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Received — 2 April 2026 ⏭

Cellular Senescence in Gastric Cancer: Molecular Mechanisms, Microenvironment Remodeling and Therapeutic Implications

✇Omics in Gastric
By: Zhiyuan Shi · Zhao Sun · Yuan Liu · Yuping Ge · Ning Jia · Yuwei Hua · Lin Zhao · Xiang Wang · Yi Ba
30 March 2026 at 18:00

Aging Dis. 2026 Mar 19. doi: 10.14336/AD.2025.1571. Online ahead of print.

ABSTRACT

Gastric cancer (GC) remains a leading cause of cancer-related morbidity and mortality worldwide, with poor prognosis for advanced-stage patients. Therefore, in-depth exploration of the mechanisms underlying GC initiation and progression, as well as the development of novel therapeutic strategies, is of crucial importance. Cellular senescence is a stable cell cycle arrest program that plays a dual role in GC. It exerts tumor-suppressive effects via growth arrest but also promotes tumor progression and immune evasion by remodeling the tumor microenvironment (TME) through senescence-associated secretory phenotype (SASP). This review comprehensively elucidates the molecular mechanisms of cellular senescence in GC and the core regulatory networks involving gene regulation, epigenetic modifications, metabolic reprogramming, and cell cycle arrest. Additionally, the review highlights how senescent cells foster an immunosuppressive microenvironment via SASP, forming a self-reinforcing feed-forward loop. Regarding therapeutic strategies, we summarize potential approaches targeting cellular senescence, including senescence induction, senescent cell clearance, SASP modulation, and multi-target synergistic therapy by integrating epigenetic regulation, metabolic intervention, and immune microenvironment modulation. Despite progress, numerous challenges remain. Future studies should leverage multi-omics technologies, novel models' development, and large-scale clinical trials to advance the clinical translation of GC cellular senescence research, providing new insights for improving prognosis.

PMID:41910653 | DOI:10.14336/AD.2025.1571

The Yin and Yang of tertiary lymphoid structures in primary liver cancer

✇Omics in Hepatocellular
By: Hui Liu · Shi Chen · Muzi Cao · Lizhuang Shu · Mufan Ji · Jian Zhou · Jia Fan · Pengxiang Wang · Yunfan Sun
29 March 2026 at 18:00

Cancer Lett. 2026 Mar 27;648:218461. doi: 10.1016/j.canlet.2026.218461. Online ahead of print.

ABSTRACT

Tertiary lymphoid structures (TLSs) have emerged as key regulators of anti-tumor immunity and biomarkers for immunotherapy response in liver cancer, including hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (iCCA), and combined hepatocellular-cholangiocarcinoma (cHCC-iCCA). Advances in single-cell and spatial multi-omics technologies have revealed unprecedented complexity in TLSs, challenging the traditional binary classification of TLSs as simply "good" or "bad". Their functional diversity appears to be shaped by spatiotemporal context, cellular composition, and maturation status. This review provides a comprehensive synthesis of TLSs in liver cancer, employing the Yin-Yang paradigm to navigate their functional dualism and prognostic contradictions through a detailed analysis of their identification, classification, and spatiotemporal interactions within the TME. Mechanistically, we elucidate how TLS functions are orchestrated by complex interactions between tumor cells, immune cell subsets, stromal components, and systemic factors. Within this framework, key metabolic drivers, notably ATP citrate lyase (ACLY), and signaling axes such as cGAS-STING/mTOR have emerged as pivotal regulators of TLS ontogeny. In addition, we evaluate current preclinical animal models and therapeutic strategies for clinical TLS induction. Furthermore, we have discussed the key unanswered questions in the field, including the three-dimensional architecture of TLSs and the mechanisms by which they establish durable immunological memory independent of the primary tumor. Clinically, TLSs exhibit great promise as prognostic and predictive biomarkers, particularly in the context of immune checkpoint blockade and locoregional therapies. Finally, we identify challenges in standardization, mechanistic understanding, and translational applications, providing directions for future research to harness TLSs for improving liver cancer outcomes.

PMID:41905709 | DOI:10.1016/j.canlet.2026.218461

Received — 26 March 2026 ⏭

Reasoning over Semantic IDs Enhances Generative Recommendation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yingzhi He · Yan Sun · Junfei Tan · Yuxin Chen · Xiaoyu Kong · Chunxu Shen · Xiang Wang · An Zhang · Tat-Seng Chua
25 March 2026 at 12:00
arXiv:2603.23183v1 Announce Type: cross Abstract: Recent advances in generative recommendation have leveraged pretrained LLMs by formulating sequential recommendation as autoregressive generation over a unified token space comprising language tokens and itemic identifiers, where each item is represented by a compact sequence of discrete tokens, namely Semantic IDs (SIDs). This SID-based formulation enables efficient decoding over large-scale item corpora and provides a natural interface for LLM-based recommenders to leverage rich world knowledge. Meanwhile, breakthroughs in LLM reasoning motivate reasoning-enhanced recommendation, yet effective reasoning over SIDs remains underexplored and challenging. Itemic tokens are not natively meaningful to LLMs; moreover, recommendation-oriented SID reasoning is hard to evaluate, making high-quality supervision scarce. To address these challenges, we propose SIDReasoner, a two-stage framework that elicits reasoning over SIDs by strengthening SID--language alignment to unlock transferable LLM reasoning, rather than relying on large amounts of recommendation-specific reasoning traces. Concretely, SIDReasoner first enhances SID-language alignment via multi-task training on an enriched SID-centered corpus synthesized by a stronger teacher model, grounding itemic tokens in diverse semantic and behavioral contexts. Building on this enhanced alignment, SIDReasoner further improves recommendation reasoning through outcome-driven reinforced optimization, which guides the model toward effective reasoning trajectories without requiring explicit reasoning annotations. Extensive experiments on three real-world datasets demonstrate the effectiveness of our reasoning-augmented SID-based generative recommendation. Beyond accuracy, the results highlight the broader potential of large reasoning models for generative recommendation, including improved interpretability and cross-domain generalization.

EVA: Aligning Video World Models with Executable Robot Actions via Inverse Dynamics Rewards

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ruixiang Wang · Qingming Liu · Yueci Deng · Guiliang Liu · Zhen Liu · Kui Jia
25 March 2026 at 12:00
arXiv:2603.17808v2 Announce Type: replace-cross Abstract: Video generative models are increasingly used as world models for robotics, where a model generates a future visual rollout conditioned on the current observation and task instruction, and an inverse dynamics model (IDM) converts the generated frames into executable robot actions. However, current video world models lack explicit executability constraints. As a result, visually coherent rollouts may still violate rigid-body and kinematic consistency, producing unstable or infeasible control commands when decoded by an IDM. We refer to this mismatch between visual generation and physically executable control as the executability gap. While this gap can be mitigated at inference time using techniques such as rejection sampling, such approaches are inefficient due to the high cost of video generation. In this paper, we leverage the executability gap as a training signal and introduce Executable Video Alignment (EVA), a reinforcement-learning post-training framework for aligning video world models. EVA trains an inverse dynamics model on real robot trajectories and repurposes it as a reward model that evaluates generated videos through the action sequences they induce, encouraging smooth motions measured by velocity, acceleration, and jerk while penalizing actions that violate embodiment constraints. Importantly, the reward remains informative even when generated videos contain severe visual artifacts, since such artifacts typically translate into unstable or out-of-bound actions. Experiments on the RoboTwin benchmark and a real bimanual robot show that EVA reduces embodiment-specific artifacts in generated rollouts and improves downstream task execution success.
Received — 11 March 2026 ⏭

How Long Can Unified Multimodal Models Generate Images Reliably? Taming Long-Horizon Interleaved Image Generation via Context Curation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haoyu Chen · Qing Liu · Yuqian Zhou · He Zhang · Zhaowen Wang · Mengwei Ren · Jingjing Ren · Xiang Wang · Zhe Lin · Lei Zhu
10 March 2026 at 12:00
arXiv:2603.07540v1 Announce Type: cross Abstract: Unified multimodal models hold the promise of generating extensive, interleaved narratives, weaving text and imagery into coherent long-form stories. However, current systems suffer from a critical reliability gap: as sequences grow, generation quality rapidly collapses. In this work, we investigate the mechanism behind this failure and argue that it is distinct from standard long-context challenges. We reveal that in generation, accumulated visual history acts as a source of active pollution, a decay governed specifically by the number of image events rather than raw token count. We identify a structural vulnerability where dense visual tokens overwhelm the attention mechanism, creating noise that distorts future synthesis. Guided by these mechanistic insights, we propose UniLongGen, a training-free inference strategy that prioritizes safe conditioning over total recall. Instead of retaining all history, UniLongGen dynamically curates the model's memory, identifying and discarding interfering visual signals based on the model's own internal relevance rankings. Extensive experiments demonstrate that this active forgetting approach is essential for stability: UniLongGen significantly outperforms baselines in long-horizon fidelity and consistency, while simultaneously reducing memory footprint and inference time.
Received — 5 March 2026 ⏭

NExT-Guard: Training-Free Streaming Safeguard without Token-Level Labels

✇cs.AI, q-bio.NC updates on arXiv.org
By: Junfeng Fang · Nachuan Chen · Houcheng Jiang · Dan Zhang · Fei Shen · Xiang Wang · Xiangnan He · Tat-Seng Chua
4 March 2026 at 13:00
arXiv:2603.02219v1 Announce Type: cross Abstract: Large language models are increasingly deployed in streaming scenarios, rendering conventional post-hoc safeguards ineffective as they fail to interdict unsafe content in real-time. While streaming safeguards based on token-level supervised training could address this, they necessitate expensive annotations and suffer from severe overfitting. In this work, we challenge the paradigm that streaming safety must rely on token-level supervised training. Instead, it is an inherent capability of well-trained post-hoc safeguards, as they already encode token-level risk signals in hidden representations. Hence, we introduce NExT-Guard, a training-free framework that achieves streaming safeguards by monitoring interpretable latent features from Sparse Autoencoders (SAEs). It uses pretrained SAEs from publicly available base LLMs, enabling flexible, low-cost deployment without token-level supervision. Experimental results show that NExT-Guard outperforms both post-hoc and streaming safeguards based on supervised training, with superior robustness across models, SAE variants, and risk scenarios. These results make NExT-Guard a universal and scalable paradigm for real-time safety, accelerating the practical deployment of streaming safeguards.

Reducing Belief Deviation in Reinforcement Learning for Active Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Deyu Zou · Yongqiang Chen · Jianxiang Wang · Haochen Yang · Mufei Li · James Cheng · Pan Li · Yu Gong
4 March 2026 at 13:00
arXiv:2510.12264v2 Announce Type: replace Abstract: Active reasoning requires large language model (LLM) agents to interact with external sources and strategically gather information to solve problems in multiple turns. Central to this process is belief tracking: maintaining an accurate representation of the underlying state and uncertainty in understanding and solving the problem. However, due to limited reasoning capabilities, LLM-based agents often suffer belief deviation: their internal beliefs drift from the true problem state, leading to loss of state awareness and uninformative or repetitive actions. Once this happens, errors compound in the trajectories used for reinforcement learning (RL), leading to misattributed credits and limited exploration. To address this issue, we propose to track belief deviation and develop $\mathbf{T^3}$, a simple yet principled method that detects excessive deviation and truncates training trajectories to suppress uninformative tail effects. Hence, $\mathbf{T^3}$ preserves credits for informative prefixes and systematically improves policy optimization. Across 5 challenging tasks, $\mathbf{T^3}$ consistently enhances training stability and yields performance gains of up to 30 points while cutting token cost by up to 34%. These results highlight belief control as a key principle for building robust LLM agents capable of active reasoning.
Received — 25 February 2026 ⏭

MemOCR: Layout-Aware Visual Memory for Efficient Long-Horizon Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yaorui Shi · Shugui Liu · Yu Yang · Wenyu Mao · Yuxin Chen · Qi GU · Hui Su · Xunliang Cai · Xiang Wang · An Zhang
24 February 2026 at 13:00
arXiv:2601.21468v3 Announce Type: replace Abstract: Long-horizon agentic reasoning necessitates effectively compressing growing interaction histories into a limited context window. Most existing memory systems serialize history as text, where token-level cost is uniform and scales linearly with length, often spending scarce budget on low-value details. To this end, we introduce MemOCR, a multimodal memory agent that improves long-horizon reasoning under tight context budgets by allocating memory space with adaptive information density through visual layout. Concretely, MemOCR maintains a structured rich-text memory (e.g., headings, highlights) and renders it into an image that the agent consults for memory access, visually prioritizing crucial evidence while aggressively compressing auxiliary details. To ensure robustness across varying memory budgets, we train MemOCR with reinforcement learning under budget-aware objectives that expose the agent to diverse compression levels. Across long-context multi-hop and single-hop question-answering benchmarks, MemOCR outperforms strong text-based baselines and achieves more effective context utilization under extreme budgets.

Look Back to Reason Forward: Revisitable Memory for Long-Context LLM Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yaorui Shi · Yuxin Chen · Siyuan Wang · Sihang Li · Hengxing Cai · Qi Gu · Xiang Wang · An Zhang
24 February 2026 at 13:00
arXiv:2509.23040v4 Announce Type: replace-cross Abstract: Large language models face challenges in long-context question answering, where key evidence of a query may be dispersed across millions of tokens. Existing works equip large language models with a memory buffer that is dynamically updated via a linear document scan, also known as the "memorize while reading" methods. While this approach scales efficiently, it suffers from pruning of latent evidence, information loss through overwriting, and sparse reinforcement learning signals. To tackle these challenges, we present ReMemR1, which integrates the mechanism of memory retrieval into the memory update process, enabling the agent to selectively callback historical memories for non-linear reasoning. To further strengthen training, we propose a multi-level reward design, which combines final-answer rewards with dense, step-level signals that guide effective memory use. Together, these contributions mitigate information degradation, improve supervision, and support complex multi-hop reasoning. Extensive experiments demonstrate that ReMemR1 significantly outperforms state-of-the-art baselines on long-context question answering while incurring negligible computational overhead, validating its ability to trade marginal cost for robust long-context reasoning.

Transport and Merge: Cross-Architecture Merging for Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chenhang Cui · Binyun Yang · Fei Shen · Yuxin Chen · Jingnan Zheng · Xiang Wang · An Zhang · Tat-Seng Chua
24 February 2026 at 13:00
arXiv:2602.05495v2 Announce Type: replace-cross Abstract: Large language models (LLMs) achieve strong capabilities by scaling model capacity and training data, yet many real-world deployments rely on smaller models trained or adapted from low-resource data. This gap motivates the need for mechanisms to transfer knowledge from large, high-resource models to smaller, low-resource targets. While model merging provides an effective transfer mechanism, most existing approaches assume architecture-compatible models and therefore cannot directly transfer knowledge from large high-resource LLMs to heterogeneous low-resource targets. In this work, we propose a cross-architecture merging framework based on optimal transport (OT) that aligns activations to infer cross-neuron correspondences between heterogeneous models. The resulting transport plans are then used to guide direct weight-space fusion, enabling effective high-resource to low-resource transfer using only a small set of inputs. Extensive experiments across low-resource languages and specialized domains demonstrate consistent improvements over target models.
Received — 17 February 2026 ⏭

GeoEyes: On-Demand Visual Focusing for Evidence-Grounded Understanding of Ultra-High-Resolution Remote Sensing Imagery

✇cs.AI, q-bio.NC updates on arXiv.org
By: Fengxiang Wang · Mingshuo Chen · Yueying Li · Yajie Yang · Yifan Zhang · Long Lan · Xue Yang · Hongda Sun · Yulin Wang · Di Wang · Jun Song · Jing Zhang · Bo Du
17 February 2026 at 13:00
arXiv:2602.14201v1 Announce Type: cross Abstract: The "thinking-with-images" paradigm enables multimodal large language models (MLLMs) to actively explore visual scenes via zoom-in tools. This is essential for ultra-high-resolution (UHR) remote sensing VQA, where task-relevant cues are sparse and tiny. However, we observe a consistent failure mode in existing zoom-enabled MLLMs: Tool Usage Homogenization, where tool calls collapse into task-agnostic patterns, limiting effective evidence acquisition. To address this, we propose GeoEyes, a staged training framework consisting of (1) a cold-start SFT dataset, UHR Chain-of-Zoom (UHR-CoZ), which covers diverse zooming regimes, and (2) an agentic reinforcement learning method, AdaZoom-GRPO, that explicitly rewards evidence gain and answer improvement during zoom interactions. The resulting model learns on-demand zooming with proper stopping behavior and achieves substantial improvements on UHR remote sensing benchmarks, with 54.23% accuracy on XLRS-Bench.

DiffusionNFT: Online Diffusion Reinforcement with Forward Process

✇cs.AI, q-bio.NC updates on arXiv.org
By: Kaiwen Zheng · Huayu Chen · Haotian Ye · Haoxiang Wang · Qinsheng Zhang · Kai Jiang · Hang Su · Stefano Ermon · Jun Zhu · Ming-Yu Liu
17 February 2026 at 13:00
arXiv:2509.16117v2 Announce Type: replace-cross Abstract: Online reinforcement learning (RL) has been central to post-training language models, but its extension to diffusion models remains challenging due to intractable likelihoods. Recent works discretize the reverse sampling process to enable GRPO-style training, yet they inherit fundamental drawbacks, including solver restrictions, forward-reverse inconsistency, and complicated integration with classifier-free guidance (CFG). We introduce Diffusion Negative-aware FineTuning (DiffusionNFT), a new online RL paradigm that optimizes diffusion models directly on the forward process via flow matching. DiffusionNFT contrasts positive and negative generations to define an implicit policy improvement direction, naturally incorporating reinforcement signals into the supervised learning objective. This formulation enables training with arbitrary black-box solvers, eliminates the need for likelihood estimation, and requires only clean images rather than sampling trajectories for policy optimization. DiffusionNFT is up to $25\times$ more efficient than FlowGRPO in head-to-head comparisons, while being CFG-free. For instance, DiffusionNFT improves the GenEval score from 0.24 to 0.98 within 1k steps, while FlowGRPO achieves 0.95 with over 5k steps and additional CFG employment. By leveraging multiple reward models, DiffusionNFT significantly boosts the performance of SD3.5-Medium in every benchmark tested.
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