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  submitted by   /u/Distinct-Question-16   to   r/singularity [link]   [comments]
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The paper examines how LLM agents' multi-role workflows transform state into intermediate artifacts, which can unintentionally weaken action constraints by turning mandatory requirements into optional information. This
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We present LAION-BVD, a large-scale open video dataset for multimodal learning, which contains 1.3B platform-specific video URLs collected from CommonCrawl. From these, we download 80M videos with a t
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Game2World (G2W) Engine is a full-stack framework designed to extract clean, world-model-ready training data from in-the-wild gameplay videos. It introduces a GameUI-Taxonomy to systematically categorize and ground game …
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ClawProBench introduces a trace-aware benchmark for evaluating AI agents within stateful runtimes, addressing the limitation of benchmarks that only assess final answers. It evaluates model-plus-runtime configurations ac…
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Unsloth has released next‑day zero
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OpenAI's newly announced Jalapeno chip has demonstrated superior performance compared to the Vera Rubin benchmark in initial tests, according to the company's first results. The findings were shared on Reddit's r/singula…
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FP8 quantization boosted serving throughput for Qwen3-8B on an RTX PRO 6000 Blackwell GPU from 1,725 to 2,597 tokens per second at concurrency 32 using vLLM, achieving
→ View original sourceClaude-obsidian is an open-source personal knowledge management tool that integrates Claude Code with Obsidian to create a self-organizing AI second brain. It automatically processes sources into a connected knowledge gr…
→ View original source![[Benchmarks] Qwen3.8-27B on one DGX Spark across SGLang, vLLM and llama.cpp](https://preview.redd.it/3p2la32nwglh1.png?width=640&crop=smart&auto=webp&s=a686bda0d1dbca3de4f3540a3fe69aa32cd1548b)
I ran a 12-way Qwen3.8-27B comparison on one DGX Spark. Each engine used plain decoding plus MTP, DSpark, and DFlash2. The coding workload was a seeded 50-task HumanEval+ slice with
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