{
  "date": "2026-04-29",
  "stories": [
    {
      "story_id": "arxiv:oai:arXiv.org:2604.24001v1",
      "title": "CT-FineBench: A Diagnostic Fidelity Benchmark for Fine-Grained Evaluation of CT Report Generation",
      "url": "https://arxiv.org/abs/2604.24001",
      "overall": 6.57,
      "metrics": {
        "signal": 9.43,
        "novelty": 5.1,
        "impact": 2.0,
        "confidence": 9.5,
        "actionability": 6.5,
        "freshness": 8.09
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2604.24001",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.25700v1",
      "title": "Bug-Report-Driven Fault Localization: Industrial Benchmarking and Lesson Learned at ABB Robotics",
      "url": "https://arxiv.org/abs/2604.25700",
      "overall": 6.57,
      "metrics": {
        "signal": 9.43,
        "novelty": 5.1,
        "impact": 2.0,
        "confidence": 9.5,
        "actionability": 6.5,
        "freshness": 8.09
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2604.25700",
        "Demo": "",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2603.16877v2",
      "title": "Enhancing Financial Report Question-Answering: A Retrieval-Augmented Generation System with Reranking Analysis",
      "url": "https://arxiv.org/abs/2603.16877",
      "overall": 6.37,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 9.5,
        "actionability": 6.5,
        "freshness": 8.09
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2603.16877",
        "Demo": "",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.22837v1",
      "title": "OAMVOS:2nd Report for 5th PVUW MOSE Track",
      "url": "https://arxiv.org/abs/2604.22837",
      "overall": 6.24,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 8.09
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2604.22837"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.23251v1",
      "title": "AI-Assisted Code Review as a Scaffold for Code Quality and Self-Regulated Learning: An Experience Report",
      "url": "https://arxiv.org/abs/2604.23251",
      "overall": 6.24,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 8.09
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2604.23251"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.24432v1",
      "title": "Kwai Summary Attention Technical Report",
      "url": "https://arxiv.org/abs/2604.24432",
      "overall": 6.24,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 8.09
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2604.24432"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.24519v1",
      "title": "Why AI Harms Can't Be Fixed One Identity at a Time: What 5300 Incident Reports Reveal About Intersectionality",
      "url": "https://arxiv.org/abs/2604.24519",
      "overall": 6.24,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 8.09
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2604.24519"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2511.16518v2",
      "title": "MiMo-Embodied: X-Embodied Foundation Model Technical Report",
      "url": "https://arxiv.org/abs/2511.16518",
      "overall": 6.24,
      "metrics": {
        "signal": 9.43,
        "novelty": 4.0,
        "impact": 2.0,
        "confidence": 8.7,
        "actionability": 6.5,
        "freshness": 8.09
      },
      "badges": {
        "Repo": "",
        "Paper": "https://arxiv.org/abs/2511.16518",
        "Benchmarks": "https://github.com/XiaomiMiMo/MiMo-Embodied."
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "hn:47947490",
      "title": "He asked AI to count carbs 27000 times. It couldn't give the same answer twice",
      "url": "https://www.diabettech.com/i-asked-ai-to-count-my-carbs-27000-times-it-couldnt-give-me-the-same-answer-twice/",
      "overall": 6.23,
      "metrics": {
        "signal": 8.72,
        "novelty": 4.0,
        "impact": 5.5,
        "confidence": 6.25,
        "actionability": 3.5,
        "freshness": 9.89
      },
      "badges": {},
      "corroboration_count": 1,
      "corroboration_sources": [
        "hackernews"
      ],
      "source": "hackernews"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2511.01188v3",
      "title": "ZoFia: Zero-Shot Fake News Detection with Entity-Guided Retrieval and Multi-LLM Interaction",
      "url": "https://arxiv.org/abs/2511.01188",
      "overall": 6.22,
      "metrics": {
        "signal": 9.43,
        "novelty": 5.1,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 5.2,
        "freshness": 8.09
      },
      "badges": {
        "Repo": "",
        "Paper": "https://arxiv.org/abs/2511.01188",
        "Benchmarks": "https://github.com/SakiRinn/ZoFia."
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.23580v1",
      "title": "PhysCodeBench: Benchmarking Physics-Aware Symbolic Simulation of 3D Scenes via Self-Corrective Multi-Agent Refinement",
      "url": "https://arxiv.org/abs/2604.23580",
      "overall": 6.21,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 3.5,
        "freshness": 8.09
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2604.23580",
        "Demo": "",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.24477v1",
      "title": "GAMMAF: A Common Framework for Graph-Based Anomaly Monitoring Benchmarking in LLM Multi-Agent Systems",
      "url": "https://arxiv.org/abs/2604.24477",
      "overall": 6.21,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 3.5,
        "freshness": 8.09
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2604.24477",
        "Demo": "",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.12290v2",
      "title": "Frontier-Eng: Benchmarking Self-Evolving Agents on Real-World Engineering Tasks with Generative Optimization",
      "url": "https://arxiv.org/abs/2604.12290",
      "overall": 6.21,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 3.5,
        "freshness": 8.09
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2604.12290",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.24964v1",
      "title": "Odysseys: Benchmarking Web Agents on Realistic Long Horizon Tasks",
      "url": "https://arxiv.org/abs/2604.24964",
      "overall": 6.21,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 3.5,
        "freshness": 8.09
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2604.24964",
        "Demo": "https://odysseys-website.pages.dev",
        "Benchmarks": "https://odysseys-website.pages.dev"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.24929v1",
      "title": "GAIA-v2-LILT: Multilingual Adaptation of Agent Benchmark beyond Translation",
      "url": "https://arxiv.org/abs/2604.24929",
      "overall": 6.21,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 3.5,
        "freshness": 8.09
      },
      "badges": {
        "Repo": "",
        "Paper": "https://arxiv.org/abs/2604.24929",
        "Benchmarks": "https://huggingface.co/datasets/Fujitsu-FRE/MAPS/viewer/GAIA-v2-LILT."
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.24955v1",
      "title": "BenchGuard: Who Guards the Benchmarks? Automated Auditing of LLM Agent Benchmarks",
      "url": "https://arxiv.org/abs/2604.24955",
      "overall": 6.21,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 3.5,
        "freshness": 8.09
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2604.24955",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.25914v1",
      "title": "DV-World: Benchmarking Data Visualization Agents in Real-World Scenarios",
      "url": "https://arxiv.org/abs/2604.25914",
      "overall": 6.21,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 3.5,
        "freshness": 8.09
      },
      "badges": {
        "Repo": "",
        "Paper": "https://arxiv.org/abs/2604.25914",
        "Benchmarks": "https://github.com/DA-Open/DV-World}{this"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2602.11224v3",
      "title": "Agent-Diff: Benchmarking LLM Agents on Enterprise API Tasks via Code Execution with State-Diff-Based Evaluation",
      "url": "https://arxiv.org/abs/2602.11224",
      "overall": 6.21,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 8.3,
        "actionability": 3.5,
        "freshness": 8.09
      },
      "badges": {
        "Repo": "",
        "Paper": "https://arxiv.org/abs/2602.11224",
        "Benchmarks": "https://github.com/agent-diff-bench/agent-diff."
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "hn:47947356",
      "title": "Show HN: I scanned 16 AI agent repos \u2013 76% of tool calls had no guards",
      "url": "https://github.com/Diplomat-ai/diplomat-agent",
      "overall": 6.2,
      "metrics": {
        "signal": 8.36,
        "novelty": 5.1,
        "impact": 2.35,
        "confidence": 7.45,
        "actionability": 6.5,
        "freshness": 9.84
      },
      "badges": {
        "Repo": "https://github.com/Diplomat-ai/diplomat-agent"
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "hackernews"
      ],
      "source": "hackernews"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.23970v1",
      "title": "LLM-Guided Agentic Floor Plan Parsing for Accessible Indoor Navigation of Blind and Low-Vision People",
      "url": "https://arxiv.org/abs/2604.23970",
      "overall": 6.09,
      "metrics": {
        "signal": 9.43,
        "novelty": 5.1,
        "impact": 2.0,
        "confidence": 7.5,
        "actionability": 5.2,
        "freshness": 8.09
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2604.23970",
        "Demo": "",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.24021v1",
      "title": "QED: An Open-Source Multi-Agent System for Generating Mathematical Proofs on Open Problems",
      "url": "https://arxiv.org/abs/2604.24021",
      "overall": 6.09,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 7.5,
        "actionability": 3.5,
        "freshness": 8.09
      },
      "badges": {
        "Repo": "",
        "Paper": "https://arxiv.org/abs/2604.24021",
        "Benchmarks": "https://github.com/proofQED/QED."
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.23940v1",
      "title": "Constraint-Guided Multi-Agent Decompilation for Executable Binary Recovery",
      "url": "https://arxiv.org/abs/2604.23940",
      "overall": 6.09,
      "metrics": {
        "signal": 9.43,
        "novelty": 5.1,
        "impact": 2.0,
        "confidence": 7.5,
        "actionability": 5.2,
        "freshness": 8.09
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2604.23940",
        "Demo": "",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.24831v1",
      "title": "FGDM: Reasoning Aware Multi-Agentic Framework for Software Bug Detection using Chain of Thought and Tree of Thought Prompting",
      "url": "https://arxiv.org/abs/2604.24831",
      "overall": 6.09,
      "metrics": {
        "signal": 9.43,
        "novelty": 5.1,
        "impact": 2.0,
        "confidence": 7.5,
        "actionability": 5.2,
        "freshness": 8.09
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2604.24831",
        "Demo": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    },
    {
      "story_id": "arxiv:oai:arXiv.org:2604.25135v1",
      "title": "FAMA: Failure-Aware Meta-Agentic Framework for Open-Source LLMs in Interactive Tool Use Environments",
      "url": "https://arxiv.org/abs/2604.25135",
      "overall": 6.09,
      "metrics": {
        "signal": 9.43,
        "novelty": 6.2,
        "impact": 2.0,
        "confidence": 7.5,
        "actionability": 3.5,
        "freshness": 8.09
      },
      "badges": {
        "Paper": "https://arxiv.org/abs/2604.25135",
        "Demo": "",
        "Benchmarks": ""
      },
      "corroboration_count": 1,
      "corroboration_sources": [
        "arxiv"
      ],
      "source": "arxiv"
    }
  ],
  "deep_dives": [
    {
      "story_id": "arxiv:oai:arXiv.org:2604.24001v1",
      "title": "CT-FineBench: A Diagnostic Fidelity Benchmark for Fine-Grained Evaluation of CT Report Generation",
      "url": "https://arxiv.org/abs/2604.24001",
      "source_domain": "arxiv.org",
      "category_label": "Cs.Ai",
      "overall": 6.57,
      "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": "arXiv:2604.24001v1 Announce Type: new Abstract: The evaluation of generated reports remains a critical challenge in Computed Tomography (CT) report generation, due to the large volume of text, the diversity and complexity of findings, and the presence of fi...",
      "whats_new": "arXiv:2604.24001v1 Announce Type: new Abstract: The evaluation of generated reports remains a critical challenge in Computed Tomography (CT) report generation, due to the large volume of text, the diversity and complexity of findings, and the presence of fi...",
      "key_details": [
        "Conventional evaluation metrics offer only coarse measures of lexical overlap or entity matching and fail to reflect the granular diagnostic accuracy required for clinical use.",
        "To address this gap, we propose CT-FineBench, a benchmark built from CT-RATE and Merlin to evaluate the fine-grained factual consistency of CT reports, constructed from CT-RATE and Merlin.",
        "Our benchmark is constructed through a meticulous, Question-Answering (QA) based process: first, we identify and structure key, finding-specific clinical attributes (like location, size, margin).",
        "Second, we systematically transform these attributes into a QA dataset, where questions probe for specific clinical details grounded in gold-standard reports."
      ],
      "results_evidence": [
        "arXiv:2604.24001v1 Announce Type: new Abstract: The evaluation of generated reports remains a critical challenge in Computed Tomography (CT) report generation, due to the large volume of text, the diversity and complexity of findings, and the presence of fi...",
        "Computer Science > Artificial Intelligence [Submitted on 27 Apr 2026] Title:CT-FineBench: A Diagnostic Fidelity Benchmark for Fine-Grained Evaluation of CT Report Generation View PDF HTML (experimental)Abstract:The evaluation of generated reports remains a..."
      ],
      "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:47947356",
      "title": "Show HN: I scanned 16 AI agent repos \u2013 76% of tool calls had no guards",
      "url": "https://github.com/Diplomat-ai/diplomat-agent",
      "source_domain": "github.com",
      "category_label": "Hn",
      "overall": 6.2,
      "metrics": {
        "signal": 8.36,
        "novelty": 5.1,
        "impact": 2.35,
        "confidence": 7.45,
        "actionability": 6.5
      },
      "why_made_cut": "Signal 8.4, Confidence 7.5, and Impact 2.4 combined to rank this in the top set.",
      "badges": [
        "repo"
      ],
      "context": "Do you know every function it can call that writes to a database, sends an email, charges a card, or deletes data \u2014 and which ones have zero checks?",
      "whats_new": "Do you know every function it can call that writes to a database, sends an email, charges a card, or deletes data \u2014 and which ones have zero checks?",
      "key_details": [
        "diplomat-agent runs a static AST scan and tells you exactly that.",
        "pip install diplomat-agent diplomat-agent scan .",
        "diplomat-agent \u2014 governance scan Scanned: ./my-agent Tool calls with side effects: 12 \u26a0 process_refund(amount, customer_id) Write protection: NONE Rate limit: NONE \u2192 stripe.Refund.create() with no amount limit Governance: \u274c UNGUARDED \u26a0 delete_user_data(user...",
        "The UI has validation, confirmation dialogs, rate limits per session."
      ],
      "results_evidence": [
        "diplomat-agent \u2014 governance scan Scanned: ./my-agent Tool calls with side effects: 12 \u26a0 process_refund(amount, customer_id) Write protection: NONE Rate limit: NONE \u2192 stripe.Refund.create() with no amount limit Governance: \u274c UNGUARDED \u26a0 delete_user_data(user...",
        "We scanned 16 open-source agent repos.",
        "40+ patterns across 8 categories: | Category | Examples | |---|---| | Database writes | session.commit() , .save() , .create() , .update() | | Database deletes | session.delete() , .remove() , DELETE FROM | | HTTP writes | requests.post() , httpx.put() , cl..."
      ],
      "limitations_unknowns": [
        "diplomat-agent \u2014 governance scan Scanned: ./my-agent Tool calls with side effects: 12 \u26a0 process_refund(amount, customer_id) Write protection: NONE Rate limit: NONE \u2192 stripe.Refund.create() with no amount limit Governance: \u274c UNGUARDED \u26a0 delete_user_data(user...",
        "The UI has validation, confirmation dialogs, rate limits per session."
      ],
      "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": "arxiv:oai:arXiv.org:2604.25700v1",
      "title": "Bug-Report-Driven Fault Localization: Industrial Benchmarking and Lesson Learned at ABB Robotics",
      "url": "https://arxiv.org/abs/2604.25700",
      "source_domain": "arxiv.org",
      "category_label": "Cs.Lg",
      "overall": 6.57,
      "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",
        "demo"
      ],
      "context": "arXiv:2604.25700v1 Announce Type: cross Abstract: Software quality assurance remains a major challenge in industrial environments, where large-scale and long-lived systems inevitably accumulate defects.",
      "whats_new": "By relying only on textual information, our approach requires no access to source code, execution traces, or static analysis artifacts, making it directly deployable within existing industrial maintenance workflows.",
      "key_details": [
        "Identifying the location of a fault is often time-consuming and costly, particularly during maintenance phases when developers must rely primarily on textual bug reports rather than complete runtime or code-level context.",
        "In this study, we investigated if artificial intelligence can support fault localization using only the natural-language content of bug reports.",
        "By relying only on textual information, our approach requires no access to source code, execution traces, or static analysis artifacts, making it directly deployable within existing industrial maintenance workflows.",
        "We framed fault localization as a supervised text classification problem and evaluated three traditional machine learning models (Logistic Regression, Support Vector Machine, and Random Forest) and two fine-tuned transformer-based language models (RoBERTa-B..."
      ],
      "results_evidence": [
        "arXiv:2604.25700v1 Announce Type: cross Abstract: Software quality assurance remains a major challenge in industrial environments, where large-scale and long-lived systems inevitably accumulate defects.",
        "Computer Science > Software Engineering [Submitted on 28 Apr 2026] Title:Bug-Report-Driven Fault Localization: Industrial Benchmarking and Lesson Learned at ABB Robotics View PDF HTML (experimental)Abstract:Software quality assurance remains a major challen..."
      ],
      "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:2604.24001v1",
        "title": "CT-FineBench: A Diagnostic Fidelity Benchmark for Fine-Grained Evaluation of CT Report Generation",
        "url": "https://arxiv.org/abs/2604.24001",
        "source_domain": "arxiv.org",
        "category_label": "Cs.Ai",
        "overall": 6.57,
        "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:2604.25700v1",
        "title": "Bug-Report-Driven Fault Localization: Industrial Benchmarking and Lesson Learned at ABB Robotics",
        "url": "https://arxiv.org/abs/2604.25700",
        "source_domain": "arxiv.org",
        "category_label": "Cs.Lg",
        "overall": 6.57,
        "metrics": {
          "signal": 9.43,
          "novelty": 5.1,
          "impact": 2.0,
          "confidence": 9.5,
          "actionability": 6.5
        },
        "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": "hn:47947356",
        "title": "Show HN: I scanned 16 AI agent repos \u2013 76% of tool calls had no guards",
        "url": "https://github.com/Diplomat-ai/diplomat-agent",
        "source_domain": "github.com",
        "category_label": "Hn",
        "overall": 6.2,
        "metrics": {
          "signal": 8.36,
          "novelty": 5.1,
          "impact": 2.35,
          "confidence": 7.45,
          "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": "gh:lukilabs/craft-agents-oss",
        "title": "lukilabs/craft-agents-oss: AI-related trending repo",
        "url": "https://github.com/lukilabs/craft-agents-oss",
        "source_domain": "github.com",
        "category_label": "Agent",
        "overall": 5.98,
        "metrics": {
          "signal": 8.0,
          "novelty": 5.1,
          "impact": 2.0,
          "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."
      }
    ]
  },
  "lab_notes": {
    "tool_repo_of_the_day": {
      "title": "MiMo-Embodied: X-Embodied Foundation Model Technical Report",
      "url": "https://arxiv.org/abs/2511.16518",
      "source_domain": "arxiv.org"
    },
    "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"
    }
  }
}