OpenRaiser/Intern-Atlas huggingface dataset:
Neuronal Attention Circuit (NAC) for Representation Learning
Venue: arXiv cs.AI 2026
Year: 2026

Attention improves representation learning over RNNs, but its discrete nature limits continuous-time (CT) modeling. We introduce Neuronal Attention Circuit (NAC), a novel, biologically inspired CT-Attention mechanism that reformulates attention logit computation as the solution to a linear first-order ODE with nonlinear interlinked gates derived from repurposing C.elegans Neuronal Circuit Policies (NCPs) wiring. NAC replaces dense projections with sparse sensory gates for key-query projections and a sparse backbone network with two heads for computing content-target and learnable time-constant gates, enabling efficient adaptive dynamics. To improve efficiency and memory consumption, we implemented an adaptable subquadratic sparse Top-K pairwise concatenation mechanism that selectively curates key-query interactions. We provide rigorous theoretical guarantees, including state stability and bounded approximation errors. Empirically, we implemented NAC in diverse domains, including irregular time-series classification, lane-keeping for autonomous vehicles, and industrial prognostics. We observed that NAC matches or outperforms competing baselines in accuracy and occupies an intermediate position in runtime and memory consumption compared with several CT state-of-the-art baselines, while being interpretable at the neuron cell level.
User-Feedback-Driven Adaptation for Vision-and-Language Navigation
Venue: arXiv cs.AI 2026
Year: 2026

Real-world deployment of Vision-and-Language Navigation (VLN) agents is constrained by the scarcity of reliable supervision after offline training. While recent adaptation methods attempt to mitigate distribution shifts via environment-driven self-supervision (e.g., entropy minimization), these signals are often noisy and can cause the agent to amplify its own mistakes during long-horizon sequential decision-making. In this paper, we propose a paradigm shift that positions user feedback, specifically episode-level success confirmations and goal-level corrections, as a primary and general-purpose supervision signal for VLN. Unlike internal confidence scores, user feedback is intent-aligned and in-situ consistent, directly correcting the agent's decoupling from user instructions. To effectively leverage this supervision, we introduce a user-feedback-driven learning framework featuring a topology-aware trajectory construction pipeline. This mechanism lifts sparse, goal-level corrections into dense path-level supervision by generating feasible paths on the agent's incrementally built topological graph, enabling sample-efficient imitation learning without requiring step-by-step human demonstrations. Furthermore, we develop a persistent memory bank mechanism for warm-start initialization, supporting the reuse of previously acquired topology and cached representations across navigation sessions. Extensive experiments on the GSA-R2R benchmark demonstrate that our approach transforms sparse interaction into robust supervision, consistently outperforming environment-driven baselines while exhibiting strong adaptability across diverse instruction styles.
Bit of a Close Talker: A Practical Guide to Serverless Cloud Co-Location Attacks
Venue: arXiv cs.CR 2026
Year: 2026

Serverless computing has revolutionized cloud computing by offering users an efficient, cost-effective way to develop and deploy applications without managing infrastructure details. However, serverless cloud users remain vulnerable to various types of attacks, including micro-architectural side-channel attacks. These attacks typically rely on the physical co-location of victim and attacker instances, and attackers need to exploit cloud schedulers to achieve co-location with victims. Therefore, it is crucial to study vulnerabilities in serverless cloud schedulers and assess the security of different serverless scheduling algorithms. This study addresses the gap in understanding and constructing co-location attacks in serverless clouds. We present a comprehensive methodology to uncover exploitable features in serverless scheduling algorithms and to devise strategies for constructing co-location attacks via normal user interfaces. In our experiments, we successfully reveal exploitable vulnerabilities and achieve instance co-location on prevalent open-source infrastructures and Microsoft Azure Functions. We also present a mitigation strategy, the Double-Dip scheduler, to defend against co-location attacks in serverless clouds. Our work highlights critical areas for security enhancements in current cloud schedulers, offering insights to fortify serverless computing environments against potential co-location attacks.
MultiHateLoc: Towards Temporal Localisation of Multimodal Hate Content in Online Videos
Venue: arXiv cs.CV 2026
Year: 2026

The rapid growth of video content on platforms such as TikTok and YouTube has intensified the spread of multimodal hate speech, where harmful cues emerge subtly and asynchronously across visual, acoustic, and textual streams. Existing research primarily focuses on video-level classification, leaving the practically crucial task of temporal localisation, identifying when hateful segments occur, largely unaddressed. This challenge is even more noticeable under weak supervision, where only video-level labels are available, and static fusion or classification-based architectures struggle to capture cross-modal and temporal dynamics. To address these challenges, we propose MultiHateLoc, the first framework designed for weakly-supervised multimodal hate localisation. MultiHateLoc incorporates (1) modality-aware temporal encoders to model heterogeneous sequential patterns, including a tailored text-based preprocessing module for feature enhancement; (2) dynamic cross-modal fusion to adaptively emphasise the most informative modality at each moment and a cross-modal contrastive alignment strategy to enhance multimodal feature consistency; (3) a modality-aware MIL objective to identify discriminative segments under video-level supervision. Despite relying solely on coarse labels, MultiHateLoc produces fine-grained, interpretable frame-level predictions. Experiments on HateMM and MultiHateClip show that our method achieves state-of-the-art performance in the localisation task.
Renormalizable Spectral-Shell Dynamics as the Origin of Neural Scaling Laws
Venue: arXiv cs.LG 2026
Year: 2026

Neural scaling laws and double-descent phenomena suggest that deep-network training obeys a simple macroscopic structure despite highly nonlinear optimization dynamics. We derive such structure directly from gradient descent in function space. For mean-squared error loss, the training error evolves as $\dot e_t=-M(t)e_t$ with $M(t)=J_{θ(t)}J_{θ(t)}^{\!*}$, a time-dependent self-adjoint operator induced by the network Jacobian. Using Kato perturbation theory, we obtain an exact system of coupled modewise ODEs in the instantaneous eigenbasis of $M(t)$. To extract macroscopic behavior, we introduce a logarithmic spectral-shell coarse-graining and track quadratic error energy across shells. Microscopic interactions within each shell cancel identically at the energy level, so shell energies evolve only through dissipation and external inter-shell interactions. We formalize this via a \emph{renormalizable shell-dynamics} assumption, under which cumulative microscopic effects reduce to a controlled net flux across shell boundaries. Assuming an effective power-law spectral transport in a relevant resolution range, the shell dynamics admits a self-similar solution with a moving resolution frontier and explicit scaling exponents. This framework explains neural scaling laws and double descent, and unifies lazy (NTK-like) training and feature learning as two limits of the same spectral-shell dynamics.
Bandwidth-constrained Variational Message Encoding for Cooperative Multi-agent Reinforcement Learning
Venue: arXiv cs.LG 2026
Year: 2026

Graph-based multi-agent reinforcement learning (MARL) enables coordinated behavior under partial observability by modeling agents as nodes and communication links as edges. While recent methods excel at learning sparse coordination graphs-determining who communicates with whom-they do not address what information should be transmitted under hard bandwidth constraints. We study this bandwidth-limited regime and show that naive dimensionality reduction consistently degrades coordination performance. Hard bandwidth constraints force selective encoding, but deterministic projections lack mechanisms to control how compression occurs. We introduce Bandwidth-constrained Variational Message Encoding (BVME), a lightweight module that treats messages as samples from learned Gaussian posteriors regularized via KL divergence to an uninformative prior. BVME's variational framework provides principled, tunable control over compression strength through interpretable hyperparameters, directly constraining the representations used for decision-making. Across SMACv1, SMACv2, and MPE benchmarks, BVME achieves comparable or superior performance while using 67--83% fewer message dimensions, with gains most pronounced on sparse graphs where message quality critically impacts coordination. Ablations reveal U-shaped sensitivity to bandwidth, with BVME excelling at extreme ratios while adding minimal overhead.
The Affective Bridge: Preserving Speech Representations while Enhancing Deepfake Detection vian emotional Constraints
Venue: arXiv cs.SD 2026
Year: 2026

Speech deepfake detection (DFD) has benefited from diverse acoustic and semantic speech representations, many of which encode valuable speech information and are costly to train. Existing approaches typically enhance DFD by tuning the representations or applying post-hoc classification on frozen features, limiting control over improving discriminative DF cues without distorting original semantics. We find that emotion is encoded across diverse speech features and correlates with DFD. Therefore, we introduce a unified, feature-agnostic, and non-destructive training framework that uses emotion as a bridging constraint to guide speech features toward DFD, treating emotion recognition as a representation alignment objective rather than an auxiliary task, while preserving the original semantic information. Experiments on FakeOrReal and IntheWild show accuracy improvements of up to 6\% and 2\%, respectively, with corresponding reductions in equal error rate. Code is in the supplementary material.
The Universal Language of Mathematics (Introduction to Binary Principle)
Venue: arXiv cs.IT 2026
Year: 2026

This book invites readers to see mathematics not just as formulas and rules, but as the deepest expression of human thought. It begins by exploring the timeless idea of mathematics as a universal language, contrasting its precision with the richness of natural speech. From the foundations of pure and applied mathematics to the revolutionary insights of Claude Shannon's information theory, the narrative shows how numbers, symbols, and structures have shaped science, technology, and communication. At the heart of the work lies the Binary Principle -- the idea that zero and one are not merely digits, but primitive building blocks of all mathematical reasoning. By treating absence and presence as fundamental units, the book reveals how complex theories and applications emerge from the simplest distinctions. This perspective makes mathematics more intuitive, more engaging, and more accessible, transforming how it can be taught and understood. Discover the Binary Principle: Mathematics as the True Universal Language. This book invites readers to see mathematics not just as formulas and rules, but as the deepest expression of human thought.
JoyAvatar-Flash: Real-time and Infinite Audio-Driven Avatar Generation with Autoregressive Diffusion
Venue: arXiv cs.CV 2026
Year: 2026

Existing DiT-based audio-driven avatar generation methods have achieved considerable progress, yet their broader application is constrained by limitations such as high computational overhead and the inability to synthesize long-duration videos. Autoregressive methods address this problem by applying block-wise autoregressive diffusion methods. However, these methods suffer from the problem of error accumulation and quality degradation. To address this, we propose JoyAvatar-Flash, an audio-driven autoregressive model capable of real-time inference and infinite-length video generation with the following contributions: (1) Progressive Step Bootstrapping (PSB), which allocates more denoising steps to initial frames to stabilize generation and reduce error accumulation; (2) Motion Condition Injection (MCI), enhancing temporal coherence by injecting noise-corrupted previous frames as motion condition; and (3) Unbounded RoPE via Cache-Resetting (URCR), enabling infinite-length generation through dynamic positional encoding. Our 1.3B-parameter causal model achieves 16 FPS on a single GPU and achieves competitive results in visual quality, temporal consistency, and lip synchronization.
Beyond Automation: Rethinking Work, Creativity, and Governance in the Age of Generative AI
Venue: arXiv cs.CY 2026
Year: 2026

The rapid expansion of generative artificial intelligence (AI) is transforming work, creativity, and economic security in ways that extend beyond automation and productivity. This paper examines four interconnected dimensions of contemporary AI deployment: (1) transformations in employment and task composition (2) unequal diffusion of AI across sectors and socio-demographic groups (3) the role of universal basic income (UBI) as a stabilising response to AI-induced volatility (4) the effects of model alignment and content governance on human creativity, autonomy, and decision-making Using a hybrid approach that integrates labour market task exposure modelling, sectoral diffusion analysis, policy review, and qualitative discourse critique, the study develops an Inclusive AI Governance Framework. It introduces Level 1.5 autonomy as a human centred design principle that preserves evaluative authority while enabling partial automation, and highlights evidence of creative regression and emergent sycophancy in newer model generations. The paper argues that UBI should be embedded within a broader socio-technical governance ecosystem encompassing skills development, proportional regulation, and creativity preservation.
TA-KAND: Two-stage Attention Triple Enhancement and U-KAN based Diffusion For Few-shot Knowledge Graph Completion
Venue: arXiv cs.AI 2026
Year: 2026

Knowledge Graphs have become fundamental infrastructure for applications such as intelligent question answering and recommender systems due to their expressive representation. Nevertheless, real-world knowledge is heterogeneous, leading to a pronounced long-tailed distribution over relations. Previous studies mainly based on metric matching or meta learning. However, they often overlook the distributional characteristics of positive and negative triple samples. In this paper, we propose a few-shot knowledge graph completion framework that integrates two-stage attention triple enhancer with U-KAN based diffusion model. Extensive experiments on two public datasets show significant advantages of our methods.
Training Versatile Coding Agents in Synthetic Environments
Venue: arXiv cs.SE 2026
Year: 2026

Prior works on training software engineering agents have explored utilizing existing resources such as issues on GitHub repositories to construct software engineering tasks and corresponding test suites. These approaches face two key limitations: (1) their reliance on pre-existing GitHub repositories offers limited flexibility, and (2) their primary focus on issue resolution tasks restricts their applicability to the much wider variety of tasks a software engineer must handle. To overcome these challenges, we introduce SWE-Playground, a novel pipeline for generating environments and trajectories which supports the training of versatile coding agents. Unlike prior efforts, SWE-Playground synthetically generates projects and tasks from scratch with strong language models and agents, eliminating reliance on external data sources. This allows us to tackle a much wider variety of coding tasks, such as reproducing issues by generating unit tests and implementing libraries from scratch. We demonstrate the effectiveness of this approach on three distinct benchmarks, and results indicate that SWE-Playground produces trajectories with dense training signal, enabling agents to reach comparable performance with significantly fewer trajectories than previous works.
Eventually LIL Regret: Almost Sure $\ln\ln T$ Regret for a sub-Gaussian Mixture on Unbounded Data
Venue: arXiv cs.LG 2026
Year: 2026

We prove that a classic sub-Gaussian mixture proposed by Robbins in a stochastic setting actually satisfies a path-wise (deterministic) regret bound. For every path in a natural ``Ville event'' $E_α$, this regret till time $T$ is bounded by $\ln^2(1/α)/V_T + \ln (1/α) + \ln \ln V_T$ up to universal constants, where $V_T$ is a nonnegative, nondecreasing, cumulative variance process. (The bound reduces to $\ln(1/α) + \ln \ln V_T$ if $V_T \geq \ln(1/α)$.) If the data were stochastic, then one can show that $E_α$ has probability at least $1-α$ under a wide class of distributions (eg: sub-Gaussian, symmetric, variance-bounded, etc.). In fact, we show that on the Ville event $E_0$ of probability one, the regret on every path in $E_0$ is eventually bounded by $\ln \ln V_T$ (up to constants). We explain how this work helps bridge the world of adversarial online learning (which usually deals with regret bounds for bounded data), with game-theoretic statistics (which can handle unbounded data, albeit using stochastic assumptions). In short, conditional regret bounds serve as a bridge between stochastic and adversarial betting.
Breaking the Curse of Dimensionality: On the Stability of Modern Vector Retrieval
Venue: arXiv cs.IR 2026
Year: 2026

Modern vector databases enable efficient retrieval over high-dimensional neural embeddings, powering applications from web search to retrieval-augmented generation. However, classical theory predicts such tasks should suffer from the curse of dimensionality, where distances between points become nearly indistinguishable, thereby crippling efficient nearest-neighbor search. We revisit this paradox through the lens of stability, the property that small perturbations to a query do not radically alter its nearest neighbors. Building on foundational results, we extend stability theory to three key retrieval settings widely used in practice: (i) multi-vector search, where we prove that the popular Chamfer distance metric preserves single-vector stability, while average pooling aggregation may destroy it; (ii) filtered vector search, where we show that sufficiently large penalties for mismatched filters can induce stability even when the underlying search is unstable; and (iii) sparse vector search, where we formalize and prove novel sufficient stability conditions. Across synthetic and real datasets, our experimental results match our theoretical predictions, offering concrete guidance for model and system design to avoid the curse of dimensionality.
ATLAS: Automated Tree-based Language Analysis System for C and C++ source programs
Venue: arXiv cs.SE 2026
Year: 2026

Analyzing non-compilable C/C++ submodules without a resolved build environment remains a critical bottleneck for industrial software evolution. Traditional static analysis tools often fail in these scenarios due to their reliance on successful compilation, while Large Language Models (LLMs) lack the structural context necessary to reason about complex program logic. We introduce ATLAS, a Python-based CLI that generates unified multi-view representations for large-scale C/C++ projects with high accuracy, achieving success rates up to 96.80% for CFGs and 91.38% for DFGs. ATLAS is characterized by: (i) inter-procedural, type-aware analysis across function boundaries; (ii) support for both full and partial analysis of non-compilable projects; (iii) graph optimizations such as variable collapsing and node blacklisting; and (iv) synchronized multi-view graphs that align syntax, execution paths, and data-flow logic. Evaluating ATLAS with DeepSeek V3.2 for automated test generation demonstrates a 34.71% increase in line coverage and 32.66% in branch coverage, matching or exceeding the performance of the symbolic execution tool KLEE on complex projects. With polynomial scalability, ATLAS provides a robust infrastructure for generating the information-dense datasets required by next-generation, graph-aware ML4SE models. Video demonstration: https://youtu.be/QGuJZhj9CTA Tool github repository: https://github.com/jaid-monwar/ATLAS-multi-view-code-representation-tool.git
Coupled Variational Reinforcement Learning for Language Model General Reasoning
Venue: arXiv cs.CL 2026
Year: 2026

While reinforcement learning has achieved impressive progress in language model reasoning, it is constrained by the requirement for verifiable rewards. Recent verifier-free RL methods address this limitation by utilizing the probabilities that LLMs generate reference answers as reward signals. However, these approaches typically sample reasoning traces conditioned only on the question. This design decouples reasoning-trace sampling from answer information, leading to inefficient exploration and incoherence between traces and final answers. In this paper, we propose \textit{\b{Co}upled \b{V}ariational \b{R}einforcement \b{L}earning} (CoVRL), which bridges variational inference and reinforcement learning by coupling prior and posterior distributions through a hybrid sampling strategy. By constructing and optimizing a composite distribution that integrates these two distributions, CoVRL enables efficient exploration while preserving strong thought-answer coherence. Extensive experiments on mathematical and general reasoning benchmarks show that CoVRL improves performance by 12.4\% over the base model and achieves an additional 2.3\% improvement over state-of-the-art verifier-free RL baselines, providing a principled framework for enhancing the general reasoning capabilities of language models.
Network Level Evaluation of Hangup Susceptibility of HRGCs using Deep Learning and Sensing Techniques: A Goal Towards Safer Future
Venue: arXiv cs.LG 2026
Year: 2026

Steep-profiled Highway Railway Grade Crossings (HRGCs) pose safety hazards to vehicles with low ground clearance, which may become stranded on the tracks, creating risks of train vehicle collisions. This research develops a framework for network level evaluation of hang-up susceptibility of HRGCs. Profile data from different crossings in Oklahoma were collected using both a walking profiler and the Pave3D8K Laser Imaging System. A hybrid deep learning model, combining Long Short Term Memory (LSTM) and Transformer architectures, was developed to reconstruct accurate HRGC profiles from Pave3D8K Laser Imaging System data. Vehicle dimension data from around 350 specialty vehicles were collected at various locations across Oklahoma to enable up-to-date statistical design dimensions. Hang-up susceptibility was analyzed using three vehicle dimension scenarios: (a) median dimension (median wheelbase and ground clearance), (b) 75-25 percentile dimension (75 percentile wheelbase, 25 percentile ground clearance), and (c) worst case dimension (maximum wheelbase and minimum ground clearance). Results indicate 70, 80, and 95 crossings at the highest hang-up risk levels under these scenarios, respectively. An ArcGIS database and a software interface were developed to support transportation agencies in mitigating crossing hazards. This framework advances safety evaluation by integrating next-generation sensing, deep learning, and infrastructure datasets into practical decision support tools.