RoundPipe achieves near-zero-bubble pipeline parallelism for LLM training on consumer GPUs by dynamically dispatching computation stages round-robin, yielding 1.48-2.16x speedups and enabling 235B model fine-tuning on 8x RTX 4090.
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TIDAL recovers temporal phase signals from LLM-derived semantics of provisioning metadata to enable complementary CVD placement, reducing overload frequency by 79.1% on production traces.
ProAgent uses on-demand tiered perception and context-aware LLM reasoning to deliver proactive assistance on AR glasses, achieving up to 27.7% higher prediction accuracy and 20.5% lower false detections than baselines.
HyperEmo-RAG uses hierarchical hyperbolic embeddings and graph-based evidence injection to outperform prior methods in multimodal emotion recognition.
CoGPU resolves the tradeoff in GPU sharing by introducing GPU coroutines for semantic-preserving resource migration, delivering up to 79.2% higher training throughput and zero token mismatch in inference.
eLLM unifies LLM memory management with virtual tensors and elastic ballooning to CPU memory, reporting 2.32x higher decoding throughput and 3x larger batch sizes for 128K inputs.
AffectAgent deploys a query planner, evidence filter, and emotion generator as collaborative agents trained via MAPPO with shared reward, plus MB-MoE and RAAF modules, to achieve superior multimodal emotion recognition on MER-UniBench.
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ProAgent: Harnessing On-Demand Sensory Contexts for Proactive LLM Agent Systems in the Wild
ProAgent uses on-demand tiered perception and context-aware LLM reasoning to deliver proactive assistance on AR glasses, achieving up to 27.7% higher prediction accuracy and 20.5% lower false detections than baselines.