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arXiv 最近完成扫描:2026-11-16 01:34 · 全站最近入库:2026-11-14 15:09各信源进度 →

统计口径与数据新鲜度

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版本更新 · v2arXiv:2608.28990

Agentic AI uncovers conserved cross-tissue protein co-abundance programs inaccessible to single-dataset analysis

Dehao Wu, Runyu Guan, Qiqi Xie, Yang Li, Haohan Wang

原文摘要

Protein co-abundance clusters preserved across tissues can nominate shared disease mechanisms and therapeutic targets, particularly where proteins implicated in organ-confined diseases converge in other tissues. Yet cross-tissue proteomic studies have examined pre-selected tissue pairs, leaving most combinations, and non-obvious relationships among them, unexplored. Exhaustive comparison requires consistently assessing thousands of candidate clusters from hundreds of tissue-pair networks across heterogeneous evidence, a scale beyond manual curation. We present an LLM-agent framework for large-scale, evidence-grounded comparison of tissue-specific protein co-abundance networks. From sample-level proteomic data, it constructs tissue networks, derives conserved clusters for each tissue pair, integrates evidence from expression atlases, interaction and complex databases, pathways, disease associations and the literature, and proposes mechanistic hypotheses in which every conclusion cites its evidence. Applied to all 231 pairs among 22 human tissues and sample groups, it identified 1,825 conserved clusters in 216 tissue pairs. Co-abundance relationships were shared beyond chance in 76% of pairs, including unrelated organs, and shared clusters were only weakly associated with abundance similarity. Clusters were enriched 3.7- to 6.9-fold for known interactions and complex memberships, and 379 of 793 testable clusters replicated in independent CPTAC proteomes. Some clusters pointed to relationships absent from current databases: one shared by liver and bone marrow linked three genes causing inherited white matter disorders, suggesting a common mechanism via the integrated stress response. Conserved co-abundance clusters thus offer a hypothesis-generating resource for studying disease proteins beyond the affected organ. Code: https://github.com/jacob20030316-crypto/Protein_Co_Abundance_Agent

cs.AI · q-bio.MN

新论文 · v1arXiv:2611.07461

The Last Mile to the Real World: Agent-Native Primitives as Reusable Building Blocks for Solving Real-World Tasks

Haibo Jin, Peng Kuang, Xiaopeng Yuan, Lucheng Fu, Jerry Wang, Xucheng Yu, Yaoning Yu, Dehao Wu 等 14 位作者

原文摘要

How can an agent handle a real-world task end to end? Frontier models with their own harnesses still fall short on real-world benchmarks: on Terminal-Bench 4.0, GPT-5.6 Sol with Codex resolves 37.3% of the tasks and Claude Opus 5 with Claude Code 51.8%. These tasks fail at the step from reaching a resource to applying it, which we call the last mile to the real world. Existing approaches put the resource within reach but leave the competence to apply it with the caller, so each harness rebuilds that competence. We introduce agent-native primitives, reusable units that equip a resource with a resident LLM that applies it in context and communicates with other primitives, and HEELO (Harness Engineering around Evolving Agent-Native Primitives for the Last Mile to Real-WOrld Tasks), a harness engineering framework that builds each task's harness from a library of such primitives. HEELO composes the primitives a task needs into a task-specific graph, verifies the result against evidence from the environment, revises the primitives a failure implicates, and admits new or improved primitives after validation. We instantiate HEELO with eight primitive families, each built for one kind of task, and evaluate them on more than twenty benchmarks. Dev-Primitives raise Terminal-Bench 4.0 resolution from 37.3% under Codex to 55.5% on the same backbone, Harness Primitives composed per task raise Terminal-Bench 2.0 Pass@1 from 60.0% for the seed harness to 72.2%, Tool Primitives raise ToolBench pass rate from 55.8% to 75.1%, and Navigation Primitives raise GAIA-Text-103 accuracy from 45.6% to 58.3%. On the 46 real-world tasks of our heelo.dev platform, Claude Opus 5.5 with Claude Code and GPT-6.1 Sol with Codex complete 9 each on their own and 39 and 35 when connected to HEELO through our MCP server, with 1.9$\times$ and 2.3$\times$ less time and 1.55$\times$ lower cost per completed task.

cs.CL · cs.AI · cs.LG · cs.MA · cs.SE