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to optimize prompt design across different clinical contexts remains underexplored.", "whats_new": "The proposed framework is applied to evaluate and compare the performance of several advanced LLMs, including Kimi-K2-Instruct-0905, Qwen3-235B-A22B-Instruct-2507, DeepSeek-V3, and ByteDance-Seed-OSS-36B-Instruct, using the SiliconFlow platform.", "key_details": [ "However, systematic guidance on how to optimize prompt design across different clinical contexts remains underexplored.", "Moreover, a comprehensive and standardized framework for assessing the trustworthiness of LLM-generated radiology reports is yet to be established.", "This study aims to enhance the trustworthiness of LLM-generated liver MRI reports by introducing a Multi-Dimensional Credibility Assessment (MDCA) framework and providing guidance on institution-specific prompt optimization.", "The proposed framework is applied to evaluate and compare the performance of several advanced LLMs, including Kimi-K2-Instruct-0905, Qwen3-235B-A22B-Instruct-2507, DeepSeek-V3, and ByteDance-Seed-OSS-36B-Instruct, using the SiliconFlow platform." ], "results_evidence": [ "arXiv:2510.23008v3 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated promising performance in generating diagnostic conclusions from imaging findings, thereby supporting radiology reporting, trainee education, and quality control.", "The proposed framework is applied to evaluate and compare the performance of several advanced LLMs, including Kimi-K2-Instruct-0905, Qwen3-235B-A22B-Instruct-2507, DeepSeek-V3, and ByteDance-Seed-OSS-36B-Instruct, using the SiliconFlow platform.", "Computer Science > Artificial Intelligence [Submitted on 27 Oct 2025 (v1), last revised 25 May 2026 (this version, v3)] Title:From Prompt Optimization to Multi-Dimensional Credibility Evaluation: Enhancing Trustworthiness of Chinese LLM-Generated Liver MRI..." ], "limitations_unknowns": [ "However, systematic guidance on how to optimize prompt design across different clinical contexts remains underexplored." ], "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: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": 8.02, "metrics": { "signal": 10.0, "novelty": 6.2, "impact": 8.19, "confidence": 7.03, "actionability": 6.5 }, "why_made_cut": "Signal 10.0, Confidence 7.0, and Impact 8.2 combined to rank this in the top set.", "badges": [ "repo" ], "context": "| Topic | What You'll Learn | |---|---| | Token Optimization | Model selection, system prompt slimming, background processes | | Memory Persistence | Hooks that save/load context across sessions automatically | | Continuous Learning | Auto-extract patterns...", "whats_new": "Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.", "key_details": [ "Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.", "Language: English | Portugu\u00eas (Brasil) | \u7b80\u4f53\u4e2d\u6587 | \u7e41\u9ad4\u4e2d\u6587 | \u65e5\u672c\u8a9e | \ud55c\uad6d\uc5b4 | T\u00fcrk\u00e7e | \u0420\u0443\u0441\u0441\u043a\u0438\u0439 | Ti\u1ebfng Vi\u1ec7t | \u0e44\u0e17\u0e22 | Deutsch 182K+ stars | 28K+ forks | 170+ contributors | 12+ language ecosystems | Anthropic Hackathon Winner Language / \u8bed\u8a00 / \u8a9e\u8a00 / Dil / \u042f\u0437\u044b\u043a / Ng\u00f4n ng\u1eef E...", "From an Anthropic hackathon winner.", "A complete system: skills, instincts, memory optimization, continuous learning, security scanning, and research-first development." ], "results_evidence": [ "Language: English | Portugu\u00eas (Brasil) | \u7b80\u4f53\u4e2d\u6587 | \u7e41\u9ad4\u4e2d\u6587 | \u65e5\u672c\u8a9e | \ud55c\uad6d\uc5b4 | T\u00fcrk\u00e7e | \u0420\u0443\u0441\u0441\u043a\u0438\u0439 | Ti\u1ebfng Vi\u1ec7t | \u0e44\u0e17\u0e22 | Deutsch 182K+ stars | 28K+ forks | 170+ contributors | 12+ language ecosystems | Anthropic Hackathon Winner Language / \u8bed\u8a00 / \u8a9e\u8a00 / Dil / \u042f\u0437\u044b\u043a / Ng\u00f4n ng\u1eef E...", "Production-ready agents, skills, hooks, rules, MCP configurations, and legacy command shims evolved over 10+ months of intensive daily use building real products.", "ECC v2.0.0-rc.1 adds the public Hermes operator story on top of that reusable layer: start with the Hermes setup guide, then review the rc.1 release notes and cross-harness architecture." ], "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": "hn:48279163", "title": "Show HN: Decoding the Language Machine \u2013 AI video series and CC repo", "url": "https://github.com/SkepticCTO/decoding_the_language_machine", "source_domain": "github.com", "category_label": "Hn", "overall": 6.04, "metrics": { "signal": 8.37, "novelty": 4.0, "impact": 2.56, "confidence": 7.45, "actionability": 6.5 }, "why_made_cut": "Signal 8.4, Confidence 7.5, and Impact 2.6 combined to rank this in the top set.", "badges": [ "repo", "demo" ], "context": "I released 3 parts of an educational video series (out of 6 planned), paired with a GitHub repository containing scripts and artifacts (released under Creative Commons).

- Main Site: https:&#...", "whats_new": "I released 3 parts of an educational video series (out of 6 planned), paired with a GitHub repository containing scripts and artifacts (released under Creative Commons).

- Main Site: https:&#...", "key_details": [ "in CS (U Penn, 1999 in computer vision and ML), and a PI in the NIST AI Safety Initiative Consortium.", "I spent a 4-month sabbatical making this because I wanted to demystify how LLMs work through a historical perspective (starting in 1948 with Claude Shannon) and scientific skepticism.

The project is old enough to be fleshed out, but young enough to be abl...", "I look forward to questions and feedback." ], "results_evidence": [ "I released 3 parts of an educational video series (out of 6 planned), paired with a GitHub repository containing scripts and artifacts (released under Creative Commons).

- Main Site: https:&#...", "in CS (U Penn, 1999 in computer vision and ML), and a PI in the NIST AI Safety Initiative Consortium.", "I spent a 4-month sabbatical making this because I wanted to demystify how LLMs work through a historical perspective (starting in 1948 with Claude Shannon) and scientific skepticism.

The project is old enough to be fleshed out, but young enough to be abl..." ], "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": "arxiv:oai:arXiv.org:2510.23008v3", "title": "From Prompt Optimization to Multi-Dimensional Credibility Evaluation: Enhancing Trustworthiness of Chinese LLM-Generated Liver MRI Reports -- with Preliminary Extension to Lung Cancer", "url": "https://arxiv.org/abs/2510.23008", "source_domain": "arxiv.org", "category_label": "Cs.Ai", "overall": 6.56, "metrics": { "signal": 9.43, "novelty": 4.0, "impact": 2.0, "confidence": 9.5, "actionability": 8.2 }, "badges": [ "paper", "demo" ], "checklist": { "primary_source": "yes", "demo": "yes", "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": "gh:1201656210", "title": "MemPalace/mempalace: The best-benchmarked open-source AI memory system. And it's free.", "url": "https://github.com/MemPalace/mempalace", "source_domain": "github.com", "category_label": "Benchmark", "overall": 8.0, "metrics": { "signal": 10.0, "novelty": 6.2, "impact": 7.53, "confidence": 7.83, "actionability": 6.5 }, "badges": [ "repo" ], "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": "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": 8.02, "metrics": { "signal": 10.0, "novelty": 6.2, "impact": 8.19, "confidence": 7.03, "actionability": 6.5 }, "badges": [ "repo" ], "checklist": { "primary_source": "yes", "demo": "no", "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": "arxiv:oai:arXiv.org:2605.23912v1", "title": "Raon-Speech Technical Report", "url": "https://arxiv.org/abs/2605.23912", "source_domain": "arxiv.org", "category_label": "Cs.Ai", "overall": 6.22, "metrics": { "signal": 9.43, "novelty": 4.0, "impact": 2.0, "confidence": 8.7, "actionability": 6.5 }, "badges": [ "paper", "demo" ], "checklist": { "primary_source": "yes", "demo": "yes", "benchmarks_evals": "yes", "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." } ] }, "lab_notes": { "tool_repo_of_the_day": { "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" }, "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": [ "agent", "llm", "cs.ai", "cs.lg", "rss", "cs.cl", "python", "benchmark" ], "subscribe": { "label": "Subscribe for Daily Emails", "url": "mailto:morning-singularity-digest@localhost?subject=Subscribe%20for%20Daily%20Emails" } } }