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"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 }, "why_made_cut": "Signal 9.4, Confidence 9.5, and Impact 2.0 combined to rank this in the top set.", "badges": [ "paper" ], "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." ], "results_evidence": [ "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." ] }, { "story_id": "hn:49371211", "title": "Show HN: Do-over, undo for AI agent shell commands", "url": "https://github.com/CaydenChik/doover", "source_domain": "github.com", "category_label": "Hn", "overall": 5.86, "metrics": { "signal": 8.37, "novelty": 5.1, "impact": 2.76, "confidence": 7.45, "actionability": 3.5 }, "why_made_cut": "Signal 8.4, Confidence 7.5, and Impact 2.8 combined to rank this in the top set.", "badges": [ "repo" ], "context": "Hello HN,

I built this after a coding agent deleted my files while tidying a project.", "whats_new": "Hello HN,

I built this after a coding agent deleted my files while tidying a project.", "key_details": [ "Do-over plugs into Claude Code's hooks and snapshots what a command is about to touch right before it runs.

I know what you're thinking.", "Why don't you just commit more often?

This tool doesn't seek to replace git.", "What it does is: [1] Protect the stuff git doesn't protect: your untracked/ignored files and files that exist outside your repo (which is how a guy lost 50GB of data - Claude code issue tracker) [2] Protects you when git itself is weaponized by yo...", "And it's a last mile effort that secures what current measures can't." ], "results_evidence": [ "What it does is: [1] Protect the stuff git doesn't protect: your untracked/ignored files and files that exist outside your repo (which is how a guy lost 50GB of data - Claude code issue tracker) [2] Protects you when git itself is weaponized by yo..." ], "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." ] }, { "story_id": "gh:1174820787", "title": "karpathy/autoresearch: AI agents running research on single-GPU nanochat training automatically", "url": "https://github.com/karpathy/autoresearch", "source_domain": "github.com", "category_label": "Agent", "overall": 7.74, "metrics": { "signal": 10.0, "novelty": 5.1, "impact": 7.82, "confidence": 7.03, "actionability": 6.5 }, "why_made_cut": "Signal 10.0, Confidence 7.0, and Impact 7.8 combined to rank this in the top set.", "badges": [ "repo" ], "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." ], "results_evidence": [ "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." ], "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. 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