QueST adapts LLMs at test time by generating query-specific problem-solution pairs for self-supervised fine-tuning, improving reasoning performance without external data.
arXiv preprint arXiv:2511.16301 (2025)
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Low-rank decoder adaptation enables efficient test-time optimization for zero-shot depth completion by updating only the subspace containing depth-relevant information.
SPARK constructs unified knowledge graphs from multi-document scientific literature to ground self-play RL with asymmetric roles and verifiable rewards, outperforming flat-corpus baselines especially on longer-hop reasoning tasks.
Strong generalist vision foundation models match or outperform electro-optical specific models in remote sensing retrieval with better cross-scene stability.
citing papers explorer
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Query-Conditioned Test-Time Self-Training for Large Language Models
QueST adapts LLMs at test time by generating query-specific problem-solution pairs for self-supervised fine-tuning, improving reasoning performance without external data.
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Efficient Test-Time Optimization for Depth Completion via Low-Rank Decoder Adaptation
Low-rank decoder adaptation enables efficient test-time optimization for zero-shot depth completion by updating only the subspace containing depth-relevant information.
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SPARK: Self-Play with Asymmetric Reward from Knowledge Graphs
SPARK constructs unified knowledge graphs from multi-document scientific literature to ground self-play RL with asymmetric roles and verifiable rewards, outperforming flat-corpus baselines especially on longer-hop reasoning tasks.
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Rethinking Electro-Optical Vision Foundation Models for Remote Sensing Retrieval: A Controlled Comparison with Generalist VFM
Strong generalist vision foundation models match or outperform electro-optical specific models in remote sensing retrieval with better cross-scene stability.