Georgia Institute of Technology

Headlines · 4

1. 3d
   Georgia Tech and MIT propose MASCOT for diverse AI group conversations

   Researchers from Georgia Tech and MIT proposed MASCOT, a two-stage training framework for multi-agent socio-collaborative companion systems. MASCOT first aligns each agent with a role-specific behavior and then trains a Director Agent to select the next speaker and response strategy as the conversation evolves. The paper reports stronger role consistency, group-interaction scores, and human preference results than compared baselines on Empathetic Dialogues and QMSum.

   机器之心 · 🔥 0
2. 3d
   Hugging Face launches Project ProtoStar for AI-ready public science

   Hugging Face announced Project ProtoStar, a multi-institution initiative to help publicly funded scientific datasets, models, and code be released in AI-ready, machine-actionable formats on open infrastructure. Carnegie Mellon University will host the first open challenge, which opens on November 2, with participating organizations formalizing their involvement through a memorandum of understanding.

   Hugging Face · 🔥 0
3. 5d
   Pat Gelsinger Backs Nagare Labs’ Z2RAM Research

   Former Intel CEO Pat Gelsinger is supporting Nagare Labs and its Z2RAM memory technology, according to TechNews and Wccftech. The laboratory-stage technology is designed to combine SRAM-like speed, DRAM-like density and Flash-like scalability. Z2RAM uses an oxide semiconductor and a two-transistor cell, and Nagare Labs is researching vertical stacking and direct integration with logic chips through 3D hybrid bonding. The company says its first generation could provide more than five times the bit density of SRAM, with comparable bandwidth. Nagare Labs has raised a $5 million seed round and is seeking research and manufacturing partners. The technology remains a laboratory prototype, and no standards yet exist for integrating this type of memory with accelerators.

   科技新报 · 🔥 3
4. Oct 22
   NetEase Cloud Music's CloudEar wins MIREX 2026 music-evaluation track at 73.9%

   NetEase Cloud Music's in-house CloudEar music-evaluation model took first place in the MIREX 2026 track "Music Evaluation via CMI-RewardBench" with 73.9% accuracy, according to PingWest. The track asks a model to judge which of two generated songs better matches human preference given the songs and a text prompt, scored mainly on judgement accuracy. The report says CloudEar beat the organisers' CMI-RM Baseline (65.9%) by 8 percentage points and the open SongEval baseline (72.7%) built by Northwestern Polytechnical University, Shanghai Conservatory of Music, the University of Surrey and Hong Kong University of Science and Technology. NetEase says the model, developed by its AI Music Lab, combines perception evaluation, audio-quality diagnosis and cross-modal alignment and is already used in its products; the report's framing that CloudEar is closest to human aesthetic judgement is presented as its own inference rather than an independently verified result.

   品玩 实时要闻 · 🔥 0

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