TxAgent: An AI Agent for Therapeutic Reasoning Across a Universe of Tools
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Updated
Apr 21, 2025 - Python
TxAgent: An AI Agent for Therapeutic Reasoning Across a Universe of Tools
Project Page For "Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement"
Latest Advances on Long Chain-of-Thought Reasoning
Deep Reasoning Translation via Reinforcement Learning (arXiv preprint 2025); DRT: Deep Reasoning Translation via Long Chain-of-Thought (arXiv preprint 2024)
ToolUniverse is a collection of biomedical tools designed for AI agents
OpenVLThinker: An Early Exploration to Vision-Language Reasoning via Iterative Self-Improvement
Official Implementation of "Reasoning Language Models: A Blueprint"
A Comprehensive Survey on Evaluating Reasoning Capabilities in Multimodal Large Language Models.
This is the repo of developing reasoning models in the specific domain of financial, aim to enhance models capabilities in handling financial reasoning tasks.
Lightweight replication study of DeepSeek-R1-Zero. Interesting findings include "No Aha Moment", "Longer CoT ≠ Accuracy", and "Language Mixing in Instruct Models".
Pure RL to post-train base models for social reasoning capabilities. Lightweight replication of DeepSeek-R1-Zero with Social IQa dataset.
🔥🔥🔥Breaking long thought processes of o1-like LLMs, such as DeepSeek-R1, QwQ
☁️ KUMO: Generative Evaluation of Complex Reasoning in Large Language Models
Reasoning-from-Zero using gemma.JAX.nnx on TPUs
Official code for "Divide and Translate: Compositional First-Order Logic Translation and Verification for Complex Logical Reasoning", ICLR 2025.
📖Curated list about reasoning abilitiy of MLLM, including OpenAI o1, OpenAI o3-mini, and Slow-Thinking.
An effective weight-editing method for mitigating overly short reasoning in LLMs, and a mechanistic study uncovering how reasoning length is encoded in the model’s representation space.
Structured test tasks and model tuning scripts for multiple subjects from ZNO - the Ukrainian External Independent Evaluation (ЗНО)
This repository contains the implementation of our research on optimizing Retrieval-Augmented Generation (RAG) systems for technical domains. Our work addresses the unique challenges of precise information extraction from complex, domain-specific documents by introducing token-aware evaluation metrics and synthetic data generation pipeline.
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