OpenRaiser/Intern-Atlas huggingface dataset:
Multi-Context Fusion Transformer for Pedestrian Crossing Intention Prediction in Urban Environments
Venue: arXiv cs.CV 2025
Year: 2025

Pedestrian crossing intention prediction is essential for autonomous vehicles to improve pedestrian safety and reduce traffic accidents. However, accurate pedestrian intention prediction in urban environments remains challenging due to the multitude of factors affecting pedestrian behavior. In this paper, we propose a multi-context fusion Transformer (MFT) that leverages diverse numerical contextual attributes across four key dimensions, encompassing pedestrian behavior context, environmental context, pedestrian localization context and vehicle motion context, to enable accurate pedestrian intention prediction. MFT employs a progressive fusion strategy, where mutual intra-context attention enables reciprocal interactions within each context, thereby facilitating feature sequence fusion and yielding a context token as a context-specific representation. This is followed by mutual cross-context attention, which integrates features across contexts with a global CLS token serving as a compact multi-context representation. Finally, guided intra-context attention refines context tokens within each context through directed interactions, while guided cross-context attention strengthens the global CLS token to promote multi-context fusion via guided information propagation, yielding deeper and more efficient integration. Experimental results validate the superiority of MFT over state-of-the-art methods, achieving accuracy rates of 73%, 93%, and 90% on the JAADbeh, JAADall, and PIE datasets, respectively. Extensive ablation studies are further conducted to investigate the effectiveness of the network architecture and contribution of different input context. Our code is open-source: https://github.com/ZhongHang0307/Multi-Context-Fusion-Transformer.
Energy Costs and Neural Complexity Evolution in Changing Environments
Venue: arXiv cs.NE 2025
Year: 2025

The Cognitive Buffer Hypothesis (CBH) posits that larger brains evolved to enhance survival in changing conditions. However, larger brains also carry higher energy demands, imposing additional metabolic burdens. Alongside brain size, brain organization plays a key role in cognitive ability and, with suitable architectures, may help mitigate energy challenges. This study evolves Artificial Neural Networks (ANNs) used by Reinforcement Learning (RL) agents to investigate how environmental variability and energy costs influence the evolution of neural complexity, defined in terms of ANN size and structure. Results indicate that under energy constraints, increasing seasonality led to smaller ANNs. This challenges CBH and supports the Expensive Brain Hypothesis (EBH), as highly seasonal environments reduced net energy intake and thereby constrained brain size. ANN structural complexity primarily emerged as a byproduct of size, where energy costs promoted the evolution of more efficient networks. These results highlight the role of energy constraints in shaping neural complexity, offering in silico support for biological theory and energy-efficient robotic design.
Lower Bounds for Bit Pigeonhole Principles in Bounded-Depth Resolution over Parities
Venue: arXiv cs.CC 2025
Year: 2025

We prove lower bounds for proofs of the bit pigeonhole principle (BPHP) and its generalizations in bounded-depth resolution over parities (Res$(\oplus)$). For weak BPHP$_n^m$ with $m = cn$ pigeons (for any constant $c>1$) and $n$ holes, for all $ε>0$, we prove that any depth $N^{1.5 - ε}$ proof in Res$(\oplus)$ must have exponential size, where $N = cn\log n$ is the number of variables. Inspired by recent work in TFNP on multicollision-finding, we consider a generalization of the bit pigeonhole principle, denoted $t$-BPHP$_n^m$, asserting that there is a map from $[m]$ to $[n]$ ($m > (t-1)n$) such that each $i \in [n]$ has fewer than $t$ preimages. We prove that any depth $N^{2-1/t-ε}$ proof in Res$(\oplus)$ of $t$-BPHP$_n^{ctn}$ (for any constant $c \geq 1$) must have exponential size. For the usual bit pigeonhole principle, we show that any depth $N^{2-ε}$ Res$(\oplus)$ proof of BPHP$_n^{n+1}$ must have exponential size. As a byproduct of our proof, we obtain that any randomized parity decision tree for the collision-finding problem with $n+1$ pigeons and $n$ holes must have depth $Ω(n)$, which matches the upper bound coming from a deterministic decision tree. We also prove a lifting theorem for bounded-depth Res$(\oplus)$ with a constant size gadget which lifts from $(p, q)$-DT-hardness, recently defined by Bhattacharya and Chattopadhyay. By combining our lifting theorem with the $(Ω(n), Ω(n))$-DT-hardness of the $n$-variate Tseitin contradiction over a suitable expander, proved by Bhattacharya and Chattopadhyay, we obtain an $N$-variate constant-width unsatisfiable CNF formula with $O(N)$ clauses for which any depth $N^{2-ε}$ Res$(\oplus)$ proof requires size $\exp(Ω(N^ε))$. Previously no superpolynomial lower bounds were known for Res$(\oplus)$ proofs when the depth is superlinear in the size of the formula.
DeLightMono: Enhancing Self-Supervised Monocular Depth Estimation in Endoscopy by Decoupling Uneven Illumination
Venue: arXiv cs.CV 2025
Year: 2025

Self-supervised monocular depth estimation serves as a key task in the development of endoscopic navigation systems. However, performance degradation persists due to uneven illumination inherent in endoscopic images, particularly in low-intensity regions. Existing low-light enhancement techniques fail to effectively guide the depth network. Furthermore, solutions from other fields, like autonomous driving, require well-lit images, making them unsuitable and increasing data collection burdens. To this end, we present DeLight-Mono - a novel self-supervised monocular depth estimation framework with illumination decoupling. Specifically, endoscopic images are represented by a designed illumination-reflectance-depth model, and are decomposed with auxiliary networks. Moreover, a self-supervised joint-optimizing framework with novel losses leveraging the decoupled components is proposed to mitigate the effects of uneven illumination on depth estimation. The effectiveness of the proposed methods was rigorously verified through extensive comparisons and an ablation study performed on two public datasets.
PRADA: Probability-Ratio-Based Attribution and Detection of Autoregressive-Generated Images
Venue: arXiv cs.CV 2025
Year: 2025

Autoregressive (AR) image generation has recently emerged as a powerful paradigm for image synthesis. Leveraging the generation principle of large language models, they allow for efficiently generating deceptively real-looking images, further increasing the need for reliable detection methods. However, to date there is a lack of work specifically targeting the detection of images generated by AR image generators. In this work, we present PRADA (Probability-Ratio-Based Attribution and Detection of Autoregressive-Generated Images), a simple and interpretable approach that can reliably detect AR-generated images and attribute them to their respective source model. The key idea is to inspect the ratio of a model's conditional and unconditional probability for the autoregressive token sequence representing a given image. Whenever an image is generated by a particular model, its probability ratio shows unique characteristics which are not present for images generated by other models or real images. We exploit these characteristics for threshold-based attribution and detection by calibrating a simple, model-specific score function. Our experimental evaluation shows that PRADA is highly effective against eight class-to-image and four text-to-image models.
MTA: A Merge-then-Adapt Framework for Personalized Large Language Model
Venue: arXiv cs.CL 2025
Year: 2025

Personalized Large Language Models (PLLMs) aim to align model outputs with individual user preferences, a crucial capability for user-centric applications. However, the prevalent approach of fine-tuning a separate module for each user faces two major limitations: (1) storage costs scale linearly with the number of users, rendering the method unscalable; and (2) fine-tuning a static model from scratch often yields suboptimal performance for users with sparse data. To address these challenges, we propose MTA, a Merge-then-Adapt framework for PLLMs. MTA comprises three key stages. First, we construct a shared Meta-LoRA Bank by selecting anchor users and pre-training meta-personalization traits within meta-LoRA modules. Second, to ensure scalability and enable dynamic personalization combination beyond static models, we introduce an Adaptive LoRA Fusion stage. This stage retrieves and dynamically merges the most relevant anchor meta-LoRAs to synthesize a user-specific one, thereby eliminating the need for user-specific storage and supporting more flexible personalization. Third, we propose a LoRA Stacking for Few-Shot Personalization stage, which applies an additional ultra-low-rank, lightweight LoRA module on top of the merged LoRA. Fine-tuning this module enables effective personalization under few-shot settings. Extensive experiments on the LaMP benchmark demonstrate that our approach outperforms existing SOTA methods across multiple tasks.
Adaptive LLM Agents: Toward Personalized Empathetic Care
Venue: arXiv cs.HC 2025
Year: 2025

Current mental-health conversational systems are usually based on fixed, generic dialogue patterns. This paper proposes an adaptive framework based on large language models that aims to personalize therapeutic interaction according to a user's psychological state, quantified with the Acceptance of Illness Scale (AIS). The framework defines three specialized agents, L, M, and H, each linked to a different level of illness acceptance, and adjusts conversational behavior over time using continuous feedback signals. The AIS-stratified architecture is treated as a diegetic prototype placed in a plausible near-future setting and examined through the method of design fiction. By embedding the architecture in narrative scenarios, the study explores how such agents might influence access to care and therapeutic relationship. The goal is to show how clinically informed personalization, technical feasibility, and speculative scenario analysis can together inform the responsible design of LLM-based companions for mental-health support.
Mobility Stream Processing on NebulaStream and MEOS
Venue: arXiv cs.DB 2025
Year: 2025

The increasing use of Internet-of-Things (IoT) sensors in moving objects has resulted in vast amounts of spatiotemporal streaming data. To analyze this data in situ, real-time spatiotemporal processing is needed. However, current stream processing systems designed for IoT environments often lack spatiotemporal processing capabilities, and existing spatiotemporal libraries primarily focus on analyzing historical data. This gap makes performing real-time spatiotemporal analytics challenging. In this demonstration, we present NebulaMEOS, which combines MEOS (Mobility Engine Open Source), a spatiotemporal processing library, with NebulaStream, a scalable data management system for IoT applications. By integrating MEOS into NebulaStream, NebulaMEOS utilizes spatiotemporal functionalities to process and analyze streaming data in real-time. We demonstrate NebulaMEOS by querying data streamed from edge devices on trains by the Société Nationale des Chemins de fer Belges (SNCB). Visitors can experience demonstrations of geofencing and geospatial complex event processing, visualizing real-time train operations and environmental impacts.
The Devil in the Details: Emergent Misalignment, Format and Coherence in Open-Weights LLMs
Venue: arXiv cs.LG 2025
Year: 2025

Prior work has shown that fine-tuning models on a narrow domain with misaligned data can lead to broad misalignment - a phenomenon termed "emergent misalignment" (Betley et al. 2025). While all tested models were susceptible to emergent misalignment, some models showed more resistance than others. Specifically the Qwen-2.5 family proved to be relatively resistant, while GPT-4o exhibited the strongest misalignment. In this paper we evaluate if current-generation open-weights models exhibit similar resistance to the Qwen-2.5 family and measure misalignment robustness over a range of model architectures and scales. We replicate the effect across nine modern open-weights models (Gemma 3 and Qwen 3 families, 1B-32B parameters). Models fine-tuned on insecure code generation show a 0.68% misalignment rate (compared to 0.07% for base models), matching the lower end of prior open-model results but dramatically lower than GPT-4o's 20%. We identify a critical format-dependent vulnerability: requiring JSON output doubles misalignment rates compared to natural language prompts (0.96% vs 0.42%). This suggests that structural constraints may bypass safety training by reducing the model's 'degrees of freedom' to refuse. These findings confirm emergent misalignment as a reproducible phenomenon in modern open-weights models, with rates substantially lower than observed in proprietary systems.
Explainable Deep Learning for Secrecy Energy-Efficiency Maximization in Ambient Backscatter Multi-User NOMA Systems
Venue: arXiv cs.IT 2025
Year: 2025

In this paper, we investigate the secrecy energy-efficiency (SEE) of a multi-user downlink non-orthogonal multiple access (NOMA) system assisted by multiple ambient backscatter communications (AmBC) in the presence of a passive eavesdropper. We analyze both the trade-off and the ratio between the achievable secrecy sum-rate and total power consumption. In the special case of two backscatter devices (BDs), we derive closed-form solutions for the optimal reflection coefficients and power allocation by exploiting the structure of the SEE objective and the Pareto boundary of the feasible set. When more than two BDs are present, the problem becomes analytically intractable. To address this, we propose two efficient optimization techniques: (i) an exhaustive grid-based benchmark method, and (ii) a scalable particle swarm optimization algorithm. Furthermore, we design a deep learning-based predictor using a feedforward neural network (FNN), which closely approximates the optimal solutions. Numerical results show that the inclusion of AmBC significantly improves SEE, with gains up to 615% compared to conventional NOMA in high-noise regimes. Additionally, the FNN model achieves more than 95% accuracy compared to the optimal baseline, while reducing complexity. Finally, we employ SHAP (SHapley Additive exPlanations) to interpret the learned model, revealing that the most influential features correspond to the dominant composite channel components, in accordance with the theoretical system model. This demonstrates the potential of explainable artificial intelligence to build trust in energy-efficient and secure AmBC-NOMA systems for next-generation internet of things applications.
CLIMATEAGENT: Multi-Agent Orchestration for Complex Climate Data Science Workflows
Venue: arXiv cs.LG 2025
Year: 2025

Climate science demands automated workflows to transform comprehensive questions into data-driven statements across massive, heterogeneous datasets. However, generic LLM agents and static scripting pipelines lack climate-specific context and flexibility, thus, perform poorly in practice. We present ClimateAgent, an autonomous multi-agent framework that orchestrates end-to-end climate data analytic workflows. ClimateAgent decomposes user questions into executable sub-tasks coordinated by an Orchestrate-Agent and a Plan-Agent; acquires data via specialized Data-Agents that dynamically introspect APIs to synthesize robust download scripts; and completes analysis and reporting with a Coding-Agent that generates Python code, visualizations, and a final report with a built-in self-correction loop. To enable systematic evaluation, we introduce Climate-Agent-Bench-85, a benchmark of 85 real-world tasks spanning atmospheric rivers, drought, extreme precipitation, heat waves, sea surface temperature, and tropical cyclones. On Climate-Agent-Bench-85, ClimateAgent achieves 100% task completion and a report quality score of 8.32, outperforming GitHub-Copilot (6.27) and a GPT-5 baseline (3.26). These results demonstrate that our multi-agent orchestration with dynamic API awareness and self-correcting execution substantially advances reliable, end-to-end automation for climate science analytic tasks.
One Action Too Many: Inapproximability of Budgeted Combinatorial Contracts
Venue: arXiv cs.GT 2025
Year: 2025

We study multi-agent contract design with combinatorial actions, under budget constraints, and for a broad class of objective functions, including profit (principal's utility), reward, and welfare. Our first result is a strong impossibility: For submodular reward functions, no randomized poly-time algorithm can approximate the optimal budget-feasible value within \textit{any finite factor}, even with demand-oracle access. This result rules out extending known constant-factor guarantees from either (i) unbudgeted settings with combinatorial actions or (ii) budgeted settings with binary actions, to their combination. The hardness is tight: It holds even when all but one agent have binary actions and the remaining agent has just one additional action. On the positive side, we show that gross substitutes rewards (a well-studied strict subclass of submodular functions) admit a deterministic poly-time $O(1)$-approximation, using only value queries. Our results thus draw the first sharp separation between budgeted and unbudgeted settings in combinatorial contracts, and identifies gross substitutes as a tractable frontier for budgeted combinatorial contracts. Finally, we present an FPTAS for additive rewards, demonstrating that arbitrary approximation is tractable under any budget. This constitutes the first FPTAS for the multi-agent combinatorial-actions setting, even in the absence of budget constraints.
On hierarchical secure aggregation against relay and user collusion
Venue: arXiv cs.IT 2025
Year: 2025

Secure aggregation (SA) is fundamental to privacy preservation in federated learning (FL), enabling model aggregation while preventing disclosure of individual user updates. This paper addresses hierarchical secure aggregation (HSA) against relay and user collusion in homogeneous networks, where each user connects to $n$ relays and each relay serves $m$ users. In the two-phase communication framework, users transmit masked data to relays, which then process and forward compiled messages to the server for exact sum recovery. The primary objective is to devise a transmission scheme such that the server can finish the aggregation task, while any group of $T_h$ colluding relays and $T_u$ colluding users cannot reveal any information about the data owned by the non-colluding users. In this study, we establish fundamental limits on the communication load, defined as the ratio of transmitted information size to original data size, for each user-relay link and each relay-server link. Achievable thresholds for collusion resilience are also derived. When the number of colluding relays and users falls below certain critical thresholds, we construct communication-optimal schemes using methods from network function computation. A limitation of these schemes is their reliance on large random keys. To address this, we derive a lower bound on the required key size and prove its achievability in cyclic networks, where users are connected to relays in a cyclic wrap-around manner. By establishing a connection between HSA and network function computation, this work advances the theoretical limits of communication efficiency and information-theoretic security in secure aggregation.
General Multi-User Distributed Computing
Venue: arXiv cs.IT 2025
Year: 2025

This work develops a unified {learning- and information-theoretic} framework for distributed computation and inference across multiple users and servers. The proposed \emph{General Multi-User Distributed Computing (GMUDC)} model characterizes how computation, communication, and accuracy can be jointly optimized when users demand heterogeneous target functions that are arbitrary transformations of shared real-valued subfunctions. Without any separability assumption, and requiring only that each target function lies in a reproducing-kernel Hilbert space associated with a shift-invariant kernel, the framework remains valid for arbitrary connectivity and task-assignment topologies. A dual analysis is introduced: the \emph{quenched design} considers fixed assignments of subfunctions and network topology, while the \emph{annealed design} captures the averaged performance when assignments and links are drawn uniformly at random from a given ensemble. These formulations reveal the fundamental limits governing the trade-offs among computing load, communication load, and reconstruction distortion under computational and communication budgets~$Γ$ and~$Δ$. The analysis establishes a spectral-coverage duality linking generalization capability with network topology and resource allocation, leading to provably efficient and topology-aware distributed designs. The resulting principles provide an \emph{information-energy foundation} for scalable and resource-optimal distributed and federated learning systems, with direct applications to aeronautical, satellite, and edge-intelligent networks where energy and data efficiency are critical.
Beluga: A CXL-Based Memory Architecture for Scalable and Efficient LLM KVCache Management
Venue: arXiv cs.DC 2025
Year: 2025

The rapid increase in LLM model sizes and the growing demand for long-context inference have made memory a critical bottleneck in GPU-accelerated serving systems. Although high-bandwidth memory (HBM) on GPUs offers fast access, its limited capacity necessitates reliance on host memory (CPU DRAM) to support larger working sets such as the KVCache. However, the maximum DRAM capacity is constrained by the limited number of memory channels per CPU socket. To overcome this limitation, current systems often adopt RDMA-based disaggregated memory pools, which introduce significant challenges including high access latency, complex communication protocols, and synchronization overhead. Fortunately, the emerging CXL technology introduces new opportunities in KVCache design. In this paper, we propose Beluga, a novel memory architecture that enables GPUs and CPUs to access a shared, large-scale memory pool through CXL switches. By supporting native load/store access semantics over the CXL fabric, our design delivers near-local memory latency, while reducing programming complexity and minimizing synchronization overhead. We conduct a systematic characterization of a commercial CXL switch-based memory pool and propose a set of design guidelines. Based on Beluga, we design and implement Beluga-KVCache, a system tailored for managing the large-scale KVCache in LLM inference. Beluga-KVCache achieves an 89.6% reduction in Time-To-First-Token (TTFT) and 7.35x throughput improvement in the vLLM inference engine compared to RDMA-based solutions. To the best of our knowledge, Beluga is the first system that enables GPUs to directly access large-scale memory pools through CXL switches, marking a significant step toward low-latency, shared access to vast memory resources by GPUs.
KyrgyzBERT: A Compact, Efficient Language Model for Kyrgyz NLP
Venue: arXiv cs.CL 2025
Year: 2025

Kyrgyz remains a low-resource language with limited foundational NLP tools. To address this gap, we introduce KyrgyzBERT, the first publicly available monolingual BERT-based language model for Kyrgyz. The model has 35.9M parameters and uses a custom tokenizer designed for the language's morphological structure. To evaluate performance, we create kyrgyz-sst2, a sentiment analysis benchmark built by translating the Stanford Sentiment Treebank and manually annotating the full test set. KyrgyzBERT fine-tuned on this dataset achieves an F1-score of 0.8280, competitive with a fine-tuned mBERT model five times larger. All models, data, and code are released to support future research in Kyrgyz NLP.