An NLP framework shows that liberals and conservatives use different semantic frames within the same metaphorical source domains when discussing immigration, while also uncovering nuanced frames in climate change coverage.
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6 Pith papers cite this work. Polarity classification is still indexing.
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GraphMind equips LLM agents with graph awareness to construct human-like social networks, producing botnets that substantially degrade performance of both text-based and graph-based detectors.
HydroAgent fine-tunes Qwen3-4B on 2,576 expert calibration trajectories and applies Group-Relative Policy Optimization with NSE reward from live CREST simulations to improve hydrologic model calibration over frontier LLMs.
The paper delivers a unified review and roadmap of Earth science foundation models, structured by capability depth from perception to agentic reasoning and by application breadth across atmosphere, hydrosphere, lithosphere, biosphere, anthroposphere, and cryosphere, while compiling over 200 datasets
Fine-tuned LLaMA 3.1-8B variants for the energy sector outperform the base model on domain QA benchmarks, with LoRA delivering similar gains at lower training cost.
citing papers explorer
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Not all ANIMALs are equal: metaphorical framing through source domains and semantic frames
An NLP framework shows that liberals and conservatives use different semantic frames within the same metaphorical source domains when discussing immigration, while also uncovering nuanced frames in climate change coverage.
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Beyond Individual Mimicry: Constructing Human-Like Social network with Graph-Augmented LLM Agents
GraphMind equips LLM agents with graph awareness to construct human-like social networks, producing botnets that substantially degrade performance of both text-based and graph-based detectors.
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HydroAgent: Closing the Gap Between Frontier LLMs and Human Experts in Hydrologic Model Calibration via Simulator-Grounded RL
HydroAgent fine-tunes Qwen3-4B on 2,576 expert calibration trajectories and applies Group-Relative Policy Optimization with NSE reward from live CREST simulations to improve hydrologic model calibration over frontier LLMs.
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Earth Science Foundation Models: From Perception to Reasoning and Discovery
The paper delivers a unified review and roadmap of Earth science foundation models, structured by capability depth from perception to agentic reasoning and by application breadth across atmosphere, hydrosphere, lithosphere, biosphere, anthroposphere, and cryosphere, while compiling over 200 datasets
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Towards EnergyGPT: A Large Language Model Specialized for the Energy Sector
Fine-tuned LLaMA 3.1-8B variants for the energy sector outperform the base model on domain QA benchmarks, with LoRA delivering similar gains at lower training cost.
- GHGbench: A Unified Multi-Entity, Multi-Task Benchmark for Carbon Emission Prediction