❌

Normal view

Received — 27 May 2026 ⏭

Solving Combinatorial Counting Problems with Weighted First-Order Model Counting

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuanhong Wang · Juhua Pu · Yuxu Zhou · Yuyi Wang · Ond\v{r}ej Ku\v{z}elka
26 May 2026 at 12:00
arXiv:2605.24845v1 Announce Type: new Abstract: Combinatorial counting problems pervade artificial intelligence, statistics, and discrete mathematics. Whether the task is enumerating subsets, multisets, permutations, partitions, or compositions under structural and arithmetic constraints, solving it remains a stubbornly manual exercise. Closed-form derivations are powerful but brittle, while naive encodings to propositional model counting or constraint satisfaction destroy the exchangeability that makes counting tractable in the first place. We present Cofola (COmbinatorial counting LAnguage with First-Order logic), a typed declarative language whose primitives are the combinatorial objects that recur in everyday counting questions, including sets, bags, tuples, sequences, circles, partitions, and compositions, together with natural relational and arithmetic constraints over them. A denotational semantics maps every Cofola program to a well-defined combinatorial counting problem, and a three-phase compilation pipeline (preprocessing, decomposition, and symmetry-preserving encoding) reduces this problem to a weighted first-order model counting (WFOMC) instance augmented with coefficient-extraction constraints. To stay inside known domain-liftable fragments whenever possible, the encoding groups indistinguishable entities, breaks the symmetry of unordered groupings lexicographically, and encodes sequences and circles via order axioms. On a suite of representative combinatorial counting problems, ranging from textbook math problems to multi-object scenarios that the closest prior framework cannot express, Cofola produces concise specifications and a uniform solving pipeline that is practical end-to-end.

DBPnet: Damper Characteristics-Based Bayesian Physics-Informed Neural Network for Wheel Load Estimation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tianyi Wang · Tianyi Zeng · Zimo Zeng · Feiyang Zhang · Yujin Wang · Xiangyu Li · Yiming Xu · Sikai Chen · Junfeng Jiao · Christian Claudel · Xinbo Chen
26 May 2026 at 12:00
arXiv:2605.24860v1 Announce Type: cross Abstract: Advanced driver assistance systems (ADAS) play an important role in modern automotive intelligence, significantly enhancing vehicle safety and stability. The performance of ADAS critically relies on accurate and reliable vehicle state estimation, particularly from vehicle dynamic sensors. Among these signals, wheel load is a key variable for chassis control and safety-critical functions, yet it remains difficult to estimate robustly due to complex suspension geometry, nonlinear dynamics, and measurement noise. To address this issue, we propose DBPnet, a Bayesian physics-informed neural network (PINN) with a physics-aware embedding module inspired by damper characteristics. First, this paper presents a suspension linkage-level modeling (SLLM) approach that constructs a nonlinear instantaneous dynamic model by explicitly considering the complex geometric structure of the suspension. Building upon SLLM, Bayesian inference is integrated into the PINN to effectively cope with noise and uncertainty in the vehicle chassis system, thereby improving the model's robustness. Then, a physics-informed loss function is employed to ensure consistency with fundamental physical principles, while the damper characteristics-inspired embedding module extracts temporal variation features of input signals and incorporates them into each layer of the PINN, ensuring that physical observations guide the neural network without being constrained by fixed physical models. Extensive evaluations on high-fidelity simulations and real-world experiments demonstrate that our DBPnet consistently achieves lower RMSE and MaxError than baseline methods. These results highlight the potential of our DBPnet to advance wheel load estimation and contribute to the development of more reliable ADAS actuator functions.

GL-LFGNN:A Global-Local Dual-branch Causal Graph Neural Network Based on Liang-Kleeman Information Flow for EEG Emotion Recognition

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ziyi Wang · Dongyang Kuang
26 May 2026 at 12:00
arXiv:2605.25061v1 Announce Type: cross Abstract: EEG-based emotion recognition holds significant promise for objective diagnosis of mood disorders. Graph neural networks (GNNs) have emerged as the dominant paradigm for modeling inter-channel dependencies in EEG, yet existing approaches rely on symmetric adjacency matrices derived from spatial proximity or functional correlations that fundamentally capture statistical associations rather than directed causal influences, which conflicts with the inherently asymmetric, causally-driven nature of neural information flow. To bridge this gap, we propose GL-LFGNN, a Global-Local Dual-branch Causal Graph Neural Network grounded in Liang-Kleeman information flow theory. Unlike Granger causality that merely assesses temporal precedence, our approach rigorously quantifies causal strength from a dynamical systems perspective, yielding neurophysiologically interpretable directed graphs. A dual-branch architecture further integrates whole-brain connectivity with region-specific processing aligned to established functional neuroanatomy. On the MEEG dataset, GL-LFGNN achieves 86.17% (Arousal) and 86.71% (Valence) accuracy with only 37K parameters -- approximately 10% of the current state-of-the-art -- demonstrating that principled causal modeling can simultaneously enhance interpretability, generalization, and computational efficiency. Code will be released.

Decoding ML Decision: An Agentic Reasoning Framework for Large-Scale Ranking System

✇cs.AI, q-bio.NC updates on arXiv.org
By: Longfei Yun · Yihan Wu · Haoran Liu · Xiaoxuan Liu · Ziyun Xu · Yi Wang · Yang Xia · Pengfei Wang · Mingze Gao · Yunxiang Wang · Changfan Chen · Wenjie Fu · Hong Yan · Junfeng Pan
26 May 2026 at 12:00
arXiv:2602.18640v2 Announce Type: replace Abstract: Modern large-scale ranking systems operate within a sophisticated landscape of competing objectives, operational constraints, and evolving product requirements. Progress in this domain is increasingly bottlenecked by the engineering context constraint: the arduous process of translating ambiguous product intent into reasonable, executable, verifiable hypotheses, rather than by modeling techniques alone. We present GEARS (Generative Engine for Agentic Ranking Systems), a framework that reframes ranking optimization as an autonomous discovery process within a programmable experimentation environment. Rather than treating optimization as static model selection, GEARS leverages Specialized Agent Skills to encapsulate ranking expert knowledge into reusable reasoning capabilities, enabling operators to steer systems via high-level intent vibe personalization. Furthermore, to ensure production reliability, the framework incorporates validation hooks to enforce statistical robustness and filter out brittle policies that overfit short-term signals. Experimental validation across diverse product surfaces demonstrates that GEARS consistently identifies superior, near-Pareto-efficient policies by synergizing algorithmic signals with deep ranking context while maintaining rigorous deployment stability.

SEA-Eval: A Benchmark for Evaluating Self-Evolving Agents Beyond Episodic Assessment

✇cs.AI, q-bio.NC updates on arXiv.org
By: Sihang Jiang · Lipeng Ma · Zhonghua Hong · Keyi Wang · Zhiyu Lu · Tengfei Wang · Shisong Chen · Jinghao Zhang · Tianjun Pan · Weijia Li · Jiaqing Liang · Yanghua Xiao
26 May 2026 at 12:00
arXiv:2604.08988v3 Announce Type: replace Abstract: Current LLM-based agents demonstrate strong performance in episodic task execution but remain constrained by static toolsets and episodic amnesia, failing to accumulate experience across task boundaries. This paper formalizes the Self-Evolving Agent (SEA) from the perspective of digital embodiment and continuous cross-task evolution, introduces the Evolutionary Flywheel as its minimal sufficient architecture, and presents SEA-Eval -- the first benchmark designed specifically for evaluating SEAs. Grounded in Flywheel theory, SEA-Eval establishes SR and T as primary metrics and, through sequential task stream design, is designed to quantify evolutionary gain, evolutionary stability, and implicit alignment convergence. Empirical evaluation reveals that, under comparable success rates, token consumption differs by up to 31.2 times between frameworks on individual tasks, with divergent evolutionary trajectories emerging under sequential analysis -- demonstrating that success rate alone creates a capability illusion and that the sequential convergence of $T$ is the key criterion for distinguishing genuine evolution from pseudo-evolution.

High-salt diet in macrophage-associated metabolic disorders: Mechanisms and therapeutic implications

✇(Multiomics OR Omics) AND (Pancreatic)
By: Ke Xiao · Lin Gu · Jiayi Ma · Shanjie Rong · Xiongtao Yue · Yuechen Liu · Shiwei Liu · Fei Sun · Shu Zhang · Congyi Wang
19 May 2026 at 18:00

Chin Med J (Engl). 2026 May 19. doi: 10.1097/CM9.0000000000004098. Online ahead of print.

ABSTRACT

High-salt diet (HSD) has emerged as a prevalent environmental factor that exacerbates chronic inflammation and insulin resistance in obesity-associated type 2 diabetes (T2D) by modulating macrophage polarization, metabolic reprogramming, and epigenetic imprinting. Current evidence demonstrates that HSD activates p38/mitogen-activated protein kinase (MAPK), nuclear factor kappa-B (NF-κB), and NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome signaling pathways, by which it drives macrophage polarization toward a proinflammatory M1 phenotype while inducing a glycolysis-dominant metabolic shift, thereby establishing a persistent "metabolic memory". Moreover, HSD orchestrates metabolic memory in macrophages through coordinated epigenetic machinery, including histone modifications (Trimethylation of histone H3 at lysine 4 [H3K4me3] and Acetylation of histone H3 at lysine 27 [H3K27ac]), DNA methylation, and noncoding RNAs (e.g., long non-coding RNA MALAT1 and miR-155), leading to sustained inflammatory phenotypes. In multiple metabolic organs (e.g., adipose tissue, liver, pancreas, and gut), the HSD-macrophage axis aggravates systemic insulin resistance through shared proinflammatory signaling and other tissue-specific mechanisms. Most importantly, therapeutic strategies targeting the NLRP3 inflammasome, metabolic pathways, and epigenetic alterations offer novel approaches for managing metabolic inflammation. Future investigations are encouraged to leverage lineage tracing, single-cell sequencing, and spatial multi-omics technologies to advance the development of precision medicine for macrophage-associated metabolic disorders.

PMID:42156155 | DOI:10.1097/CM9.0000000000004098

Received — 13 April 2026 ⏭

NAT10 promotes cisplatin resistance and immune escape by increasing the expression of DUSP1 and PD-L1 in gastric cancer

✇Cell Death Discovery nature.com science feeds
By: Lilin Qian · Wenrong Gao · Xinyi Wang · Shuqi Cui · Xiaoqi Han · Xia Xu · Jihui Jia · Zhifang Liu
10 April 2026 at 08:00

Cell Death Discovery, Published online: 10 April 2026; doi:10.1038/s41420-026-03107-w

NAT10 promotes cisplatin resistance and immune escape by increasing the expression of DUSP1 and PD-L1 in gastric cancer
Received — 4 April 2026 ⏭

Unified modeling of 3D molecular generation via atomic interactions with PocketXMol

✇Cell
By: Xingang Peng · Ruihan Guo · Fenglin Guo · Ziyi Wang · Jiayu Sun · Jiaqi Guan · Yinjun Jia · Yan Xu · Yanwen Huang · Muhan Zhang · Jian Peng · Xinquan Wang · Chuanhui Han · Zihua Wang · Jianzhu Ma
18 February 2026 at 08:00
A versatile, atom-level generative AI model enables unified pocket-interacting tasks, from docking to de novo design, and demonstrates robust experimental validation for both small-molecule and peptide therapeutics.
Received — 2 April 2026 ⏭

Efficient and Scalable Granular-ball Graph Coarsening Method for Large-scale Graph Node Classification

✇cs.AI, q-bio.NC updates on arXiv.org
By: Guan Wang · Shuyin Xia · Lei Qian · Guoyin Wang · Yi Liu · Yi Wang · Wei Wang
1 April 2026 at 12:00
arXiv:2603.29148v1 Announce Type: cross Abstract: Graph Convolutional Network (GCN) is a model that can effectively handle graph data tasks and has been successfully applied. However, for large-scale graph datasets, GCN still faces the challenge of high computational overhead, especially when the number of convolutional layers in the graph is large. Currently, there are many advanced methods that use various sampling techniques or graph coarsening techniques to alleviate the inconvenience caused during training. However, among these methods, some ignore the multi-granularity information in the graph structure, and the time complexity of some coarsening methods is still relatively high. In response to these issues, based on our previous work, in this paper, we propose a new framework called Efficient and Scalable Granular-ball Graph Coarsening Method for Large-scale Graph Node Classification. Specifically, this method first uses a multi-granularity granular-ball graph coarsening algorithm to coarsen the original graph to obtain many subgraphs. The time complexity of this stage is linear and much lower than that of the exiting graph coarsening methods. Then, subgraphs composed of these granular-balls are randomly sampled to form minibatches for training GCN. Our algorithm can adaptively and significantly reduce the scale of the original graph, thereby enhancing the training efficiency and scalability of GCN. Ultimately, the experimental results of node classification on multiple datasets demonstrate that the method proposed in this paper exhibits superior performance. The code is available at https://anonymous.4open.science/r/1-141D/.

From Efficiency to Adaptivity: A Deeper Look at Adaptive Reasoning in Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chao Wu · Baoheng Li · Mingchen Gao · Yu Tian · Zhenyi Wang
1 April 2026 at 12:00
arXiv:2511.10788v3 Announce Type: replace Abstract: Recent advances in large language models (LLMs) have made reasoning a central benchmark for evaluating intelligence. While prior surveys focus on efficiency by examining how to shorten reasoning chains or reduce computation, this view overlooks a fundamental challenge: current LLMs apply uniform reasoning strategies regardless of task complexity, generating long traces for trivial problems while failing to extend reasoning for difficult tasks. This survey reframes reasoning through the lens of {adaptivity}: the capability to allocate reasoning effort based on input characteristics such as difficulty and uncertainty. We make three contributions. First, we formalize deductive, inductive, and abductive reasoning within the LLM context, connecting these classical cognitive paradigms with their algorithmic realizations. Second, we formalize adaptive reasoning as a control-augmented policy optimization problem balancing task performance with computational cost, distinguishing learned policies from inference-time control mechanisms. Third, we propose a systematic taxonomy organizing existing methods into training-based approaches that internalize adaptivity through reinforcement learning, supervised fine-tuning, and learned controllers, and training-free approaches that achieve adaptivity through prompt conditioning, feedback-driven halting, and modular composition. This framework clarifies how different mechanisms realize adaptive reasoning in practice and enables systematic comparison across diverse strategies. We conclude by identifying open challenges in self-evaluation, meta-reasoning, and human-aligned reasoning control.

The 1000 Chinese Pangenome empowers medical and population genetics

✇Nature - Issue - nature.com science feeds
By: Yifei Wang · Zhongqu Duan · Dan Chen · Dandan Shi · Yi Ding · Zhibin Wang · Baoqing Li · Zhiyi Wang · Minmin Guo · Wen Yang · Junren Hou · Wenhao Chen · Yazhou Guo · Wenjie Wei · Yujie Cao · Xiwei Sun · Weiyang Bai · Mingdong Lu · Ting Qi · Xian Shen · Jian Yang
1 April 2026 at 08:00

Nature, Published online: 01 April 2026; doi:10.1038/s41586-026-10315-y

Development of the pangenome-informed genome assembly (PIGA) workflow enabled the generation of 1,116 diploid genome assemblies (55 de novo and 1,061 pangenome-informed), representing an extensive resource of medically relevant genic variations.

Electric dipole moment drives the dynamics of the TNFR1 complex I signalosome

✇Nature - Issue - nature.com science feeds
By: Jianping Liu · Jing Zhao · Jiayang Gao · Kun Zhao · Yaoyao Han · Jing Yang · Zefei Li · Jianyu Ye · Ziyu Sun · Fengyi Wang · Xinyi Liu · Zekai Li · Siyu Ji · Bo Liu · Cong Liu · Yixiao Zhang · Junying Yuan · James J. Chou
1 April 2026 at 08:00

Nature, Published online: 01 April 2026; doi:10.1038/s41586-026-10304-1

Long-range interactions mediated by protein electric dipole moments have a role in driving the assembly and disassembly of super-signalling complex I for promoting NF-κB signalling.
Received — 26 March 2026 ⏭

Three Creates All: You Only Sample 3 Steps

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuren Cai · Guangyi Wang · Zongqing Li · Li Li · Zhihui Liu · Songzhi Su
25 March 2026 at 12:00
arXiv:2603.22375v1 Announce Type: cross Abstract: Diffusion models deliver high-fidelity generation but remain slow at inference time due to many sequential network evaluations. We find that standard timestep conditioning becomes a key bottleneck for few-step sampling. Motivated by layer-dependent denoising dynamics, we propose Multi-layer Time Embedding Optimization (MTEO), which freeze the pretrained diffusion backbone and distill a small set of step-wise, layer-wise time embeddings from reference trajectories. MTEO is plug-and-play with existing ODE solvers, adds no inference-time overhead, and trains only a tiny fraction of parameters. Extensive experiments across diverse datasets and backbones show state-of-the-art performance in the few-step sampling and substantially narrow the gap between distillation-based and lightweight methods. Code will be available.

Genomic atlas of Bifidobacterium infantis and B. longum informs infant probiotic design

✇Cell
By: Yan Shao · Shuyi Wang · Bonface M. Gichuki · Mark D. Stares · Timothy J. Rozday · Nitin Kumar · Hilary P. Browne · Nicholas J.R. Dawson · James M. Njunge · Caroline Tigoi · Narshion Ngao · Mohammod Jobayer Chisti · Benson O. Singa · Samuel Kariuki · Abdoulaye Hama Diallo · Ali Faisal Saleem · Syed Asad Ali · Ezekiel Mupere · Emmie Mbale · Kirkby D. Tickell · Wieger P. Voskuijl · Christina L. Lancioni · Robert H.J. Bandsma · Tahmeed Ahmed · Judd L. Walson · James A. Berkley · Trevor D. Lawley
18 February 2026 at 08:00
A global genomic survey of infant gut bifidobacteria shows that B. infantis remains highly prevalent and diverse in infants from low- and middle-income countries but scarce in Western, industrialized populations and poorly represented in current probiotics. This genomic and culture collection catalogs geo-specific B. infantis strains and provides a blueprint for developing probiotics tailored to local diets and populations to support infant health.
Received — 17 March 2026 ⏭

SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xiangyi Li · Wenbo Chen · Yimin Liu · Shenghan Zheng · Xiaokun Chen · Yifeng He · Yubo Li · Bingran You · Haotian Shen · Jiankai Sun · Shuyi Wang · Binxu Li · Qunhong Zeng · Di Wang · Xuandong Zhao · Yuanli Wang · Roey Ben Chaim · Zonglin Di · Yipeng Gao · Junwei He · Yizhuo He · Liqiang Jing · Luyang Kong · Xin Lan · Jiachen Li · Songlin Li · Yijiang Li · Yueqian Lin · Xinyi Liu · Xuanqing Liu · Haoran Lyu · Ze Ma · Bowei Wang · Runhui Wang · Tianyu Wang · Wengao Ye · Yue Zhang · Hanwen Xing · Yiqi Xue · Steven Dillmann · Han-chung Lee
16 March 2026 at 12:00
arXiv:2602.12670v3 Announce Type: replace Abstract: Agent Skills are structured packages of procedural knowledge that augment LLM agents at inference time. Despite rapid adoption, there is no standard way to measure whether they actually help. We present SkillsBench, a benchmark of 86 tasks across 11 domains paired with curated Skills and deterministic verifiers. Each task is evaluated under three conditions: no Skills, curated Skills, and self-generated Skills. We test 7 agent-model configurations over 7,308 trajectories. Curated Skills raise average pass rate by 16.2 percentage points(pp), but effects vary widely by domain (+4.5pp for Software Engineering to +51.9pp for Healthcare) and 16 of 84 tasks show negative deltas. Self-generated Skills provide no benefit on average, showing that models cannot reliably author the procedural knowledge they benefit from consuming. Focused Skills with 2--3 modules outperform comprehensive documentation, and smaller models with Skills can match larger models without them.
Received — 14 March 2026 ⏭

CircRNA-encoded RIPK1-98 protein drives lung adenocarcinoma progression

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

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

ABSTRACT

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

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

CircRNA-encoded RIPK1-98 protein drives lung adenocarcinoma progression

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

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

ABSTRACT

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

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

Received — 11 March 2026 ⏭

SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xiangyi Li · Wenbo Chen · Yimin Liu · Shenghan Zheng · Xiaokun Chen · Yifeng He · Yubo Li · Bingran You · Haotian Shen · Jiankai Sun · Shuyi Wang · Binxu Li · Qunhong Zeng · Di Wang · Xuandong Zhao · Yuanli Wang · Roey Ben Chaim · Zonglin Di · Yipeng Gao · Junwei He · Yizhuo He · Liqiang Jing · Luyang Kong · Xin Lan · Jiachen Li · Songlin Li · Yijiang Li · Yueqian Lin · Xinyi Liu · Xuanqing Liu · Haoran Lyu · Ze Ma · Bowei Wang · Runhui Wang · Tianyu Wang · Wengao Ye · Yue Zhang · Hanwen Xing · Yiqi Xue · Steven Dillmann · Han-chung Lee
10 March 2026 at 12:00
arXiv:2602.12670v2 Announce Type: replace Abstract: Agent Skills are structured packages of procedural knowledge that augment LLM agents at inference time. Despite rapid adoption, there is no standard way to measure whether they actually help. We present SkillsBench, a benchmark of 86 tasks across 11 domains paired with curated Skills and deterministic verifiers. Each task is evaluated under three conditions: no Skills, curated Skills, and self-generated Skills. We test 7 agent-model configurations over 7,308 trajectories. Curated Skills raise average pass rate by 16.2 percentage points(pp), but effects vary widely by domain (+4.5pp for Software Engineering to +51.9pp for Healthcare) and 16 of 84 tasks show negative deltas. Self-generated Skills provide no benefit on average, showing that models cannot reliably author the procedural knowledge they benefit from consuming. Focused Skills with 2--3 modules outperform comprehensive documentation, and smaller models with Skills can match larger models without them.
Received — 5 March 2026 ⏭

SycoEval-EM: Sycophancy Evaluation of Large Language Models in Simulated Clinical Encounters for Emergency Care

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dongshen Peng · Yi Wang · Austin Schoeffler · Carl Preiksaitis · Christian Rose
5 March 2026 at 13:00
arXiv:2601.16529v2 Announce Type: replace Abstract: Large language models (LLMs) show promise in clinical decision support yet risk acquiescing to patient pressure for inappropriate care. We introduce SycoEval-EM, a multi-agent simulation framework evaluating LLM robustness through adversarial patient persuasion in emergency medicine. Across 20 LLMs and 1,875 encounters spanning three Choosing Wisely scenarios, acquiescence rates ranged from 0-100\%. Models showed higher vulnerability to imaging requests (38.8\%) than opioid prescriptions (25.0\%), with model capability poorly predicting robustness. All persuasion tactics proved equally effective (30.0-36.0\%), indicating general susceptibility rather than tactic-specific weakness. Our findings demonstrate that static benchmarks inadequately predict safety under social pressure, necessitating multi-turn adversarial testing for clinical AI certification.

Generalized Discrete Diffusion with Self-Correction

✇cs.AI, q-bio.NC updates on arXiv.org
By: Linxuan Wang · Ziyi Wang · Yikun Bai · Wei Deng · Guang Lin · Qifan Song
4 March 2026 at 13:00
arXiv:2603.02230v1 Announce Type: cross Abstract: Self-correction is an effective technique for maintaining parallel sampling in discrete diffusion models with minimal performance degradation. Prior work has explored self-correction at inference time or during post-training; however, such approaches often suffer from limited generalization and may impair reasoning performance. GIDD pioneers pretraining-based self-correction via a multi-step BERT-style uniform-absorbing objective. However, GIDD relies on a continuous interpolation-based pipeline with opaque interactions between uniform transitions and absorbing masks, which complicates hyperparameter tuning and hinders practical performance. In this work, we propose a Self-Correcting Discrete Diffusion (SCDD) model to reformulate pretrained self-correction with explicit state transitions and learn directly in discrete time. Our framework also simplifies the training noise schedule, eliminates a redundant remasking step, and relies exclusively on uniform transitions to learn self-correction. Experiments at the GPT-2 scale demonstrate that our method enables more efficient parallel decoding while preserving generation quality.
❌