufficient.<\/p>\n\n\n\n

In a recent paper: Automatic Bug Detection in LLM-Powered Text-Based Games Using LLMs<\/a>, accepted for presentation at the Association of Computational Linguistics (ACL) 2024 (opens in new tab)<\/span><\/a> conference, researchers from Microsoft and external colleagues propose a systematic LLM-based method for automatically identifying such bugs from player game logs, eliminating the need for collecting additional data such as post-play surveys. Applied to a text-based game, DejaBoom!, their approach identifies bugs inherent in LLM-powered interactive games, surpassing unstructured LLM-powered bug-catching methods and filling the gap in automated detection of logical and design flaws.<\/p>\n\n\n\n

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Read the paper<\/a><\/div>\n<\/div>\n\n\n\n
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NEW RESEARCH<\/h3>\n\n\n\n

MAIRA-2: Grounded Radiology Report Generation<\/h2>\n\n\n\n

Radiology reporting is a complex task that requires detailed image understanding, integration of multiple inputs, including comparison with prior imaging, and precise language generation. This makes it ideal for the development and use of generative multimodal models. In a recent preprint: MAIRA-2: Grounded Radiology Report Generation<\/a>, researchers from Microsoft extend report generation to include the localization of individual findings on the image \u2013 or grounded report generation. Prior work indicates that grounding helps clarify image understanding and interpret AI-generated text. Therefore, grounded reporting should improve the utility and transparency of automated report drafting. <\/p>\n\n\n\n

To enable evaluation of grounded reporting, the researchers propose a novel framework \u2013 RadFact \u2013 leveraging the reasoning capabilities of LLMs. RadFact (opens in new tab)<\/span><\/a> assesses the factuality of individual generated sentences, as well as correctness of generated spatial localizations, when present. The researchers introduce MAIRA-2, a large multimodal model combining a radiology-specific image encoder with an LLM, which is trained for the new task of grounded report generation on chest x-rays. MAIRA-2 uses more comprehensive inputs than explored previously: the current frontal image, the current lateral image, the prior frontal image and prior report, as well as the Indication, Technique and Comparison sections of the current report. These additions significantly improve report quality and reduce model hallucinations, establishing a new state of the art on findings generation (without grounding) on MIMIC-CXR, while demonstrating the feasibility of grounded reporting as a novel and richer task.<\/p>\n\n\n\n

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Read the paper<\/a><\/div>\n\n\n\n
Get the code<\/a><\/div>\n<\/div>\n\n\n\n
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In this issue: RENC makes 5G vRAN servers more energy efficient; CoExplorer uses AI to keep video meetings on track; Automatic bug detection in LLM-powered text-based games; MAIRA-2: Grounded radiology report generation.<\/p>\n","protected":false},"author":37583,"featured_media":1048971,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_hide_image_in_river":0,"footnotes":""},"categories":[1],"tags":[],"research-area":[13556,13545,13554,13553,13559,13547],"msr-region":[],"msr-event-type":[],"msr-locale":[268875],"msr-post-option":[243984],"msr-impact-theme":[],"msr-promo-type":[],"msr-podcast-series":[],"class_list":["post-1048935","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-research-blog","msr-research-area-artificial-intelligence","msr-research-area-human-language-technologies","msr-research-area-human-computer-interaction","msr-research-area-medical-health-genomics","msr-research-area-social-sciences","msr-research-area-systems-and-networking","msr-locale-en_us","msr-post-option-blog-homepage-featured"],"msr_event_details":{"start":"","end":"","location":""},"podcast_url":"","podcast_episode":"","msr_research_lab":[199561,199565,849856],"msr_impact_theme":[],"related-publications":[],"related-downloads":[],"related-videos":[],"related-academic-programs":[],"related-groups":[144736,780706],"related-projects":[978063,483294],"related-events":[],"related-researchers":[{"type":"user_nicename","value":"Xenofon 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