OpenRaiser/Intern-Atlas huggingface dataset:
Boundary Point Jailbreaking of Black-Box LLMs
Venue: arXiv cs.LG 2026
Year: 2026

Frontier LLMs are safeguarded against attempts to extract harmful information via adversarial prompts known as "jailbreaks". Recently, defenders have developed classifier-based systems that have survived thousands of hours of human red teaming. We introduce Boundary Point Jailbreaking (BPJ), a new class of automated jailbreak attacks that evade the strongest industry-deployed safeguards. Unlike previous attacks that rely on white/grey-box assumptions (such as classifier scores or gradients) or libraries of existing jailbreaks, BPJ is fully black-box and uses only a single bit of information per query: whether or not the classifier flags the interaction. To achieve this, BPJ addresses the core difficulty in optimising attacks against robust real-world defences: evaluating whether a proposed modification to an attack is an improvement. Instead of directly trying to learn an attack for a target harmful string, BPJ converts the string into a curriculum of intermediate attack targets and then actively selects evaluation points that best detect small changes in attack strength ("boundary points"). We believe BPJ is the first fully automated attack algorithm that succeeds in developing universal jailbreaks against Constitutional Classifiers, as well as the first automated attack algorithm that succeeds against GPT-5's input classifier without relying on human attack seeds. BPJ is difficult to defend against in individual interactions but incurs many flags during optimisation, suggesting that effective defence requires supplementing single-interaction methods with batch-level monitoring.
Learning User Interests via Reasoning and Distillation for Cross-Domain News Recommendation
Venue: arXiv cs.CL 2026
Year: 2026

News recommendation plays a critical role in online news platforms by helping users discover relevant content. Cross-domain news recommendation further requires inferring user's underlying information needs from heterogeneous signals that often extend beyond direct news consumption. A key challenge lies in moving beyond surface-level behaviors to capture deeper, reusable user interests while maintaining scalability in large-scale production systems. In this paper, we present a reinforcement learning framework that trains large language models to generate high-quality lists of interest-driven news search queries from cross-domain user signals. We formulate query-list generation as a policy optimization problem and employ GRPO with multiple reward signals. We systematically study two compute dimensions: inference-time sampling and model capacity, and empirically observe consistent improvements with increased compute that exhibit scaling-like behavior. Finally, we perform on-policy distillation to transfer the learned policy from a large, compute-intensive teacher to a compact student model suitable for scalable deployment. Extensive offline experiments, ablation studies and large-scale online A/B tests in a production news recommendation system demonstrate consistent gains in both interest modeling quality and downstream recommendation performance.
CircuChain: Disentangling Competence and Compliance in LLM Circuit Analysis
Venue: arXiv cs.SE 2026
Year: 2026

As large language models (LLMs) advance toward expert-level performance in engineering domains, reliable reasoning under user-specified constraints becomes critical. In circuit analysis, for example, a numerically correct solution is insufficient if it violates established methodological conventions such as mesh directionality or polarity assignments, errors that can propagate in safety-critical systems. Yet it remains unclear whether frontier models truly apply first-principles reasoning or rely on entrenched training priors that conflict with explicit instructions. We introduce CircuChain, a diagnostic benchmark designed to disentangle instruction compliance from physical reasoning competence in electrical circuit analysis. CircuChain consists of counterbalanced Control/Trap problem pairs across five canonical circuit topologies, augmented with systematic variations in sign conventions, current orientations, and polarity definitions. A multi-stage verification pipeline, combining symbolic solvers, SPICE simulation, and an LLM-based error taxonomy, enables fine-grained attribution of failures to convention errors, physics errors, arithmetic mistakes, or hallucinations. Across 100 tasks per model, we observe a consistent Compliance-Competence Divergence. The strongest model evaluated exhibits near-perfect physical reasoning but a high rate of convention violations when Trap conditions deliberately invert natural sign patterns. Conversely, weaker models display lower physical fidelity yet superior adherence to explicit instructions. These results suggest that increased model capability does not guarantee improved constraint alignment and highlight the need for new evaluation frameworks that stress instruction-following under mathematically rigid domains. CircuChain provides one such framework and offers actionable insights for both engineering education and AI alignment research.
A Quantum-inspired Hybrid Swarm Intelligence and Decision-Making for Multi-Criteria ADAS Calibration
Venue: arXiv cs.NE 2026
Year: 2026

The tuning of Advanced Driver Assistance Systems (ADAS) involves resolving trade-offs among several competing objectives, including operational safety, system responsiveness, energy usage, and passenger comfort. This work introduces a novel optimization framework based on Quantum-Inspired Hybrid Swarm Intelligence (QiHSI), in which quantum-inspired mechanisms are embedded within a multi-objective salp swarm optimization process to strengthen global search capability and preserve population diversity in complex, high-dimensional decision spaces. In addition, a decision-maker-in-the-loop strategy is incorporated to incorporate adaptive expert guidance, enabling the optimization process to respond dynamically to changing design priorities and system constraints. The effectiveness of QiHSI is assessed using established multi-objective benchmark problems as well as a practical ADAS calibration scenario. Experimental comparisons with several state-of-the-art evolutionary and swarm-based algorithms, including MSSA, MOPSO, MOEA/D, SPEA2, NSGA-III, and RVEA, show that the proposed method consistently produces well-distributed Pareto-optimal solutions with faster convergence and improved adaptability. These findings demonstrate that QiHSI offers a reliable and scalable approach for intelligent ADAS calibration, supporting the development of more responsive, efficient, and safety-oriented autonomous driving technologies.
Beyond Context Sharing: A Unified Agent Communication Protocol (ACP) for Secure, Federated, and Autonomous Agent-to-Agent (A2A) Orchestration
Venue: arXiv cs.MA 2026
Year: 2026

In the artificial intelligence space, as we transition from isolated large language models to autonomous agents capable of complex reasoning and tool use. While foundational architectures and local context management protocols have been established, the challenge of cross-platform, decentralized, and secure interaction remains a significant barrier to the realization of a truly Agentic Web. Building upon the foundations of AI agent architectures and the Model Context Protocol (MCP) for multi-agent coordination, this paper introduces the Agent Communication Protocol (ACP). ACP provides a standardized framework for Agent-to-Agent (AA) interaction, enabling heterogeneous agents to discover, negotiate, and execute collaborative workflows across disparate environments. We propose a federated orchestration model that integrates decentralized identity verification, semantic intent mapping, and automated service-level agreements. Our evaluation demonstrates that ACP reduces inter-agent communication latency by % while maintaining a zero-trust security posture. This work represents a critical advancement toward a scalable and interoperable ecosystem of autonomous digital entities
Structure-Aware Piano Accompaniment via Style Planning and Dataset-Aligned Pattern Retrieval
Venue: arXiv cs.SD 2026
Year: 2026

We introduce a structure-aware approach for symbolic piano accompaniment that decouples high-level planning from note-level realization. A lightweight transformer predicts an interpretable, per-measure style plan conditioned on section/phrase structure and functional harmony, and a retriever then selects and reharmonizes human-performed piano patterns from a corpus. We formulate retrieval as pattern matching under an explicit energy with terms for harmonic feasibility, structural-role compatibility, voice-leading continuity, style preferences, and repetition control. Given a structured lead sheet and optional keyword prompts, the system generates piano-accompaniment MIDI. In our experiments, transformer style-planner-guided retrieval produces diverse long-form accompaniments with strong style realization. We further analyze planner ablations and quantify inter-style isolation. Experimental results demonstrate the effectiveness of our inference-time approach for piano accompaniment generation.
M-polynomial Based Mathematical Formulation of the Hyperbolic Sombor Index
Venue: arXiv cs.DM 2026
Year: 2026

The numerical values extracted from a graph that indicates its topology are called topological indices. A contemporary and efficient method is to compute a graph's topological indices using the graph polynomial that corresponds to it. This method of identifying degree-based topological indices involves the use of the M-polynomial. Very recently, in 2025, the hyperbolic Sombor index (HSO) was proposed and shows its chemical applicability for octane isomers and the structure sensitivity and abruptness for octane, nonane, and decane isomers, respectively. In this work, we establish the closed derivation formula for the above-mentioned index of a graph based on its M-polynomial. Additionally, we use our proposed derivation formula to calculate the hyperbolic Sombor index of a few standard graphs and chemical families. Moreover, we provide the numerical and graphical representations for the M-polynomial and the computed HSO index of the chemical families.
CGRA-DeBERTa Concept Guided Residual Augmentation Transformer for Theologically Islamic Understanding
Venue: arXiv cs.CL 2026
Year: 2026

Accurate QA over classical Islamic texts remains challenging due to domain specific semantics, long context dependencies, and concept sensitive reasoning. Therefore, a new CGRA DeBERTa, a concept guided residual domain augmentation transformer framework, is proposed that enhances theological QA over Hadith corpora. The CGRA DeBERTa builds on a customized DeBERTa transformer backbone with lightweight LoRA based adaptations and a residual concept aware gating mechanism. The customized DeBERTa embedding block learns global and positional context, while Concept Guided Residual Blocks incorporate theological priors from a curated Islamic Concept Dictionary of 12 core terms. Moreover, the Concept Gating Mechanism selectively amplifies semantically critical tokens via importance weighted attention, applying differential scaling from 1.04 to 3.00. This design preserves contextual integrity, strengthens domain-specific semantic representations, and enables accurate, efficient span extraction while maintaining computational efficiency. This paper reports the results of training CGRA using a specially constructed dataset of 42591 QA pairs from the text of Sahih alBukhari and Sahih Muslim. While BERT achieved an EM score of 75.87 and DeBERTa one of 89.77, our model scored 97.85 and thus surpassed them by 8.08 on an absolute scale, all while adding approximately 8 inference overhead due to parameter efficient gating. The qualitative evaluation noted better extraction and discrimination and theological precision. This study presents Hadith QA systems that are efficient, interpretable, and accurate and that scale provide educational materials with necessary theological nuance.
Protecting Language Models Against Unauthorized Distillation through Trace Rewriting
Venue: arXiv cs.AI 2026
Year: 2026

Knowledge distillation is a widely adopted technique for transferring capabilities from LLMs to smaller, more efficient student models. However, unauthorized use of knowledge distillation takes unfair advantage of the considerable effort and cost put into developing frontier models. We investigate methods for modifying teacher-generated reasoning traces to achieve two objectives that deter unauthorized distillation: (1) \emph{anti-distillation}, or degrading the training usefulness of query responses, and (2) \emph{API watermarking}, which embeds verifiable signatures in student models. We introduce several approaches for dynamically rewriting a teacher's reasoning outputs while preserving answer correctness and semantic coherence. Two of these leverage the rewriting capabilities of LLMs, while others use gradient-based techniques. Our experiments show that a simple instruction-based rewriting approach achieves a strong anti-distillation effect while maintaining or even improving teacher performance. Furthermore, we show that our rewriting approach also enables highly reliable watermark detection with essentially no false alarms.
Fast and Fusiest: An Optimal Fusion-Aware Mapper for Accelerator Modeling and Evaluation
Venue: arXiv cs.AR 2026
Year: 2026

The latency and energy of tensor algebra accelerators depend on how data movement and operations are scheduled (i.e., mapped) onto accelerators, so determining the potential of an accelerator architecture requires both a performance model and a mapper to search for the optimal mapping. A key optimization that the mapper must explore is fusion, meaning holding data on-chip between computation steps, which has been shown to reduce energy and latency by reducing DRAM accesses. However, prior mappers cannot find optimal mappings with fusion (i.e., fused mappings) in a feasible runtime because the number of fused mappings to search increases exponentially with the number of workload computation steps. In this paper, we introduce the Fast and Fusiest Mapper (FFM), the first mapper to quickly find optimal mappings in a comprehensive fused mapspace for tensor algebra workloads. FFM shrinks the search space by pruning subsets of mappings (i.e., partial mappings) that are shown to never be a part of optimal mappings, quickly eliminating all suboptimal mappings with those partial mappings as subsets. Then FFM joins partial mappings to construct optimal fused mappings. We evaluate FFM and show that, although the mapspace size grows exponentially with the number of computation steps, FFM's runtime scales approximately linearly. FFM is orders of magnitude faster ($>1000\times$) than prior state-of-the-art approaches at finding optimal mappings for Transformers.
Interbank Lending Games
Venue: arXiv cs.GT 2026
Year: 2026

We define and study a lending game to model the interbank money market, in which lending banks strategically allocate their cash to borrowing banks. The interest rate offered by each borrowing bank is within the interest rate corridor set by the central bank and ultimately depends on the demand and the supply of cash in the interbank market. Lending banks naturally aim to maximise the income coming from the interest repayments. In its purest form, this is an infinite-strategy game that we show to be an exact potential game which has a unique pure strategy Nash equilibrium. We then define and solve a constrained optimisation problem and propose a strongly polynomial-time algorithm to compute this Nash equilibrium. We also study some variants of best-response dynamics of this lending game, showing that they converge to the Nash equilibrium in both discrete and continuous-time scenarios.
COMPOT: Calibration-Optimized Matrix Procrustes Orthogonalization for Transformers Compression
Venue: arXiv cs.LG 2026
Year: 2026

Post-training compression of Transformer models commonly relies on truncated singular value decomposition (SVD). However, enforcing a single shared subspace can degrade accuracy even at moderate compression. Sparse dictionary learning provides a more flexible union-of-subspaces representation, but existing approaches often suffer from iterative dictionary and coefficient updates. We propose COMPOT (Calibration-Optimized Matrix Procrustes Orthogonalization for Transformers), a training-free compression framework that uses a small calibration dataset to estimate a sparse weight factorization. COMPOT employs orthogonal dictionaries that enable closed-form Procrustes updates for the dictionary and analytical single-step sparse coding for the coefficients, eliminating iterative optimization. To handle heterogeneous layer sensitivity under a global compression budget, COMPOT further introduces a one-shot dynamic allocation strategy that adaptively redistributes layer-wise compression rates. Extensive experiments across diverse architectures and tasks show that COMPOT consistently delivers a superior quality-compression trade-off over strong low-rank and sparse baselines, while remaining fully compatible with post-training quantization for extreme compression. Code is available $\href{https://github.com/mts-ai/COMPOT}{here}$.
Secure and Energy-Efficient Wireless Agentic AI Networks
Venue: arXiv cs.AI 2026
Year: 2026

In this paper, we introduce a secure wireless agentic AI network comprising one supervisor AI agent and multiple other AI agents to provision quality of service (QoS) for users' reasoning tasks while ensuring confidentiality of private knowledge and reasoning outcomes. Specifically, the supervisor AI agent can dynamically assign other AI agents to participate in cooperative reasoning, while the unselected AI agents act as friendly jammers to degrade the eavesdropper's interception performance. To extend the service duration of AI agents, an energy minimization problem is formulated that jointly optimizes AI agent selection, base station (BS) beamforming, and AI agent transmission power, subject to latency and reasoning accuracy constraints. To address the formulated problem, we propose two resource allocation schemes, ASC and LAW, which first decompose it into three sub-problems. Specifically, ASC optimizes each sub-problem iteratively using the proposed alternating direction method of multipliers (ADMM)-based algorithm, semi-definite relaxation (SDR), and successive convex approximation (SCA), while LAW tackles each sub-problem using the proposed large language model (LLM) optimizer within an agentic workflow. The experimental results show that the proposed solutions can reduce network energy consumption by up to 59.1% compared to other benchmark schemes. Furthermore, the proposed schemes are validated using a practical agentic AI system based on Qwen, demonstrating satisfactory reasoning accuracy across various public benchmarks.
Decomposing Docker Container Startup Performance: A Three-Tier Measurement Study on Heterogeneous Infrastructure
Venue: arXiv cs.PF 2026
Year: 2026

Container startup latency is a critical performance metric for CI/CD pipelines, serverless computing, and auto-scaling systems, yet practitioners lack empirical guidance on how infrastructure choices affect this latency. We present a systematic measurement study that decomposes Docker container startup into constituent operations across three heterogeneous infrastructure tiers: Azure Premium SSD (cloud SSD), Azure Standard HDD (cloud HDD), and macOS Docker Desktop (developer workstation with hypervisor-based virtualization). Using a reproducible benchmark suite that executes 50 iterations per test across 10 performance dimensions, we quantify previously under-characterized relationships between infrastructure configuration and container runtime behavior. Our key findings include: (1) container startup is dominated by runtime overhead rather than image size, with only 2.5% startup variation across images ranging from 5 MB to 155 MB on SSD; (2) storage tier selection imposes a 2.04x startup penalty (HDD 1157 ms vs. SSD 568 ms); (3) Docker Desktop's hypervisor layer introduces a 2.69x startup penalty and 9.5x higher CPU throttling variance compared to native Linux; (4) OverlayFS write performance collapses by up to two orders of magnitude compared to volume mounts on SSD-backed storage; and (5) Linux namespace creation contributes only 8-10 ms (<1.5%) of total startup time. All measurement scripts, raw data, and analysis tools are publicly available.
An Empirical Study on the Effects of System Prompts in Instruction-Tuned Models for Code Generation
Venue: arXiv cs.SE 2026
Year: 2026

Instruction-tuned Language Models (ILMs) have become essential components of modern AI systems, demonstrating exceptional versatility across natural language and reasoning tasks. Among their most impactful applications is code generation, where ILMs -- commonly referred to as Code Language Models (CLMs) -- translate human intent into executable programs. While progress has been driven by advances in scaling and training methodologies, one critical aspect remains underexplored: the impact of system prompts on both general-purpose ILMs and specialized CLMs for code generation. We systematically evaluate how system prompts of varying instructional detail, along with model scale, prompting strategy, and programming language, affect code assistant. Our experimental setting spans 360 configurations across four models, five system prompts, three prompting strategies, two languages, and two temperature settings. We find that (1) increasing system-prompt constraint specificity does not monotonically improve correctness -- prompt effectiveness is configuration-dependent and can help or hinder based on alignment with task requirements and decoding context; (2) for larger code-specialized models, few-shot examples can degrade performance relative to zero-shot generation, contrary to conventional wisdom; and (3) programming language matters, with Java exhibiting significantly greater sensitivity to system prompt variations than Python, suggesting language-specific prompt engineering strategies may be necessary.
tensorFM: Low-Rank Approximations of Cross-Order Feature Interactions
Venue: arXiv cs.LG 2026
Year: 2026

We address prediction problems on tabular categorical data, where each instance is defined by multiple categorical attributes, each taking values from a finite set. These attributes are often referred to as fields, and their categorical values as features. Such problems frequently arise in practical applications, including click-through rate prediction and social sciences. We introduce and analyze {tensorFM}, a new model that efficiently captures high-order interactions between attributes via a low-rank tensor approximation representing the strength of these interactions. Our model generalizes field-weighted factorization machines. Empirically, tensorFM demonstrates competitive performance with state-of-the-art methods. Additionally, its low latency makes it well-suited for time-sensitive applications, such as online advertising.