Exclusive | Caltech Researchers Claim Radical Compression of High-Fidelity AI Models - WSJ
<a href="https://news.google.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?oc=5" target="_blank">Exclusive | Caltech Researchers Claim Radical Compression of High-Fidelity AI Models</a> <font color="#6f6f6f">WSJ</font>
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LLM+Graph@VLDB'2025 Workshop Summary
arXiv:2604.02861v1 Announce Type: new Abstract: The integration of large language models (LLMs) with graph-structured data has become a pivotal and fast evolving research frontier, drawing strong interest from both academia and industry. The 2nd LLM+Graph Workshop, co-located with the 51st International Conference on Very Large Data Bases (VLDB 2025) in London, focused on advancing algorithms and systems that bridge LLMs, graph data management, and graph machine learning for practical applications. This report highlights the key research directions, challenges, and innovative solutions presented by the workshop's speakers.
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LLM+Graph@VLDB'2025 Workshop Summary
arXiv:2604.02861v1 Announce Type: new Abstract: The integration of large language models (LLMs) with graph-structured data has become a pivotal and fast evolving research frontier, drawing strong interest from both academia and industry. The 2nd LLM+Graph Workshop, co-located with the 51st International Conference on Very Large Data Bases (VLDB 2025) in London, focused on advancing algorithms and systems that bridge LLMs, graph data management, and graph machine learning for practical applications. This report highlights the key research directions, challenges, and innovative solutions presented by the workshop's speakers.
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arXiv:2604.02655v1 Announce Type: new Abstract: Semantic operators have increasingly become integrated within data systems to enable processing data using Large Language Models (LLMs). Despite significant recent effort in improving these operators, their accuracy is limited due to a critical flaw in their implementation: lack of holistic data understanding. In existing systems, semantic operators often process each data record independently using an LLM, without considering data context, only leveraging LLM's dataset-agnostic interpretation of the user-provided task. However, natural language is imprecise, so a task can only be accurately performed if it is correctly interpreted in the context of the dataset. For example, for classification and scoring tasks, which are typical semantic map
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I technically got an LLM running locally on a 1998 iMac G3 with 32 MB of RAM
Hardware: • Stock iMac G3 Rev B (October 1998). 233 MHz PowerPC 750, 32 MB RAM, Mac OS 8.5. No upgrades. • Model: Andrej Karpathy’s 260K TinyStories (Llama 2 architecture). ~1 MB checkpoint. Toolchain: • Cross-compiled from a Mac mini using Retro68 (GCC for classic Mac OS → PEF binaries) • Endian-swapped model + tokenizer from little-endian to big-endian for PowerPC • Files transferred via FTP to the iMac over Ethernet Challenges: • Mac OS 8.5 gives apps a tiny memory partition by default. Had to use MaxApplZone() + NewPtr() from the Mac Memory Manager to get enough heap • RetroConsole crashes on this hardware, so all output writes to a text file you open in SimpleText • The original llama2.c weight layout assumes n_kv_heads == n_heads. The 260K model uses grouped-query attention (kv_heads
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