{
  "date": "2026-09-01",
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        "| | 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..."
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        "However, due to their reliance on sparse trajectory-level signals, existing methods cannot identify which per-turn actions are effective and often discover correct code regions during exploration but fail to commit them.",
        "To address these limitations, we propose an action-aware reinforcement learning method that combines a per-turn reward sequence rewarding both the discovery and commitment of gold code regions with an action-level advantage estimation scheme that isolates e...",
        "Extensive evaluations show that our method improves the average F1 over the state-of-the-art (SOTA) by 1.58% on SWE-Bench Verified and 8.55% on SWE-Bench Pro, with our 4B model outperforming baselines up to 8x larger.",
        "Our code is available at https://github.com/donian00/A2Agent."
      ],
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        "Extensive evaluations show that our method improves the average F1 over the state-of-the-art (SOTA) by 1.58% on SWE-Bench Verified and 8.55% on SWE-Bench Pro, with our 4B model outperforming baselines up to 8x larger.",
        "Computer Science > Computation and Language [Submitted on 30 Aug 2026] Title:A^2Agent: Action-Aware Reinforcement Learning for Repository-Level Code Localization Agents View PDF HTML (experimental) Abstract:Localizing issue-relevant code regions is a critic..."
      ],
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        "However, due to their reliance on sparse trajectory-level signals, existing methods cannot identify which per-turn actions are effective and often discover correct code regions during exploration but fail to commit them.",
        "To address these limitations, we propose an action-aware reinforcement learning method that combines a per-turn reward sequence rewarding both the discovery and commitment of gold code regions with an action-level advantage estimation scheme that isolates e..."
      ],
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        "actionability": 6.5
      },
      "why_made_cut": "Signal 9.4, Confidence 8.7, and Impact 2.0 combined to rank this in the top set.",
      "badges": [
        "paper"
      ],
      "context": "Our evaluation reveals two key insights: (1) Vulnerability is highly context-dependent, with task type creating up to a 4.5-fold difference in ASR, with test-execution task forming a silent attack surface (high ASR, low AR).",
      "whats_new": "We term these user-side choices Prompt-Level Configurations (PLCs) and introduce CIPR (Coding In Poisoned Repos), the first benchmark that systematically varies PLCs in poisoned real-world repositories.",
      "key_details": [
        "Prior work on repository poisoning largely focuses on attacker-controlled injection and disguise, but developers also shape risk through everyday invocation choices: what task to delegate, how to phrase the request, and which skills or rules to supply.",
        "We term these user-side choices Prompt-Level Configurations (PLCs) and introduce CIPR (Coding In Poisoned Repos), the first benchmark that systematically varies PLCs in poisoned real-world repositories.",
        "CIPR comprises 1,920 instances across 20 repositories, four task types, three social-media-grounded prompt styles, and three skill/rule conditions, and measures attack success rate (ASR) and agent alert rate (AR) using automated runtime and trace-based orac...",
        "Our evaluation reveals two key insights: (1) Vulnerability is highly context-dependent, with task type creating up to a 4.5-fold difference in ASR, with test-execution task forming a silent attack surface (high ASR, low AR)."
      ],
      "results_evidence": [
        "arXiv:2608.30686v1 Announce Type: cross Abstract: Coding agents are increasingly used for software engineering tasks, including bootstrapping projects from third-party repositories whose integrity cannot be assumed.",
        "CIPR comprises 1,920 instances across 20 repositories, four task types, three social-media-grounded prompt styles, and three skill/rule conditions, and measures attack success rate (ASR) and agent alert rate (AR) using automated runtime and trace-based orac...",
        "Our evaluation reveals two key insights: (1) Vulnerability is highly context-dependent, with task type creating up to a 4.5-fold difference in ASR, with test-execution task forming a silent attack surface (high ASR, low AR)."
      ],
      "limitations_unknowns": [
        "Prior work on repository poisoning largely focuses on attacker-controlled injection and disguise, but developers also shape risk through everyday invocation choices: what task to delegate, how to phrase the request, and which skills or rules to supply.",
        "(2) Prompt expression shifts risk indirectly: underspecified prompts reduce ASR by truncating execution depth; noisy prompts exhibit a directional trend toward suppressing alerts by making malicious content less conspicuous."
      ],
      "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": 8.14,
        "metrics": {
          "signal": 10.0,
          "novelty": 7.3,
          "impact": 7.82,
          "confidence": 7.03,
          "actionability": 6.5
        },
        "badges": [
          "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": 8.31,
          "confidence": 7.03,
          "actionability": 6.5
        },
        "badges": [
          "repo"
        ],
        "checklist": {
          "primary_source": "yes",
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          "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:2601.16753v2",
        "title": "Standardizing Longitudinal Radiology Report Evaluation via Large Language Model Annotation",
        "url": "https://arxiv.org/abs/2601.16753",
        "source_domain": "arxiv.org",
        "category_label": "Cs.Ai",
        "overall": 6.33,
        "metrics": {
          "signal": 9.43,
          "novelty": 4.0,
          "impact": 2.0,
          "confidence": 9.5,
          "actionability": 6.5
        },
        "badges": [
          "repo",
          "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:2608.29831v1",
        "title": "A^2Agent: Action-Aware Reinforcement Learning for Repository-Level Code Localization Agents",
        "url": "https://arxiv.org/abs/2608.29831",
        "source_domain": "arxiv.org",
        "category_label": "Cs.Cl",
        "overall": 6.4,
        "metrics": {
          "signal": 9.43,
          "novelty": 5.1,
          "impact": 2.0,
          "confidence": 8.7,
          "actionability": 6.5
        },
        "badges": [
          "repo",
          "paper"
        ],
        "checklist": {
          "primary_source": "yes",
          "demo": "no",
          "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": "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"
    }
  }
}