OpenRaiser/Intern-Atlas huggingface dataset:
Beyond speculation: Measuring the growing presence of LLM-generated texts in multilingual disinformation
Venue: arXiv cs.CL 2026
Year: 2026

Increased sophistication of large language models (LLMs) and the consequent quality of generated multilingual text raises concerns about potential disinformation misuse. While humans struggle to distinguish LLM-generated content from human-written texts, the scholarly debate about their impact remains divided. Some argue that heightened fears are overblown due to natural ecosystem limitations, while others contend that specific "longtail" contexts face overlooked risks. Our study bridges this debate by providing the first empirical evidence of LLM presence in the latest real-world disinformation datasets, documenting the increase of machine-generated content following ChatGPT's release, and revealing crucial patterns across languages, platforms, and time periods.
Navigating Uncertainties: How GenAI Developers Document Their Models on Open-Source Platforms
Venue: arXiv cs.HC 2026
Year: 2026

Model documentation plays a crucial role in promoting transparency and responsible development of AI systems. With the rise of Generative AI (GenAI), open-source platforms have increasingly become hubs for hosting and distributing these models, prompting platforms like Hugging Face to develop dedicated model documentation guidelines that align with responsible AI principles. Despite these growing efforts, there remains a lack of understanding of how developers document their GenAI models on open-source platforms. Through interviews with 13 GenAI developers active on open-source platforms, we provide empirical insights into their documentation practices and challenges. Our analysis reveals that despite existing resources, developers of GenAI models still face multiple layers of uncertainties in their model documentation: (1) uncertainties about what specific content should be included; (2) uncertainties about how to effectively report key components of their models; and (3) uncertainties in deciding who should take responsibilities for various aspects of model documentation. Based on our findings, we discuss the implications for policymakers, open-source platforms, and the research community to support meaningful, effective and actionable model documentation in the GenAI era, including cultivating better community norms, building robust evaluation infrastructures, and clarifying roles and responsibilities.
OrchMLLM: Orchestrate Multimodal Data with Batch Post-Balancing to Accelerate Multimodal Large Language Model Training
Venue: arXiv cs.DC 2026
Year: 2026

Multimodal large language models (MLLMs), such as GPT-4o, are garnering significant attention. During the exploration of MLLM training, we identified Modality Composition Incoherence, a phenomenon that the proportion of a certain modality varies dramatically across different examples. It exacerbates the challenges of addressing mini-batch imbalances, which lead to uneven GPU utilization between Data Parallel (DP) instances and severely degrades the efficiency and scalability of MLLM training, ultimately affecting training speed and hindering further research on MLLMs. To address these challenges, we introduce OrchMLLM, a comprehensive framework designed to mitigate the inefficiencies in MLLM training caused by Modality Composition Incoherence. First, we propose Batch Post-Balancing Dispatcher, a technique that efficiently eliminates mini-batch imbalances in sequential data. Additionally, we integrate MLLM Global Orchestrator into the training framework to orchestrate multimodal data and tackle the issues arising from Modality Composition Incoherence. We evaluate OrchMLLM across various MLLM sizes, demonstrating its efficiency and scalability. Experimental results reveal that OrchMLLM achieves a Model FLOPs Utilization (MFU) of $41.6\%$ when training an 84B MLLM with three modalities on $2560$ H100 GPUs, outperforming Megatron-LM by up to $3.1\times$ in throughput.
DenseFormer: Learning Dense Depth Map from Sparse Depth and Image via Conditional Diffusion Model
Venue: arXiv cs.CV 2026
Year: 2026

The depth completion task is a critical problem in autonomous driving, involving the generation of dense depth maps from sparse depth maps and RGB images. Most existing methods employ a spatial propagation network to iteratively refine the depth map after obtaining an initial dense depth. In this paper, we propose DenseFormer, a novel method that integrates the diffusion model into the depth completion task. By incorporating the denoising mechanism of the diffusion model, DenseFormer generates the dense depth map by progressively refining an initial random depth distribution through multiple iterations. We propose a feature extraction module that leverages a feature pyramid structure, along with multi-layer deformable attention, to effectively extract and integrate features from sparse depth maps and RGB images, which serve as the guiding condition for the diffusion process. Additionally, this paper presents a depth refinement module that applies multi-step iterative refinement across various ranges to the dense depth results generated by the diffusion process. The module utilizes image features enriched with multi-scale information and sparse depth input to further enhance the accuracy of the predicted depth map. Extensive experiments on the KITTI outdoor scene dataset demonstrate that DenseFormer outperforms classical depth completion methods.
Beyond Prompts: Space-Time Decoupling Control-Plane Jailbreaks in LLM Structured Output
Venue: arXiv cs.CR 2026
Year: 2026

Content Warning: This paper may contain unsafe or harmful content generated by LLMs that may be offensive to readers. Large Language Models (LLMs) are extensively used as tooling platforms through structured output APIs to ensure syntax compliance so that robust integration with existing software, like agent systems, can be achieved. However, the feature enabling the functionality of grammar-guided structured output presents significant security vulnerabilities. In this work, we reveal a critical control-plane attack surface orthogonal to traditional data-plane vulnerabilities. We introduce Constrained Decoding Attack (CDA), a novel jailbreak class that weaponizes structured output constraints to bypass both external auditing and internal safety alignment. Unlike prior attacks focused on input prompt designs, CDA operates by embedding malicious intent in schema-level grammar rules (control-plane) while maintaining benign surface prompts (data-plane). We instantiate this with two proof-of-concept attacks: EnumAttack, which embeds malicious content in enum fields; and the more evasive DictAttack, which decouples the malicious payload across a benign prompt and a dictionary-based grammar. Our evaluation spans a broad spectrum of 13 proprietary/open-weight models. In particular, DictAttack achieves 94.3--99.5% ASR across five benchmarks on gpt-5, gemini-2.5-pro, deepseek-r1, and gpt-oss-120b. Furthermore, we demonstrate the significant challenge in defending against these threats: while basic grammar auditing mitigates EnumAttack, the more sophisticated DictAttack maintains a 75.8% ASR even against multiple state-of-the-art jailbreak guardrails. This exposes a critical "semantic gap" in current safety architectures and underscores the urgent need for cross-plane defenses that can bridge the data and control planes to secure the LLM generation pipeline.
Past-Discounting is Key for Learning Markovian Fairness with Long Horizons
Venue: arXiv cs.AI 2026
Year: 2026

Fairness is an important consideration for dynamic resource allocation in multi-agent systems. Many existing methods treat fairness as a one-shot problem without considering temporal dynamics, which misses the nuances of accumulating inequalities over time. Recent approaches overcome this limitation by tracking allocations over time, assuming perfect recall of all past utilities. While the former neglects long-term equity, the latter introduces a critical challenge: the augmented state space required to track cumulative utilities grows unboundedly with time, hindering the scalability and convergence of learning algorithms. Motivated by behavioral insights that human fairness judgments discount distant events, we introduce a framework for temporal fairness that incorporates past-discounting into the learning problem. This approach offers a principled interpolation between instantaneous and perfect-recall fairness. Our central contribution is a past-discounted framework for memory tracking and a theoretical analysis of fairness memories, showing past-discounting guarantees a bounded, horizon-independent state space, a property that we prove perfect-recall methods lack. This result unlocks the ability to learn fair policies tractably over arbitrarily long horizons. We formalize this framework, demonstrate its necessity with experiments showing that perfect recall fails where past-discounting succeeds, and provide a clear path toward building scalable and equitable resource allocation systems.
Chain of Correction for Full-text Speech Recognition with Large Language Models
Venue: arXiv cs.CL 2026
Year: 2026

Full-text error correction with Large Language Models (LLMs) for Automatic Speech Recognition (ASR) is attracting increased attention for its ability to address a wide range of error types, such as punctuation restoration and inverse text normalization, across long context. However, challenges remain regarding stability, controllability, completeness, and fluency. To mitigate these issues, this paper proposes the Chain of Correction (CoC), which uses a multi-turn chat format to correct errors segment by segment, guided by pre-recognized text and full-text context for better semantic understanding. Utilizing the open-sourced ChFT dataset, we fine-tune a pre-trained LLM to evaluate CoC's performance. Experiments show that CoC significantly outperforms baseline and benchmark systems in correcting full-text ASR outputs. We also analyze correction thresholds to balance under-correction and over-rephrasing, extrapolate CoC on extra-long ASR outputs, and explore using other types of information to guide error correction.
Undefinability of Approximation of 2-to-2 Games
Venue: arXiv cs.LO 2026
Year: 2026

Recent work by Atserias and Dawar (J. Log. Comp 2019) and Tucker-Foltz (LMCS 2024) has established undefinability results in fixed-point logic with counting (FPC) corresponding to many classical complexity results from the hardness of approximation. In this line of work, NP-hardness results are turned into unconditional FPC undefinability results. We extend this work by showing the FPC undefinability of any constant factor approximation of weighted 2-to-2 games, based on the NP-hardness results of Khot, Minzer and Safra. Our result shows that the completely satisfiable 2-to-2 games are not FPC-separable from those that are not epsilon-satisfiable, for arbitrarily small epsilon. The perfect completeness of our inseparability is an improvement on the complexity result, as the NP-hardness of such a separation is still only conjectured. This perfect completeness enables us to show the FPC undefinability of other problems whose NP-hardness is conjectured. In particular, we are able to show that no FPC formula can separate the 3-colourable graphs from those that are not t-colourable, for any constant t.
Evaluating Large Language Models for Fair and Reliable Organ Allocation
Venue: arXiv cs.LG 2026
Year: 2026

Medical institutions are considering the use of LLMs in high-stakes clinical decision-making, such as organ allocation. In such sensitive use cases, evaluating fairness is imperative. However, existing evaluation methods often fall short; benchmarks are too simplistic to capture real-world complexity, and accuracy-based metrics fail to address the absence of a clear ground truth. To realistically and fairly model organ allocation, specifically kidney allocation, we begin by testing the medical knowledge of LLMs to determine whether they understand the clinical factors required to make sound allocation decisions. Building on this foundation, we design two tasks: (1) Choose-One and (2) Rank-All. In Choose-One, LLMs select a single candidate from a list of potential candidates to receive a kidney. In this scenario, we assess fairness across demographics using traditional fairness metrics, such as proportional parity. In Rank-All, LLMs rank all candidates waiting for a kidney, reflecting real-world allocation processes more closely, where an organ is passed down a ranked list until allocated. Our evaluation on three LLMs reveals a divergence between fairness metrics: while exposure-based metrics suggest equitable outcomes, probability-based metrics uncover systematic preferential sorting, where specific groups were clustered in upper-ranking tiers. Furthermore, we observe that demographic preferences are highly task-dependent, showing inverted trends between Choose-One and Rank-All tasks, even when considering the topmost rank. Overall, our results indicate that current LLMs can introduce inequalities in real-world allocation scenarios, underscoring the urgent need for rigorous fairness evaluation and human oversight before their use in high-stakes decision-making.
A Reconfigurable Time-Domain In-Memory Computing Macro using FeFET-Based CAM with Multilevel Delay Calibration in 28 nm CMOS
Venue: arXiv cs.ET 2026
Year: 2026

Time-domain nonvolatile in-memory computing (TD-nvIMC) offers a promising pathway to reduce data movement and improve energy efficiency by encoding computation in delay rather than voltage or current. This work presents a fully integrated and reconfigurable TD-nvIMC macro, fabricated in 28 nm CMOS, that combines a ferroelectric FET (FeFET)-based content-addressable memory array, a cascaded delay element chain, and a time-to-digital converter. The architecture supports binary multiply-and-accumulate (MAC) operations using XOR- and AND-based matching, as well as in-memory Boolean logic and arithmetic functions. Sub-nanosecond MAC resolution is achieved through experimentally demonstrated 550 ps delay steps, representing a 2000$\times$ improvement over prior FeFET TD-nvIMC work, enabled by multilevel-state calibration with $\leq$ 100 ps resolution. Write-disturb resilience is ensured via isolated triple-well bulks. The proposed macro achieves a measured throughput of 222.2 MOPS/cell and energy efficiency of 1887 TOPS/W at 0.85 V, establishing a viable path toward scalable, energy-efficient TD-nvIMC accelerators.
A Benchmark for End-to-End Zero-Shot Biomedical Relation Extraction with LLMs: Experiments with OpenAI Models
Venue: arXiv cs.CL 2026
Year: 2026

Extracting relations from scientific literature is a fundamental task in biomedical NLP because entities and relations among them drive hypothesis generation and knowledge discovery. As literature grows rapidly, relation extraction (RE) is indispensable to curate knowledge graphs to be used as computable structured and symbolic representations. With the rise of LLMs, it is pertinent to examine if it is better to skip tailoring supervised RE methods, save annotation burden, and just use zero shot RE (ZSRE) via LLM API calls. In this paper, we propose a benchmark with seven biomedical RE datasets with interesting characteristics and evaluate three Open AI models (GPT-4, o1, and GPT-OSS-120B) for end-to-end ZSRE. We show that LLM-based ZSRE is inching closer to supervised methods in performances on some datasets but still struggles on complex inputs expressing multiple relations with different predicates. Our error analysis reveals scope for improvements.
Introducing COGENT3: An AI Architecture for Emergent Cognition
Venue: arXiv cs.AI 2026
Year: 2026

This paper presents COGENT3 (or Collective Growth and Entropy-modulated Triads System), a novel approach for emergent cognition integrating pattern formation networks with group influence dynamics. Contrasting with traditional strategies that rely on predetermined architectures, computational structures emerge dynamically in our framework through agent interactions. This enables a more flexible and adaptive system exhibiting characteristics reminiscent of human cognitive processes. The incorporation of temperature modulation and memory effects in COGENT3 closely integrates statistical mechanics, machine learning, and cognitive science.
PVASS Reachability is Decidable
Venue: arXiv cs.LO 2026
Year: 2026

Reachability in pushdown vector addition systems with states (PVASS) is among the longest standing open problems in Theoretical Computer Science. We show that the problem is decidable in full generality. Our decision procedure is similar in spirit to the KLMST algorithm for VASS reachability, but works over objects that support an elaborate form of procedure summarization as known from pushdown reachability.
Federated Hierarchical Reinforcement Learning for Adaptive Traffic Signal Control
Venue: arXiv cs.LG 2026
Year: 2026

Multi-agent reinforcement learning (MARL) has shown promise for adaptive traffic signal control (ATSC), enabling multiple intersections to coordinate signal timings in real time. However, in large-scale settings, MARL faces constraints due to extensive data sharing and communication requirements. Federated learning (FL) mitigates these challenges by training shared models without directly exchanging raw data, yet traditional FL methods such as FedAvg struggle with highly heterogeneous intersections. Different intersections exhibit varying traffic patterns, demands, and road structures, so performing FedAvg across all agents is inefficient. To address this gap, we propose Hierarchical Federated Reinforcement Learning (HFRL) for ATSC. HFRL employs clustering-based or optimization-based techniques to dynamically group intersections and perform FedAvg independently within groups of intersections with similar characteristics, enabling more effective coordination and scalability than standard FedAvg.Our experiments on synthetic and real-world traffic networks demonstrate that HFRL consistently outperforms decentralized and standard federated RL approaches, and achieves competitive or superior performance compared to centralized RL as network scale and heterogeneity increase, particularly in real-world settings. The method also identifies suitable grouping patterns based on network structure or traffic demand, resulting in a more robust framework for distributed, heterogeneous systems.
Curved representational Bregman divergences and their applications
Venue: arXiv cs.IT 2026
Year: 2026

By analogy to the terminology of curved exponential families in statistics, we define curved Bregman divergences as Bregman divergences restricted to non-affine parameter subspaces and sub-dimensional Bregman divergences when the restrictions are affine. A common example of curved Bregman divergence is the cosine dissimilarity between normalized vectors: a curved squared Euclidean divergence. We prove that the barycenter of a finite weighted set of parameters under a curved Bregman divergence amounts to the right Bregman projection onto the non-affine subspace of the barycenter with respect to the full Bregman divergence, and interpret a generalization of the weighted Bregman centroid of $n$ parameters as a $n$-fold sub-dimensional Bregman divergence. We demonstrate the significance of curved Bregman divergences with several examples: (1) symmetrized Bregman divergences, (2) pointwise symmetrized Bregman divergences, and (3) the Kullback-Leibler divergence between circular complex normal distributions. We explain how to reparameterize sub-dimensional Bregman divergences on simplicial sub-dimensional domains. We then consider monotonic embeddings to define representational curved Bregman divergences and show that the $α$-divergences are representational curved Bregman divergences with respect to $α$-embeddings of the probability simplex into the positive measure cone. As an application, we report an efficient method to calculate the intersection of a finite set of $α$-divergence spheres. As an application, we report an efficient method to calculate the intersection of a finite set of $α$-divergence spheres.
Architecture independent generalization bounds for overparametrized deep ReLU networks
Venue: arXiv cs.LG 2026
Year: 2026

We prove that overparametrized neural networks are able to generalize with a test error that is independent of the level of overparametrization, and independent of the Vapnik-Chervonenkis (VC) dimension. We prove explicit bounds that only depend on the metric geometry of the test and training sets, on the regularity properties of the activation function, and on the operator norms of the weights and norms of biases. For overparametrized deep ReLU networks with a training sample size bounded by the input space dimension, we explicitly construct zero loss minimizers without use of gradient descent, and prove a uniform generalization bound that is independent of the network architecture. We perform computational experiments of our theoretical results with MNIST, and obtain agreement with the true test error within a 22 % margin on average.
SAP-CoPE: Social-Aware Planning using Cooperative Pose Estimation with Infrastructure Sensor Nodes
Venue: arXiv cs.RO 2026
Year: 2026

Autonomous driving systems must operate smoothly in human-populated indoor environments, where challenges arise including limited perception and occlusions when relying only on onboard sensors, as well as the need for socially compliant motion planning that accounts for human psychological comfort zones. These factors complicate accurate recognition of human intentions and the generation of comfortable, socially aware trajectories. To address these challenges, we propose SAP-CoPE, an indoor navigation system that integrates cooperative infrastructure with a novel 3D human pose estimation method and a socially-aware model predictive control (MPC)-based motion planner. In the perception module, an optimization problem is formulated to account for uncertainty propagation in the camera projection matrix while enforcing human joint coherence. The proposed method is adaptable to both single- and multi-camera configurations and can incorporate sparse LiDAR point-cloud data. For motion planning, we integrate a psychology inspired personal-space field using the information from estimated human poses into an MPC framework to enhance socially comfort in human-populated environments. Extensive real-world evaluations demonstrate the effectiveness of the proposed approach in generating socially aware trajectories for autonomous systems.
CAT: Circular-Convolutional Attention for Sub-Quadratic Transformers
Venue: arXiv cs.LG 2026
Year: 2026

Transformers have driven remarkable breakthroughs in natural language processing and computer vision, yet their standard attention mechanism still imposes O(N^2) complexity, hindering scalability to longer sequences. We introduce Circular-convolutional ATtention (CAT), a Fourier-based approach that efficiently applies circular convolutions to reduce complexity without sacrificing representational power. CAT achieves O(NlogN) computations, requires fewer learnable parameters by streamlining fully connected layers, and introduces no additional heavy operations, resulting in consistent accuracy improvements and about a 10% speedup in naive PyTorch implementations. Based on the Engineering-Isomorphic Transformers (EITs) framework, CAT's design not only offers practical efficiency and ease of implementation, but also provides insights to guide the development of future high-performance Transformer architectures. Finally, our ablation studies highlight the key conditions underlying CAT's success, shedding light on broader principles for scalable attention mechanisms.