✨✨Latest Papers and Benchmarks in Reasoning with Foundation Models
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Updated
Apr 25, 2025
✨✨Latest Papers and Benchmarks in Reasoning with Foundation Models
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
a-m-team's exploration in large language modeling
OpenVLThinker: An Early Exploration to Vision-Language Reasoning via Iterative Self-Improvement
A Comprehensive Survey on Evaluating Reasoning Capabilities in Multimodal Large Language Models.
Official Implementation of "Reasoning Language Models: A Blueprint"
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.
Pivotal Token Search
To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models
The official implement of "VisionReasoner: Unified Visual Perception and Reasoning via Reinforcement Learning"
[arxiv: 2505.02156] Adaptive Thinking via Mode Policy Optimization for Social Language Agents
☁️ KUMO: Generative Evaluation of Complex Reasoning in Large Language Models
Official code for "Divide and Translate: Compositional First-Order Logic Translation and Verification for Complex Logical Reasoning", ICLR 2025.
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.
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