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Received — 27 May 2026 ⏭

Reason--Imagine--Act: Closed-Loop LLM Decision Making with World Models for Autonomous Driving

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhengqi Sun · Yiwen Sun · Boxuan Liu · Tailai Chen · Tianxu Guo · Jiabin Liu
26 May 2026 at 12:00
arXiv:2605.24004v1 Announce Type: new Abstract: Large language models (LLMs) are promising for autonomous driving, but semantics-only decision policies can yield physically unsafe behavior in dynamic traffic. Existing methods either perform online language reasoning without explicit dynamics verification or use world models mainly in offline pipelines, leaving a gap between semantic intent and physical feasibility at decision time. We propose Reason--Imagine--Act (RIA), a closed-loop framework that couples an LLM reasoner with an action-conditioned world model for online safety verification. At each step, the LLM proposes an action template and candidate sub-actions, the world model performs short-horizon rollouts, and a safety scorer selects the safest executable action with feedback to the next reasoning step. Under a unified CARLA point-goal protocol (1000 episodes), RIA achieves 80.05% route completion, 51.10% arrival rate, and 0.20% collision rate. Under the same closed-loop interface, RIA consistently outperforms training-free baselines, including CARLA TM and MADA, on core closed-loop metrics. For reproducibility, code is available at https://github.com/pku-smart-city/source_code/tree/main/RIA.
Received — 8 April 2026 ⏭

Search, Do not Guess: Teaching Small Language Models to Be Effective Search Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yizhou Liu · Qi Sun · Yulin Chen · Siyue Zhang · Chen Zhao
7 April 2026 at 12:00
arXiv:2604.04651v1 Announce Type: new Abstract: Agents equipped with search tools have emerged as effective solutions for knowledge-intensive tasks. While Large Language Models (LLMs) exhibit strong reasoning capabilities, their high computational cost limits practical deployment for search agents. Consequently, recent work has focused on distilling agentic behaviors from LLMs into Small Language Models (SLMs). Through comprehensive evaluation on complex multi-hop reasoning tasks, we find that despite possessing less parametric knowledge, SLMs invoke search tools less frequently and are more prone to hallucinations. To address this issue, we propose \policy, a lightweight fine-tuning approach that explicitly trains SLMs to reliably retrieve and generate answers grounded in retrieved evidence. Compared to agent distillation from LLMs, our approach improves performance by 17.3 scores on Bamboogle and 15.3 scores on HotpotQA, achieving LLM-level results across benchmarks. Our further analysis reveals that adaptive search strategies in SLMs often degrade performance, highlighting the necessity of consistent search behavior for reliable reasoning.
Received — 17 March 2026 ⏭

ESG-Bench: Benchmarking Long-Context ESG Reports for Hallucination Mitigation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Siqi Sun · Ben Peng Wu · Mali Jin · Peizhen Bai · Hanpei Zhang · Xingyi Song
16 March 2026 at 12:00
arXiv:2603.13154v1 Announce Type: cross Abstract: As corporate responsibility increasingly incorporates environmental, social, and governance (ESG) criteria, ESG reporting is becoming a legal requirement in many regions and a key channel for documenting sustainability practices and assessing firms' long-term and ethical performance. However, the length and complexity of ESG disclosures make them difficult to interpret and automate the analysis reliably. To support scalable and trustworthy analysis, this paper introduces ESG-Bench, a benchmark dataset for ESG report understanding and hallucination mitigation in large language models (LLMs). ESG-Bench contains human-annotated question-answer (QA) pairs grounded in real-world ESG report contexts, with fine-grained labels indicating whether model outputs are factually supported or hallucinated. Framing ESG report analysis as a QA task with verifiability constraints enables systematic evaluation of LLMs' ability to extract and reason over ESG content and provides a new use case: mitigating hallucinations in socially sensitive, compliance-critical settings. We design task-specific Chain-of-Thought (CoT) prompting strategies and fine-tune multiple state-of-the-art LLMs on ESG-Bench using CoT-annotated rationales. Our experiments show that these CoT-based methods substantially outperform standard prompting and direct fine-tuning in reducing hallucinations, and that the gains transfer to existing QA benchmarks beyond the ESG domain.
Received — 14 March 2026 ⏭

CircRNA-encoded RIPK1-98 protein drives lung adenocarcinoma progression

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Qi Sun · Runqiu Chi · Xiao Zhang · Tianxiang Chen · Xin Xu · Lifang Ma · Xiaoting Tian · Yayou Miao · Yongchun Yu · Rui Kang · Guido Kroemer · Yi Shi · Daolin Tang · Jiayi Wang
13 March 2026 at 18:00

Dev Cell. 2026 Mar 12:S1534-5807(26)00079-1. doi: 10.1016/j.devcel.2026.02.014. Online ahead of print.

ABSTRACT

Unexplored biological matter-including uncharacterized genetic elements, molecular entities, and microbial components-remains poorly understood. Here, we use integrated multi-omics approaches to identify and characterize previously unrecognized protein products encoded by circular RNAs (circRNAs) in human tissue specimens and to delineate their roles in the progression of lung adenocarcinoma (LUAD). The transcription of precursor mRNA by RNA polymerase Ⅱ subunit A (RPB1) is crucial for the biogenesis of these potential circRNA-encoded proteins. Functional and translational analyses link their expression to distinct pathological stages of LUAD in patients. The protein RIPK1-98, encoded by circRIPK1, was identified as functionally distinct from its parental gene product, receptor-interacting serine/threonine kinase 1 (RIPK1). RIPK1-98 modulates cyclin-dependent kinase 2 (CDK2)-dependent cell-cycle regulation, thereby facilitating tumor proliferation in cellular and animal models. Together, these findings suggest that RIPK1-98 serves as a biomarker for cell-cycle progression in LUAD and highlight its potential as a therapeutic target to counteract resistance to first-line treatments, such as osimertinib.

PMID:41825439 | DOI:10.1016/j.devcel.2026.02.014

CircRNA-encoded RIPK1-98 protein drives lung adenocarcinoma progression

✇Omics In Lung
By: Qi Sun · Runqiu Chi · Xiao Zhang · Tianxiang Chen · Xin Xu · Lifang Ma · Xiaoting Tian · Yayou Miao · Yongchun Yu · Rui Kang · Guido Kroemer · Yi Shi · Daolin Tang · Jiayi Wang
13 March 2026 at 18:00

Dev Cell. 2026 Mar 12:S1534-5807(26)00079-1. doi: 10.1016/j.devcel.2026.02.014. Online ahead of print.

ABSTRACT

Unexplored biological matter-including uncharacterized genetic elements, molecular entities, and microbial components-remains poorly understood. Here, we use integrated multi-omics approaches to identify and characterize previously unrecognized protein products encoded by circular RNAs (circRNAs) in human tissue specimens and to delineate their roles in the progression of lung adenocarcinoma (LUAD). The transcription of precursor mRNA by RNA polymerase Ⅱ subunit A (RPB1) is crucial for the biogenesis of these potential circRNA-encoded proteins. Functional and translational analyses link their expression to distinct pathological stages of LUAD in patients. The protein RIPK1-98, encoded by circRIPK1, was identified as functionally distinct from its parental gene product, receptor-interacting serine/threonine kinase 1 (RIPK1). RIPK1-98 modulates cyclin-dependent kinase 2 (CDK2)-dependent cell-cycle regulation, thereby facilitating tumor proliferation in cellular and animal models. Together, these findings suggest that RIPK1-98 serves as a biomarker for cell-cycle progression in LUAD and highlight its potential as a therapeutic target to counteract resistance to first-line treatments, such as osimertinib.

PMID:41825439 | DOI:10.1016/j.devcel.2026.02.014

Received — 11 March 2026 ⏭

HarmonyCell: Automating Single-Cell Perturbation Modeling under Semantic and Distribution Shifts

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wenxuan Huang · Mingyu Tsoi · Yanhao Huang · Xinjie Mao · Xue Xia · Hao Wu · Jiaqi Wei · Yuejin Yang · Lang Yu · Cheng Tan · Xiang Zhang · Zhangyang Gao · Siqi Sun
10 March 2026 at 12:00
arXiv:2603.01396v2 Announce Type: replace Abstract: Single-cell perturbation studies face dual heterogeneity bottlenecks: (i) semantic heterogeneity--identical biological concepts encoded under incompatible metadata schemas across datasets; and (ii) statistical heterogeneity--distribution shifts from biological variation demanding dataset-specific inductive biases. We propose HarmonyCell, an end-to-end agent framework resolving each challenge through a dedicated mechanism: an LLM-driven Semantic Unifier autonomously maps disparate metadata into a canonical interface without manual intervention; and an adaptive Monte Carlo Tree Search engine operates over a hierarchical action space to synthesize architectures with optimal statistical inductive biases for distribution shifts. Evaluated across diverse perturbation tasks under both semantic and distribution shifts, HarmonyCell achieves a 95% valid execution rate on heterogeneous input datasets (versus 0% for general agents) while matching or even exceeding expert-designed baselines in rigorous out-of-distribution evaluations. This dual-track orchestration enables scalable automatic virtual cell modeling without dataset-specific engineering.
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