Harbor Adapters and Harbor-Index: Infrastructure and a Curated Meta-Dataset for Large-Scale Agentic Evaluation
Updated 6 h ago · first seen 11 Sept 2026
paper_01M294G6R7XEKXRHZJDTBSA7K2
- Published
- 11 Sept 2026
- T1 · 6 h ago
- arXiv
- 2609.04298
- T1 · 6 h ago
- Category
- cs.AI
- T1 · 6 h ago
Abstract
-cross Abstract: Evaluating agents on the growing number of agentic benchmarks is challenging because they often require complex environments and agent integrations. We introduce Harbor Adapters, a unified evaluation infrastructure for agentic benchmarks. Our work makes three contributions. First, we develop benchmark adapters that port more than 80 benchmarks to evaluate arbitrary agents, and validate them through rigorous code review and parity experiments. Second, we conduct a large-scale evaluation of 8 models spanning capability tiers across 54 benchmarks; every model is run with Terminus-2 and with one of 3 native harnesses. This enables a broader analysis of agent capabilities and failure modes than was previously possible. Third, we introduce Harbor-Index, a curated set of 82 difficult, diverse, and high-quality tasks spanning 29 benchmarks, refined from the adapted suite through difficulty filtering, AI and human audit, and an audit-and-fix loop. Harbor-Index preserves the challenge and breadth of large-scale agentic evaluations while being affordable to run; no evaluated model-harness configuration exceeds 30% pass rate, and the strongest (GPT-5.5 with Codex) reaches 28.0%. We release the adapters, evaluation results, in-depth analysis, and Harbor-Index as open-source artifacts to support more reliable and comprehensive evaluation of language-model agents.
Authors 100
Lin Shi (Audrey), Haowei Lin (Audrey), Zixuan Zhu (Audrey), Xiaoyue Zhou (Audrey), Xiang Li (Audrey), Xiangning Lin (Audrey), Yaxuan Deng (Audrey), Han Xu (Audrey), Yuangang Li (Audrey), Shanda Li (Audrey), Zizhao Chen (Audrey), Hanwen Xing (Audrey), Harsh Raj (Audrey), Bo Chen (Audrey), Quan Shi (Audrey), Steven Dillmann (Audrey), Yipeng Gao (Audrey), Puneesh Khanna (Audrey), Ruofan Lu (Audrey), Chao Beyond Zhou (Audrey), Michael Yang (Audrey), Robert Zhang (Audrey), Siyuan Chai (Audrey), Jiayu Chang (Audrey), Yizhao Chen (Audrey), Xiaokun Chen (Audrey), Yiwei Dai (Audrey), Wenting Yang (Audrey), Hange Liu (Audrey), Minghao Liu (Audrey), Zihan Wang (Audrey), Adnan El Assadi (Audrey), Benedikt Stroebl (Audrey), E. Kelly Buchanan (Audrey), Han Meng (Audrey), Junwei He (Audrey), Longxuan Yu (Audrey), Radin Shayanfar (Audrey), Yukyung Lee (Audrey), Zhikang Dong (Audrey), Allen G Hart (Audrey), Anjiang Wei (Audrey), Anurag Kashyap (Audrey), Arpandeep Khatua (Audrey), Audrey Jixin Zheng (Audrey), Chengrui Ma (Audrey), David Heineman (Audrey), Dubing Chen (Audrey), Hai-Anh Trinh (Audrey), Haishuo Fang (Audrey), Hefan Zhang (Audrey), Hui Shen (Audrey), Issa Sugiura (Audrey), Jiankai Sun (Audrey), Jiechao Gao (Audrey), Junhong Lin (Audrey), Junnan Li (Audrey), Kai Yang (Audrey), Lei Hsiung (Audrey), Maoyu Wang (Audrey), Mengze Tang (Audrey), Nabil Omi (Audrey), Negin Raoof (Audrey), Nicholas Edwards (Audrey), Octavia Guo (Audrey), Orfeas Menis Mastromichalakis (Audrey), Pengliang Ji (Audrey), Przemys{\l}aw Hejman (Audrey), Qi Qi (Audrey), Qunshu Lin (Audrey), Richard Zhuang (Audrey), Rui Yang (Audrey), Ruichen Zheng (Audrey), Ryan Marten (Audrey), Shaghayegh Fazliani (Audrey), Shizheng Hou (Audrey), Sicong Jiang (Audrey), Sijie Li (Audrey), Boqin Yuan (Audrey), Michael Glass (Audrey), Song Bian (Audrey), Terry Yue Zhuo (Audrey), Tianqing Wu (Audrey), Tom Tang (Audrey), Wanjia Zhao (Audrey), Weihao Xuan (Audrey), Wenhua Liang (Audrey), Xian Liu (Audrey), Xin Lan (Audrey), Xuan Zhang (Audrey), Xuandong Zhao (Audrey), Yanchuan Tang (Audrey), Yifan Jiang (Audrey), Yijiang Li (Audrey), Yitong Guan (Audrey), Yizhi Li (Audrey), Yonghui Liu (Audrey), Yuheng Tang (Audrey), Yujun (Audrey), Mao
Specification
- Official page
Source:arXiv (Atom API + RSS)T1observed 6 h agohigh
- Arxiv announce type
- replace
Source:arXiv (Atom API + RSS)T1observed 6 h agohigh
- arXiv id
- 2609.04298
Source:arXiv (Atom API + RSS)T1observed 6 h agohigh
- Categories
- cs.AI, cs.CL
Source:arXiv (Atom API + RSS)T1observed 6 h agohigh
Source:arXiv (Atom API + RSS)T1observed 6 h agohigh
- Primary category
- cs.AI
Source:arXiv (Atom API + RSS)T1observed 6 h agohigh
- Published
- 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 6 h agohigh
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9
Source tiers
T19
Freshest observation
6 h ago
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Claim history · Abstract
Abstractabstract1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| -cross Abstract: Evaluating agents on the growing number of agentic benchmarks is challenging because they often require complex environments and agent integrations. We introduce Harbor Adapters, a unified evaluation infrastructure for agentic benchmarks. Our work makes three contributions. First, we develop benchmark adapters that port more than 80 benchmarks to evaluate arbitrary agents, and validate them through rigorous code review and parity experiments. Second, we conduct a large-scale evaluation of 8 models spanning capability tiers across 54 benchmarks; every model is run with Terminus-2 and with one of 3 native harnesses. This enables a broader analysis of agent capabilities and failure modes than was previously possible. Third, we introduce Harbor-Index, a curated set of 82 difficult, diverse, and high-quality tasks spanning 29 benchmarks, refined from the adapted suite through difficulty filtering, AI and human audit, and an audit-and-fix loop. Harbor-Index preserves the challenge and breadth of large-scale agentic evaluations while being affordable to run; no evaluated model-harness configuration exceeds 30% pass rate, and the strongest (GPT-5.5 with Codex) reaches 28.0%. We release the adapters, evaluation results, in-depth analysis, and Harbor-Index as open-source artifacts to support more reliable and comprehensive evaluation of language-model agents. | → current | current | arXiv (Atom API + RSS)T1 | high | deterministic |
Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
- New paperPaperHarbor Adapters and Harbor-Index: Infrastructure and a Curated Meta-Dataset for Large-Scale Agentic Evaluation
New paper: Harbor Adapters and Harbor-Index: Infrastructure and a Curated Meta-Dataset for Large-Scale Agentic Evaluation
arxiv
| Source | Document | Type | Tier | Last observed | Snapshots |
|---|---|---|---|---|---|
| arXiv (Atom API + RSS) | rss.arxiv.org/rss/cs.CL | feed | T1· Official | 5 h ago | 1 |
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.