More
PulseAugur coverage of More — every cluster mentioning More across labs, papers, and developer communities, ranked by signal.
- 2026-06-08 research_milestone A new adaptive multi-objective reinforcement learning framework called MORE was introduced for e-commerce dialogue systems. source
- 2026-06-08 research_milestone A new adaptive multi-objective reinforcement learning framework called MORE was detailed in a research paper, showing significant improvements in e-commerce dialogue systems. source
1 day(s) with sentiment data
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New MORE architecture integrates multi-task learning into ranking model backbones
Researchers have developed MORE (Multi-task cO-evolving Ranking modEl), a novel architecture that integrates multi-task learning directly within the ranking model's backbone. Unlike previous methods that applied multi-t…
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New UG-UMRE framework enhances multimodal relation extraction
Researchers have developed a new framework called UG-UMRE to improve unified multimodal relation extraction (UMRE). This approach addresses issues of noise propagation from inherent uncertainty and the heterogeneity bet…
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New MoRe method accelerates multi-objective learning convergence
Researchers have developed a new stochastic multi-objective learning method called MoRe, which improves convergence rates for optimizing multiple objectives simultaneously. The method addresses limitations in existing s…
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New benchmark MORE evaluates multilingual document parsing across 149 languages
Researchers have introduced MORE, a new benchmark designed to evaluate multilingual document parsing capabilities across 149 languages. This benchmark addresses the current lack of evaluation for models on languages bey…
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New AI framework MORE boosts e-commerce dialogue conversion
Researchers have developed a new adaptive multi-objective reinforcement learning framework called MORE, designed to optimize both reasoning accuracy and linguistic naturalness in e-commerce dialogue systems. This approa…
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New methods learn domain-invariant representations for continual learning
Researchers have developed new methods for continual learning that focus on learning domain-invariant representations. This approach aims to prevent models from overfitting to specific domain cues, thereby improving gen…