Update README.md

README.md

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-title: TalkingAIResearcher
+title: DeepResearchEvaluator
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-short_description: TalkingAIResearcher
+short_description: Deep Research Evaluator for Long Horizon Learning Tasks
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-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
-#OPENAI_API_KEY=your_key
-#ANTHROPIC_API_KEY=your_key
-#HF_KEY=your_key
+A Deep Research Evaluator is a conceptual AI system designed to analyze and synthesize information from extensive research literature, such as arXiv papers, to learn about specific topics and generate code applicable to long-horizon tasks in AI. This involves understanding complex subjects, identifying relevant methodologies, and implementing solutions that require planning and execution over extended sequences.
+
+Key Topics and Related Papers:
+
+Long-Horizon Task Planning in Robotics:
+
+"MLDT: Multi-Level Decomposition for Complex Long-Horizon Robotic Task Planning with Open-Source Large Language Model"
+Authors: Yike Wu, Jiatao Zhang, Nan Hu, LanLing Tang, Guilin Qi, Jun Shao, Jie Ren, Wei Song
+This paper introduces a method that decomposes complex tasks at multiple levels to enhance planning capabilities using open-source large language models. 
+ARXIV
+
+"ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task Planning"
+Authors: Zhehua Zhou, Jiayang Song, Kunpeng Yao, Zhan Shu, Lei Ma
+The study presents a framework that improves LLM-based planning through an iterative self-refinement process, enhancing feasibility and correctness in task plans. 
+ARXIV
+
+Skill-Based Reinforcement Learning:
+
+"Skill Reinforcement Learning and Planning for Open-World Long-Horizon Tasks"
+Authors: Haoqi Yuan, Chi Zhang, Hongcheng Wang, Feiyang Xie, Penglin Cai, Hao Dong, Zongqing Lu
+This research focuses on building multi-task agents in open-world environments by learning basic skills and planning over them to accomplish long-horizon tasks efficiently. 
+ARXIV
+
+"SkillTree: Explainable Skill-Based Deep Reinforcement Learning for Long-Horizon Control Tasks"
+Authors: Yongyan Wen, Siyuan Li, Rongchang Zuo, Lei Yuan, Hangyu Mao, Peng Liu
+The paper proposes a framework that integrates a differentiable decision tree within the high-level policy to generate skill embeddings, enhancing explainability in decision-making for complex tasks. 
+ARXIV
+
+Neuro-Symbolic Approaches:
+
+"Learning for Long-Horizon Planning via Neuro-Symbolic Abductive Imitation"
+Authors: Jie-Jing Shao, Hao-Ran Hao, Xiao-Wen Yang, Yu-Feng Li
+This work introduces a framework that combines data-driven learning and symbolic-based reasoning to enable long-horizon planning through abductive imitation learning. 
+ARXIV
+
+"CaStL: Constraints as Specifications through LLM Translation for Long-Horizon Task and Motion Planning"
+Authors: [Authors not specified]
+The study presents a method that utilizes large language models to translate constraints into formal specifications, facilitating long-horizon task and motion planning. 
+ARXIV
+
+Evaluation Frameworks for AI Models:
+
+"ASI: Accuracy-Stability Index for Evaluating Deep Learning Models"
+Authors: Wei Dai, Daniel Berleant
+The paper introduces the Accuracy-Stability Index (ASI), a quantitative measure that incorporates both accuracy and stability for assessing deep learning models. 
+ARXIV
+
+"Benchmarks for Deep Off-Policy Evaluation"
+Authors: Justin Fu, Mohammad Norouzi, Ofir Nachum, George Tucker, Ziyu Wang, Alexander Novikov, Mengjiao Yang, Michael R. Zhang, Yutian Chen, Aviral Kumar, Cosmin Paduraru, Sergey Levine, Tom Le Paine
+This research provides a collection of policies that, in conjunction with existing offline datasets, can be used for benchmarking off-policy evaluation in deep learning. 
+ARXIV
+
+These topics and papers contribute to the development of AI systems capable of understanding research literature and applying the acquired knowledge to complex, long-horizon tasks, thereby advancing the field of artificial intelligence.
+
+---
+
 
 Features: