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FactorEngram, a factorized n-gram memory with basis-level contextual gating that improves language modeling and downstream task performance and retrieves sparsity-regularized coefficients over a dictionary of basis vectors shared across patterns, so related patterns can reuse common components.
OmegaUse-OfficeVal is introduced, a benchmark for evaluating LLM agents on long-horizon office-suite tasks with task-level economic grounding, and code-based verifiers from fine-grained rubrics are developed to support stable evaluation.
TS-RAG is proposed, which leverages RAG to enhance forecasting performance and introduces specially designed reference tokens to effectively fuse information from the input sequence with that from retrieved similar sequences, enabling a more robust capture of complex temporal dynamics.
Experiments on GUI agent benchmarks show that LACL-GUI provides more effective learning signals and consistently improves agent performance over prior methods, highlighting the value of trajectory-level supervision in contrastive RLVR.
OmegaUse-SOP is introduced, a human-in-the-loop SOP Engineering system for transforming human demonstrations of professional computer use into reusable SOP skills for GUI agents, and the results suggest that OmegaUse-SOP can improve GUI-agent reliability on professional SOP tasks.
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