OpenRaiser/Intern-Atlas huggingface dataset:
AutoPCR: Automated Phenotype Concept Recognition by Prompting
Venue: arXiv cs.CL 2026
Year: 2026

Phenotype concept recognition (CR) is a fundamental task in biomedical text mining, enabling applications such as clinical diagnostics and knowledge graph construction. However, existing methods often require ontology-specific training and struggle to generalize across diverse text types and evolving biomedical terminology. We present AutoPCR, a prompt-based phenotype CR method that does not require ontology-specific training. AutoPCR performs CR in three stages: entity extraction using a hybrid of rule-based and neural tagging strategies, candidate retrieval via SapBERT, and entity linking through prompting a large language model. Experiments on four benchmark datasets show that AutoPCR achieves the best average and most robust performance across both mention-level and document-level evaluations, surpassing prior state-of-the-art methods. Further ablation and transfer studies demonstrate its inductive capability and generalizability to new ontologies.
Order in Partial Markov Categories
Venue: arXiv cs.LO 2026
Year: 2026

Partial Markov categories are a recent framework for categorical probability theory that provide an abstract account of partial probabilistic computation with updating semantics. In this article, we discuss two order relations on the morphisms of a partial Markov category. In particular, we prove that every partial Markov category is canonically preorder-enriched, recovering several well-known order enrichments. We also demonstrate that the existence of codiagonal maps (comparators) is closely related to order properties of partial Markov categories. Finally, we introduce a synthetic version of the Cauchy--Schwarz inequality and, from it, we prove that updating increases validity.
ChipletPart: Cost-Aware Partitioning for 2.5D Systems
Venue: arXiv cs.AR 2026
Year: 2026

Industry adoption of chiplets has been growing as chiplets are a cost-effective option for making large, high-performance systems. Consequently, partitioning large systems into chiplets is increasingly important. In this work, we introduce ChipletPart, a cost-driven 2.5D system partitioner that addresses the constraints of chiplet systems, including complex objective functions, limited reach of inter-chiplet I/O transceivers, and the assignment of heterogeneous manufacturing technologies to different chiplets. ChipletPart integrates a sophisticated chiplet cost model with a genetic algorithm (GA)-based technology assignment and partitioning methodology, along with a simulated annealing (SA)-based chiplet floorplanner. Our results show that ChipletPart: (i) reduces chiplet cost by up to 58% (20% geometric mean) compared to state-of-the-art min-cut partitioners, which often yield floorplan-infeasible solutions; (ii) generates partitions with up to 47% (6% geometric mean) lower cost compared to the prior work Floorplet; (iii) reduces chiplet cost up to 48% (30% geometric mean) compared to Chipletizer, while consistently producing I/O-feasible chiplet solutions across all testcases; and (iv) for the testcases we study, heterogeneous integration reduces cost by up to 43% (15% geometric mean) compared to homogeneous implementations. Additionally, we explore Bayesian optimization (BO) for finding low cost and floorplan-feasible chiplet solutions with technology assignments. On some testcases, our BO framework achieves better system cost (up to 5.3% improvement) with higher runtime overhead (up to 4x) compared to our GA-based framework. We also present case studies that show how changes in packaging and inter-chiplet signaling technologies can affect partitioning solutions. Finally, ChipletPart, the underlying cost model, and our testcase generator are available as open-source tools.
They See Me Rolling: High-Speed Event Vision-Based Tactile Roller Sensor for Large Surface Inspection
Venue: arXiv cs.RO 2026
Year: 2026

Inspecting large-scale industrial surfaces like aircraft fuselages for quality control requires capturing their precise 3D surface geometry at high resolution. Vision-based tactile sensors (VBTSs) offer high local resolution but require slow 'press-and-lift' measurements stitched for large areas. Approaches with sliding or roller/belt VBTS designs provide measurements continuity. However, they face significant challenges respectively: sliding struggles with friction/wear and both approaches are speed-limited by conventional camera frame rates and motion blur, making large-area scanning time consuming. Thus, a rapid, continuous, high-resolution method is needed. We introduce a novel tactile sensor integrating a neuromorphic camera in a rolling mechanism to achieve this. Leveraging its high temporal resolution and robustness to motion blur, our system uses a modified event-based multi-view stereo approach for 3D reconstruction. We demonstrate state-of-the-art scanning speeds up to 0.5 m/s, achieving Mean Absolute Error below 100 microns -- 11 times faster than prior continuous tactile sensing methods. A multi-reference Bayesian fusion strategy enhances accuracy (reducing MAE by 25.2\% compared to EMVS) and mitigates curvature errors. We also validate high-speed feature recognition via Braille reading 2.6 times faster than previous approaches.
Prometheus: Towards Long-Horizon Codebase Navigation for Repository-Level Problem Solving
Venue: arXiv cs.SE 2026
Year: 2026

Large Language Models (LLMs) have shown remarkable capabilities in automating software engineering tasks, spurring the emergence of coding agents that scaffold LLMs with external tools to resolve repository-level problems. However, existing agents still struggle to navigate large-scale codebases, as the Needle-in-a-Haystack problem persists even with million-token context windows, where relevant evidence is often overwhelmed by large volumes of irrelevant code and documentation. Prior codebase navigation approaches, including embedding-based retrieval, file-system exploration, and graph-based retrieval, address parts of this challenge but fail to capture the temporal continuity of agent reasoning, rendering agents stateless and causing repeated repository traversals that hinder scalable planning and reasoning. To address these limitations, we present Prometheus, a memory-centric coding agent framework for long-horizon codebase navigation. Prometheus represents the repository as a unified knowledge graph to encode semantic dependencies and employs a context engine augmented with working memory that retains and reuses previously explored contexts to ensure continuity across reasoning steps. Built upon this engine, Prometheus integrates memory-enhanced navigation into a multi-agent system for automated issue resolution, encompassing issue classification, bug reproduction, patch generation, and verification. Comprehensive experiments are conducted on two widely used issue resolution benchmarks, i.e., SWE-bench Verified and SWE-PolyBench Verified. Powered by GPT-5, Prometheus achieves state-of-the-art performance with 74.4% and 33.8% resolution rates on the two benchmarks, ranking Top-6 and Top-1 among open-source agent systems, respectively. Our data and code are available at https://github.com/EuniAI/Prometheus.
Silent Self-Stabilising Leader Election in Programmable Matter Systems with Holes
Venue: arXiv cs.DC 2026
Year: 2026

Leader election is a fundamental problem in distributed computing, particularly within programmable matter systems, where coordination among simple computational entities is crucial for solving complex tasks. In these systems, particles (i.e., constant-memory computational entities) operate in a regular triangular grid as described in the geometric Amoebot model. While leader election has been extensively studied in non self-stabilising settings, self-stabilising solutions remain more limited. In this work, we study the problem of self-stabilising leader election in connected (but not necessarily simply connected) configurations. We present the first self-stabilising algorithm for connected programmable matter systems that guarantees the election of a unique leader under an unfair scheduler, for oblivious particles (i.e., particles with no persistent memory) that share a common sense of direction. Our approach leverages particle movement, a capability not previously exploited in the self-stabilising context. We show that movement in conjunction with particles sharing a sense of orientation and operating in a grid can overcome classical impossibility results for constant-memory systems established by Dolev, Gouda and Schneider (1999).
Kimi K2: Open Agentic Intelligence
Venue: arXiv cs.LG 2026
Year: 2026

We introduce Kimi K2, a Mixture-of-Experts (MoE) large language model with 32 billion activated parameters and 1 trillion total parameters. We propose the MuonClip optimizer, which improves upon Muon with a novel QK-clip technique to address training instability while enjoying the advanced token efficiency of Muon. Based on MuonClip, K2 was pre-trained on 15.5 trillion tokens with zero loss spike. During post-training, K2 undergoes a multi-stage post-training process, highlighted by a large-scale agentic data synthesis pipeline and a joint reinforcement learning (RL) stage, where the model improves its capabilities through interactions with real and synthetic environments. Kimi K2 achieves state-of-the-art performance among open-source non-thinking models, with strengths in agentic capabilities. Notably, K2 obtains 66.1 on Tau2-Bench, 76.5 on ACEBench (En), 65.8 on SWE-Bench Verified, and 47.3 on SWE-Bench Multilingual -- surpassing most open and closed-sourced baselines in non-thinking settings. It also exhibits strong capabilities in coding, mathematics, and reasoning tasks, with a score of 53.7 on LiveCodeBench v6, 49.5 on AIME 2025, 75.1 on GPQA-Diamond, and 27.1 on OJBench, all without extended thinking. These results position Kimi K2 as one of the most capable open-source large language models to date, particularly in software engineering and agentic tasks. We release our base and post-trained model checkpoints to facilitate future research and applications of agentic intelligence.
Exposing the Illusion of Fairness: Auditing Vulnerabilities to Distributional Manipulation Attacks
Venue: arXiv cs.LG 2026
Year: 2026

The rapid deployment of AI systems in high-stakes domains, including those classified as high-risk under the The EU AI Act (Regulation (EU) 2024/1689), has intensified the need for reliable compliance auditing. For binary classifiers, regulatory risk assessment often relies on global fairness metrics such as the Disparate Impact ratio, widely used to evaluate potential discrimination. In typical auditing settings, the auditee provides a subset of its dataset to an auditor, while a supervisory authority may verify whether this subset is representative of the full underlying distribution. In this work, we investigate to what extent a malicious auditee can construct a fairness-compliant yet representative-looking sample from a non-compliant original distribution, thereby creating an illusion of fairness. We formalize this problem as a constrained distributional projection task and introduce mathematically grounded manipulation strategies based on entropic and optimal transport projections. These constructions characterize the minimal distributional shift required to satisfy fairness constraints. To counter such attacks, we formalize representativeness through distributional distance based statistical tests and systematically evaluate their ability to detect manipulated samples. Our analysis highlights the conditions under which fairness manipulation can remain statistically undetected and provides practical guidelines for strengthening supervisory verification. We validate our theoretical findings through experiments on standard tabular datasets for bias detection. Code is publicly available at https://github.com/ValentinLafargue/Inspection.
Uncertainty-driven Embedding Convolution
Venue: arXiv cs.LG 2026
Year: 2026

Text embeddings are essential components in modern NLP pipelines. Although numerous embedding models have been proposed, no single model consistently dominates across domains and tasks. This variability motivates the use of ensemble techniques to combine complementary strengths. However, most existing ensemble methods operate on deterministic embeddings and fail to account for model-specific uncertainty, limiting their robustness and reliability in downstream applications. To address these limitations, we propose Uncertainty-driven Embedding Convolution (UEC). UEC first transforms deterministic embeddings into probabilistic ones in a post-hoc manner. It then computes adaptive ensemble coefficients based on embedding uncertainty, derived from a principled surrogate-loss formulation. Additionally, UEC employs an uncertainty-aware similarity function that directly incorporates uncertainty into the similarity scoring, providing a theoretically grounded and efficient surrogate to distributional distances. Extensive experiments on diverse benchmarks demonstrate that UEC consistently improves both performance and robustness by leveraging principled uncertainty modeling.
Repairing vulnerabilities without invisible hands. A differentiated replication study on LLMs
Venue: arXiv cs.SE 2026
Year: 2026

Background: Automated Vulnerability Repair (AVR) is a fast-growing branch of program repair. Recent studies show that large language models (LLMs) outperform traditional techniques, extending their success beyond code generation and fault detection. Hypothesis: These gains may be driven by hidden factors -- "invisible hands" such as training-data leakage or perfect fault localization -- that let an LLM reproduce human-authored fixes for the same code. Objective: We replicate prior AVR studies under controlled conditions by deliberately adding errors to the reported vulnerability location in the prompt. If LLMs merely regurgitate memorized fixes, both small and large localization errors should yield the same number of correct patches, because any offset should divert the model from the original fix. Method: Our pipeline repairs vulnerabilities from the Vul4J and VJTrans benchmarks after shifting the fault location by n lines from the ground truth. A first LLM generates a patch, a second LLM reviews it, and we validate the result with regression and proof-of-vulnerability tests. Finally, we manually audit a sample of patches and estimate the error rate with the Agresti-Coull-Wilson method.
A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence
Venue: arXiv cs.AI 2026
Year: 2026

Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks but remain fundamentally static, unable to adapt their internal parameters to novel tasks, evolving knowledge domains, or dynamic interaction contexts. As LLMs are increasingly deployed in open-ended, interactive environments, this static nature has become a critical bottleneck, necessitating agents that can adaptively reason, act, and evolve in real time. This paradigm shift -- from scaling static models to developing self-evolving agents -- has sparked growing interest in architectures and methods enabling continual learning and adaptation from data, interactions, and experiences. This survey provides the first systematic and comprehensive review of self-evolving agents, organizing the field around three foundational dimensions: what, when, and how to evolve. We examine evolutionary mechanisms across agent components (e.g., models, memory, tools, architecture), categorize adaptation methods by stages (e.g., intra-test-time, inter-test-time), and analyze the algorithmic and architectural designs that guide evolutionary adaptation (e.g., scalar rewards, textual feedback, single-agent and multi-agent systems). Additionally, we analyze evaluation metrics and benchmarks tailored for self-evolving agents, highlight applications in domains such as coding, education, and healthcare, and identify critical challenges and research directions in safety, scalability, and co-evolutionary dynamics. By providing a structured framework for understanding and designing self-evolving agents, this survey establishes a roadmap for advancing more adaptive, robust, and versatile agentic systems in both research and real-world deployments, and ultimately sheds light on the realization of Artificial Super Intelligence (ASI) where agents evolve autonomously and perform beyond human-level intelligence across tasks.
Evaluating the cognitive reality of Spanish irregular morphomic patterns: Humans vs. Transformers
Venue: arXiv cs.CL 2026
Year: 2026

Do transformer models generalize morphological patterns like humans do? We investigate this by directly comparing transformers to human behavioral data on Spanish irregular morphomic patterns from \citet{Nevins2015TheRA}. We adopt the same analytical framework as the original human study. Under controlled input conditions, we evaluate whether transformer models can replicate human-like sensitivity to the morphome, a complex linguistic phenomenon. Our experiments focus on three frequency conditions: natural, low-frequency, and high-frequency distributions of verbs exhibiting irregular morphomic patterns. Transformer models achieve higher stem-accuracy than human participants. However, response preferences diverge: humans consistently favor the "natural" inflection across all items, whereas models preferred the irregular forms, and their choices are modulated by the proportion of irregular verbs present during training. Moreover, models trained on the natural and low-frequency distributions, but not the high-frequency distribution, exhibit sensitivity to phonological similarity between test items and Spanish L-shaped verbs, mirroring a limited aspect of human phonological generalization.
Categorical Distributions are Effective Neural Network Outputs for Event Prediction
Venue: arXiv cs.LG 2026
Year: 2026

We demonstrate the effectiveness of the categorical distribution as a neural network output for next event prediction. This is done for both discrete-time and continuous-time event sequences. To model continuous-time processes, the categorical distribution is interpreted as a piecewise-constant density function and is shown to be competitive across a range of datasets. We then argue for the importance of studying discrete-time processes by introducing a neuronal spike prediction task motivated by retinal prosthetics, where discretization of event times is consequent on the task description. Separately, we show evidence that commonly used datasets favour smaller models. Finally, we introduce new synthetic datasets for testing larger models, as well as synthetic datasets with discrete event times.
When Proximity Falls Short: Inequalities in Commuting and Accessibility by Public Transport in Santiago, Chile
Venue: arXiv cs.CY 2026
Year: 2026

Traditional measures of urban accessibility often rely on static models or survey data. However, location information from mobile networks now enables large-scale, dynamic analyses of how people navigate cities. This study uses eXtended Detail Records (XDRs) derived from mobile phone activity to analyze commuting patterns and accessibility inequalities in Santiago, Chile. First, we identify residential and work locations and model commuting routes using the R5 multimodal routing engine, which combines public transport and walking. To explore spatial patterns, we apply a bivariate spatial clustering analysis (LISA) alongside regression techniques to identify distinct commuting behaviors and their alignment with vulnerable population groups. Our findings reveal that average commuting times remain consistent across socioeconomic groups. However, despite residing in areas with greater opportunity density, higher-income populations do not consistently experience shorter commuting times. This highlights a disconnect between spatial proximity to opportunities and actual travel experience. Our analysis reveals significant disparities between sociodemographic groups, particularly regarding the distribution of indigenous populations and gender. Overall, the findings of our study suggest that commuting and accessibility inequalities in Santiago are closely linked to broader social and demographic structures.
Transport and Delivery of Objects with a Soft Everting Robot
Venue: arXiv cs.RO 2026
Year: 2026

Soft everting robots present significant advantages over traditional rigid robots, including enhanced dexterity, improved environmental interaction, and safe navigation in unpredictable environments. While soft everting robots have been widely demonstrated for exploration type tasks, their potential to move and deploy payloads in such tasks has been less investigated, with previous work focusing on sensors and tools for the robot. Leveraging the navigation capabilities, and deployed body, of the soft everting robot to deliver payloads in hazardous areas, e.g. carrying a water bottle to a person stuck under debris, would represent a significant capability in many applications. In this work, we present an analysis of how soft everting robots can be used to deploy larger, heavier payloads through the inside of the robot. We analyze both what objects can be deployed and what terrain features they can be carried through. Building on existing models, we present methods to quantify the effects of payloads on robot growth and self-support, and develop a model to predict payload slip. We then experimentally quantify payload transport using soft everting robot with a variety of payload shapes, sizes, and weights and though a series of tasks: steering, vertical transport, movement through holes, and movement across gaps. Overall, the results show that we can transport payloads in a variety of shapes and up to 1.5kg in weight and that we can move through circular apertures with as little as 0.01cm clearance around payloads, carry out discrete turns up to 135 degrees, and move across unsupported gaps of 1.15m in length.
Nonlinear reconciliation: Error reduction theorems
Venue: arXiv cs.LG 2026
Year: 2026

Forecast reconciliation, an ex-post technique applied to forecasts that must satisfy constraints, has been a prominent topic in the forecasting literature over the past two decades. Recently, several efforts have sought to extend reconciliation methods to the probabilistic settings. Nevertheless, formal theorems demonstrating error reduction in nonlinear constraints, analogous to those presented in Panagiotelis et al.(2021), are still lacking. This paper addresses that gap by establishing such theorems for various classes of nonlinear hypersurfaces and vector-valued functions. Specifically, we derive an exact analog of Theorem 3.1 from Panagiotelis et al.(2021) for hypersurfaces with constant-sign curvature. Additionally, we provide an error reduction theorem for the broader case of hypersurfaces with non-constant-sign curvature and for general manifolds with codimension > 1. To support reproducibility and practical adoption, we release a JAX-based Python package, JNLR, implementing the presented theorems and reconciliation procedures.