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Received — 27 May 2026 ⏭

Human pancreatic progenitor organoids define genetic and epigenetic barriers to early PDAC transformation

✇(Multiomics OR Omics) AND (Pancreatic)
By: Xian Zhang · Wilfred Wong · Hyein S Cho · Dapeng Yang · Gary Dixon · Renhe Luo · Dingyu Liu · Denis Torre · Shigeaki Umeda · Federico González · Gokce Askan · Aslihan Yavas · Jeyaram Ravichandran Damodaran · Robert W Rickert · Rajya Kappagantula · Fong Cheng Pan · Victoria G Aveson · Adrien Grimont · William B Fall · Xianming Wang · Julian Pulecio · Samuel J Kaplan · Heng Pan · Xianlu Laura Peng · Jen Jen Yeh · Steven D Leach · Christine A Iacobuzio-Donahue · Rohit Chandwani · Christina S Leslie · Danwei Huangfu
20 May 2026 at 18:00

Dev Cell. 2026 May 19:S1534-5807(26)00159-0. doi: 10.1016/j.devcel.2026.04.012. Online ahead of print.

ABSTRACT

The lack of accurate human models that recapitulate pancreatic ductal adenocarcinoma (PDAC) initiation has hindered therapeutic development. Using pluripotent stem cell-derived pancreatic progenitor organoids, we established a human PDAC model that faithfully reproduces the genetic, epigenetic, and transcriptomic trajectories of tumor initiation and progression, validated against clinical datasets and tumor histopathology. We demonstrate that CDKN2A loss, which is nearly universal in patients but dispensable in mouse models, is essential for neoplastic transformation when combined with KRAS and TP53 mutations, whereas SMAD4 loss promotes tumor progression. Multi-omics profiling reveals epigenetic repression of the pancreatic lineage program during PDAC initiation, alongside AP-1-driven chromatin remodeling. We identify TET1 suppression as a mechanistic link between oncogenic ERK signaling and hypermethylation of essential pancreatic transcription factors. This model captures genetic and epigenetic determinants of human PDAC, reveals antagonism between oncogenic and lineage restriction programs, and supports TET-based lineage restoration as a potential early intervention strategy.

PMID:42161274 | PMC:PMC13196429 | DOI:10.1016/j.devcel.2026.04.012

Received — 13 April 2026 ⏭

A Genetically Engineered Human Organoid Model Reveals Distinct Genetic and Epigenetic Barriers of Lineage Plasticity in Early PDAC Transformation

✇(Multiomics OR Omics) AND (Pancreatic)
By: Xian Zhang · Wilfred Wong · Hyein S Cho · Dapeng Yang · Gary Dixon · Renhe Luo · Dingyu Liu · Denis Torre · Shigeaki Umeda · Federico González · Gokce Askan · Aslihan Yavas · Jeyaram Ravichandran Damodaran · Robert W Rickert · Rajya Kappagantula · Fong Cheng Pan · Victoria G Aveson · Adrien Grimont · Julian Pulecio · Samuel J Kaplan · Heng Pan · Steven D Leach · Christine A Iacobuzio-Donahue · Rohit Chandwani · Christina S Leslie · Danwei Huangfu
10 April 2026 at 18:00

bioRxiv [Preprint]. 2026 Mar 11:2026.03.09.710586. doi: 10.64898/2026.03.09.710586.

ABSTRACT

The lack of accurate, human-based models recapitulating early-stage pancreatic ductal adenocarcinoma (PDAC) has hindered therapeutic development. Using pluripotent stem cell-derived pancreatic progenitor organoids, we established a human PDAC model that faithfully reproduces the genetic, epigenetic, and transcriptomic trajectory of tumor initiation and progression in vitro , validated against clinical datasets and histopathology. We demonstrate that CDKN2A loss, nearly universal in patients but dispensable in mouse models, is essential for neoplastic transformation when combined with KRAS and TP53 mutations, while SMAD4 loss promotes tumor progression. Multi-omics profiling reveals epigenetic repression of pancreatic lineage program during PDAC initiation, alongside oncogenic AP-1-driven chromatin remodeling. Notably, we identify TET1 suppression as a mechanistic link between oncogenic ERK signaling and the hypermethylation and silencing of essential pancreatic transcription factors. This model captures the genetic and epigenetic determinants of human PDAC, reveals antagonism between oncogenic and lineage restriction programs, and supports TET-based lineage restoration as a promising early intervention strategy for high-risk individuals.

PMID:41959451 | PMC:PMC13060829 | DOI:10.64898/2026.03.09.710586

Received — 4 April 2026 ⏭
Received — 26 March 2026 ⏭

ImplicitRM: Unbiased Reward Modeling from Implicit Preference Data for LLM alignment

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hao Wang · Haocheng Yang · Licheng Pan · Lei Shen · Xiaoxi Li · Yinuo Wang · Zhichao Chen · Yuan Lu · Haoxuan Li · Zhouchen Lin
25 March 2026 at 12:00
arXiv:2603.23184v1 Announce Type: cross Abstract: Reward modeling represents a long-standing challenge in reinforcement learning from human feedback (RLHF) for aligning language models. Current reward modeling is heavily contingent upon experimental feedback data with high collection costs. In this work, we study \textit{implicit reward modeling} -- learning reward models from implicit human feedback (e.g., clicks and copies) -- as a cost-effective alternative. We identify two fundamental challenges in implicit reward modeling: (1) Implicit preference data lacks definitive negative samples, which makes standard positive-negative classification methods inapplicable; (2) Implicit preference data suffers from user preference bias, where different responses have different propensities to elicit user feedback actions, which exacerbates the difficulty of distinguishing definitive negative samples. To address these challenges, we propose ImplicitRM, which aims to learn unbiased reward models from implicit preference data. ImplicitRM stratifies training samples into four latent groups via a stratification model. Building on this, it derives a learning objective through likelihood maximization, which we prove is theoretically unbiased, effectively resolving both challenges. Experiments demonstrate that ImplicitRM learns accurate reward models across implicit preference datasets. Code is available on our project website.
Received — 14 March 2026 ⏭

Adaptive Collaboration with Humans: Metacognitive Policy Optimization for Multi-Agent LLMs with Continual Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wei Yang · Defu Cao · Jiacheng Pang · Muyan Weng · Yan Liu
10 March 2026 at 12:00
arXiv:2603.07972v1 Announce Type: new Abstract: While scaling individual Large Language Models (LLMs) has delivered remarkable progress, the next frontier lies in scaling collaboration through multi-agent systems (MAS). However, purely autonomous MAS remain ''closed-world'' systems, constrained by the static knowledge horizon of pre-trained models. This limitation makes them brittle on tasks requiring knowledge beyond training data, often leading to collective failure under novel challenges. To address this, we propose the Human-In-the-Loop Multi-Agent Collaboration (HILA) framework, a principled paradigm for human--agent collaboration. HILA trains agents to learn a metacognitive policy that governs when to solve problems autonomously and when to defer to a human expert. To operationalize this policy, we introduce Dual-Loop Policy Optimization, which disentangles immediate decision-making from long-term capability growth. The inner loop applies Group Relative Policy Optimization (GRPO) with a cost-aware reward to optimize deferral decisions, while the outer loop implements continual learning, transforming expert feedback into high-quality supervised signals that strengthen the agent's reasoning ability. Experiments on challenging mathematical and problem-solving benchmarks show that HILA, equipped with Dual-Loop Policy Optimization, consistently outperforms advanced MAS, establishing a principled foundation for collaborative and continually improving agentic systems.
Received — 17 February 2026 ⏭

Debiasing Central Fixation Confounds Reveals a Peripheral "Sweet Spot" for Human-like Scanpaths in Hard-Attention Vision

✇cs.AI, q-bio.NC updates on arXiv.org
By: Pengcheng Pan · Yonekura Shogo · Yasuo Kuniyosh
17 February 2026 at 13:00
arXiv:2602.14834v1 Announce Type: cross Abstract: Human eye movements in visual recognition reflect a balance between foveal sampling and peripheral context. Task-driven hard-attention models for vision are often evaluated by how well their scanpaths match human gaze. However, common scanpath metrics can be strongly confounded by dataset-specific center bias, especially on object-centric datasets. Using Gaze-CIFAR-10, we show that a trivial center-fixation baseline achieves surprisingly strong scanpath scores, approaching many learned policies. This makes standard metrics optimistic and blurs the distinction between genuine behavioral alignment and mere central tendency. We then analyze a hard-attention classifier under constrained vision by sweeping foveal patch size and peripheral context, revealing a peripheral sweet spot: only a narrow range of sensory constraints yields scanpaths that are simultaneously (i) above the center baseline after debiasing and (ii) temporally human-like in movement statistics. To address center bias, we propose GCS (Gaze Consistency Score), a center-debiased composite metric augmented with movement similarity. GCS uncovers a robust sweet spot at medium patch size with both foveal and peripheral vision, that is not obvious from raw scanpath metrics or accuracy alone, and also highlights a "shortcut regime" when the field-of-view becomes too large. We discuss implications for evaluating active perception on object-centric datasets and for designing gaze benchmarks that better separate behavioral alignment from center bias.

Reinforcement Learning with Promising Tokens for Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jing-Cheng Pang · Liang Lu · Xian Tang · Kun Jiang · Sijie Wu · Kai Zhang · Xubin Li
17 February 2026 at 13:00
arXiv:2602.03195v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has emerged as a key paradigm for aligning and optimizing large language models (LLMs). Standard approaches treat the LLM as the policy and apply RL directly over the full vocabulary space. However, this formulation includes the massive tail of contextually irrelevant tokens in the action space, which could distract the policy from focusing on decision-making among the truly reasonable tokens. In this work, we verify that valid reasoning paths could inherently concentrate within a low-rank subspace. Based on this insight, we introduce Reinforcement Learning with Promising Tokens (RLPT), a framework that mitigates the action space issue by decoupling strategic decision-making from token generation. Specifically, RLPT leverages the semantic priors of the base model to identify a dynamic set of promising tokens and constrains policy optimization exclusively to this refined subset via masking. Theoretical analysis and empirical results demonstrate that RLPT effectively reduces gradient variance, stabilizes the training process, and improves sample efficiency. Experiment results on math, coding, and telecom reasoning show that RLPT outperforms standard RL baselines and integrates effectively across various model sizes (4B and 8B) and RL algorithms (GRPO and DAPO).
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