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      "title": "MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports",
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      "context": "Submission history From: Wang Yu [view email] [v1] Mon, 4 May 2026 19:37:21 UTC (7,126 KB) [v2] Wed, 19 Aug 2026 05:46:04 UTC (7,125 KB) Current browse context: cs.CL References & Citations Loading...",
      "whats_new": "arXiv:2605.03103v2 Announce Type: replace-cross Abstract: Semi-structured information extraction (IE) from OCR-derived clinical reports is crucial for efficiently reconstructing patients' longitudinal medical histories.",
      "key_details": [
        "In practice, this scenario commonly involves three tasks: (i) field-header (key) discovery, (ii) key-conditioned question answering (QA), and (iii) end-to-end key-value pair extraction.",
        "However, existing evaluations often under-model two factors: heterogeneous and incompletely known key representations, and OCR-induced noise.",
        "This makes it difficult to assess model robustness in real-world settings.",
        "We present MedStruct-S, a benchmark specifically designed to evaluate these tasks under unknown keys and OCR noise."
      ],
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        "arXiv:2605.03103v2 Announce Type: replace-cross Abstract: Semi-structured information extraction (IE) from OCR-derived clinical reports is crucial for efficiently reconstructing patients' longitudinal medical histories.",
        "MedStruct-S contains 3,582 annotated real-world clinical report pages.",
        "Using MedStruct-S, we benchmark two representative paradigms: encoder-only sequence labeling with post-processing and decoder-only structured generation, covering four encoder-only and five decoder-only models spanning 0.11B to 103B parameters."
      ],
      "limitations_unknowns": [
        "However, existing evaluations often under-model two factors: heterogeneous and incompletely known key representations, and OCR-induced noise.",
        "We present MedStruct-S, a benchmark specifically designed to evaluate these tasks under unknown keys and OCR noise."
      ],
      "practical_next_steps": [
        "Reproduce one claim with a public baseline and fixed evaluation settings.",
        "Check robustness on out-of-distribution or long-context cases.",
        "Track whether independent teams report matching results."
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      "title": "karpathy/autoresearch: AI agents running research on single-GPU nanochat training automatically",
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      "badges": [
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      "context": "Instead, you are programming the program.md Markdown files that provide context to the AI agents and set up your autonomous research org.",
      "whats_new": "AI agents running research on single-GPU nanochat training automatically One day, frontier AI research used to be done by meat computers in between eating, sleeping, having other fun, and synchronizing once in a while using sound wave interconnect in the ri...",
      "key_details": [
        "Research is now entirely the domain of autonomous swarms of AI agents running across compute cluster megastructures in the skies.",
        "The agents claim that we are now in the 10,205th generation of the code base, in any case no one could tell if that's right or wrong as the \"code\" is now a self-modifying binary that has grown beyond human comprehension.",
        "This repo is the story of how it all began.",
        "The idea: give an AI agent a small but real LLM training setup and let it experiment autonomously overnight."
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        "The agents claim that we are now in the 10,205th generation of the code base, in any case no one could tell if that's right or wrong as the \"code\" is now a self-modifying binary that has grown beyond human comprehension.",
        "It modifies the code, trains for 5 minutes, checks if the result improved, keeps or discards, and repeats."
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      "title": "DeltaML-Bench: Evaluating Machine Learning Agents on Real-World Research Repositories",
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      "whats_new": "arXiv:2608.19653v1 Announce Type: new Abstract: Autonomous agents for machine learning experimentation must navigate heterogeneous repositories, repair training pipelines, and evaluate candidate improvements under realistic compute constraints.",
      "key_details": [
        "Existing benchmarks only partially capture these conditions.",
        "We introduce DeltaML-Bench, a benchmark comprising 48 tasks sourced from research papers that require agents to improve published baselines within imperfect, open-source repositories.",
        "We evaluate GPT-5 and Claude Sonnet 4 with a standard Modular agent and a search-based ARG scaffolding.",
        "In the 4 x 6h allocation, ARG raises GPT-5's per-run success rate from 9.4% to 33.9%; in the 2 x 12h allocation, GPT-5 ARG reaches 49.0%."
      ],
      "results_evidence": [
        "arXiv:2608.19653v1 Announce Type: new Abstract: Autonomous agents for machine learning experimentation must navigate heterogeneous repositories, repair training pipelines, and evaluate candidate improvements under realistic compute constraints.",
        "We introduce DeltaML-Bench, a benchmark comprising 48 tasks sourced from research papers that require agents to improve published baselines within imperfect, open-source repositories.",
        "We evaluate GPT-5 and Claude Sonnet 4 with a standard Modular agent and a search-based ARG scaffolding."
      ],
      "limitations_unknowns": [
        "Generalization outside curated tasks is still unclear."
      ],
      "practical_next_steps": [
        "Reproduce one claim with a public baseline and fixed evaluation settings.",
        "Check robustness on out-of-distribution or long-context cases.",
        "Track whether independent teams report matching results."
      ]
    }
  ],
  "reality_check": {
    "read_time": "1-2 min",
    "items": [
      {
        "story_id": "gh:1223170290",
        "title": "nexu-io/open-design: \ud83c\udfa8 Best DeepSeek Harness Design Plugin. The open-source Claude Design alternative. \ud83d\udda5\ufe0f Local-first desktop app. \ud83d\uddbc\ufe0f Your coding agent becomes the design engine: prototypes, landing pages, dashboards, slides, images & video \u2014 real files, HTML/PDF/PPTX/MP4 export. \ud83e\udd16 Claude Code / Codex / Cursor / DeepSeek Harness / OpenCode & 20+ CLIs via BYOK.",
        "url": "https://github.com/nexu-io/open-design",
        "source_domain": "github.com",
        "category_label": "Agent",
        "overall": 8.13,
        "metrics": {
          "signal": 10.0,
          "novelty": 7.3,
          "impact": 7.8,
          "confidence": 7.03,
          "actionability": 6.5
        },
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          "repo",
          "demo"
        ],
        "checklist": {
          "primary_source": "yes",
          "demo": "yes",
          "benchmarks_evals": "no",
          "baselines_ablations": "no",
          "third_party_corroboration": "no",
          "reproducibility_details": "yes"
        },
        "what_would_change_my_mind": [
          "Independent replication with comparable or better results.",
          "Public benchmark numbers with clear baseline comparisons."
        ],
        "likely_failure_mode": "Performance may collapse outside curated demos or narrow tasks."
      },
      {
        "story_id": "gh:1136590548",
        "title": "affaan-m/ECC: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.",
        "url": "https://github.com/affaan-m/ECC",
        "source_domain": "github.com",
        "category_label": "Agent",
        "overall": 8.05,
        "metrics": {
          "signal": 10.0,
          "novelty": 6.2,
          "impact": 8.3,
          "confidence": 7.03,
          "actionability": 6.5
        },
        "badges": [
          "repo"
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          "primary_source": "yes",
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          "third_party_corroboration": "no",
          "reproducibility_details": "yes"
        },
        "what_would_change_my_mind": [
          "Independent replication with comparable or better results.",
          "Public benchmark numbers with clear baseline comparisons."
        ],
        "likely_failure_mode": "Performance may collapse outside curated demos or narrow tasks."
      },
      {
        "story_id": "arxiv:oai:arXiv.org:2605.03103v2",
        "title": "MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports",
        "url": "https://arxiv.org/abs/2605.03103",
        "source_domain": "arxiv.org",
        "category_label": "Cs.Ai",
        "overall": 6.59,
        "metrics": {
          "signal": 9.43,
          "novelty": 5.1,
          "impact": 2.0,
          "confidence": 9.5,
          "actionability": 6.5
        },
        "badges": [
          "paper"
        ],
        "checklist": {
          "primary_source": "yes",
          "demo": "no",
          "benchmarks_evals": "yes",
          "baselines_ablations": "yes",
          "third_party_corroboration": "no",
          "reproducibility_details": "yes"
        },
        "what_would_change_my_mind": [
          "Independent replication with comparable or better results.",
          "Public benchmark numbers with clear baseline comparisons."
        ],
        "likely_failure_mode": "Performance may collapse outside curated demos or narrow tasks."
      },
      {
        "story_id": "arxiv:oai:arXiv.org:2608.19653v1",
        "title": "DeltaML-Bench: Evaluating Machine Learning Agents on Real-World Research Repositories",
        "url": "https://arxiv.org/abs/2608.19653",
        "source_domain": "arxiv.org",
        "category_label": "Cs.Ai",
        "overall": 6.59,
        "metrics": {
          "signal": 9.43,
          "novelty": 5.1,
          "impact": 2.0,
          "confidence": 9.5,
          "actionability": 6.5
        },
        "badges": [
          "paper"
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        "checklist": {
          "primary_source": "yes",
          "demo": "no",
          "benchmarks_evals": "yes",
          "baselines_ablations": "yes",
          "third_party_corroboration": "no",
          "reproducibility_details": "yes"
        },
        "what_would_change_my_mind": [
          "Independent replication with comparable or better results.",
          "Public benchmark numbers with clear baseline comparisons."
        ],
        "likely_failure_mode": "Performance may collapse outside curated demos or narrow tasks."
      }
    ]
  },
  "lab_notes": {
    "tool_repo_of_the_day": {
      "title": "nexu-io/open-design: \ud83c\udfa8 Best DeepSeek Harness Design Plugin. The open-source Claude Design alternative. \ud83d\udda5\ufe0f Local-first desktop app. \ud83d\uddbc\ufe0f Your coding agent becomes the design engine: prototypes, landing pages, dashboards, slides, images & video \u2014 real files, HTML/PDF/PPTX/MP4 export. \ud83e\udd16 Claude Code / Codex / Cursor / DeepSeek Harness / OpenCode & 20+ CLIs via BYOK.",
      "url": "https://github.com/nexu-io/open-design",
      "source_domain": "github.com"
    },
    "prompt_workflow_of_the_day": "summarize claim -> evidence -> risk in three passes before acting",
    "tiny_snippet": "uv run python -m msd.run --scheduled"
  },
  "forecast_watchlist": {
    "read_time": "1-2 min",
    "watch_prefix": "Watch:",
    "topics": [
      "cs.ai",
      "cs.lg",
      "rss",
      "cs.cl",
      "python",
      "benchmark",
      "eval",
      "repo"
    ],
    "subscribe": {
      "label": "Subscribe for Daily Emails",
      "url": "mailto:morning-singularity-digest@localhost?subject=Subscribe%20for%20Daily%20Emails"
    }
  }
}