In this tutorial, we build a self-organizing memory system for an agent that goes beyond storing raw conversation history and instead structures interactions into persistent, meaningful knowledge units. We design the system so that reasoning and memory management are clearly separated, allowing a dedicated component to extract, compress, and organize information. At the same…

metadata_dict = metadata.to_dict() diagnostic = DiagnosticReport() diagnostic.generate(real_data=real, synthetic_data=synthetic_sdv, metadata=metadata_dict, verbose=True) print(“Diagnostic score:”, diagnostic.get_score()) quality = QualityReport() quality.generate(real_data=real, synthetic_data=synthetic_sdv, metadata=metadata_dict, verbose=True) print(“Quality score:”, quality.get_score()) def show_report_details(report, title): print(f”\n===== {title} details =====”) props = report.get_properties() for p in props: print(f”\n— {p} —“) details = report.get_details(property_name=p) try: display(details.head(10)) except Exception: display(details) show_report_details(diagnostic, “DiagnosticReport”) show_report_details(quality, “QualityReport”) train_real, test_real…

In the world of Large Language Models (LLMs), speed is the only feature that matters once accuracy is solved. For a human, waiting 1 second for a search result is fine. For an AI agent performing 10 sequential searches to solve a complex task, a 1-second delay per search creates a 10-second lag. This…

Kyutai has released Hibiki-Zero, a new model for simultaneous speech-to-speech translation (S2ST) and speech-to-text translation (S2TT). The system translates source speech into a target language in real-time. It handles non-monotonic word dependencies during the process. Unlike previous models, Hibiki-Zero does not require word-level aligned data for training. This eliminates a major bottleneck in scaling…

Google DeepMind team has introduced Aletheia, a specialized AI agent designed to bridge the gap between competition-level math and professional research. While models achieved gold-medal standards at the 2025 International Mathematical Olympiad (IMO), research requires navigating vast literature and constructing long-horizon proofs. Aletheia solves this by iteratively generating, verifying,…

In this tutorial, we implement an end-to-end Direct Preference Optimization workflow to align a large language model with human preferences without using a reward model. We combine TRL’s DPOTrainer with QLoRA and PEFT to make preference-based alignment feasible on a single Colab GPU. We train directly on the UltraFeedback binarized dataset, where each prompt…