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}, { "story_id": "arxiv:oai:arXiv.org:2604.02947v2", "title": "AgentHazard: A Benchmark for Evaluating Harmful Behavior in Computer-Use Agents", "url": "https://arxiv.org/abs/2604.02947", "overall": 6.17, "metrics": { "signal": 9.43, "novelty": 6.2, "impact": 2.0, "confidence": 8.3, "actionability": 3.5, "freshness": 7.52 }, "badges": { "Paper": "https://arxiv.org/abs/2604.02947", "Benchmarks": "" }, "corroboration_count": 1, "corroboration_sources": [ "arxiv" ], "source": "arxiv" } ], "deep_dives": [ { "story_id": "arxiv:oai:arXiv.org:2609.26480v1", "title": "FeatLens: Feature-Guided Dynamic Code Graph Construction and Retrieval for Repository-Level Code Generation", "url": "https://arxiv.org/abs/2609.26480", "source_domain": "arxiv.org", "category_label": "Cs.Ai", "overall": 6.53, "metrics": { "signal": 9.43, "novelty": 4.0, "impact": 2.0, "confidence": 9.5, "actionability": 8.2 }, "why_made_cut": "Signal 9.4, Confidence 9.5, and Impact 2.0 combined to rank this in the top set.", "badges": [ "paper" ], "context": "Existing retrieval methods provide such context through code similarity search, persistent whole-repository graphs, or LLM-driven graph exploration, but often incur high graph construction, reasoning, and token costs.", "whats_new": "Existing retrieval methods provide such context through code similarity search, persistent whole-repository graphs, or LLM-driven graph exploration, but often incur high graph construction, reasoning, and token costs.", "key_details": [ "To implement a target function correctly, an LLM must identify reusable repository dependencies such as existing functions, APIs, and cross-file definitions.", "Existing retrieval methods provide such context through code similarity search, persistent whole-repository graphs, or LLM-driven graph exploration, but often incur high graph construction, reasoning, and token costs.", "Feature-oriented methods offer a natural view of software functionality, yet they mainly support requirement decomposition, planning, or feature editing rather than code dependency retrieval.", "This paper presents \\textbf{FeatLens}, a feature-guided dynamic code graph construction and retrieval approach for repository-level code generation." ], "results_evidence": [ "arXiv:2609.26480v1 Announce Type: cross Abstract: Recent code generation research has moved from isolated function completion toward repository-level generation in existing codebases.", "Experiments on DevEval and EvoCodeBench show that FeatLens achieves the best DR@15 among sparse, dense, and graph-based baselines (0.501 and 0.460).", "On DevEval generation, it obtains the highest DIR@1, reaching 52.91\\% with DeepSeek-V3.2 and 53.58\\% with GPT-5-mini, while maintaining competitive Pass@1 and producing shorter code." ], "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:1170821064", "title": "paperclipai/paperclip: The open-source app everyone uses to manage agents at work", "url": "https://github.com/paperclipai/paperclip", "source_domain": "github.com", "category_label": "Agent", "overall": 7.93, "metrics": { "signal": 10.0, "novelty": 6.2, "impact": 7.75, "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", "paper" ], "context": "The open-source app everyone uses to manage agents at work Quickstart \u00b7 Docs \u00b7 GitHub \u00b7 Discord \u00b7 Twitter \u00b7 Website full-tour.webm Open-source orchestration for teams of AI agents.", "whats_new": "The open-source app everyone uses to manage agents at work Quickstart \u00b7 Docs \u00b7 GitHub \u00b7 Discord \u00b7 Twitter \u00b7 Website full-tour.webm Open-source orchestration for teams of AI agents.", "key_details": [ "If OpenClaw is an employee, Paperclip is the company.", "Paperclip is a Node.js server and React UI that orchestrates a team of AI agents to run a business.", "Bring your own agents, assign goals, and track work and costs from one dashboard.", "Under the hood: org charts, budgets, governance, goal alignment, and agent coordination." ], "results_evidence": [ "| | Step | Example | |---|---|---| | 01 | Define the goal | \"Build the #1 AI note-taking app to $1M MRR.\" | | 02 | Hire the team | CEO, CTO, engineers, designers, marketers \u2014 any bot, any provider.", "| | 03 | Approve and run | Review strategy.", "| - \u2705 You want to build autonomous AI organizations - \u2705 You coordinate many different agents (OpenClaw, Codex, Claude, Cursor) toward a common goal - \u2705 You have 20 simultaneous Claude Code terminals open and lose track of what everyone is doing - \u2705 You want..." ], "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": "arxiv:oai:arXiv.org:2609.27607v1", "title": "Can Jev Judge Radiology Reports? Evaluating a System One Model for Clinical Factuality", "url": "https://arxiv.org/abs/2609.27607", "source_domain": "arxiv.org", "category_label": "Cs.Ai", "overall": 6.32, "metrics": { "signal": 9.43, "novelty": 4.0, "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:2609.27607v1 Announce Type: new Abstract: An AI-generated radiology report can resemble a physician's report while omitting an abnormality, adding an unsupported finding, or reversing its presence.", "whats_new": "arXiv:2609.27607v1 Announce Type: new Abstract: An AI-generated radiology report can resemble a physician's report while omitting an abnormality, adding an unsupported finding, or reversing its presence.", "key_details": [ "Measuring these factual differences is essential for evaluating report generators.", "We study Jev, a System One decision model, as a simple, low-cost judge of agreement with physician-written reference reports.", "Our evaluator checks whether each statement is supported by the other report and combines these judgments in both directions to capture unsupported claims and omissions.", "A single-question configuration reaches Kendall correlations of 0.573 on RadEvalX and 0.398 on RadEvalExpert with expert error counts, outperforming an open natural language inference judge under matched decomposition and aggregation." ], "results_evidence": [ "arXiv:2609.27607v1 Announce Type: new Abstract: An AI-generated radiology report can resemble a physician's report while omitting an abnormality, adding an unsupported finding, or reversing its presence.", "A single-question configuration reaches Kendall correlations of 0.573 on RadEvalX and 0.398 on RadEvalExpert with expert error counts, outperforming an open natural language inference judge under matched decomposition and aggregation.", "One support question per statement retains similar expert agreement to seven while using 43-45% fewer judgment input tokens." ], "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:2609.26480v1", "title": "FeatLens: Feature-Guided Dynamic Code Graph Construction and Retrieval for Repository-Level Code Generation", "url": "https://arxiv.org/abs/2609.26480", "source_domain": "arxiv.org", "category_label": "Cs.Ai", "overall": 6.53, "metrics": { "signal": 9.43, "novelty": 4.0, "impact": 2.0, "confidence": 9.5, "actionability": 8.2 }, "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": "gh:1223170290", "title": "nexu-io/open-design: \ud83c\udfa8 Best DeepSeek Harness Design Plugin. 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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" } } }