TS-Reasoner is a domain-oriented agent using LLMs, computational tools, and error feedback for multi-step time series inference, showing better performance than general LLMs on understanding and reasoning benchmarks.
Visual programming: Compositional visual reasoning without training
3 Pith papers cite this work. Polarity classification is still indexing.
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VILASR integrates visual drawing operations with reasoning in LVLMs via cold-start synthetic training, reflective rejection sampling, and reinforcement learning, yielding an 18.4% average gain on spatial reasoning benchmarks.
Chat Modeling is a multi-agent LLM framework with modeling memory and dynamic chat widgets that translates text inputs into interactive 3D modeling operations for literature-grounded biological structures.
citing papers explorer
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TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis
TS-Reasoner is a domain-oriented agent using LLMs, computational tools, and error feedback for multi-step time series inference, showing better performance than general LLMs on understanding and reasoning benchmarks.
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Reinforcing Spatial Reasoning in Vision-Language Models with Interwoven Thinking and Visual Drawing
VILASR integrates visual drawing operations with reasoning in LVLMs via cold-start synthetic training, reflective rejection sampling, and reinforcement learning, yielding an 18.4% average gain on spatial reasoning benchmarks.
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Chat Modeling: Interaction-Enhanced Agent Framework for Visualizing Literature-Grounded Biological Structures
Chat Modeling is a multi-agent LLM framework with modeling memory and dynamic chat widgets that translates text inputs into interactive 3D modeling operations for literature-grounded biological structures.