OpenRaiser/Intern-Atlas huggingface dataset:
The Information Theory of Similarity
Venue: arXiv cs.IT 2025
Year: 2025

We establish a precise mathematical equivalence between witness-based similarity systems (REWA) and Shannon's information theory. We prove that witness overlap is mutual information, that REWA bit complexity bounds arise from channel capacity limitations, and that ranking-preserving encodings obey rate-distortion constraints. This unification reveals that fifty years of similarity search research -- from Bloom filters to locality-sensitive hashing to neural retrieval -- implicitly developed information theory for relational data. We derive fundamental lower bounds showing that REWA's $O(Δ^{-2} \log N)$ complexity is optimal: no encoding scheme can preserve similarity rankings with fewer bits. The framework establishes that semantic similarity has physical units (bits of mutual information), search is communication (query transmission over a noisy channel), and retrieval systems face fundamental capacity limits analogous to Shannon's channel coding theorem.
Framework-Aware Code Generation with API Knowledge Graph-Constructed Data: A Study on HarmonyOS
Venue: arXiv cs.SE 2025
Year: 2025

In the context of software frameworks with limited resources (such as HarmonyOS), large language models (LLMs) often exhibit poor code generation performance because they lack sufficient exposure to such environments during pre-training. Although LLMs can usually maintain correct logical structures across programming languages, they frequently struggle when dealing with framework-specific APIs or syntax, resulting in errors. This indicates that while pre-training equips LLMs with general algorithmic capabilities, they remain unfamiliar with the distinctive syntax and API usage of underrepresented frameworks. As a result, even advanced commercial models like GPT-4o cannot reliably generate correct code without prior adaptation. To address this issue, we propose APIKG4SYN, a framework designed to exploit API knowledge graphs for the construction of API-oriented question-code pairs, specifically tailored for low-resource frameworks without requiring executable code. APIKG4SYN integrates both single-API and multi-API knowledge, where the latter is derived through uncertainty estimation (UE)-driven Monte Carlo Tree Search (MCTS), enabling the creation of a diverse and informative dataset for fine-tuning LLMs. Using HarmonyOS as a case study, we build the first benchmark for HarmonyOS code generation. Experimental results show that fine-tuning Qwen with APIKG4SYN raises pass@1 accuracy to 25.00%, compared with 17.59% for the baseline GPT model. These results confirm that API-oriented data significantly enhance LLM performance in low-resource software development scenarios.
Mitigating the Threshold Priming Effect in Large Language Model-Based Relevance Judgments via Personality Infusing
Venue: arXiv cs.CL 2025
Year: 2025

Recent research has explored LLMs as scalable tools for relevance labeling, but studies indicate they are susceptible to priming effects, where prior relevance judgments influence later ones. Although psychological theories link personality traits to such biases, it is unclear whether simulated personalities in LLMs exhibit similar effects. We investigate how Big Five personality profiles in LLMs influence priming in relevance labeling, using multiple LLMs on TREC 2021 and 2022 Deep Learning Track datasets. Our results show that certain profiles, such as High Openness and Low Neuroticism, consistently reduce priming susceptibility. Additionally, the most effective personality in mitigating priming may vary across models and task types. Based on these findings, we propose personality prompting as a method to mitigate threshold priming, connecting psychological evidence with LLM-based evaluation practices.
A Taxonomy of Errors in English as she is spoke: Toward an AI-Based Method of Error Analysis for EFL Writing Instruction
Venue: arXiv cs.CL 2025
Year: 2025

This study describes the development of an AI-assisted error analysis system designed to identify, categorize, and correct writing errors in English. Utilizing Large Language Models (LLMs) like Claude 3.5 Sonnet and DeepSeek R1, the system employs a detailed taxonomy grounded in linguistic theories from Corder (1967), Richards (1971), and James (1998). Errors are classified at both word and sentence levels, covering spelling, grammar, and punctuation. Implemented through Python-coded API calls, the system provides granular feedback beyond traditional rubric-based assessments. Initial testing on isolated errors refined the taxonomy, addressing challenges like overlapping categories. Final testing used "English as she is spoke" by Jose da Fonseca (1855), a text rich with authentic linguistic errors, to evaluate the system's capacity for handling complex, multi-layered analysis. The AI successfully identified diverse error types but showed limitations in contextual understanding and occasionally generated new error categories when encountering uncoded errors. This research demonstrates AI's potential to transform EFL instruction by automating detailed error analysis and feedback. While promising, further development is needed to improve contextual accuracy and expand the taxonomy to stylistic and discourse-level errors.
GreenPlanner: Practical Floorplan Layout Generation via an Energy-Aware and Function-Feasible Generative Framework
Venue: arXiv cs.AI 2025
Year: 2025

Building design directly affects human well-being and carbon emissions, yet generating spatial-functional and energy-compliant floorplans remains manual, costly, and non-scalable. Existing methods produce visually plausible layouts but frequently violate key constraints, yielding invalid results due to the absence of automated evaluation. We present GreenPlanner, an energy- and functionality-aware generative framework that unifies design evaluation and generation. It consists of a labeled Design Feasibility Dataset for learning constraint priors; a fast Practical Design Evaluator (PDE) for predicting energy performance and spatial-functional validity; a Green Plan Dataset (GreenPD) derived from PDE-guided filtering to pair user requirements with regulation-compliant layouts; and a GreenFlow generator trained on GreenPD with PDE feedback for controllable, regulation-aware generation. Experiments show that GreenPlanner accelerates evaluation by over $10^{5}\times$ with $>$99% accuracy, eliminates invalid samples, and boosts design efficiency by 87% over professional architects.
CryptoBench: A Dynamic Benchmark for Expert-Level Evaluation of LLM Agents in Cryptocurrency
Venue: arXiv cs.CL 2025
Year: 2025

This paper introduces CryptoBench, the first expert-curated, dynamic benchmark designed to rigorously evaluate the real-world capabilities of Large Language Model (LLM) agents in the uniquely demanding and fast-paced cryptocurrency domain. Unlike general-purpose agent benchmarks for search and prediction, professional crypto analysis presents specific challenges: \emph{extreme time-sensitivity}, \emph{a highly adversarial information environment}, and the critical need to synthesize data from \emph{diverse, specialized sources}, such as on-chain intelligence platforms and real-time Decentralized Finance (DeFi) dashboards. CryptoBench thus serves as a much more challenging and valuable scenario for LLM agent assessment. To address these challenges, we constructed a live, dynamic benchmark featuring 50 questions per month, expertly designed by crypto-native professionals to mirror actual analyst workflows. These tasks are rigorously categorized within a four-quadrant system: Simple Retrieval, Complex Retrieval, Simple Prediction, and Complex Prediction. This granular categorization enables a precise assessment of an LLM agent's foundational data-gathering capabilities alongside its advanced analytical and forecasting skills. Our evaluation of ten LLMs, both directly and within an agentic framework, reveals a performance hierarchy and uncovers a failure mode. We observe a \textit{retrieval-prediction imbalance}, where many leading models, despite being proficient at data retrieval, demonstrate a pronounced weakness in tasks requiring predictive analysis. This highlights a problematic tendency for agents to appear factually grounded while lacking the deeper analytical capabilities to synthesize information.
What about gravity in video generation? Post-Training Newton's Laws with Verifiable Rewards
Venue: arXiv cs.CV 2025
Year: 2025

Recent video diffusion models can synthesize visually compelling clips, yet often violate basic physical laws-objects float, accelerations drift, and collisions behave inconsistently-revealing a persistent gap between visual realism and physical realism. We propose $\texttt{NewtonRewards}$, the first physics-grounded post-training framework for video generation based on $\textit{verifiable rewards}$. Instead of relying on human or VLM feedback, $\texttt{NewtonRewards}$ extracts $\textit{measurable proxies}$ from generated videos using frozen utility models: optical flow serves as a proxy for velocity, while high-level appearance features serve as a proxy for mass. These proxies enable explicit enforcement of Newtonian structure through two complementary rewards: a Newtonian kinematic constraint enforcing constant-acceleration dynamics, and a mass conservation reward preventing trivial, degenerate solutions. We evaluate $\texttt{NewtonRewards}$ on five Newtonian Motion Primitives (free fall, horizontal/parabolic throw, and ramp sliding down/up) using our newly constructed large-scale benchmark, $\texttt{NewtonBench-60K}$. Across all primitives in visual and physics metrics, $\texttt{NewtonRewards}$ consistently improves physical plausibility, motion smoothness, and temporal coherence over prior post-training methods. It further maintains strong performance under out-of-distribution shifts in height, speed, and friction. Our results show that physics-grounded verifiable rewards offer a scalable path toward physics-aware video generation.
Whose Personae? Synthetic Persona Experiments in LLM Research and Pathways to Transparency
Venue: arXiv cs.CY 2025
Year: 2025

Synthetic personae experiments have become a prominent method in Large Language Model alignment research, yet the representativeness and ecological validity of these personae vary considerably between studies. Through a review of 63 peer-reviewed studies published between 2023 and 2025 in leading NLP and AI venues, we reveal a critical gap: task and population of interest are often underspecified in persona-based experiments, despite personalization being fundamentally dependent on these criteria. Our analysis shows substantial differences in user representation, with most studies focusing on limited sociodemographic attributes and only 35% discussing the representativeness of their LLM personae. Based on our findings, we introduce a persona transparency checklist that emphasizes representative sampling, explicit grounding in empirical data, and enhanced ecological validity. Our work provides both a comprehensive assessment of current practices and practical guidelines to improve the rigor and ecological validity of persona-based evaluations in language model alignment research.
Smart-TCP: An Agentic AI-based Autonomous and Adaptive TCP Protocol
Venue: arXiv cs.NI 2025
Year: 2025

The Transmission Control Protocol (TCP) relies on a state machine and deterministic arithmetic to ensure reliable connections. However, traditional protocol logic driven by hard-coded state machines struggles to meet the demands of intelligent and autonomous network architectures. Here, we adopt the agentic AI-based paradigm, driven by Large Language Models (LLMs), characterized by context perception, autonomous reasoning, and tool use. Based on this, we propose Smart-TCP, which re-imagines TCP's core control logic as an autonomous agent. Specifically, the proposed architecture employs a context aggregation mechanism to synthesize the protocol context, utilizes the LLM for autonomous logical reasoning, and invokes an Arithmetic Logic Unit (ALU) as a tool for computation. Furthermore, we establish a dual-agent interaction framework based on this architecture and implement TCP protocol interactions. Experiments demonstrate that the Smart-TCP agent excels in static prediction and error detection, achieving a 93.33% success rate in end-to-end sessions. These results strongly validate the technical feasibility of an agentic AI-based TCP protocol.
G-KV: Decoding-Time KV Cache Eviction with Global Attention
Venue: arXiv cs.CL 2025
Year: 2025

Recent reasoning large language models (LLMs) excel in complex tasks but encounter significant computational and memory challenges due to long sequence lengths. KV cache compression has emerged as an effective approach to greatly enhance the efficiency of reasoning. However, existing methods often focus on prompt compression or token eviction with local attention score, overlooking the long-term importance of tokens. We propose G-KV, a KV cache eviction method that employs a global scoring mechanism, combining local and historical attention scores to more accurately assess token importance. Additionally, we introduce post-training techniques, including reinforcement learning and distillation, to optimize models for compressed KV cache settings. The code of this paper is available on: https://github.com/microsoft/G-KV.
Terrain Sensing with Smartphone Structured Light: 2D Dynamic Time Warping for Grid Pattern Matching
Venue: arXiv cs.CV 2025
Year: 2025

Low-cost mobile rovers often operate on uneven terrain where small bumps or tilts are difficult to perceive visually but can significantly affect locomotion stability. To address this problem, we explore a smartphone-based structured-light system that projects a grid pattern onto the ground and reconstructs local terrain unevenness from a single handheld device. The system is inspired by face-recognition projectors, but adapted for ground sensing. A key technical challenge is robustly matching the projected grid with its deformed observation under perspective distortion and partial occlusion. Conventional one-dimensional dynamic time warping (1D-DTW) is not directly applicable to such two-dimensional grid patterns. We therefore propose a topology-constrained two-dimensional dynamic time warping (2D-DTW) algorithm that performs column-wise alignment under a global grid consistency constraint. The proposed method is designed to be simple enough to run on resource limited platforms while preserving the grid structure required for accurate triangulation. We demonstrate that our 2D-DTW formulation can be used not only for terrain sensing but also as a general tool for matching structured grid patterns in image processing scenarios. This paper describes the overall system design as well as the 2D-DTW extension that emerged from this application.
Speculating on the Role of Media Architecture in Post-disaster Rebuilding and Recovery: Insights from Architects and Interaction Designers
Venue: arXiv cs.HC 2025
Year: 2025

In post-disaster contexts, design is not only about rebuilding structures but also about reimagining how architecture can become a communicative medium that supports recovery, resilience, and collective memory. While recent studies have expanded the understanding of media architecture from aesthetic urban screens to participatory civic infrastructures, there remains limited empirical research on its potential role in post-disaster contexts. In particular, opportunities exist to explore how architecture and interaction design might speculate on media architecture's role in rebuilding and recovery efforts for post-disaster permanent housing, especially when conceptualizing disasters as active agents that reshape design processes. Following to Kahramanmaras earthquake on February 6, 2023, we conducted two focus groups with architects and interaction designers in the case of Antakya, Turkey, building on affected residents' expectations for post-earthquake permanent housing. Our analysis revealed three critical dimensions of how future media architecture may support post-disaster housing: (1) as a facilitator of individuals' social connections to their community, (2) as an enabler of multispecies participation and collective efforts, and (3) as a mediator of heritage preservation and revival. With novel perspectives, we contribute a three-dimension lens for media architecture in permanent homes; a co-speculative, card-based process bridging residents' insights and expert design; and ten situated speculative design ideas with implications for design of post-disaster permanent homes.
Asset-Driven Sematic Reconstruction of Dynamic Scene with Multi-Human-Object Interactions
Venue: arXiv cs.CV 2025
Year: 2025

Real-world human-built environments are highly dynamic, involving multiple humans and their complex interactions with surrounding objects. While 3D geometry modeling of such scenes is crucial for applications like AR/VR, gaming, and embodied AI, it remains underexplored due to challenges like diverse motion patterns and frequent occlusions. Beyond novel view rendering, 3D Gaussian Splatting (GS) has demonstrated remarkable progress in producing detailed, high-quality surface geometry with fast optimization of the underlying structure. However, very few GS-based methods address multihuman, multiobject scenarios, primarily due to the above-mentioned inherent challenges. In a monocular setup, these challenges are further amplified, as maintaining structural consistency under severe occlusion becomes difficult when the scene is optimized solely based on GS-based rendering loss. To tackle the challenges of such a multihuman, multiobject dynamic scene, we propose a hybrid approach that effectively combines the advantages of 1) 3D generative models for generating high-fidelity meshes of the scene elements, 2) Semantic-aware deformation, \ie rigid transformation of the rigid objects and LBS-based deformation of the humans, and mapping of the deformed high-fidelity meshes in the dynamic scene, and 3) GS-based optimization of the individual elements for further refining their alignments in the scene. Such a hybrid approach helps maintain the object structures even under severe occlusion and can produce multiview and temporally consistent geometry. We choose HOI-M3 for evaluation, as, to the best of our knowledge, this is the only dataset featuring multihuman, multiobject interactions in a dynamic scene. Our method outperforms the state-of-the-art method in producing better surface reconstruction of such scenes.
Catch Me If You Can: How Smaller Reasoning Models Pretend to Reason with Mathematical Fidelity
Venue: arXiv cs.CL 2025
Year: 2025

Current evaluation of mathematical reasoning in language models relies primarily on answer accuracy, potentially masking fundamental failures in logical computation. We introduce a diagnostic framework that distinguishes genuine mathematical reasoning from superficial pattern matching through four complementary axes: forward-backward consistency, transitivity coverage, counterfactual sensitivity, and perturbation robustness. Through a case study applying this framework to Qwen3-0.6B on the MenatQA dataset, we reveal a striking disconnect between surface performance and reasoning fidelity. While the model achieves reasonable answer accuracy (70%+), it demonstrates poor backward consistency (15%), limited transitivity coverage (32.2%), and brittle sensitivity to perturbations. Our diagnostics expose reasoning failures invisible to traditional accuracy metrics, suggesting that this small model relies heavily on pattern matching rather than genuine logical computation. While our empirical findings are based on a single 600M-parameter model, the diagnostic framework itself is model-agnostic and generalizable. We release our evaluation protocols to enable the research community to assess reasoning fidelity across different model scales and architectures, moving beyond surface-level accuracy toward verifiable mathematical reasoning.
Enhancing Analogy-Based Software Effort Estimation with Firefly Algorithm Optimization
Venue: arXiv cs.SE 2025
Year: 2025

Analogy-Based Estimation (ABE) is a popular method for non-algorithmic estimation due to its simplicity and effectiveness. The Analogy-Based Estimation (ABE) model was proposed by researchers, however, no optimal approach for reliable estimation was developed. Achieving high accuracy in the ABE might be challenging for new software projects that differ from previous initiatives. This study (conducted in June 2024) proposes a Firefly Algorithm-guided Analogy-Based Estimation (FAABE) model that combines FA with ABE to improve estimation accuracy. The FAABE model was tested on five publicly accessible datasets: Cocomo81, Desharnais, China, Albrecht, Kemerer and Maxwell. To improve prediction efficiency, feature selection was used. The results were measured using a variety of evaluation metrics; various error measures include MMRE, MAE, MSE, and RMSE. Compared to conventional models, the experimental results show notable increases in prediction precision, demonstrating the efficacy of the Firefly-Analogy ensemble.
Scaling Down to Scale Up: Towards Operationally-Efficient and Deployable Clinical Models via Cross-Modal Low-Rank Adaptation for Medical Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Foundation models trained via vision-language pretraining have demonstrated strong zero-shot capabilities across diverse image domains, yet their application to volumetric medical imaging remains limited. We introduce MedCT-VLM: Medical CT Vision-Language Model, a parameter-efficient vision-language framework designed to adapt large-scale CT foundation models for downstream clinical tasks. MedCT-VLM uses a parameter-efficient approach to adapt CT-CLIP, a contrastive vision-language model trained on 25,692 chest CT volumes, for multi-label pathology classification using Low-Rank Adaptation (LoRA). Rather than fine-tuning the model's 440 M parameters directly, we insert low-rank decomposition matrices into attention layers of both vision and text encoders, training only 1.67M parameters (0.38\% of total). We evaluate on zero-shot classification across 18 thoracic pathologies, where the model must align CT embeddings with unseen text prompts at inference without task-specific training. LoRA fine-tuning improves mean AUROC from 61.3\% to 68.9\% (+7.6 pp), accuracy from 67.2\% to 73.6\% (+6.4 pp), and macro-F1 from 32.1\% to 36.9\% (+4.8 pp). These results demonstrate that parameter-efficient methods can effectively transfer large-scale pretraining to downstream medical imaging tasks, particularly for zero-shot scenarios where labeled data is scarce.