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Received — 10 September 2026 ⏭

Harbor Adapters and Harbor-Index: Infrastructure and a Curated Meta-Dataset for Large-Scale Agentic Evaluation

✇cs.AI, q-bio.NC updates on arXiv.org
By: 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 (Aud
10 September 2026 at 12:00
arXiv:2609.04298v2 Announce Type: replace 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.
Received — 27 May 2026 ⏭

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization

✇cs.AI, q-bio.NC updates on arXiv.org
By: Keane Ong · Sabri Boughorbel · Luwei Xiao · Chanakya Ekbote · Wei Dai · Ao Qu · Jingyao Wu · Rui Mao · Ehsan Hoque · Erik Cambria · Gianmarco Mengaldo · Paul Pu Liang
26 May 2026 at 12:00
arXiv:2602.10635v2 Announce Type: replace Abstract: Socially intelligent AI systems must entail reasoning across diverse human behavioral tasks, and generalization to new contexts. However, AI has yet to achieve this level of social intelligence. Existing models remain fundamentally constrained by the imbalanced learning dynamics induced by training on behavioral data. Namely, behavioral data is inherently heterogeneous, comprising diverse modalities and prediction targets that often produce uneven training signals across samples. To address this, we develop Omnisapiens-7B 2.0, a foundation model for social behavior processing that explicitly addresses learning from heterogeneous behavioral data. This is enabled through Heterogeneity-Aware Relative Policy Optimization, a novel reasoning RL method that explicitly rebalances learning signals across samples. The core insight is to approximate contribution signals to the policy update, using them to inform geometrically centered and intertially smoothed advantage modulation. Results demonstrate that Omnisapiens-7B 2.0 achieves the best and most consistent performance across 10 diverse behavioral tasks, while also attaining the best performance on all five held-out zero-shot generalization benchmarks, with gains of up to +12.02% and +9.37% respectively. Furthermore, Omnisapiens-7B 2.0 demonstrates more consistent and interpretable reasoning traces, supporting reliable real-world behavioral applications. Our model and codes can be found at https://github.com/MIT-MI/human_behavior_atlas.
Received — 13 April 2026 ⏭

Integrated analysis of network pharmacology and multi-omics reveals the mechanisms of Zuogui Jiangtang Qinggan formula ameliorates MASLD via fatty acid metabolic reprogramming

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Junju Zou · Yuwei Dai · Yungfeng Yu · Yongjun Wu · Qin Xiang · Rong Yu
10 April 2026 at 18:00

Phytomedicine. 2026 Mar 30;155:158128. doi: 10.1016/j.phymed.2026.158128. Online ahead of print.

ABSTRACT

BACKGROUND: The global prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) continues to rise, and its pathogenesis is complex, creating an urgent need to discover novel and effective therapeutic strategies. The Zuogui Jiangtang Qinggan formula (ZGJTQGF), an approved in-hospital preparation, has demonstrated significant clinical efficacy in treating diabetes over several decades. However, the mechanisms underlying its potential therapeutic effects on MASLD remain unclear PURPOSE: This study systematically investigates the therapeutic effects and molecular mechanisms of ZGJTQGF on MASLD through the integration of network pharmacology and multi-omics strategies.

METHODS: The model of MASLD was successfully induced in db/db mice by a high-fat diet (HFD), which displayed characteristic dyslipidaemia. Serum biomarkers, histology, and hepatic multi-omics analyses were employed to assess metabolic status, steatosis, targets, and pathways. Ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), molecular docking analysis and in vitro verification were applied to explore the active ingredients of ZGJTQGF.

RESULTS: ZGJTQGF significantly reduced dyslipidemia in HFD-fed mice, inhibited pro-inflammatory cytokines, and restored glucose metabolic balance by lowering levels of glucose, insulin, OGTT, and HOMA-IR. Histopathology showed reduced lipid deposition and hepatocyte damage. Comprehensive multi-omics analysis suggested that regulating the AMPK/PGC-1α/PPARα and FXR-BSEP signaling pathways could be potential targets for ZGJTQGF in reprogramming glucose and lipid metabolism in MASLD treatment. Blood component analysis identified 52 ZGJTQGF-derived compounds. In molecular docking experiments, Wogonin, Naringenin, Quercetin, Tanshinone IIA and Berberine showed high-affinity binding to core targets in AMPK, PPARα, PGC-1α, FXR and FAS. Mechanistically, ZGJTQGF activated AMPK/PPARα /PGC-1α and FXR-BSEP signaling pathway, promotes fatty acid β oxidation and enhances energy consumption in AML-2 and 3T3-L1 cells, downregulates SREBP-1-dependent adipogenesis (reduces ACC1 and FAS expression), alleviates MASLD driven reprogramming of glucose and lipid metabolism, and regulates lipid metabolism and fatty acid synthesis.

CONCLUSIONS: ZGJTQGF activates the AMPK/PPARα /PGC-1α pathway and inhibits abnormal lipid accumulation in diabetic fatty liver by promoting fatty acid β-oxidation, energy consumption, and bile acid metabolism. These findings provide new insights into the mechanism of ZGJTQGF in the treatment of diabetic fatty liver disease.

PMID:41962267 | DOI:10.1016/j.phymed.2026.158128

Integrated analysis of network pharmacology and multi-omics reveals the mechanisms of Zuogui Jiangtang Qinggan formula ameliorates MASLD via fatty acid metabolic reprogramming

✇Omics in Hepatocellular
By: Junju Zou · Yuwei Dai · Yungfeng Yu · Yongjun Wu · Qin Xiang · Rong Yu
10 April 2026 at 18:00

Phytomedicine. 2026 Mar 30;155:158128. doi: 10.1016/j.phymed.2026.158128. Online ahead of print.

ABSTRACT

BACKGROUND: The global prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) continues to rise, and its pathogenesis is complex, creating an urgent need to discover novel and effective therapeutic strategies. The Zuogui Jiangtang Qinggan formula (ZGJTQGF), an approved in-hospital preparation, has demonstrated significant clinical efficacy in treating diabetes over several decades. However, the mechanisms underlying its potential therapeutic effects on MASLD remain unclear PURPOSE: This study systematically investigates the therapeutic effects and molecular mechanisms of ZGJTQGF on MASLD through the integration of network pharmacology and multi-omics strategies.

METHODS: The model of MASLD was successfully induced in db/db mice by a high-fat diet (HFD), which displayed characteristic dyslipidaemia. Serum biomarkers, histology, and hepatic multi-omics analyses were employed to assess metabolic status, steatosis, targets, and pathways. Ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), molecular docking analysis and in vitro verification were applied to explore the active ingredients of ZGJTQGF.

RESULTS: ZGJTQGF significantly reduced dyslipidemia in HFD-fed mice, inhibited pro-inflammatory cytokines, and restored glucose metabolic balance by lowering levels of glucose, insulin, OGTT, and HOMA-IR. Histopathology showed reduced lipid deposition and hepatocyte damage. Comprehensive multi-omics analysis suggested that regulating the AMPK/PGC-1α/PPARα and FXR-BSEP signaling pathways could be potential targets for ZGJTQGF in reprogramming glucose and lipid metabolism in MASLD treatment. Blood component analysis identified 52 ZGJTQGF-derived compounds. In molecular docking experiments, Wogonin, Naringenin, Quercetin, Tanshinone IIA and Berberine showed high-affinity binding to core targets in AMPK, PPARα, PGC-1α, FXR and FAS. Mechanistically, ZGJTQGF activated AMPK/PPARα /PGC-1α and FXR-BSEP signaling pathway, promotes fatty acid β oxidation and enhances energy consumption in AML-2 and 3T3-L1 cells, downregulates SREBP-1-dependent adipogenesis (reduces ACC1 and FAS expression), alleviates MASLD driven reprogramming of glucose and lipid metabolism, and regulates lipid metabolism and fatty acid synthesis.

CONCLUSIONS: ZGJTQGF activates the AMPK/PPARα /PGC-1α pathway and inhibits abnormal lipid accumulation in diabetic fatty liver by promoting fatty acid β-oxidation, energy consumption, and bile acid metabolism. These findings provide new insights into the mechanism of ZGJTQGF in the treatment of diabetic fatty liver disease.

PMID:41962267 | DOI:10.1016/j.phymed.2026.158128

Received — 8 April 2026 ⏭

Enhancing Foundation VLM Robustness to Missing Modality: Scalable Diffusion for Bi-directional Feature Restoration

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wei Dai · Haoyu Wang · Honghao Chang · Lijun He · Fan Li · Jian Sun · Haixia Bi
7 April 2026 at 12:00
arXiv:2602.03151v2 Announce Type: replace Abstract: Vision Language Model (VLM) typically assume complete modality input during inference. However, their effectiveness drops sharply when certain modalities are unavailable or incomplete. Current research on missing modality primarily faces two dilemmas: Prompt-based methods struggle to restore missing yet indispensable features and degrade the generalizability of VLM. Imputation-based approaches, lacking effective guidance, are prone to generating semantically irrelevant noise. Restoring precise semantics while sustaining VLM's generalization remains challenging. Therefore, we propose a general missing modality restoration strategy in this paper. We introduce an enhanced diffusion model as a pluggable mid-stage training module to effectively restore missing features. Our strategy introduces two key innovations: (I) Dynamic Modality Gating, which adaptively leverages conditional features to guide the generation of semantically consistent features; (II) Cross-Modal Mutual Learning mechanism, which bridges the semantic spaces of the dual models to achieve bi-directional alignment. Notably, our strategy maintains the original integrity of the pre-trained VLM, requiring no fine-tuning of the backbone models while significantly boosting resilience to information loss. Zero-shot evaluations across benchmark datasets demonstrate that our approach consistently outperforms existing baselines, establishing it as a robust and scalable extension that ensures VLM reliability across diverse missing rates and conditions. Our code and models will be publicly available.
Received — 4 April 2026 ⏭
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