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Normal view

Received — 27 May 2026 ⏭

EchoDistill:Alignment Noisy-to-Clean Self-Distillation for Robust Audio LLMs

✇cs.AI, q-bio.NC updates on arXiv.org
By: Liang Lin · Chunxi Luo · Kaiwen Luo · Jie Zhang · Jin Wang · Yuanhe Zhang · Cai Yuchen · Qiankun Li · Gongli Xi · Zhenhong Zhou · Kun Wang · Junhao Dong
26 May 2026 at 12:00
arXiv:2605.23954v1 Announce Type: cross Abstract: Audio Large Language Models (ALLMs) are highly vulnerable to real-world noise, which often induces severe semantic drift and hallucinations. Existing robustness methods primarily rely on waveform-level acoustic enhancement, answer-level supervision, or the internal suppression of noise representations. To address these issues, we propose echodistill, an alignment-based noisy-to-clean self-distillation framework. Echodistill leverages a frozen clean-audio teacher to provide semantic references for an inference-time noisy-audio student. Specifically, the student samples candidate responses under noisy conditions to expose its test-time behavior. These trajectories are then optimized via group-relative policy optimization (GRPO), where the token-level consistency with the teacher acts as a reward bonus. By aligning the noisy student's candidate responses with clean semantic evidence, and applying audio-aware reward shaping, our method encourages reasoning trajectories that are both correct and genuinely acoustically grounded. Echodistill significantly improves the semantic reliability and task performance of Audio LLMs under complex noise, without introducing any additional inference costs. Extensive experiments show that: (I) Compared with the strongest baseline, echodistill achieves average improvements of 4.18\%$\uparrow$ in GSR under strong noise. (II) Ablation results on Qwen-Omni further show that echodistill improves over the GRPO-only variant by 3.02\%$\uparrow$ in Acc, 3.89\%$\uparrow$ in Noisy, and 4.53\%$\uparrow$ in GSR on average. Our codes are available at https://anonymous.4open.science/r/echodistill-10DE.

AutoSG: LLM-Driven Solver Generation Solely from Task Prompts for Expensive Optimization

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haoran Gu · Handing Wang · Yi Mei · Mengjie Zhang
26 May 2026 at 12:00
arXiv:2605.25658v1 Announce Type: cross Abstract: Expensive optimization tasks are ubiquitous in real-world applications, demanding highly specialized solvers. While LLM-driven automated solver generation shows promise, current paradigms face three critical issues when tackling expensive optimization: factual hallucinations due to deficient domain knowledge, the frequent dismantling of previously established locally optimal structures during refinement, and the prohibitive evaluation costs alongside restricted generalization caused by executing on training instances. To address these issues, we introduce AutoSG, a fully automated workflow directly translating natural language prompts into executable customized solvers. AutoSG features three core innovations: a retrieval-augmented solver generation module strictly grounding code in verified literature; a one-step self-refinement operator introducing task-specific improvements while preserving critical structural components; and an instance-free Elo-based LLM-as-a-Judge evaluation mechanism rapidly establishing global rankings. Extensive evaluations across diverse expensive optimization tasks confirm AutoSG significantly outperforms human-designed state-of-the-art frameworks and existing LLM-generated solvers.

HiTeC: Hierarchical Contrastive Learning on Text-Attributed Hypergraph with Semantic-Aware Augmentation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Mengting Pan · Fan Li · Chen Chen · Xiaoyang Wang · Wenjie Zhang
26 May 2026 at 12:00
arXiv:2508.03104v3 Announce Type: replace-cross Abstract: Contrastive learning (CL) has become a dominant paradigm for self-supervised hypergraph learning, enabling effective training without costly labels. However, node entities in real-world hypergraphs are often associated with rich textual information, which has been largely ignored in prior works. Directly applying existing CL-based methods to such text-attributed hypergraphs (TAHGs) leads to three key limitations: (1) The common use of graph-agnostic text encoders fails to capture the correlations between textual semantics and hypergraph topology, resulting in less expressive representations. (2) Their reliance on random data augmentations introduces noise and weakens the contrastive signals. (3) The primary focus on node- and hyperedge-level contrastive signals limits the ability to capture long-range dependencies, which is essential for effective representation learning. To address these challenges, we introduce HiTeC, a two-stage hierarchical contrastive learning framework for effective self-supervised learning on TAHGs. In the first stage, we pre-train the text encoder with a structure-aware contrastive objective to overcome the graph-agnostic nature of conventional methods. In the second stage, we begin by introducing semantic-aware augmentations, including structure-contextualized text augmentation and semantic-aware hyperedge dropping, to facilitate informative view generation. Subsequently, we propose a multi-scale contrastive loss with an $s$-walk-based subgraph-level objective to capture long-range dependencies. Extensive experiments on six real-world datasets validate the effectiveness of our proposed method.

HiGraph: A Large-Scale Hierarchical Graph Dataset for Malware Analysis

✇cs.AI, q-bio.NC updates on arXiv.org
By: Han Chen · Hanchen Wang · Hongmei Chen · Ying Zhang · Lu Qin · Wenjie Zhang
26 May 2026 at 12:00
arXiv:2509.02113v2 Announce Type: replace-cross Abstract: The advancement of graph-based malware analysis is critically limited by the absence of large-scale datasets that capture the inherent hierarchical structure of software. Existing methods often oversimplify programs into single level graphs, failing to model the crucial semantic relationship between high-level functional interactions and low-level instruction logic. To bridge this gap, we introduce \dataset, the largest public hierarchical graph dataset for malware analysis, comprising over \textbf{200M} Control Flow Graphs (CFGs) nested within \textbf{595K} Function Call Graphs (FCGs). This two-level representation preserves structural semantics essential for building robust detectors resilient to code obfuscation and malware evolution. We demonstrate HiGraph's utility through a large-scale analysis that reveals distinct structural properties of benign and malicious software, establishing it as a foundational benchmark for the community. The dataset and tools are publicly available at https://higraph.org.

SNHG12 drives gastric cancer progression by activating the Wnt/beta-catenin-mediated serine synthesis pathway

✇Omics in Gastric
By: Nianjie Zhang · Taolang Li · Kunming Wen
1 May 2026 at 18:00

J Transl Med. 2026 Apr 30;24(1):638. doi: 10.1186/s12967-026-08173-3.

ABSTRACT

BACKGROUND: Metabolic reprogramming is a hallmark of gastric cancer and is essential for sustaining rapid proliferation and malignant progression. The serine synthesis pathway (SSP), a key branch of glycolysis coupled to one-carbon metabolism (OCM), plays a central role in nucleotide biosynthesis, redox homeostasis, and epigenetic regulation. Although aberrant SSP activation has been implicated in gastric cancer, its upstream regulatory mechanisms remain poorly defined. Long non-coding RNAs (lncRNAs) have emerged as critical modulators of oncogenic signaling and metabolism. This study aimed to elucidate the role of the lncRNA SNHG12 in gastric cancer progression and to determine whether it drives metabolic reprogramming through the Wnt/β-catenin-SSP axis.

METHODS: SNHG12 expression and clinical relevance were analyzed using public datasets, clinical gastric cancer specimens, and cell lines. Gain- and loss-of-function experiments were performed to assess the effects of SNHG12 on proliferation, apoptosis, migration, and invasion. Transcriptomic profiling, targeted metabolomics, and integrative multi-omics analyses were used to characterize metabolic alterations. Pharmacological inhibition of SSP (NCT503) and Wnt/β-catenin signaling (IWR-1) was applied in vitro and in vivo. A subcutaneous xenograft mouse model was used to validate tumor-promoting effects and therapeutic responses.

RESULTS: SNHG12 was significantly upregulated in gastric cancer tissues and cell lines and was associated with poor overall and progression-free survival. Functionally, SNHG12 promoted gastric cancer cell proliferation, migration, and invasion while suppressing apoptosis. Transcriptomic and targeted metabolomic analyses revealed broad metabolic alterations associated with SNHG12, including changes in serine/one-carbon metabolism, purine biosynthesis, and glutathione-related pathways. Mechanistically, SNHG12 increased Wnt/β-catenin reporter activity, promoted β-catenin nuclear accumulation, and was accompanied by increased expression of key SSP-associated enzymes, including PHGDH, PSAT1, and SHMT2. Pharmacological inhibition of SSP or Wnt/β-catenin signaling partially reversed SNHG12-induced malignant phenotypes in vitro and suppressed tumor growth in xenograft models.

CONCLUSIONS: This study identifies SNHG12 as an important regulator of metabolic reprogramming in gastric cancer. Our data support a model in which SNHG12 promotes gastric cancer cell proliferation, invasion, and migration through SSP regulation, and suggest that its effects on the SSP may be mediated, at least in part, through modulation of SSP-associated enzymes via the Wnt/β-catenin pathway. These findings support SNHG12 as a candidate biomarker and a potential therapeutic target for combined metabolic and signaling-based interventions in gastric cancer.

PMID:42063161 | PMC:PMC13151230 | DOI:10.1186/s12967-026-08173-3

Received — 8 April 2026 ⏭

Empirical Characterization of Rationale Stability Under Controlled Perturbations for Explainable Pattern Recognition

✇cs.AI, q-bio.NC updates on arXiv.org
By: Abu Noman Md Sakib · Zhensen Wang · Merjulah Roby · Zijie Zhang
7 April 2026 at 12:00
arXiv:2604.04456v1 Announce Type: new Abstract: Reliable pattern recognition systems should exhibit consistent behavior across similar inputs, and their explanations should remain stable. However, most Explainable AI evaluations remain instance centric and do not explicitly quantify whether attribution patterns are consistent across samples that share the same class or represent small variations of the same input. In this work, we propose a novel metric aimed at assessing the consistency of model explanations, ensuring that models consistently reflect the intended objectives and consistency under label-preserving perturbations. We implement this metric using a pre-trained BERT model on the SST-2 sentiment analysis dataset, with additional robustness tests on RoBERTa, DistilBERT, and IMDB, applying SHAP to compute feature importance for various test samples. The proposed metric quantifies the cosine similarity of SHAP values for inputs with the same label, aiming to detect inconsistent behaviors, such as biased reliance on certain features or failure to maintain consistent reasoning for similar predictions. Through a series of experiments, we evaluate the ability of this metric to identify misaligned predictions and inconsistencies in model explanations. These experiments are compared against standard fidelity metrics to assess whether the new metric can effectively identify when a model's behavior deviates from its intended objectives. The proposed framework provides a deeper understanding of model behavior by enabling more robust verification of rationale stability, which is critical for building trustworthy AI systems. By quantifying whether models rely on consistent attribution patterns for similar inputs, the proposed approach supports more robust evaluation of model behavior in practical pattern recognition pipelines. Our code is publicly available at https://github.com/anmspro/ESS-XAI-Stability.

VLBiasBench: A Comprehensive Benchmark for Evaluating Bias in Large Vision-Language Model

✇cs.AI, q-bio.NC updates on arXiv.org
By: Sibo Wang · Xiangkui Cao · Jie Zhang · Zheng Yuan · Shiguang Shan · Xilin Chen · Wen Gao
7 April 2026 at 12:00
arXiv:2406.14194v3 Announce Type: replace-cross Abstract: The emergence of Large Vision-Language Models (LVLMs) marks significant strides towards achieving general artificial intelligence. However, these advancements are accompanied by concerns about biased outputs, a challenge that has yet to be thoroughly explored. Existing benchmarks are not sufficiently comprehensive in evaluating biases due to their limited data scale, single questioning format and narrow sources of bias. To address this problem, we introduce VLBiasBench, a comprehensive benchmark designed to evaluate biases in LVLMs. VLBiasBench, features a dataset that covers nine distinct categories of social biases, including age, disability status, gender, nationality, physical appearance, race, religion, profession, social economic status, as well as two intersectional bias categories: race x gender and race x social economic status. To build a large-scale dataset, we use Stable Diffusion XL model to generate 46,848 high-quality images, which are combined with various questions to creat 128,342 samples. These questions are divided into open-ended and close-ended types, ensuring thorough consideration of bias sources and a comprehensive evaluation of LVLM biases from multiple perspectives. We conduct extensive evaluations on 15 open-source models as well as two advanced closed-source models, yielding new insights into the biases present in these models. Our benchmark is available at https://github.com/Xiangkui-Cao/VLBiasBench.

Mind Your HEARTBEAT! Claw Background Execution Inherently Enables Silent Memory Pollution

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yechao Zhang · Shiqian Zhao · Jie Zhang · Gelei Deng · Jiawen Zhang · Xiaogeng Liu · Chaowei Xiao · Tianwei Zhang
7 April 2026 at 12:00
arXiv:2603.23064v3 Announce Type: replace-cross Abstract: We identify a critical security vulnerability in mainstream Claw personal AI agents: untrusted content encountered during heartbeat-driven background execution can silently pollute agent memory and subsequently influence user-facing behavior without the user's awareness. This vulnerability arises from an architectural design shared across the Claw ecosystem: heartbeat background execution runs in the same session as user-facing conversation, so content ingested from any external source monitored in the background (including email, message channels, news feeds, code repositories, and social platforms) can enter the same memory context used for foreground interaction, often with limited user visibility and without clear source provenance. We formalize this process as an Exposure (E) $\rightarrow$ Memory (M) $\rightarrow$ Behavior (B) pathway: misinformation encountered during heartbeat execution enters the agent's short-term session context, potentially gets written into long-term memory, and later shapes downstream user-facing behavior. We instantiate this pathway in an agent-native social setting using MissClaw, a controlled research replica of Moltbook. We find that (1) social credibility cues, especially perceived consensus, are the dominant driver of short-term behavioral influence, with misleading rates up to 61%; (2) routine memory-saving behavior can promote short-term pollution into durable long-term memory at rates up to 91%, with cross-session behavioral influence reaching 76%; (3) under naturalistic browsing with content dilution and context pruning, pollution still crosses session boundaries. Overall, prompt injection is not required: ordinary social misinformation is sufficient to silently shape agent memory and behavior under heartbeat-driven background execution.
Received — 4 April 2026 ⏭

Editing strigolactone hormone receptor for robust antiviral silencing in rice

✇Cell
By: Guoyi Yang · Ming Wu · Shuai Zhang · Yucen Huang · Yixiao Liu · Xiyuan Yu · Jiayang Hu · Le Mi · Peng Gan · Yuansheng Wu · Jing Zou · Baogang Zhang · Qun Hu · Jie Hu · Ruifeng Yao · Bojian Zhong · Xianbo Huang · Huiting Xie · Yinghua Ji · Yi Li · Jie Zhang · Liming Yan · Shou-wei Ding · Shanshan Zhao · Jianguo Wu
24 February 2026 at 08:00
Precise genome editing of the rice strigolactone receptor DWARF14 confers robust, transgene-free antiviral resistance by blocking viral suppression of endogenous RNA silencing, offering a promising strategy for durable disease protection without a yield penalty.
Received — 2 April 2026 ⏭

Grokking From Abstraction to Intelligence

✇cs.AI, q-bio.NC updates on arXiv.org
By: Junjie Zhang · Zhen Shen · Gang Xiong · Xisong Dong
1 April 2026 at 12:00
arXiv:2603.29262v1 Announce Type: new Abstract: Grokking in modular arithmetic has established itself as the quintessential fruit fly experiment, serving as a critical domain for investigating the mechanistic origins of model generalization. Despite its significance, existing research remains narrowly focused on specific local circuits or optimization tuning, largely overlooking the global structural evolution that fundamentally drives this phenomenon. We propose that grokking originates from a spontaneous simplification of internal model structures governed by the principle of parsimony. We integrate causal, spectral, and algorithmic complexity measures alongside Singular Learning Theory to reveal that the transition from memorization to generalization corresponds to the physical collapse of redundant manifolds and deep information compression, offering a novel perspective for understanding the mechanisms of model overfitting and generalization.

Spontaneous Functional Differentiation in Large Language Models: A Brain-Like Intelligence Economy

✇cs.AI, q-bio.NC updates on arXiv.org
By: Junjie Zhang · Zhen Shen · Gang Xiong · Xisong Dong
1 April 2026 at 12:00
arXiv:2603.29735v1 Announce Type: new Abstract: The evolution of intelligence in artificial systems provides a unique opportunity to identify universal computational principles. Here we show that large language models spontaneously develop synergistic cores where information integration exceeds individual parts remarkably similar to the human brain. Using Integrated Information Decomposition across multiple architectures we find that middle layers exhibit synergistic processing while early and late layers rely on redundancy. This organization is dynamic and emerges as a physical phase transition as task difficulty increases. Crucially ablating synergistic components causes catastrophic performance loss confirming their role as the physical entity of abstract reasoning and bridging artificial and biological intelligence.

ATP-Bench: Towards Agentic Tool Planning for MLLM Interleaved Generation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yinuo Liu · Zi Qian · Heng Zhou · Jiahao Zhang · Yajie Zhang · Zhihang Li · Mengyu Zhou · Erchao Zhao · Xiaoxi Jiang · Guanjun Jiang
1 April 2026 at 12:00
arXiv:2603.29902v1 Announce Type: new Abstract: Interleaved text-and-image generation represents a significant frontier for Multimodal Large Language Models (MLLMs), offering a more intuitive way to convey complex information. Current paradigms rely on either image generation or retrieval augmentation, yet they typically treat the two as mutually exclusive paths, failing to unify factuality with creativity. We argue that the next milestone in this field is Agentic Tool Planning, where the model serves as a central controller that autonomously determines when, where, and which tools to invoke to produce interleaved responses for visual-critical queries. To systematically evaluate this paradigm, we introduce ATP-Bench, a novel benchmark comprising 7,702 QA pairs (including 1,592 VQA pairs) across eight categories and 25 visual-critical intents, featuring human-verified queries and ground truths. Furthermore, to evaluate agentic planning independent of end-to-end execution and changing tool backends, we propose a Multi-Agent MLLM-as-a-Judge (MAM) system. MAM evaluates tool-call precision, identifies missed opportunities for tool use, and assesses overall response quality without requiring ground-truth references. Our extensive experiments on 10 state-of-the-art MLLMs reveal that models struggle with coherent interleaved planning and exhibit significant variations in tool-use behavior, highlighting substantial room for improvement and providing actionable guidance for advancing interleaved generation. Dataset and code are available at https://github.com/Qwen-Applications/ATP-Bench.

LLM-Meta-SR: In-Context Learning for Evolving Selection Operators in Symbolic Regression

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hengzhe Zhang · Qi Chen · Bing Xue · Wolfgang Banzhaf · Mengjie Zhang
1 April 2026 at 12:00
arXiv:2505.18602v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have revolutionized algorithm development, yet their application in symbolic regression, where algorithms automatically discover symbolic expressions from data, remains limited. In this paper, we propose a meta-learning framework that enables LLMs to automatically design selection operators for evolutionary symbolic regression algorithms. We first identify two key limitations in existing LLM-based algorithm evolution techniques: lack of semantic guidance and code bloat. The absence of semantic awareness can lead to ineffective exchange of useful code components, while bloat results in unnecessarily complex components; both can hinder evolutionary learning progress or reduce the interpretability of the designed algorithm. To address these issues, we enhance the LLM-based evolution framework for meta-symbolic regression with two key innovations: a complementary, semantics-aware selection operator and bloat control. Additionally, we embed domain knowledge into the prompt, enabling the LLM to generate more effective and contextually relevant selection operators. Our experimental results on symbolic regression benchmarks show that LLMs can devise selection operators that outperform nine expert-designed baselines, achieving state-of-the-art performance. Moreover, the evolved operator can further improve a state-of-the-art symbolic regression algorithm, achieving the best performance among 28 symbolic regression and other machine learning algorithms across 116 regression datasets. This demonstrates that LLMs can exceed expert-level algorithm design for symbolic regression.

Accelerating Diffusion Large Language Models with SlowFast Sampling: The Three Golden Principles

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qingyan Wei · Yaojie Zhang · Zhiyuan Liu · Puyu Zeng · Yuxuan Wang · Biqing Qi · Dongrui Liu · Linfeng Zhang
1 April 2026 at 12:00
arXiv:2506.10848v3 Announce Type: replace-cross Abstract: Diffusion-based language models (dLLMs) have emerged as a promising alternative to traditional autoregressive LLMs by enabling parallel token generation and significantly reducing inference latency. However, existing sampling strategies for dLLMs, such as confidence-based or semi-autoregressive decoding, often suffer from static behavior, leading to suboptimal efficiency and limited flexibility. In this paper, we propose SlowFast Sampling, a novel dynamic sampling strategy that adaptively alternates between exploratory and accelerated decoding stages. Our method is guided by three golden principles: certainty principle, convergence principle, and positional principle, which govern when and where tokens can be confidently and efficiently decoded. We further integrate our strategy with dLLM-Cache to reduce redundant computation. Extensive experiments across benchmarks and models show that SlowFast Sampling achieves up to 15.63$\times$ speedup on LLaDA with minimal accuracy drop, and up to 34.22$\times$ when combined with caching. Notably, our approach outperforms strong autoregressive baselines like LLaMA3 8B in throughput, demonstrating that well-designed sampling can unlock the full potential of dLLMs for fast and high-quality generation.

Integrated transcriptomic and proteomic analyses elucidate the stress tolerance network of <em>Saccharomyces boulardii</em> under gastrointestinal challenge

✇Omics in Gastric
By: Dingkang Wang · Lerong Liu · Xiaoru Chen · Yiting Kang · Dan Li · Bin Ma · Yuexi Zhu · Yanqin Rong · Hongfei Yu · Yongjie Zhang
31 March 2026 at 18:00

Food Funct. 2026 Mar 31. doi: 10.1039/d5fo04958j. Online ahead of print.

ABSTRACT

The probiotic yeast Saccharomyces boulardii is renowned for its clinical efficacy, which is intrinsically linked to its exceptional ability to survive the harsh gastrointestinal (GI) environment. However, a comprehensive understanding of the molecular mechanisms and regulatory pathways underlying the stress tolerance of S. boulardii remains limited. This study employed an integrated transcriptomic and proteomic approach to systematically map the dynamic responses of S. boulardii to simulated GI transit. Our analysis revealed that the intestinal phase posed a significantly greater challenge than the gastric phase, triggering extensive molecular reprogramming. A core adaptive strategy was the marked upregulation of the central carbon metabolism, particularly glycolysis, as evidenced by the concerted overexpression of key enzymes at both transcriptional and translational levels, indicating a heightened demand for energy to fuel stress defence mechanisms. Furthermore, significant enrichment was observed in the pathways related to nitrogen and fatty acid metabolism. Integration of the multi-omics datasets highlighted the complexity of the regulatory response, with frequent discordance between mRNA and protein abundance underscoring the importance of post-transcriptional regulation. This study provides a detailed molecular profile of the stress tolerance network in S. boulardii, elucidating the strategic metabolic rewiring and multi-layered regulation that underpin its probiotic resilience. The findings offer valuable insights and a foundational resource for the future development of enhanced probiotic therapies.

PMID:41914832 | DOI:10.1039/d5fo04958j

Integrated transcriptomic and proteomic analyses elucidate the stress tolerance network of <em>Saccharomyces boulardii</em> under gastrointestinal challenge

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Dingkang Wang · Lerong Liu · Xiaoru Chen · Yiting Kang · Dan Li · Bin Ma · Yuexi Zhu · Yanqin Rong · Hongfei Yu · Yongjie Zhang
31 March 2026 at 18:00

Food Funct. 2026 Mar 31. doi: 10.1039/d5fo04958j. Online ahead of print.

ABSTRACT

The probiotic yeast Saccharomyces boulardii is renowned for its clinical efficacy, which is intrinsically linked to its exceptional ability to survive the harsh gastrointestinal (GI) environment. However, a comprehensive understanding of the molecular mechanisms and regulatory pathways underlying the stress tolerance of S. boulardii remains limited. This study employed an integrated transcriptomic and proteomic approach to systematically map the dynamic responses of S. boulardii to simulated GI transit. Our analysis revealed that the intestinal phase posed a significantly greater challenge than the gastric phase, triggering extensive molecular reprogramming. A core adaptive strategy was the marked upregulation of the central carbon metabolism, particularly glycolysis, as evidenced by the concerted overexpression of key enzymes at both transcriptional and translational levels, indicating a heightened demand for energy to fuel stress defence mechanisms. Furthermore, significant enrichment was observed in the pathways related to nitrogen and fatty acid metabolism. Integration of the multi-omics datasets highlighted the complexity of the regulatory response, with frequent discordance between mRNA and protein abundance underscoring the importance of post-transcriptional regulation. This study provides a detailed molecular profile of the stress tolerance network in S. boulardii, elucidating the strategic metabolic rewiring and multi-layered regulation that underpin its probiotic resilience. The findings offer valuable insights and a foundational resource for the future development of enhanced probiotic therapies.

PMID:41914832 | DOI:10.1039/d5fo04958j

Received — 26 March 2026 ⏭

Toward Faithful Segmentation Attribution via Benchmarking and Dual-Evidence Fusion

✇cs.AI, q-bio.NC updates on arXiv.org
By: Abu Noman Md Sakib · OFM Riaz Rahman Aranya · Kevin Desai · Zijie Zhang
25 March 2026 at 12:00
arXiv:2603.22624v1 Announce Type: cross Abstract: Attribution maps for semantic segmentation are almost always judged by visual plausibility. Yet looking convincing does not guarantee that the highlighted pixels actually drive the model's prediction, nor that attribution credit stays within the target region. These questions require a dedicated evaluation protocol. We introduce a reproducible benchmark that tests intervention-based faithfulness, off-target leakage, perturbation robustness, and runtime on Pascal VOC and SBD across three pretrained backbones. To further demonstrate the benchmark, we propose Dual-Evidence Attribution (DEA), a lightweight correction that fuses gradient evidence with region-level intervention signals through agreement-weighted fusion. DEA increases emphasis where both sources agree and retains causal support when gradient responses are unstable. Across all completed runs, DEA consistently improves deletion-based faithfulness over gradient-only baselines and preserves strong robustness, at the cost of additional compute from intervention passes. The benchmark exposes a faithfulness-stability tradeoff among attribution families that is entirely hidden under visual evaluation, providing a foundation for principled method selection in segmentation explainability. Code is available at https://github.com/anmspro/DEA.

Mind Your HEARTBEAT! Claw Background Execution Inherently Enables Silent Memory Pollution

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yechao Zhang · Shiqian Zhao · Jie Zhang · Gelei Deng · Jiawen Zhang · Xiaogeng Liu · Chaowei Xiao · Tianwei Zhang
25 March 2026 at 12:00
arXiv:2603.23064v2 Announce Type: cross Abstract: We identify a critical security vulnerability in mainstream Claw personal AI agents: untrusted content encountered during heartbeat-driven background execution can silently pollute agent memory and subsequently influence user-facing behavior without the user's awareness. This vulnerability arises from an architectural design shared across the Claw ecosystem: heartbeat background execution runs in the same session as user-facing conversation, so content ingested from any external source monitored in the background (including email, message channels, news feeds, code repositories, and social platforms) can enter the same memory context used for foreground interaction, often with limited user visibility and without clear source provenance. We formalize this process as an Exposure (E) $\rightarrow$ Memory (M) $\rightarrow$ Behavior (B) pathway: misinformation encountered during heartbeat execution enters the agent's short-term session context, potentially gets written into long-term memory, and later shapes downstream user-facing behavior. We instantiate this pathway in an agent-native social setting using MissClaw, a controlled research replica of Moltbook. We find that (1) social credibility cues, especially perceived consensus, are the dominant driver of short-term behavioral influence, with misleading rates up to 61%; (2) routine memory-saving behavior can promote short-term pollution into durable long-term memory at rates up to 91%, with cross-session behavioral influence reaching 76%; (3) under naturalistic browsing with content dilution and context pruning, pollution still crosses session boundaries. Overall, prompt injection is not required: ordinary social misinformation is sufficient to silently shape agent memory and behavior under heartbeat-driven background execution.

Generalizable Heuristic Generation Through LLMs with Meta-Optimization

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yiding Shi · Jianan Zhou · Wen Song · Jieyi Bi · Yaoxin Wu · Zhiguang Cao · Jie Zhang
25 March 2026 at 12:00
arXiv:2505.20881v2 Announce Type: replace-cross Abstract: Heuristic design with large language models (LLMs) has emerged as a promising approach for tackling combinatorial optimization problems (COPs). However, existing approaches often rely on manually predefined evolutionary computation (EC) heuristic-optimizers and single-task training schemes, which may constrain the exploration of diverse heuristic algorithms and hinder the generalization of the resulting heuristics. To address these issues, we propose Meta-Optimization of Heuristics (MoH), a novel framework that operates at the optimizer level, discovering effective heuristic-optimizers through the principle of meta-learning. Specifically, MoH leverages LLMs to iteratively refine a meta-optimizer that autonomously constructs diverse heuristic-optimizers through (self-)invocation, thereby eliminating the reliance on a predefined EC heuristic-optimizer. These constructed heuristic-optimizers subsequently evolve heuristics for downstream tasks, enabling broader heuristic exploration. Moreover, MoH employs a multi-task training scheme to promote its generalization capability. Experiments on classic COPs demonstrate that MoH constructs an effective and interpretable meta-optimizer, achieving state-of-the-art performance across various downstream tasks, particularly in cross-size settings. Our code is available at: https://github.com/yiding-s/MoH.
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