Update README.md

README.md

@@ -7,6 +7,7 @@ At DAIR.AI we ❤️ reading ML papers so we've created this repo to highlight t
 Here is the weekly series:
 
 ## 2024
+- [Top ML Papers of the Week (August 12 - August 18)](./#top-ml-papers-of-the-week-august-12---august-18---2024)
 - [Top ML Papers of the Week (August 5 - August 11)](./#top-ml-papers-of-the-week-august-5---august-11---2024)
 - [Top ML Papers of the Week (July 29 - August 4)](./#top-ml-papers-of-the-week-july-29---august-4---2024)
 - [Top ML Papers of the Week (July 22 - July 28)](./#top-ml-papers-of-the-week-july-15---july-21---2024)
@@ -99,6 +100,21 @@ Here is the weekly series:
 
 [Join our Discord](https://discord.gg/SKgkVT8BGJ)
 
+## Top ML Papers of the Week (August 12 - August 18) - 2024
+| **Paper**  | **Links** |
+| ------------- | ------------- |
+| 1) **The AI Scientist** - a novel AI agent that can develop and write a full conference-level scientific paper costing less than $15; it automates scientific discovery by enabling frontier LLMs to perform independent research and summarize findings; it also uses an automated reviewer to evaluate the generated papers; claims to achieve near-human performance in evaluating paper scores; claims to produce papers that exceed the acceptance threshold at a top machine learning conference as judged by their automated reviewer.  | [Paper](https://arxiv.org/abs/2408.06292), [Tweet](https://x.com/omarsar0/status/1823189280883097788) |
+| 2) **Grok-2** - a new frontier model with strong code, math, and reasoning capabilities which includes a large and small model; outperforms both Claude 3.5 Sonnet and GPT-4-Turbo on the LMSYS Chatbot Arena; claims to improve capabilities including instruction following, retrieval, tool use, and enhancing factuality; competes with Claude 3.5 Sonnet (June release) and GPT-4o (May release) on MMLU and HumanEval.  | [Paper](https://x.ai/blog/grok-2), [Tweet](https://x.com/xai/status/1823597788573098215) |
+| 3) **LongWriter** - proposes AgentWrite to enable off-the-shelf LLMs to generate coherent outputs beyond 20K words; AgentWrite breaks down the long generation task into subtasks and in a divide-and-conquer approach generates; the agent breaks the task into multiple writing subtasks and concatenates the outputs to get a final output (i.e., plan + write); the approach is then used to build SFT datasets that are used to tune LLMs to generate coherent longer outputs automatically; a 9B parameter model, further improved through DPO, achieves state-of-the-art performance on their benchmark, and surpasses proprietary models.  | [Paper](https://arxiv.org/abs/2408.07055), [Tweet](https://x.com/omarsar0/status/1823551063946850712) |
+| 4) **EfficientRAG** - trains an auto-encoder LM to label and tag chunks; it retrieves relevant chunks, tags them as either <Terminate> or <Continue>, and annotates <Continue> chunks for continuous processing; then a filter model is trained to formulate the next-hop query based on the original question and previous annotations; this is done iteratively until all chunks are tagged as <Terminate> or the maximum # of iterations is reached; after the process above has gathered enough information to answer the initial question, the final generator (an LLM) generates the final answer.  | [Paper](https://arxiv.org/abs/2408.04259), [Tweet](https://x.com/omarsar0/status/1822744591810114044) |
+| 5) **RAGChecker** - a fine-grained evaluation framework for diagnosing retrieval and generation modules in RAG; shows that RAGChecker has better correlations with human judgment; reports several revealing insightful patterns and trade-offs in design choices of RAG architectures.  | [Paper](https://arxiv.org/abs/2408.08067), [Tweet](https://x.com/omarsar0/status/1824460245051081216) |
+| 6) **HybridRAG** - combines GraphRAG and VectorRAG leading to a HybridRAG system that outperforms both individually; it was tested on a set of financial earning call transcripts. Combining the advantages of both approaches provides more accurate answers to queries.  | [Paper](https://arxiv.org/abs/2408.04948),  [Tweet](https://x.com/omarsar0/status/1822832843455648000)  |
+| 7) **rStar** - introduces self-play mutual reasoning to improve the reasoning capabilities of small language models without fine-tuning or superior models; MCTS is augmented with human-like reasoning actions, obtained from SLMs, to build richer reasoning trajectories; a separate SLM provides unsupervised feedback on the trajectories and the target SLM selects the final reasoning trajectory as the answer; rStar boosts GSM8K accuracy from 12.51% to 63.91% for LLaMA2-7B and consistently improves the accuracy of other SLMs. | [Paper](https://arxiv.org/abs/2408.06195),  [Tweet](https://x.com/AtakanTekparmak/status/1823776878747877572)  |
+| 8) **Scaling LLM Test-Time Compute Optimally** - investigates the scaling behaviors of inference-time computation in LLMs; in particular, it analyses how much an LLM can be improved provided a fixed amount of inference-time compute; finds that the effectiveness of different scaling approaches varies by difficulty of prompt; it then proposes an adaptive compute-optimal strategy that can improve efficiency by more than 4x compared to a best-of-N baseline; reports that in a FLOPs-matched evaluation, optimally scaling test-time compute can outperform a 14x larger model.  | [Paper](https://arxiv.org/abs/2408.05109),  [Tweet](https://x.com/sea_snell/status/1821263798772363598)  |
+| 9) **MedGraphRAG** - a graph-based framework for the medical domain with a focus on enhancing LLMs and generating evidence-based results; leverages a hybrid static-semantic approach to chunk documents to improve context capture; entities and medical knowledge are represented through graphs which leads to an interconnected global graph; this approach improves precision and outperforms state-of-the-art models on multiple medical Q&A benchmarks.  | [Paper](https://arxiv.org/abs/2408.04187), [Tweet](https://x.com/Marktechpost/status/1823069406924288110)  |
+| 10) **Survey of NL2QL** - a comprehensive overview of NL2SQL techniques powered by LLMs; covers models, data collection, evaluation methods, and error analysis. | [Paper](https://arxiv.org/abs/2408.05109), [Tweet](https://x.com/_reachsumit/status/1822835969743347815) |
+
+
 ## Top ML Papers of the Week (August 5 - August 11) - 2024
 | **Paper**  | **Links** |
 | ------------- | ------------- |