The DAIR Daily - ML Papers of the Week

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{ Hono } from \"npm:hono\";\n\nconst app = new Hono();\n\nconst papers = [\n {\n title: \"Intelligent AI Delegation\",\n authors: \"Google DeepMind\",\n summary: \"A comprehensive framework for intelligent AI delegation that models the process as a sequence of decisions: whether to delegate, how to instruct, and how to verify outputs.\",\n link: \"https://arxiv.org/abs/2602.11865\",\n category: \"Front Page\",\n highlights: [\n \"Adaptive delegation structure for real-time shifts.\",\n \"Trust calibration models to prevent over/under-delegation.\",\n \"Structured verification and integration protocols.\"\n ]\n },\n {\n title: \"Emergent Socialization in AI Agent Society\",\n authors: \"Moltbook Researchers\",\n summary: \"A study on Moltbook, an AI-only social network, finding that scale alone doesn't produce meaningful social dynamics without shared memory.\",\n link: \"https://arxiv.org/abs/2602.14299\",\n category: \"Front Page\",\n highlights: [\n \"Moltbook as a natural laboratory for agent interaction.\",\n \"Agents fail to develop stable social structures or consensus.\",\n \"Shared memory identified as a prerequisite for socialization.\"\n ]\n },\n {\n title: \"Lossless Context Management (LCM)\",\n authors: \"Voltropy / Opus 4.6\",\n summary: \"A deterministic architecture for LLM memory that outperforms Claude Code on long-context tasks using recursive compression and task partitioning.\",\n link: \"https://papers.voltropy.com/LCM\",\n category: \"Front Page\",\n highlights: [\n \"Recursive context compression into hierarchical DAGs.\",\n \"Recursive task partitioning via engine-managed primitives.\",\n \"Outperforms Claude Code at 1M token context lengths.\"\n ]\n },\n {\n title: \"GLM-5: Transitioning to Agentic Engineering\",\n authors: \"Zhipu AI\",\n summary: \"A foundation model designed for end-to-end software engineering, introducing asynchronous agent RL and DSA compression.\",\n link: \"https://arxiv.org/abs/2602.15763\",\n category: \"Engineering\",\n highlights: [\n \"Asynchronous agent RL decouples generation from training.\",\n \"Focuses on project-level context and multi-file edits.\",\n \"Distributed Sparse Attention (DSA) for long-context efficiency.\"\n ]\n },\n {\n title: \"MemoryArena: Actionable Memory Benchmark\",\n authors: \"LoCoMo Researchers\",\n summary: \"A benchmark revealing that high memory recall doesn't equate to correct actions across multi-session agentic tasks.\",\n link: \"https://arxiv.org/abs/2602.16313\",\n category: \"Benchmarks\",\n highlights: [\n \"Evaluates retrieval vs. actionable decision quality.\",\n \"Spans web navigation, planning, and logical reasoning.\",\n \"Exposes blind spots in existing long-context evals.\"\n ]\n },\n {\n title: \"MAPLE: Specialized Sub-Agents\",\n authors: \"MAPLE-Personas Team\",\n summary: \"Proposes separating memory, learning, and personalization into specialized sub-agents to improve behavioral consistency.\",\n link: \"https://arxiv.org/abs/2602.13258\",\n category: \"Personalization\",\n highlights: [\n \"14.6% improvement in personalization scores.\",\n \"Asynchronous learning processes history offline.\",\n \"Context-budget-aware injection of learned knowledge.\"\n ]\n },\n {\n title: \"SkillsBench: Procedural Knowledge Gap\",\n authors: \"SkillsBench Team\",\n summary: \"Reveals that while agents benefit from curated procedural knowledge, they cannot yet reliably author it themselves.\",\n link: \"https://arxiv.org/abs/2602.12670\",\n category: \"Benchmarks\",\n highlights: [\n \"Curated skills boost pass rates by 16.2pp on average.\",\n \"Self-generated skills provide zero net benefit.\",\n \"Focused modules outperform comprehensive documentation.\"\n ]\n },\n {\n title: \"LongCLI-Bench: Complex CLI Tasks\",\n authors: \"CLI Agent Researchers\",\n summary: \"Benchmarks AI agents on 20 demanding CLI tasks, finding a success rate of less than 20% for leading models.\",\n link: \"https://arxiv.org/abs/2602.14337\",\n category: \"Benchmarks\",\n highlights: [\n \"Failures often occur early in task execution.\",\n \"Human-agent collaboration yields better results than self-correction.\"\n ]\n },\n {\n title: \"CogRouter: Adaptive Reasoning Depth\",\n authors: \"Qwen2.5 Team\",\n summary: \"Enables agents to dynamically select cognitive levels, skipping heavy reasoning on routine steps to save tokens.\",\n link: \"https://arxiv.org/abs/2602.12662\",\n category: \"Efficiency\",\n highlights: [\n \"82.3% success rate on agentic benchmarks.\",\n \"Substantially outperforms larger models with fewer tokens.\"\n ]\n },\n {\n title: \"Team of Thoughts: Test-Time Scaling\",\n authors: \"Multi-Agent Framework Team\",\n summary: \"A multi-agent framework for efficient test-time scaling through orchestrated tool calling and self-assessment.\",\n link: \"https://arxiv.org/abs/2602.16485\",\n category: \"Scaling\",\n highlights: [\n \"96.67% on AIME24 and 72.53% on LiveCodeBench.\",\n \"Uses calibrated orchestrators to coordinate specialized agents.\"\n ]\n }\n];\n\napp.get(\"/\", (c) => {\n const html = `\n \n \n \n \n \n The DAIR Daily - ML Papers of the Week\n \n \n \n \n

\n Vol. MMXXVI • No. 09\n Sunday, March 1, 2026\n Price: One Token\n

\n \n

\n ML Papers of the Week\n Elvis Saravia, Editor-in-Chief\n Val Town Edition\n

\n\n

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\n `).join('')}\n\n

Engineering & Efficiency

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\n ${p.title}\n By ${p.authors}\n

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\n\n \n

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