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Received — 6 April 2026 ⏭

HoloTrauma 3X Triadic AI Co reasoning for robot assisted emergency maxillofacial reconstruction

✇npj Digital Medicine
By: Qiao Zhou · Yao Liu · Chenfei Wang · Liang Wang · Zhenghan Chen · Yiyi Chen · Chi Yang · Dong Liu · Yuxin Zhang · Jisi Zheng · Xingmei Feng
4 April 2026 at 08:00

npj Digital Medicine, Published online: 04 April 2026; doi:10.1038/s41746-026-02573-x

HoloTrauma 3X Triadic AI Co reasoning for robot assisted emergency maxillofacial reconstruction

Isobavachalcone exerts anti-gastric cancer effects by targeting dihydroorotate dehydrogenase to induce ROS release and activating the STING pathway

✇Omics in Gastric
By: Yong Kuang · Yu Shao · Jia Chen · Chen Dai · Sachiyo Nomura · Fulin Wang · Jingyao Chen · Yulong He · Xing Xiao · Changhua Zhang · Jiahao Feng
3 April 2026 at 18:00

Phytomedicine. 2026 Mar 27;155:158126. doi: 10.1016/j.phymed.2026.158126. Online ahead of print.

ABSTRACT

BACKGROUND: Mitochondrial damage can induce the release of mitochondrial DNA (mtDNA), leading to oxidative stress and activation of immune responses. Targeting mitochondrial dysfunction may thus represent a therapeutic strategy for gastric cancer. Isobavachalcone (IBC), a prenylated chalcone derived from Psoralea corylifolia L., has demonstrated antitumor activity, but its mechanism of action remains unclear, limiting its clinical application.

PURPOSE: This study aimed to investigate the antitumor effects of IBC in gastric cancer and to elucidate the underlying molecular mechanisms, with a focus on mitochondrial damage and immune activation.

STUDY DESIGN: The study combined in vitro and in vivo assays with multi-omics sequencing and network pharmacology to identify IBC's therapeutic target and downstream signaling pathways.

METHODS: Gastric cancer cells and mouse models were treated with IBC to assess its inhibitory effects. Multi-omics approaches and network pharmacology were used to identify potential targets. ROS production, mitochondrial membrane integrity, and immune pathway activation were evaluated via biochemical and molecular assays.

RESULTS: IBC significantly suppresses gastric cancer growth both in vitro and in vivo. Integrated analysis identifies dihydroorotate dehydrogenase (DHODH) as a direct target of IBC. DHODH deficiency can induce mitochondrial membrane remodeling and STING pathway activation. Inhibition of DHODH by IBC induces ROS accumulation, mitochondrial membrane remodeling, and activation of the STING pathway, promoting antitumor immune responses. This study demonstrates that IBC enhances antitumor immunity in gastric cancer through mitochondrial damage-mediated mechanisms.

CONCLUSION: IBC exerts dual antitumor and immunostimulatory effects in gastric cancer by targeting DHODH, inducing mitochondrial damage, and activating the STING pathway, highlighting its promising therapeutic potential in gastric cancer.

PMID:41931998 | DOI:10.1016/j.phymed.2026.158126

Multi-ancestry transcriptome prediction with functionally informed variants in TOPMed MESA improves performance of transcriptome-wide association studies

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Xiaowei Hu · Daniel S Araujo · Chachrit Khunsriraksakul · Lida Wang · Quan Sun · Jia Wen · Lingbo Zhou · Lynette Ekunwe · Leslie A Lange · Ethan M Lange · Stephen B Montgomery · Alexander P Reiner · Francois Aguet · Kristin G Ardlie · Tuuli Lappalainen · Christopher R Gignoux · Esteban G Burchard · Kent D Taylor · Xiuqing Guo · Jerome I Rotter · Stephen S Rich · Elaine Cornell · Peter Durda · Russell P Tracy · Yongmei Liu · W Craig Johnson · George P Papanicolaou · Minoli A Perera · Michael H Cho · Dajiang J Liu · Laura M Raffield · Yun Li · TOPMed Multi-Omics Working Group · Heather E Wheeler · Hae Kyung Im · Ani Manichaikul
3 April 2026 at 18:00

Am J Hum Genet. 2026 Apr 2;113(4):828-841. doi: 10.1016/j.ajhg.2026.03.008.

ABSTRACT

Reliable reference transcriptome prediction models are key to accurate multi-ancestry transcriptome-wide association studies (TWASs). We propose three methods leveraging functionally informed variants (FIVs) for transcriptome prediction models to improve multi-ancestry TWASs. We trained models on 1,287 multi-ancestry participants from the Trans-Omics for Precision Medicine (TOPMed) program Multi-Ethnic Study of Atherosclerosis (MESA) with RNA sequencing (RNA-seq) data from peripheral blood mononuclear cells (PBMCs). We validated models' prediction accuracy on two external independent datasets, Geuvadis and Jackson Heart Study. To test robustness of our methods for TWASs, we integrated models with three multi-ancestry GWASs from blood cell, lipid, and pulmonary function traits, respectively. Our methods presented similar prediction accuracy while using a smaller and functionally informed set of variants compared to the benchmark method, elastic net (EN). Overall, our methods achieved higher power and accuracy (with average improved accuracy of 24% over EN) for TWASs. However, no single proposed method outperformed all GWAS traits. To further improve TWAS performance, we propose an omnibus approach that aggregates TWAS summary statistics from our methods. The omnibus approach yielded the highest number of Bonferroni-significant TWAS genes for all GWAS traits, and it further improved TWAS power and accuracy for blood cell traits. Additionally, the omnibus approach detected some trait-relevant important genes that the EN missed. Our study demonstrates the value of including FIVs in multi-ancestry transcriptome prediction models for improving TWAS performance. Further, the observed TWAS improvement depends on the GWAS trait's relevance to the PBMCs used to build our transcriptome prediction models.

PMID:41932314 | DOI:10.1016/j.ajhg.2026.03.008

Isobavachalcone exerts anti-gastric cancer effects by targeting dihydroorotate dehydrogenase to induce ROS release and activating the STING pathway

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Yong Kuang · Yu Shao · Jia Chen · Chen Dai · Sachiyo Nomura · Fulin Wang · Jingyao Chen · Yulong He · Xing Xiao · Changhua Zhang · Jiahao Feng
3 April 2026 at 18:00

Phytomedicine. 2026 Mar 27;155:158126. doi: 10.1016/j.phymed.2026.158126. Online ahead of print.

ABSTRACT

BACKGROUND: Mitochondrial damage can induce the release of mitochondrial DNA (mtDNA), leading to oxidative stress and activation of immune responses. Targeting mitochondrial dysfunction may thus represent a therapeutic strategy for gastric cancer. Isobavachalcone (IBC), a prenylated chalcone derived from Psoralea corylifolia L., has demonstrated antitumor activity, but its mechanism of action remains unclear, limiting its clinical application.

PURPOSE: This study aimed to investigate the antitumor effects of IBC in gastric cancer and to elucidate the underlying molecular mechanisms, with a focus on mitochondrial damage and immune activation.

STUDY DESIGN: The study combined in vitro and in vivo assays with multi-omics sequencing and network pharmacology to identify IBC's therapeutic target and downstream signaling pathways.

METHODS: Gastric cancer cells and mouse models were treated with IBC to assess its inhibitory effects. Multi-omics approaches and network pharmacology were used to identify potential targets. ROS production, mitochondrial membrane integrity, and immune pathway activation were evaluated via biochemical and molecular assays.

RESULTS: IBC significantly suppresses gastric cancer growth both in vitro and in vivo. Integrated analysis identifies dihydroorotate dehydrogenase (DHODH) as a direct target of IBC. DHODH deficiency can induce mitochondrial membrane remodeling and STING pathway activation. Inhibition of DHODH by IBC induces ROS accumulation, mitochondrial membrane remodeling, and activation of the STING pathway, promoting antitumor immune responses. This study demonstrates that IBC enhances antitumor immunity in gastric cancer through mitochondrial damage-mediated mechanisms.

CONCLUSION: IBC exerts dual antitumor and immunostimulatory effects in gastric cancer by targeting DHODH, inducing mitochondrial damage, and activating the STING pathway, highlighting its promising therapeutic potential in gastric cancer.

PMID:41931998 | DOI:10.1016/j.phymed.2026.158126

Received — 4 April 2026 ⏭

ClawSafety: "Safe" LLMs, Unsafe Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Bowen Wei · Yunbei Zhang · Jinhao Pan · Kai Mei · Xiao Wang · Jihun Hamm · Ziwei Zhu · Yingqiang Ge
3 April 2026 at 12:00
arXiv:2604.01438v1 Announce Type: new Abstract: Personal AI agents like OpenClaw run with elevated privileges on users' local machines, where a single successful prompt injection can leak credentials, redirect financial transactions, or destroy files. This threat goes well beyond conventional text-level jailbreaks, yet existing safety evaluations fall short: most test models in isolated chat settings, rely on synthetic environments, and do not account for how the agent framework itself shapes safety outcomes. We introduce CLAWSAFETY, a benchmark of 120 adversarial test scenarios organized along three dimensions (harm domain, attack vector, and harmful action type) and grounded in realistic, high-privilege professional workspaces spanning software engineering, finance, healthcare, law, and DevOps. Each test case embeds adversarial content in one of three channels the agent encounters during normal work: workspace skill files, emails from trusted senders, and web pages. We evaluate five frontier LLMs as agent backbones, running 2,520 sandboxed trials across all configurations. Attack success rates (ASR) range from 40\% to 75\% across models and vary sharply by injection vector, with skill instructions (highest trust) consistently more dangerous than email or web content. Action-trace analysis reveals that the strongest model maintains hard boundaries against credential forwarding and destructive actions, while weaker models permit both. Cross-scaffold experiments on three agent frameworks further demonstrate that safety is not determined by the backbone model alone but depends on the full deployment stack, calling for safety evaluation that treats model and framework as joint variables.

A Multi-Agent Human-LLM Collaborative Framework for Closed-Loop Scientific Literature Summarization

✇cs.AI, q-bio.NC updates on arXiv.org
By: Maxwell J. Jacobson · Daniel Xie · Jackson Shen · Adil Wazeer · Haiyan Wang · Xinghang Zhang · Yexiang Xue
3 April 2026 at 12:00
arXiv:2604.01452v1 Announce Type: new Abstract: Scientific discovery is slowed by fragmented literature that requires excessive human effort to gather, analyze, and understand. AI tools, including autonomous summarization and question answering, have been developed to aid in understanding scientific literature. However, these tools lack the structured, multi-step approach necessary for extracting deep insights from scientific literature. Large Language Models (LLMs) offer new possibilities for literature analysis, but remain unreliable due to hallucinations and incomplete extraction. We introduce Elhuyar, a multi-agent, human-in-the-loop system that integrates LLMs, structured AI, and human scientists to extract, analyze, and iteratively refine insights from scientific literature. The framework distributes tasks among specialized agents for filtering papers, extracting data, fitting models, and summarizing findings, with human oversight ensuring reliability. The system generates structured reports with extracted data, visualizations, model equations, and text summaries, enabling deeper inquiry through iterative refinement. Deployed in materials science, it analyzed literature on tungsten under helium-ion irradiation, showing experimentally correlated exponential helium bubble growth with irradiation dose and temperature, offering insight for plasma-facing materials (PFMs) in fusion reactors. This demonstrates how AI-assisted literature review can uncover scientific patterns and accelerate discovery.

Infeasibility Aware Large Language Models for Combinatorial Optimization

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yakun Wang · Min Chen · Zeguan Wu · Junyu Liu · Sitao Zhang · Zhenwen Shao
3 April 2026 at 12:00
arXiv:2604.01455v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly explored for NP-hard combinatorial optimization problems, but most existing methods emphasize feasible-instance solution generation and do not explicitly address infeasibility detection. We propose an infeasibility-aware framework that combines certifiable dataset construction, supervised fine-tuning, and LLM-assisted downstream search. For the minor-embedding problem, we introduce a new mathematical programming formulation together with provable zero-phase infeasibility screening, which enables scalable construction of training instances labeled either as feasible with structured certificates or as certifiably infeasible. Using training data generated through this exact optimization pipeline, we show that an 8B-parameter LLM can be fine-tuned to jointly perform solution generation and infeasibility detection. We further utilize LLM outputs as warm starts for downstream local search, providing a practical way to accelerate optimization even when the LLM outputs are imperfect. Experiments show that our fine-tuned model improves overall accuracy by up to 30\% over GPT-5.2; meanwhile LLM-guided warm starts provide up to $2\times$ speedup compared with starting from scratch in downstream local search.

Reducing Hallucinations in LLM-based Scientific Literature Analysis Using Peer Context Outlier Detection

✇cs.AI, q-bio.NC updates on arXiv.org
By: Daniel Xie · Maxwell J. Jacobson · Adil Wazeer · Haiyan Wang · Xinghang Zhang · Yexiang Xue
3 April 2026 at 12:00
arXiv:2604.01461v1 Announce Type: new Abstract: Reducing hallucinations in Large Language Models (LLMs) is essential for improving the accuracy of data extraction from large text corpora. Current methods, like prompt engineering and chain-of-thought prompting, focus on individual documents but fail to consider relationships across a corpus. This paper introduces Peer Context Outlier Detection (P-COD), a novel approach that uses the relationships between documents to improve extraction accuracy. Our application domain is in scientific literature summarization, where papers with similar experiment settings should draw similar conclusions. By comparing extracted data to validated peer information within the corpus, we adjust confidence scores and flag low-confidence results for expert review. High-confidence results, supported by peer validation, are considered reliable. Our experiments demonstrate up to 98% precision in outlier detection across 6 domains of science, demonstrating that our design reduces hallucinations, enhances trust in automated systems, and allows researchers to focus on ambiguous cases, streamlining the data extraction workflows.

AgentSocialBench: Evaluating Privacy Risks in Human-Centered Agentic Social Networks

✇cs.AI, q-bio.NC updates on arXiv.org
By: Prince Zizhuang Wang · Shuli Jiang
3 April 2026 at 12:00
arXiv:2604.01487v1 Announce Type: new Abstract: With the rise of personalized, persistent LLM agent frameworks such as OpenClaw, human-centered agentic social networks in which teams of collaborative AI agents serve individual users in a social network across multiple domains are becoming a reality. This setting creates novel privacy challenges: agents must coordinate across domain boundaries, mediate between humans, and interact with other users' agents, all while protecting sensitive personal information. While prior work has evaluated multi-agent coordination and privacy preservation, the dynamics and privacy risks of human-centered agentic social networks remain unexplored. To this end, we introduce AgentSocialBench, the first benchmark to systematically evaluate privacy risk in this setting, comprising scenarios across seven categories spanning dyadic and multi-party interactions, grounded in realistic user profiles with hierarchical sensitivity labels and directed social graphs. Our experiments reveal that privacy in agentic social networks is fundamentally harder than in single-agent settings: (1) cross-domain and cross-user coordination creates persistent leakage pressure even when agents are explicitly instructed to protect information, (2) privacy instructions that teach agents how to abstract sensitive information paradoxically cause them to discuss it more (we call it abstraction paradox). These findings underscore that current LLM agents lack robust mechanisms for privacy preservation in human-centered agentic social networks, and that new approaches beyond prompt engineering are needed to make agent-mediated social coordination safe for real-world deployment.

LLM Agents as Social Scientists: A Human-AI Collaborative Platform for Social Science Automation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Lei Wang · Yuanzi Li · Jinchao Wu · Heyang Gao · Xiaohe Bo · Xu Chen · Ji-Rong Wen
3 April 2026 at 12:00
arXiv:2604.01520v1 Announce Type: new Abstract: Traditional social science research often requires designing complex experiments across vast methodological spaces and depends on real human participants, making it labor-intensive, costly, and difficult to scale. Here we present S-Researcher, an LLM-agent-based platform that assists researchers in conducting social science research more efficiently and at greater scale by "siliconizing" both the research process and the participant pool. To build S-Researcher, we first develop YuLan-OneSim, a large-scale social simulation system designed around three core requirements: generality via auto-programming from natural language to executable scenarios, scalability via a distributed architecture supporting up to 100,000 concurrent agents, and reliability via feedback-driven LLM fine-tuning. Leveraging this system, S-Researcher supports researchers in designing social experiments, simulating human behavior with LLM agents, analyzing results, and generating reports, forming a complete human-AI collaborative research loop in which researchers retain oversight and intervention at every stage. We operationalize LLM simulation research paradigms into three canonical reasoning modes (induction, deduction, and abduction) and validate S-Researcher through systematic case studies: inductive reproduction of cultural dynamics consistent with Axelrod's theory, deductive testing of competing hypotheses on teacher attention validated against survey data, and abductive identification of a cooperation mechanism in public goods games confirmed by human experiments. S-Researcher establishes a new human--AI collaborative paradigm for social science, in which computational simulation augments human researchers to accelerate discovery across the full spectrum of social inquiry.

Analysis of LLM Performance on AWS Bedrock: Receipt-item Categorisation Case Study

✇cs.AI, q-bio.NC updates on arXiv.org
By: Gabby Sanchez · Sneha Oommen · Cassandra T. Britto · Di Wang · Jung-De Chiou · Maria Spichkova
3 April 2026 at 12:00
arXiv:2604.01615v1 Announce Type: new Abstract: This paper presents a systematic, cost-aware evaluation of large language models (LLMs) for receipt-item categorisation within a production-oriented classification framework. We compare four instruction-tuned models available through AWS Bedrock: Claude 3.7 Sonnet, Claude 4 Sonnet, Mixtral 8x7B Instruct, and Mistral 7B Instruct. The aim of the study was (1) to assess performance across accuracy, response stability, and token-level cost, and (2) to investigate what prompting methods, zero-shot or few-shot, are especially appropriate both in terms of accuracy and in terms of incurred costs. Results of our experiments demonstrated that Claude 3.7 Sonnet achieves the most favourable balance between classification accuracy and cost efficiency.

Hierarchical Memory Orchestration for Personalized Persistent Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Junming Liu · Yifei Sun · Weihua Cheng · Haodong Lei · Yuqi Li · Yirong Chen · Ding Wang
3 April 2026 at 12:00
arXiv:2604.01670v1 Announce Type: new Abstract: While long-term memory is essential for intelligent agents to maintain consistent historical awareness, the accumulation of extensive interaction data often leads to performance bottlenecks. Naive storage expansion increases retrieval noise and computational latency, overwhelming the reasoning capacity of models deployed on constrained personal devices. To address this, we propose Hierarchical Memory Orchestration (HMO), a framework that organizes interaction history into a three-tiered directory driven by user-centric contextual relevance. Our system maintains a compact primary cache, coupling recent and pivotal memories with an evolving user profile to ensure agent reasoning remains aligned with individual behavioral traits. This primary cache is complemented by a high-priority secondary layer, both of which are managed within a global archive of the full interaction history. Crucially, the user persona dictates memory redistribution across this hierarchy, promoting records mapped to long-term patterns toward more active tiers while relegating less relevant information. This targeted orchestration surfaces historical knowledge precisely when needed while maintaining a lean and efficient active search space. Evaluations on multiple benchmarks achieve state-of-the-art performance. Real-world deployments in ecosystems like OpenClaw demonstrate that HMO significantly enhances agent fluidity and personalization.

EvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hanrong Zhang · Shicheng Fan · Henry Peng Zou · Yankai Chen · Zhenting Wang · Jiayu Zhou · Chengze Li · Wei-Chieh Huang · Yifei Yao · Kening Zheng · Xue Liu · Xiaoxiao Li · Philip S. Yu
3 April 2026 at 12:00
arXiv:2604.01687v1 Announce Type: new Abstract: Anthropic proposes the concept of skills for LLM agents to tackle multi-step professional tasks that simple tool invocations cannot address. A tool is a single, self-contained function, whereas a skill is a structured bundle of interdependent multi-file artifacts. Currently, skill generation is not only label-intensive due to manual authoring, but also may suffer from human--machine cognitive misalignment, which can lead to degraded agent performance, as evidenced by evaluations on SkillsBench. Therefore, we aim to enable agents to autonomously generate skills. However, existing self-evolving methods designed for tools cannot be directly applied to skills due to their increased complexity. To address these issues, we propose EvoSkills, a self-evolving skills framework that enables agents to autonomously construct complex, multi-file skill packages. Specifically, EvoSkills couples a Skill Generator that iteratively refines skills with a Surrogate Verifier that co-evolves to provide informative and actionable feedback without access to ground-truth test content. On SkillsBench, EvoSkills achieves the highest pass rate among five baselines on both Claude Code and Codex, and also exhibits strong generalization capabilities to six additional LLMs.

Domain-constrained knowledge representation: A modal framework

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chao Li · Yuru Wang · Chunyi Zhao
3 April 2026 at 12:00
arXiv:2604.01770v1 Announce Type: new Abstract: Knowledge graphs store large numbers of relations efficiently, but they remain weak at representing a quieter difficulty: the meaning of a concept often shifts with the domain in which it is used. A triple such as Apple, instance-of, Company may be acceptable in one setting while being misleading or unusable in another. In most current systems, domain information is attached as metadata, qualifiers, or graph-level organization. These mechanisms help with filtering and provenance, but they usually do not alter the formal status of the assertion itself. This paper argues that domain should be treated as part of knowledge representation rather than as supplementary annotation. It introduces the Domain-Contextualized Concept Graph (DCG), a framework in which domain is written into the relation and interpreted as a modal world constraint. In the DCG form (C, R at D, C'), the marker at D identifies the world in which the relation holds. Formally, the relation is interpreted through a domain-indexed necessity operator, so that truth, inference, and conflict checking are all scoped to the relevant world. This move has three consequences: ambiguous concepts can be disambiguated at the point of representation; invalid assertions can be challenged against their domain; cross-domain relations can be connected through explicit predicates. The paper develops this claim through a Kripke-style semantics, a compact predicate system, a Prolog implementation, and mappings to RDF, OWL, and relational databases. The contribution is a representational reinterpretation of domain itself. The central claim is that many practical failures in knowledge systems begin when domain is treated as external to the assertion. DCG addresses that by giving domain a structural and computable role inside the representation.

SenseMath: Do LLMs Have Number Sense? Evaluating Shortcut Use, Judgment, and Generation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haomin Zhuang · Xiangqi Wang · Yili Shen · Ying Cheng · Xiangliang Zhang
3 April 2026 at 12:00
arXiv:2604.01988v1 Announce Type: new Abstract: Large language models often default to step-by-step computation even when efficient numerical shortcuts are available. This raises a basic question: do they exhibit number sense in a human-like behavioral sense, i.e., the ability to recognize numerical structure, apply shortcuts when appropriate, and avoid them when they are not? We introduce SenseMath, a controlled benchmark for evaluating structure-sensitive numerical reasoning in LLMs. SenseMath contains 4,800 items spanning eight shortcut categories and four digit scales, with matched strong-shortcut, weak-shortcut, and control variants. It supports three evaluation settings of increasing cognitive demand: Shortcut Use (whether models can apply shortcuts on shortcut-amenable problems); Applicability Judgment (whether they can recognize when a shortcut is appropriate or misleading); and Problem Generation (whether they can generate new problem items that correctly admit a given type of shortcut). Our evaluation across five LLMs, ranging from GPT-4o-mini to Llama-3.1-8B, shows a consistent pattern: when explicitly prompted, models readily adopt shortcut strategies and achieve substantial accuracy gains on shortcut-amenable items (up to 15%), yet under standard chain-of-thought prompting they spontaneously employ such strategies in fewer than 40% of cases, even when they demonstrably possess the requisite capability. Moreover, this competence is confined to the Use level; models systematically over-generalise shortcuts to problems where they do not apply, and fail to generate valid shortcut-bearing problems from scratch. Together, these results suggest that current LLMs exhibit procedural shortcut fluency without the structural understanding of when and why shortcuts work that underlies human number sense.

The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xinlei Yu · Zhangquan Chen · Yongbo He · Tianyu Fu · Cheng Yang · Chengming Xu · Yue Ma · Xiaobin Hu · Zhe Cao · Jie Xu · Guibin Zhang · Jiale Tao · Jiayi Zhang · Siyuan Ma · Kaituo Feng · Haojie Huang · Youxing Li · Ronghao Chen · Huacan Wang · Chenglin Wu · Zikun Su · Xiaogang Xu · Kelu Yao · Kun Wang · Chen Gao · Yue Liao · Ruqi Huang · Tao Jin · Cheng Tan · Jiangning Zhang · Wenqi Ren · Yanwei Fu · Yong Liu · Yu Wang · Xiangyu Yue · Yu-Gang Jiang · Shuicheng Yan
3 April 2026 at 12:00
arXiv:2604.02029v1 Announce Type: new Abstract: Latent space is rapidly emerging as a native substrate for language-based models. While modern systems are still commonly understood through explicit token-level generation, an increasing body of work shows that many critical internal processes are more naturally carried out in continuous latent space than in human-readable verbal traces. This shift is driven by the structural limitations of explicit-space computation, including linguistic redundancy, discretization bottlenecks, sequential inefficiency, and semantic loss. This survey aims to provide a unified and up-to-date landscape of latent space in language-based models. We organize the survey into five sequential perspectives: Foundation, Evolution, Mechanism, Ability, and Outlook. We begin by delineating the scope of latent space, distinguishing it from explicit or verbal space and from the latent spaces commonly studied in generative visual models. We then trace the field's evolution from early exploratory efforts to the current large-scale expansion. To organize the technical landscape, we examine existing work through the complementary lenses of mechanism and ability. From the perspective of Mechanism, we identify four major lines of development: Architecture, Representation, Computation, and Optimization. From the perspective of Ability, we show how latent space supports a broad capability spectrum spanning Reasoning, Planning, Modeling, Perception, Memory, Collaboration, and Embodiment. Beyond consolidation, we discuss the key open challenges, and outline promising directions for future research. We hope this survey serves not only as a reference for existing work, but also as a foundation for understanding latent space as a general computational and systems paradigm for next-generation intelligence.

TRACE-Bot: Detecting Emerging LLM-Driven Social Bots via Implicit Semantic Representations and AIGC-Enhanced Behavioral Patterns

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhongbo Wang · Zhiyu Lin · Zhu Wang · Haizhou Wang
3 April 2026 at 12:00
arXiv:2604.02147v1 Announce Type: new Abstract: Large Language Model-driven (LLM-driven) social bots pose a growing threat to online discourse by generating human-like content that evades conventional detection. Existing methods suffer from limited detection accuracy due to overreliance on single-modality signals, insufficient sensitivity to the specific generative patterns of Artificial Intelligence-Generated Content (AIGC), and a failure to adequately model the interplay between linguistic patterns and behavioral dynamics. To address these limitations, we propose TRACE-Bot, a unified dual-channel framework that jointly models implicit semantic representations and AIGC-enhanced behavioral patterns. TRACE-Bot constructs fine-grained representations from heterogeneous sources, including personal information data, interaction behavior data and tweet data. A dual-channel architecture captures linguistic representations via a pretrained language model and behavioral irregularities via multidimensional activity features augmented with signals from state-of-the-art (SOTA) AIGC detectors. The fused representations are then classified through a lightweight prediction head. Experiments on two public LLM-driven social bot datasets demonstrate SOTA performance, achieving accuracies of 98.46% and 97.50%, respectively. The results further indicate strong robustness against advanced bot strategies, highlighting the effectiveness of jointly leveraging implicit semantic representations and AIGC-enhanced behavioral patterns for emerging LLM-driven social bot detection.

Do Emotions in Prompts Matter? Effects of Emotional Framing on Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Minda Zhao · Yutong Yang · Chufei Peng · Rachel Gonsalves · Weiyue Li · Ruyi Yang · Zhixi Liu · Mengyu Wang
3 April 2026 at 12:00
arXiv:2604.02236v1 Announce Type: new Abstract: Emotional tone is pervasive in human communication, yet its influence on large language model (LLM) behaviour remains unclear. Here, we examine how first-person emotional framing in user-side queries affect LLM performance across six benchmark domains, including mathematical reasoning, medical question answering, reading comprehension, commonsense reasoning and social inference. Across models and tasks, static emotional prefixes usually produce only small changes in accuracy, suggesting that affective phrasing is typically a mild perturbation rather than a reliable general-purpose intervention. This stability is not uniform: effects are more variable in socially grounded tasks, where emotional context more plausibly interacts with interpersonal reasoning. Additional analyses show that stronger emotional wording induces only modest extra change, and that human-written prefixes reproduce the same qualitative pattern as LLM-generated ones. We then introduce EmotionRL, an adaptive emotional prompting framework that selects emotional framing adaptively for each query. Although no single emotion is consistently beneficial, adaptive selection yields more reliable gains than fixed emotional prompting. Together, these findings show that emotional tone is neither a dominant driver of LLM performance nor irrelevant noise, but a weak and input-dependent signal that can be exploited through adaptive control.
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