A Coding Implementation on Document Parsing Benchmarking with LlamaIndex ParseBench Using Python, Hugging Face, and Evaluation Metrics
In this tutorial, we explore how to use the ParseBench dataset to evaluate document parsing systems in a structured, practical way. We begin by loading the dataset directly…
Pročitaj više →Poolside AI Introduces Laguna XS.2 and M.1: Agentic Coding Models Reaching 68.2% and 72.5% on SWE-bench Verified
Poolside Releases Laguna XS.2 and M.1: Open-Weight Agentic Coding Models Built for Long-Horizon Tasks The post Poolside AI Introduces Laguna XS.2 and M.1: Agentic Coding Models Reaching 68.2%…
Pročitaj više →Poolside AI Introduces Laguna XS.2 and M.1: Agentic Coding Models Reaching 68.2% and 72.5% on SWE-bench Verified
Poolside Releases Laguna XS.2 and M.1: Open-Weight Agentic Coding Models Built for Long-Horizon Tasks The post Poolside AI Introduces Laguna XS.2 and M.1: Agentic Coding Models Reaching 68.2%…
Pročitaj više →Poolside AI Introduces Laguna XS.2 and M.1: Agentic Coding Models Reaching 68.2% and 72.5% on SWE-bench Verified
Poolside Releases Laguna XS.2 and M.1: Open-Weight Agentic Coding Models Built for Long-Horizon Tasks The post Poolside AI Introduces Laguna XS.2 and M.1: Agentic Coding Models Reaching 68.2%…
Pročitaj više →Poolside AI Introduces Laguna XS.2 and M.1: Agentic Coding Models Reaching 68.2% and 72.5% on SWE-bench Verified
Poolside Releases Laguna XS.2 and M.1: Open-Weight Agentic Coding Models Built for Long-Horizon Tasks The post Poolside AI Introduces Laguna XS.2 and M.1: Agentic Coding Models Reaching 68.2%…
Pročitaj više →A Deep Reinforcement Learning Approach to Automated Stock Trading, using xLSTM Networks
arXiv:2503.09655v2 Announce Type: replace-cross Abstract: Traditional Long Short-Term Memory (LSTM) networks are effective for handling sequential data but have limitations such as gradient vanishing and difficulty in capturing…
Pročitaj više →Odysseys: Benchmarking Web Agents on Realistic Long Horizon Tasks
arXiv:2604.24964v1 Announce Type: new Abstract: Existing web agent benchmarks have largely converged on short, single-site tasks that frontier models are approaching saturation on. However, real world web use…
Pročitaj više →OptProver: Bridging Olympiad and Optimization through Continual Training in Formal Theorem Proving
arXiv:2604.23712v2 Announce Type: replace Abstract: Recent advances in formal theorem proving have focused on Olympiad-level mathematics, leaving undergraduate domains largely unexplored. Optimization, fundamental to machine learning, operations research,…
Pročitaj više →CoreFlow: Low-Rank Matrix Generative Models
arXiv:2604.24959v1 Announce Type: new Abstract: Learning matrix-valued distributions from high-dimensional and possibly incomplete training data is challenging: ambient-space generative modeling is computationally expensive and statistically fragile when the…
Pročitaj više →AutoPPA: Automated Circuit PPA Optimization via Contrastive Code-based Rule Library Learning
arXiv:2604.18445v2 Announce Type: replace Abstract: Performance, power, and area (PPA) optimization is a fundamental task in RTL design, requiring a precise understanding of circuit functionality and the relationship…
Pročitaj više →Compute Aligned Training: Optimizing for Test Time Inference
arXiv:2604.24957v1 Announce Type: new Abstract: Scaling test-time compute has emerged as a powerful mechanism for enhancing Large Language Model (LLM) performance. However, standard post-training paradigms, Supervised Fine-Tuning (SFT)…
Pročitaj više →Sharp Capacity Scaling of Spectral Optimizers in Learning Associative Memory
arXiv:2603.26554v2 Announce Type: replace Abstract: Spectral optimizers such as Muon have recently shown strong empirical performance in large-scale language model training, but the source and extent of their…
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