OpenRaiser/Intern-Atlas huggingface dataset:
RobotDesignGPT: Automated Robot Design Synthesis using Vision Language Models
Venue: arXiv cs.RO 2026
Year: 2026

Robot design is a nontrivial process that involves careful consideration of multiple criteria, including user specifications, kinematic structures, and visual appearance. Therefore, the design process often relies heavily on domain expertise and significant human effort. The majority of current methods are rule-based, requiring the specification of a grammar or a set of primitive components and modules that can be composed to create a design. We propose a novel automated robot design framework, RobotDesignGPT, that leverages the general knowledge and reasoning capabilities of large pre-trained vision-language models to automate the robot design synthesis process. Our framework synthesizes an initial robot design from a simple user prompt and a reference image. Our novel visual feedback approach allows us to greatly improve the design quality and reduce unnecessary manual feedback. We demonstrate that our framework can design visually appealing and kinematically valid robots inspired by nature, ranging from legged animals to flying creatures. We justify the proposed framework by conducting an ablation study and a user study.
Optimal Thruster Configuration for 6-DOF Control of a Small Satellite
Venue: arXiv cs.RO 2026
Year: 2026

With the growing deployment of small satellites (such as CubeSats, Nanosats, Picosats, and Femtosats) in Low Earth Orbit (LEO) for targeted applications like imaging, communication, data storage, and rendezvous-docking mission, there is increasing attention on orbit maintenance and attitude control. A common approach for active orbit control involves the use of multiple thrusters, which, when properly arranged, can also generate the required torque for attitude control. Starting from a 24-thruster configuration, this paper presents a set of thruster configurations (referred to as a viable configuration group) that enable full six degrees of freedom (6-DOF) control. Further, configuration group that requires minimum total thrust to achieve 6-DOF commands are found among the viable configuration group. One configuration from each of these groups is further evaluated for its attitude control performance through a representative rendezvous-docking mission, demonstrating that even with a reduced thruster count, sufficient maneuverability can be achieved.
A Hybrid Soft Haptic Display for Rendering Lump Stiffness in Remote Palpation
Venue: arXiv cs.HC 2026
Year: 2026

Remote palpation enables noninvasive tissue examination in telemedicine, yet current tactile displays often lack the fidelity to convey both large-scale forces and fine spatial details. This study introduces a hybrid fingertip display comprising a rigid platform and a $4\times4$ soft pneumatic tactile display (4.93 mm displacement and 1.175 N per single pneumatic chamber) to render a hard lump beneath soft tissue. This study compares three rendering strategies: a Platform-Only baseline that renders the total interaction force; a Hybrid A (Position + Force Feedback) strategy that adds a dynamic, real-time soft spatial cue; and a Hybrid B (Position + Preloaded Stiffness Feedback) strategy that provides a constant, pre-calculated soft spatial cue. In a 12-participant lump detection study, both hybrid methods dramatically improved accuracy over the Platform-Only baseline (from 50\% to over 95\%). While the Hybrid B was highlighted qualitatively for realism, its event-based averaging is expected to increase interaction latency in real-time operation. This suggests a trade-off between perceived lump realism and real-time responsiveness, such that rendering choices that enhance realism may conflict with those that minimize latency.
Multi-agent DRL-based Lane Change Decision Model for Cooperative Planning in Mixed Traffic
Venue: arXiv cs.AI 2026
Year: 2026

Connected automated vehicles (CAVs) possess the ability to communicate and coordinate with one another, enabling cooperative platooning that enhances both energy efficiency and traffic flow. However, during the initial stage of CAV deployment, the sparse distribution of CAVs among human-driven vehicles reduces the likelihood of forming effective cooperative platoons. To address this challenge, this study proposes a hybrid multi-agent lane change decision model aimed at increasing CAV participation in cooperative platooning and maximizing its associated benefits. The proposed model employs the QMIX framework, integrating traffic data processed through a convolutional neural network (CNN-QMIX). This architecture addresses a critical issue in dynamic traffic scenarios by enabling CAVs to make optimal decisions irrespective of the varying number of CAVs present in mixed traffic. Additionally, a trajectory planner and a model predictive controller are designed to ensure smooth and safe lane-change execution. The proposed model is trained and evaluated within a microsimulation environment under varying CAV market penetration rates. The results demonstrate that the proposed model efficiently manages fluctuating traffic agent numbers, significantly outperforming the baseline rule-based models. Notably, it enhances cooperative platooning rates up to 26.2\%, showcasing its potential to optimize CAV cooperation and traffic dynamics during the early stage of deployment.
POLARIS: Typed Planning and Governed Execution for Agentic AI in Back-Office Automation
Venue: arXiv cs.AI 2026
Year: 2026

Enterprise back office workflows require agentic systems that are auditable, policy-aligned, and operationally predictable, capabilities that generic multi-agent setups often fail to deliver. We present POLARIS (Policy-Aware LLM Agentic Reasoning for Integrated Systems), a governed orchestration framework that treats automation as typed plan synthesis and validated execution over LLM agents. A planner proposes structurally diverse, type checked directed acyclic graphs (DAGs), a rubric guided reasoning module selects a single compliant plan, and execution is guarded by validator gated checks, a bounded repair loop, and compiled policy guardrails that block or route side effects before they occur. Applied to document centric finance tasks, POLARIS produces decision grade artifacts and full execution traces while reducing human intervention. Empirically, POLARIS achieves a micro F1 of 0.81 on the SROIE dataset and, on a controlled synthetic suite, achieves 0.95 to 1.00 precision for anomaly routing with preserved audit trails. These evaluations constitute an initial benchmark for governed Agentic AI. POLARIS provides a methodological and benchmark reference for policy-aligned Agentic AI. Keywords Agentic AI, Enterprise Automation, Back-Office Tasks, Benchmarks, Governance, Typed Planning, Evaluation
Shapelets-Enriched Selective Forecasting using Time Series Foundation Models
Venue: arXiv cs.LG 2026
Year: 2026

Time series foundation models have recently gained a lot of attention due to their ability to model complex time series data encompassing different domains including traffic, energy, and weather. Although they exhibit strong average zero-shot performance on forecasting tasks, their predictions on certain critical regions of the data are not always reliable, limiting their usability in real-world applications, especially when data exhibits unique trends. In this paper, we propose a selective forecasting framework to identify these critical segments of time series using shapelets. We learn shapelets using shift-invariant dictionary learning on the validation split of the target domain dataset. Utilizing distance-based similarity to these shapelets, we facilitate the user to selectively discard unreliable predictions and be informed of the model's realistic capabilities. Empirical results on diverse benchmark time series datasets demonstrate that our approach leveraging both zero-shot and full-shot fine-tuned models reduces the overall error by an average of 22.17% for zero-shot and 22.62% for full-shot fine-tuned model. Furthermore, our approach using zero-shot and full-shot fine-tuned models, also outperforms its random selection counterparts by up to 21.41% and 21.43% on one of the datasets.
RAPID-Serve: Resource-efficient and Accelerated P/D Intra-GPU Disaggregation
Venue: arXiv cs.DC 2026
Year: 2026

Two widely adopted techniques for LLM inference serving systems today are hybrid batching and disaggregated serving. A hybrid batch combines prefill and decode tokens of different requests in the same batch to improve resource utilization and throughput at the cost of increased latency per token. In contrast, disaggregated serving decouples compute-bound prefill and bandwidth-bound decode phases to optimize for service level objectives (SLOs) at the cost of resource under-utilization and KV-cache transfer overheads. To address the limitations of these techniques, we propose RAPID-Serve: a technique to concurrently execute prefill and decode on the same GPU(s) to meet latency SLOs while maintaining high throughput and efficient resource utilization. Furthermore, we propose Adaptive Resource Management for runtime compute resource allocation, optionally leveraging CU masking (a fine-grained Compute Unit partitioning feature on AMD Instinct\textsuperscript{TM} GPUs). RAPID-Serve provides up to 4.1x (average 1.7x) unconstrained throughput improvement and 32x and higher (average 4.9x) throughput improvement under SLO constraints, showing it as an effective strategy compared to the state-of-the-art approaches, particularly in resource-constrained environments.
Shortest-Path Flow Matching with Mixture-Conditioned Bases for OOD Generalization to Unseen Conditions
Venue: arXiv cs.LG 2026
Year: 2026

Robust generalization under distribution shift remains a key challenge for conditional generative modeling: conditional flow-based methods often fit the training conditions well but fail to extrapolate to unseen ones. We introduce SP-FM, a shortest-path flow-matching framework that improves out-of-distribution (OOD) generalization by conditioning both the base distribution and the flow field on the condition. Specifically, SP-FM learns a condition-dependent base distribution parameterized as a flexible, learnable mixture, together with a condition-dependent vector field trained via shortest-path flow matching. Conditioning the base allows the model to adapt its starting distribution across conditions, enabling smooth interpolation and more reliable extrapolation beyond the observed training range. We provide theoretical insights into the resulting conditional transport and show how mixture-conditioned bases enhance robustness under shift. Empirically, SP-FM is effective across heterogeneous domains, including predicting responses to unseen perturbations in single-cell transcriptomics and modeling treatment effects in high-content microscopy--based drug screening. Overall, SP-FM provides a simple yet effective plug-in strategy for improving conditional generative modeling and OOD generalization across diverse domains.
Imandra CodeLogician: Neuro-Symbolic Reasoning for Precise Analysis of Software Logic
Venue: arXiv cs.AI 2026
Year: 2026

Large Language Models (LLMs) have shown strong performance on code understanding tasks, yet they fundamentally lack the ability to perform precise, exhaustive mathematical reasoning about program behavior. Existing benchmarks either focus on mathematical proof automation, largely disconnected from real-world software, or on engineering tasks that do not require semantic rigor. We present CodeLogician, a neurosymbolic agent for precise analysis of software logic, integrated with ImandraX, an industrial automated reasoning engine deployed in financial markets and safety-critical systems. Unlike prior approaches that use formal methods primarily to validate LLM outputs, CodeLogician uses LLMs to construct explicit formal models of software systems, enabling automated reasoning to answer rich semantic questions beyond binary verification outcomes. To rigorously evaluate mathematical reasoning about software logic, we introduce code-logic-bench, a benchmark targeting the middle ground between theorem proving and software engineering benchmarks. It measures reasoning correctness about program state spaces, control flow, coverage constraints, and edge cases, with ground truth defined via formal modeling and region decomposition. Comparing LLM-only reasoning against LLMs augmented with CodeLogician, formal augmentation yields substantial improvements, closing a 41-47 percentage point gap in reasoning accuracy. These results demonstrate that neurosymbolic integration is essential for scaling program analysis toward rigorous, autonomous software understanding.
Human-AI Collaborative Inductive Thematic Analysis: AI Guided Analysis and Human Interpretive Authority
Venue: arXiv cs.AI 2026
Year: 2026

The increasing use of generative artificial intelligence (GenAI) in qualitative research raises important questions about analytic practice and interpretive authority. This study examines how researchers interact with an Inductive Thematic Analysis GPT (ITA-GPT), a purpose-built AI tool designed to support inductive thematic analysis through structured, semi-automated prompts aligned with reflexive thematic analysis and verbatim coding principles. Guided by a Human-Artificial Intelligence Collaborative Inductive Thematic Analysis (HACITA) framework, the study focuses on analytic process rather than substantive findings. Three experienced qualitative researchers conducted ITA-GPT assisted analyses of interview transcripts from education research in the Ghanaian teacher education context. The tool supported familiarization, verbatim in vivo coding, gerund-based descriptive coding, and theme development, while enforcing trace to text integrity, coverage checks, and auditability. Data sources included interaction logs, AI-generated tables, researcher revisions, deletions, insertions, comments, and reflexive memos. Findings show that ITA-GPT functioned as a procedural scaffold that structured analytic workflow and enhanced transparency. However, interpretive authority remained with human researchers, who exercised judgment through recurrent analytic actions including modification, deletion, rejection, insertion, and commenting. The study demonstrates how inductive thematic analysis is enacted through responsible human AI collaboration.
Utilizing Metadata for Better Retrieval-Augmented Generation
Venue: arXiv cs.IR 2026
Year: 2026

Retrieval-Augmented Generation systems depend on retrieving semantically relevant document chunks to support accurate, grounded outputs from large language models. In structured and repetitive corpora such as regulatory filings, chunk similarity alone often fails to distinguish between documents with overlapping language. Practitioners often flatten metadata into input text as a heuristic, but the impact and trade-offs of this practice remain poorly understood. We present a systematic study of metadata-aware retrieval strategies, comparing plain-text baselines with approaches that embed metadata directly. Our evaluation spans metadata-as-text (prefix and suffix), a dual-encoder unified embedding that fuses metadata and content in a single index, dual-encoder late-fusion retrieval, and metadata-aware query reformulation. Across multiple retrieval metrics and question types, we find that prefixing and unified embeddings consistently outperform plain-text baselines, with the unified at times exceeding prefixing while being easier to maintain. Beyond empirical comparisons, we analyze embedding space, showing that metadata integration improves effectiveness by increasing intra-document cohesion, reducing inter-document confusion, and widening the separation between relevant and irrelevant chunks. Field-level ablations show that structural cues provide strong disambiguating signals. Our code, evaluation framework, and the RAGMATE-10K dataset are publicly hosted.
Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces
Venue: arXiv cs.SE 2026
Year: 2026

AI agents may soon become capable of autonomously completing valuable, long-horizon tasks in diverse domains. Current benchmarks either do not measure real-world tasks, or are not sufficiently difficult to meaningfully measure frontier models. To this end, we present Terminal-Bench 2.0: a carefully curated hard benchmark composed of 89 tasks in computer terminal environments inspired by problems from real workflows. Each task features a unique environment, human-written solution, and comprehensive tests for verification. We show that frontier models and agents score less than 65\% on the benchmark and conduct an error analysis to identify areas for model and agent improvement. We publish the dataset and evaluation harness to assist developers and researchers in future work at https://www.tbench.ai/ .
Cultural Analytics for Good: Building Inclusive Evaluation Frameworks for Historical IR
Venue: arXiv cs.IR 2026
Year: 2026

This work bridges the fields of information retrieval and cultural analytics to support equitable access to historical knowledge. Using the British Library BL19 digital collection (more than 35,000 works from 1700-1899), we construct a benchmark for studying changes in language, terminology and retrieval in the 19th-century fiction and non-fiction. Our approach combines expert-driven query design, paragraph-level relevance annotation, and Large Language Model (LLM) assistance to create a scalable evaluation framework grounded in human expertise. We focus on knowledge transfer from fiction to non-fiction, investigating how narrative understanding and semantic richness in fiction can improve retrieval for scholarly and factual materials. This interdisciplinary framework not only improves retrieval accuracy but also fosters interpretability, transparency, and cultural inclusivity in digital archives. Our work provides both practical evaluation resources and a methodological paradigm for developing retrieval systems that support richer, historically aware engagement with digital archives, ultimately working towards more emancipatory knowledge infrastructures.
Taming Various Privilege Escalation in LLM-Based Agent Systems: A Mandatory Access Control Framework
Venue: arXiv cs.CR 2026
Year: 2026

Large Language Model (LLM)-based agent systems are increasingly deployed for complex real-world tasks but remain vulnerable to natural language-based attacks that exploit over-privileged tool use. This paper aims to understand and mitigate such attacks through the lens of privilege escalation, defined as agent actions exceeding the least privilege required for a user's intended task. Based on a formal model of LLM agent systems, we identify novel privilege escalation scenarios, particularly in multi-agent systems, including a variant akin to the classic confused deputy problem. To defend against both known and newly demonstrated privilege escalation, we propose SEAgent, a mandatory access control (MAC) framework built upon attribute-based access control (ABAC). SEAgent monitors agent-tool interactions via an information flow graph and enforces customizable security policies based on entity attributes. Our evaluations show that SEAgent effectively blocks various privilege escalation while maintaining a low false positive rate and negligible system overhead. This demonstrates its robustness and adaptability in securing LLM-based agent systems.
Event Detection with a Context-Aware Encoder and LoRA for Improved Performance on Long-Tailed Classes
Venue: arXiv cs.CL 2026
Year: 2026

The current state of event detection research has two notable re-occurring limitations that we investigate in this study. First, the unidirectional nature of decoder-only LLMs presents a fundamental architectural bottleneck for natural language understanding tasks that depend on rich, bidirectional context. Second, we confront the conventional reliance on Micro-F1 scores in event detection literature, which systematically inflates performance by favoring majority classes. Instead, we focus on Macro-F1 as a more representative measure of a model's ability across the long-tail of event types. Our experiments demonstrate that models enhanced with sentence context achieve superior performance over canonical decoder-only baselines. Using Low-Rank Adaptation (LoRA) during finetuning provides a substantial boost in Macro-F1 scores in particular, especially for the decoder-only models, showing that LoRA can be an effective tool to enhance LLMs' performance on long-tailed event classes.
A Method for Detecting Spatio-temporal Correlation Anomalies of WSN Nodes Based on Topological Information Enhancement and Time-frequency Feature Extraction
Venue: arXiv cs.NI 2026
Year: 2026

Existing anomaly detection methods for Wireless Sensor Networks (WSNs) generally suffer from insufficient ex-traction of spatio-temporal correlation features, reliance on either time-domain or frequency-domain information alone, and high computational overhead. To address these limitations, this paper proposes a topology-enhanced spatio-temporal feature fusion anomaly detection method, TE-MSTAD. First, building upon the RWKV model with linear attention mechanisms, a Cross-modal Feature Extraction (CFE) module is introduced to fully extract spatial correlation features among multiple nodes while reducing computational resource consumption. Second, a strategy is designed to construct an adjacency matrix by jointly learning spatial correlation from time-frequency domain features. Different graph neural networks are integrated to enhance spatial correlation feature extraction, thereby fully capturing spatial relationships among multiple nodes. Finally, a dual-branch network TE-MSTAD is designed for time-frequency domain feature fusion, overcoming the limitations of relying solely on the time or frequency domain to improve WSN anomaly detection performance. Testing on both public and real-world datasets demonstrates that the TE-MSTAD model achieves F1 scores of 92.52% and 93.28%, respectively, exhibiting superior detection performance and generalization capabilities compared to existing methods.
Enhancing Fuzz Testing Efficiency through Automated Fuzz Target Generation
Venue: arXiv cs.SE 2026
Year: 2026

Fuzzing continues to be the most effective method for identifying security vulnerabilities in software. In the context of fuzz testing, the fuzzer supplies varied inputs to fuzz targets, which are designed to comprehensively exercise critical sections of the client code. Various studies have focused on optimizing and developing advanced fuzzers, such as AFL++, libFuzzer, Honggfuzz, syzkaller, ISP-Fuzzer, which have substantially enhanced vulnerability detection in widely used software and libraries. Nevertheless, achieving greater coverage necessitates improvements in both the quality and quantity of fuzz targets. In large-scale software projects and libraries -- characterized by numerous user defined functions and data types -- manual creation of fuzz targets is both labor-intensive and time-consuming. This challenge underscores the need for automated techniques not only to generate fuzz targets but also to streamline the execution and analysis of their results. In this paper, we introduce an approach to improving fuzz target generation through static analysis of library source code. The proposed method encompasses several key aspects: it analyzes source code structures to accurately construct function calls and generate fuzz targets; it maps fuzzer input data to the corresponding function parameters; it synthesizes compilation information for the fuzz targets; and it automatically collects and analyzes execution results. Our findings are demonstrated through the application of this approach to the generation of fuzz targets for C/C++ libraries.