OpenRaiser/Intern-Atlas huggingface dataset:
End-to-End Transformer Acceleration Through Processing-in-Memory Architectures
Venue: arXiv cs.AR 2025
Year: 2025

Transformers have become central to natural language processing and large language models, but their deployment at scale faces three major challenges. First, the attention mechanism requires massive matrix multiplications and frequent movement of intermediate results between memory and compute units, leading to high latency and energy costs. Second, in long-context inference, the key-value cache (KV cache) can grow unpredictably and even surpass the model's weight size, creating severe memory and bandwidth bottlenecks. Third, the quadratic complexity of attention with respect to sequence length amplifies both data movement and compute overhead, making large-scale inference inefficient. To address these issues, this work introduces processing-in-memory solutions that restructure attention and feed-forward computation to minimize off-chip data transfers, dynamically compress and prune the KV cache to manage memory growth, and reinterpret attention as an associative memory operation to reduce complexity and hardware footprint. Moreover, we evaluate our processing-in-memory design against state-of-the-art accelerators and general-purpose GPUs, demonstrating significant improvements in energy efficiency and latency. Together, these approaches address computation overhead, memory scalability, and attention complexity, further enabling efficient, end-to-end acceleration of Transformer models.
Agentic Persona Control and Task State Tracking for Realistic User Simulation in Interactive Scenarios
Venue: arXiv cs.HC 2025
Year: 2025

Testing conversational AI systems at scale across diverse domains necessitates realistic and diverse user interactions capturing a wide array of behavioral patterns. We present a novel multi-agent framework for realistic, explainable human user simulation in interactive scenarios, using persona control and task state tracking to mirror human cognitive processes during goal-oriented conversations. Our system employs three specialized AI agents: (1) a User Agent to orchestrate the overall interaction, (2) a State Tracking Agent to maintain structured task state, and (3) a Message Attributes Generation Agent that controls conversational attributes based on task progress and assigned persona. To validate our approach, we implement and evaluate the framework for guest ordering at a restaurant with scenarios rich in task complexity, behavioral diversity, and conversational ambiguity. Through systematic ablations, we evaluate the contributory efficacy of each agentic component to overall simulation quality in terms of persona adherence, task completion accuracy, explainability, and realism. Our experiments demonstrate that the complete multi-agent system achieves superior simulation quality compared to single-LLM baselines, with significant gains across all evaluation metrics. This framework establishes a powerful environment for orchestrating agents to simulate human users with cognitive plausibility, decomposing the simulation into specialized sub-agents that reflect distinct aspects of human thought processes applicable across interactive domains.
Light Field Display Point Rendering
Venue: arXiv cs.GR 2025
Year: 2025

Rendering for light field displays (LFDs) requires rendering of dozens or hundreds of views, which must then be combined into a single image on the display, making real-time LFD rendering extremely difficult. We introduce light field display point rendering (LFDPR), which meets these challenges by improving eye-based point rendering [Gavane and Watson 2023] with texture-based splatting, which avoids oversampling of triangles mapped to only a few texels; and with LFD-biased sampling, which adjusts horizontal and vertical triangle sampling to match the sampling of the LFD itself. To improve image quality, we introduce multiview mipmapping, which reduces texture aliasing even though compute shaders do not support hardware mipmapping. We also introduce angular supersampling and reconstruction to combat LFD view aliasing and crosstalk. The resulting LFDPR is 2-8x times faster than multiview rendering, with similar comparable quality.
Hardware-Aware Model Design and Training of Silicon-based Analog Neural Networks
Venue: arXiv cs.AR 2025
Year: 2025

Silicon-based analog neural networks physically embody the ideal neural network model in an approximate way. We show that by retraining the neural network using a physics-informed hardware-aware model one can fully recover the inference accuracy of the ideal network model even in the presence of significant non-idealities. This is way more promising for scalability and integration density than the default option of improving the fidelity of the analog neural network at the cost of significant energy, area, and design overhead, through extensive calibration and conservative analog design. We first present a physics-informed hardware-aware model for a time-domain vector-matrix multiplier implemented with single-transistor floating-gate memory cells that explicitly accounts for two dominant non-idealities of the physical implementation - capacitive crosstalk and bit-line voltage drop - and integrates seamlessly with modern deep-learning workflows. The model discretizes each operation into adaptive time slots, processes activation patterns in parallel, and accumulates their contributions to predict effective multiplier outputs. Using measurements from a 16x16 silicon array, we calibrate the model, show that crosstalk is layout-dependent and often dominant, and introduce an improved weight-extraction procedure that doubles signal-to-error ratio versus an ideal vector-matrix multiplier model. Finally, we show that by training silicon-based analog neural networks using an hardware-aware model in the forward pass we can recover the accuracy of the ideal software networks across three architectures -- custom MLP on low-resolution MNIST, LeNet-5 on MNIST, and a VGG-style CNN on CIFAR-10 - establishing a complete design-to-deployment workflow for time-domain analog neuromorphic chips.
GTAC: A Generative Transformer for Approximate Circuits
Venue: arXiv cs.AR 2025
Year: 2025

Targeting error-tolerant applications, approximate circuits introduce controlled errors to significantly improve performance, power, and area (PPA) of circuits. In this work, we introduce GTAC, a novel generative Transformer-based model for producing approximate circuits. By leveraging principles of approximate computing and AI-driven EDA, our model innovatively integrates error thresholds into the design process. Experimental results show that compared with a state-of-the-art method, GTAC further reduces 6.4% area under the error rate constraint, while being 4.3x faster.
CHIME: Chiplet-based Heterogeneous Near-Memory Acceleration for Edge Multimodal LLM Inference
Venue: arXiv cs.AR 2025
Year: 2025

The proliferation of large language models (LLMs) is accelerating the integration of multimodal assistants into edge devices, where inference is executed under stringent latency and energy constraints, often exacerbated by intermittent connectivity. These challenges become particularly acute in the context of multimodal LLMs (MLLMs), as high-dimensional visual inputs are transformed into extensive token sequences, thereby inflating the key-value (KV) cache and imposing substantial data movement overheads to the LLM backbone. To address these issues, we present CHIME, a chiplet-based heterogeneous near-memory acceleration for edge MLLMs inference. CHIME leverages the complementary strengths of integrated monolithic 3D (M3D) DRAM and RRAM chiplets: DRAM supplies low-latency bandwidth for attention, while RRAM offers dense, non-volatile storage for weights. This heterogeneous hardware is orchestrated by a co-designed mapping framework that executes fused kernels near data, minimizing cross-chiplet traffic to maximize effective bandwidth. On FastVLM (0.6B/1.7B) and MobileVLM (1.7B/3B), CHIME achieves up to 54x speedup and up to 246x better energy efficiency per inference as compared to the edge GPU NVIDIA Jetson Orin NX. It sustains 116.5-266.5 token/J compared to Jetson's 0.7-1.1 token/J. Furthermore, it delivers up to 69.2x higher throughput than the state-of-the-art PIM accelerator FACIL. Compared to the M3D DRAM-only design, CHIME's heterogeneous memory further improves energy efficiency by 7% and performance by 2.4x.
C$^2$-Cite: Contextual-Aware Citation Generation for Attributed Large Language Models
Venue: arXiv cs.IR 2025
Year: 2025

The attribution technique enhances the credibility of LLMs by adding citations to the generated sentences, enabling users to trace back to the original sources and verify the reliability of the output. However, existing instruction-tuned attributed LLMs often fail to properly interpret the contextual semantics of citation symbols (e.g., [i]) during text generation. This shortcoming arises from their insufficient awareness of the context information surrounding citation markers, which in turn leads to disjointed references and poor integration of retrieved knowledge into the generated content. To address this issue, we propose a novel \textbf{C}ontextual-aware \textbf{C}itation generation framework (\textbf{C$^2$}-\textbf{Cite}) that explicitly integrates the semantic relationships between citation markers and their referenced content. Specifically, a contextual citation alignment mechanism is adopted: it first encodes the retrieved document contexts into the symbol representation of citations, then aligns the marker numbers by decoding information from a citation router function. This mechanism enables the transformation of citation markers from generic placeholders into active knowledge pointers that link to the referenced source information. Experimental results on the ALCE benchmark across three datasets validate our framework C$^2$-Cite++: it outperforms the SOTA baseline by an average of 5.8\% in citation quality and 17.4\% in response correctness. The implementation is publicly available at https://github.com/BAI-LAB/c2cite
Gamification-Based Learning Method for Hijaiyah Letters
Venue: arXiv cs.HC 2025
Year: 2025

The mastery of Hijaiyah letters is a crucial foundation for reading and comprehending the Quran, yet conventional pedagogical approaches based on repetitive memorization frequently struggle to maintain the engagement of young learners in contemporary educational contexts. This research presents the design and implementation of an innovative gamification-based methodology for Hijaiyah literacy acquisition, systematically developed through the ADDIE framework (Analysis, Design, Development, Implementation, Evaluation) to optimize student motivation, participation, and educational outcomes. The resulting technological solution, engineered using Unity 2D and Firebase, strategically incorporates game design elements such as points, badges, leaderboards, and progressive leveling, while integrating multifaceted learning components including visual animations, authentic tajwid-based audio pronunciation, and interactive letter tracing exercises to simultaneously develop cognitive recognition capabilities and fine motor skills. Empirical evaluation involving 50 elementary school participants revealed substantial quantitative improvements, with mean assessment scores increasing from 42.8 to 88.6 (107% improvement, p < 0.001), demonstrating an exceptionally large effect size (Cohen's d = 4.87), complemented by strong user engagement metrics (4.2 average daily sessions) and high satisfaction ratings (4.82 out of 5 mean motivation score). Beyond cognitive learning outcomes, the gamified approach effectively fostered intrinsic Islamic values such as perseverance, responsibility, and disciplined practice, thereby establishing an innovative educational paradigm that successfully integrates traditional Islamic pedagogical principles with modern digital learning technologies to create a transformative, engaging, and meaningful framework for Hijaiyah literacy development in contemporary Islamic education.
Althea: Human-AI Collaboration for Fact-Checking and Critical Reasoning
Venue: arXiv cs.HC 2025
Year: 2025

The web's information ecosystem demands fact-checking systems that are both scalable and epistemically trustworthy. Automated approaches offer efficiency but often lack transparency, while human verification remains slow and inconsistent. We introduce Althea, a retrieval-augmented system that integrates question generation, evidence retrieval, and structured reasoning to support user-driven evaluation of online claims. On the AVeriTeC benchmark, Althea achieves a Macro-F1 of 0.44, outperforming standard verification pipelines and improving discrimination between supported and refuted claims. We further evaluate Althea through a controlled user study and a longitudinal survey experiment (N = 642), comparing three interaction modes that vary in the degree of scaffolding: an Exploratory mode with guided reasoning, a Summary mode providing synthesized verdicts, and a Self-search mode that offers procedural guidance without algorithmic intervention. Results show that guided interaction produces the strongest immediate gains in accuracy and confidence, while self-directed search yields the most persistent improvements over time. This pattern suggests that performance gains are not driven solely by effort or exposure, but by how cognitive work is structured and internalized.
Evaluating Large Language Models on Quantum Mechanics: A Comparative Study Across Diverse Models and Tasks
Venue: arXiv cs.AI 2025
Year: 2025

We present a systematic evaluation of large language models on quantum mechanics problem-solving. Our study evaluates 15 models from five providers (OpenAI, Anthropic, Google, Alibaba, DeepSeek) spanning three capability tiers on 20 tasks covering derivations, creative problems, non-standard concepts, and numerical computation, comprising 900 baseline and 75 tool-augmented assessments. Results reveal clear tier stratification: flagship models achieve 81\% average accuracy, outperforming mid-tier (77\%) and fast models (67\%) by 4pp and 14pp respectively. Task difficulty patterns emerge distinctly: derivations show highest performance (92\% average, 100\% for flagship models), while numerical computation remains most challenging (42\%). Tool augmentation on numerical tasks yields task-dependent effects: modest overall improvement (+4.4pp) at 3x token cost masks dramatic heterogeneity ranging from +29pp gains to -16pp degradation. Reproducibility analysis across three runs quantifies 6.3pp average variance, with flagship models demonstrating exceptional stability (GPT-5 achieves zero variance) while specialized models require multi-run evaluation. This work contributes: (i) a benchmark for quantum mechanics with automatic verification, (ii) systematic evaluation quantifying tier-based performance hierarchies, (iii) empirical analysis of tool augmentation trade-offs, and (iv) reproducibility characterization. All tasks, verifiers, and results are publicly released.
Adaptive Multi-Agent Reasoning for Text-to-Video Retrieval
Venue: arXiv cs.IR 2025
Year: 2025

The rise of short-form video platforms and the emergence of multimodal large language models (MLLMs) have amplified the need for scalable, effective, zero-shot text-to-video retrieval systems. While recent advances in large-scale pretraining have improved zero-shot cross-modal alignment, existing methods still struggle with query-dependent temporal reasoning, limiting their effectiveness on complex queries involving temporal, logical, or causal relationships. To address these limitations, we propose an adaptive multi-agent retrieval framework that dynamically orchestrates specialized agents over multiple reasoning iterations based on the demands of each query. The framework includes: (1) a retrieval agent for scalable retrieval over large video corpora, (2) a reasoning agent for zero-shot contextual temporal reasoning, and (3) a query reformulation agent for refining ambiguous queries and recovering performance for those that degrade over iterations. These agents are dynamically coordinated by an orchestration agent, which leverages intermediate feedback and reasoning outcomes to guide execution. We also introduce a novel communication mechanism that incorporates retrieval-performance memory and historical reasoning traces to improve coordination and decision-making. Experiments on three TRECVid benchmarks spanning eight years show that our framework achieves a twofold improvement over CLIP4Clip and significantly outperforms state-of-the-art methods by a large margin.
Thin Plate Spline Surface Reconstruction via the Method of Matched Sections
Venue: arXiv cs.GR 2025
Year: 2025

This paper further develops the Method of Matched Sections (MMS), a robust numerical framework for the solution of boundary value problems governed by partial differential equations, specifically for surface modeling. While originating in mechanics, the method addresses critical challenges in isogeometric analysis and computer graphics by bridging the gap between physical accuracy and geometric continuity. By decomposing the domain into an assembly of 1D directional components matched along their entire boundaries, the method inherently enforces the continuity of all variational parameters, including second-order (curvature) and third-order (shear) derivatives. We demonstrate the method's advanced capabilities in high-fidelity surface reconstruction and blending, showing that it consistently generates energetically optimal, fair surfaces even from complex boundary conditions or sparse internal points. By advancing the application of MMS, this research establishes it as a powerful, physics-informed geometric tool that satisfies the dual demands of rigorous numerical analysis and aesthetic computer-aided design.
DS SERVE: A Framework for Efficient and Scalable Neural Retrieval
Venue: arXiv cs.IR 2025
Year: 2025

We present DS-Serve, a framework that transforms large-scale text datasets, comprising half a trillion tokens, into a high-performance neural retrieval system. DS-Serve offers both a web interface and API endpoints, achieving low latency with modest memory overhead on a single node. The framework also supports inference-time trade-offs between latency, accuracy, and result diversity. We anticipate that DS-Serve will be broadly useful for a range of applications, including large-scale retrieval-augmented generation (RAG), training data attribution, training search agents, and beyond.
Modelación y Visualización Tridimensional Interactiva de Variables Eléctricas en Celdas de Electro-Obtención con Electrodos Bipolares
Venue: arXiv cs.GR 2026
Year: 2026

The use of floating bipolar electrodes in copper electro-winning cells represents an emerging technology that promises economic and operational impacts. This article presents a computational tool designed for the simulation and analysis of these electrochemical systems. Based on the generalization and optimization of an existing 2D finite difference model for calculating electrical variables in rectangular cells, a new 3D model capable of processing complex geometries, not necessarily rectangular, has been developed. At the same time, a new analytical method for estimating potentials in floating electrodes is introduced, overcoming the inaccuracies of previous heuristic approaches. The analysis of the results is supported by an interactive visualization technique of three-dimensional vector fields as flow lines.
Aplicación Gráfica para el estudio de un Modelo de Celda Electrolítica usando Técnicas de Visualización de Campos Vectoriales
Venue: arXiv cs.GR 2026
Year: 2026

The use of floating bipolar electrodes in copper electro-winning cells represents an emerging technology that promises economic and operational impacts. This thesis presents EWCellCAD, a computational tool designed for the simulation and analysis of these electrochemical systems. Based on the generalization and optimization of an existing 2D finite difference model for calculating electrical variables in rectangular cells, EWCellCAD implements a new 3D model capable of processing complex geometries, not necessarily rectangular, which also accelerates calculations by several orders of magnitude. At the same time, a new analytical method for estimating potentials in floating electrodes is introduced, overcoming the inaccuracies of previous heuristic approaches. The analysis of the results is supported by an interactive visualization technique of three-dimensional vector fields as flow lines.
A New Generation of Brain-Computer Interface Based on Riemannian Geometry
Venue: arXiv cs.HC 2026
Year: 2026

Based on the cumulated experience over the past 25 years in the field of Brain-Computer Interface (BCI) we can now envision a new generation of BCI. Such BCIs will not require training; instead they will be smartly initialized using remote massive databases and will adapt to the user fast and effectively in the first minute of use. They will be reliable, robust and will maintain good performances within and across sessions. A general classification framework based on recent advances in Riemannian geometry and possessing these characteristics is presented. It applies equally well to BCI based on event-related potentials (ERP), sensorimotor (mu) rhythms and steady-state evoked potential (SSEP). The framework is very simple, both algorithmically and computationally. Due to its simplicity, its ability to learn rapidly (with little training data) and its good across-subject and across-session generalization, this strategy a very good candidate for building a new generation of BCIs, thus we hereby propose it as a benchmark method for the field.
Certificates in P and Subquadratic-Time Computation of Radius, Diameter, and all Eccentricities in Graphs
Venue: arXiv cs.DM 2026
Year: 2026

In the context of fine-grained complexity, we investigate the notion of certificate enabling faster polynomial-time algorithms. We specifically target radius (minimum eccentricity), diameter (maximum eccentricity), and all-eccentricity computations for which quadratic-time lower bounds are known under plausible conjectures. In each case, we introduce a notion of certificate as a specific set of nodes from which appropriate bounds on all eccentricities can be derived in subquadratic time when this set has sublinear size. The existence of small certificates for radius, diameter and all eccentricities is a barrier against SETH-based lower bounds for these problems. We indeed prove that for graph classes with certificates of bounded size, there exist randomized subquadratic-time algorithms for computing the radius, the diameter, and all eccentricities respectively. Moreover, these notions of certificates are tightly related to algorithms probing the graph through one-to-all distance queries and allow to explain the efficiency of practical radius and diameter algorithms from the literature. In particular, our formalization enables a novel primal-dual analysis of a classical approach for diameter computation. Based on our novel insights for these problems, we introduce several new algorithmic techniques related to eccentricity computation and propose algorithms for radius, diameter and all eccentricities with theoretical guarantees with respect to certain graph parameters. This is complemented by experimental results on various types of real-world graphs showing that these parameters appear to be low in practice. Finally, we obtain refined results in the case where the input graph is a power-law random graph, has low doubling dimension, has low hyperbolicity, is chordal, satisfies some Helly-type property, or has bounded asteroidal number.