ConRetroBert achieves 62.4% top-1 accuracy on USPTO-50k by combining contrastive pretraining, hard-negative listwise ranking, and EMA-stabilized dual encoders for template retrieval in retrosynthesis.
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PlantMarkerBench supplies 5,550 literature sentences annotated for plant marker gene evidence validity and type across Arabidopsis, maize, rice and tomato, showing frontier LLMs handle direct expression evidence but struggle with functional, indirect and weak-support cases.
NV-Embed achieves first place on the MTEB leaderboard across 56 tasks by combining a latent attention layer, causal-mask removal, two-stage contrastive training, and data curation for LLM-based embedding models.
TIDE augments standard transformers with per-layer token embedding injection via an ensemble of memory blocks and a depth-conditioned router to mitigate rare-token undertraining and contextual collapse.
SGR enhances LLM reasoning accuracy by generating external subgraphs from knowledge bases and guiding progressive inference over them, yielding consistent gains over baselines on benchmarks.
The paper defines Agent AI as interactive multimodal systems that perceive grounded data and generate embodied actions, arguing this approach can mitigate hallucinations in foundation models.
citing papers explorer
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ConRetroBert: EMA Stabilized Dual Encoders for Template-Based Single-Step Retrosynthesis
ConRetroBert achieves 62.4% top-1 accuracy on USPTO-50k by combining contrastive pretraining, hard-negative listwise ranking, and EMA-stabilized dual encoders for template retrieval in retrosynthesis.
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PlantMarkerBench: A Multi-Species Benchmark for Evidence-Grounded Plant Marker Reasoning
PlantMarkerBench supplies 5,550 literature sentences annotated for plant marker gene evidence validity and type across Arabidopsis, maize, rice and tomato, showing frontier LLMs handle direct expression evidence but struggle with functional, indirect and weak-support cases.
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NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models
NV-Embed achieves first place on the MTEB leaderboard across 56 tasks by combining a latent attention layer, causal-mask removal, two-stage contrastive training, and data curation for LLM-based embedding models.
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TIDE: Every Layer Knows the Token Beneath the Context
TIDE augments standard transformers with per-layer token embedding injection via an ensemble of memory blocks and a depth-conditioned router to mitigate rare-token undertraining and contextual collapse.
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SGR: A Stepwise Reasoning Framework for LLMs with External Subgraph Generation
SGR enhances LLM reasoning accuracy by generating external subgraphs from knowledge bases and guiding progressive inference over them, yielding consistent gains over baselines on benchmarks.
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Agent AI: Surveying the Horizons of Multimodal Interaction
The paper defines Agent AI as interactive multimodal systems that perceive grounded data and generate embodied actions, arguing this approach can mitigate hallucinations in foundation models.