OpenRaiser/Intern-Atlas huggingface dataset:
Doc2AHP: Inferring Structured Multi-Criteria Decision Models via Semantic Trees with LLMs
Venue: arXiv cs.AI 2026
Year: 2026

While Large Language Models (LLMs) demonstrate remarkable proficiency in semantic understanding, they often struggle to ensure structural consistency and reasoning reliability in complex decision-making tasks that demand rigorous logic. Although classical decision theories, such as the Analytic Hierarchy Process (AHP), offer systematic rational frameworks, their construction relies heavily on labor-intensive domain expertise, creating an "expert bottleneck" that hinders scalability in general scenarios. To bridge the gap between the generalization capabilities of LLMs and the rigor of decision theory, we propose Doc2AHP, a novel structured inference framework guided by AHP principles. Eliminating the need for extensive annotated data or manual intervention, our approach leverages the structural principles of AHP as constraints to direct the LLM in a constrained search within the unstructured document space, thereby enforcing the logical entailment between parent and child nodes. Furthermore, we introduce a multi-agent weighting mechanism coupled with an adaptive consistency optimization strategy to ensure the numerical consistency of weight allocation. Empirical results demonstrate that Doc2AHP not only empowers non-expert users to construct high-quality decision models from scratch but also significantly outperforms direct generative baselines in both logical completeness and downstream task accuracy.
Online Computation of Palindromes and Suffix Trees on Tries
Venue: arXiv cs.DS 2026
Year: 2026

We consider the problems of computing maximal palindromes and distinct palindromes in a trie. A trie is a natural generalization of a string, which can be seen as a single-path tree. There is a linear-time offline algorithm to compute maximal palindromes and distinct palindromes in a given (static) trie whose edge-labels are drawn from a linearly-sortable alphabet [Mieno et al., ISAAC 2022]. In this paper, we tackle problems of palindrome enumeration on dynamic tries which support leaf additions and leaf deletions. We propose the first sub-quadratic algorithms to enumerate palindromes in a dynamic trie. For maximal palindromes, we propose an algorithm that runs in $O(N \min(\log h, σ))$ time and uses $O(N)$ space, where $N$ is the maximum number of edges in the trie, $σ$ is the size of the alphabet, and $h$ is the height of the trie. For distinct palindromes, we develop several online algorithms based on different algorithmic frameworks, including approaches using the EERTREE (a.k.a. palindromic tree) and the suffix tree of a trie. These algorithms support leaf insertions and deletions in the trie and achieve different time and space trade-offs. Furthermore, as a by-product, we present online algorithms to construct the suffix tree and the EERTREE of the input trie, which is of independent interest.
Expert Knowledge-Guided Decision Calibration for Accurate Fine-Grained Tree Species Classification
Venue: arXiv cs.CV 2026
Year: 2026

Accurate fine-grained tree species classification is critical for forest inventory and biodiversity monitoring. Existing methods predominantly focus on designing complex architectures to fit local data distributions. However, they often overlook the long-tailed distributions and high inter-class similarity inherent in limited data, thereby struggling to distinguish between few-shot or confusing categories. In the process of knowledge dissemination in the human world, individuals will actively seek expert assistance to transcend the limitations of local thinking. Inspired by this, we introduce an external "Domain Expert" and propose an Expert Knowledge-Guided Classification Decision Calibration Network (EKDC-Net) to overcome these challenges. Our framework addresses two core issues: expert knowledge extraction and utilization. Specifically, we first develop a Local Prior Guided Knowledge Extraction Module (LPKEM). By leveraging Class Activation Map (CAM) analysis, LPKEM guides the domain expert to focus exclusively on discriminative features essential for classification. Subsequently, to effectively integrate this knowledge, we design an Uncertainty-Guided Decision Calibration Module (UDCM). This module dynamically corrects the local model's decisions by considering both overall category uncertainty and instance-level prediction uncertainty. Furthermore, we present a large-scale classification dataset covering 102 tree species, named CU-Tree102 to address the issue of scarce diversity in current benchmarks. Experiments on three benchmark datasets demonstrate that our approach achieves state-of-the-art performance. Crucially, as a lightweight plug-and-play module, EKDC-Net improves backbone accuracy by 6.42% and precision by 11.46% using only 0.08M additional learnable parameters. The dataset, code, and pre-trained models are available at https://github.com/WHU-USI3DV/TreeCLS.
Participatory Budgeting Project Strength via Candidate Control
Venue: arXiv cs.GT 2026
Year: 2026

We study the complexity of candidate control in participatory budgeting elections. The goal of constructive candidate control is to ensure that a given candidate wins by either adding or deleting candidates from the election (in the destructive setting, the goal is to prevent a given candidate from winning). We show that such control problems are NP-hard to solve for many participatory budgeting voting rules, including Phragmén and Method of Equal Shares, but there are natural cases with polynomial-time algorithms (e.g., for the GreedyAV rule and projects with costs encoded in unary). We also argue that control by deleting candidates is a useful tool for assessing the performance (or, strength) of initially losing projects, and we support this view with experiments.
SALAD: Achieve High-Sparsity Attention via Efficient Linear Attention Tuning for Video Diffusion Transformer
Venue: arXiv cs.CV 2026
Year: 2026

Diffusion Transformers have recently demonstrated remarkable performance in video generation. However, the long input sequences result in high computational latency due to the quadratic complexity of full attention. Various sparse attention mechanisms have been proposed. Training-free sparse attention is constrained by limited sparsity and thus offers modest acceleration, whereas training-based methods can reach much higher sparsity but demand substantial data and computation for training. In this work, we propose SALAD, introducing a lightweight linear attention branch in parallel with the sparse attention. By incorporating an input-dependent gating mechanism to finely balance the two branches, our method attains 90% sparsity and 1.72x inference speedup, while maintaining generation quality comparable to the full attention baseline. Moreover, our finetuning process is highly efficient, requiring only 2,000 video samples and 1,600 training steps with a batch size of 8.
Predicting Networks Before They Happen: Experimentation on a Real-Time V2X Digital Twin
Venue: arXiv cs.NI 2026
Year: 2026

Emerging safety-critical Vehicle-to-Everything (V2X) applications require networks to proactively adapt to rapid environmental changes rather than merely reacting to them. While Network Digital Twins (NDTs) offer a pathway to such predictive capabilities, existing solutions typically struggle to reconcile high-fidelity physical modeling with strict real-time constraints. This paper presents a novel, end-to-end real-time V2X Digital Twin framework that integrates live mobility tracking with deterministic channel simulation. By coupling the Tokyo Mobility Digital Twin-which provides live sensing and trajectory forecasting-with VaN3Twin-a full-stack simulator with ray tracing-we enable the prediction of network performance before physical events occur. We validate this approach through an experimental proof-of-concept deployed in Tokyo, Japan, featuring connected vehicles operating on 60 GHz links. Our results demonstrate the system's ability to predict Received Signal Strength (RSSI) with a maximum average error of 1.01 dB and reliably forecast Line-of-Sight (LoS) transitions within a maximum average end-to-end system latency of 250 ms, depending on the ray tracing level of detail. Furthermore, we quantify the fundamental trade-offs between digital model fidelity, computational latency, and trajectory prediction horizons, proving that high-fidelity and predictive digital twins are feasible in real-world urban environments.
HA2F: Dual-module Collaboration-Guided Hierarchical Adaptive Aggregation Framework for Remote Sensing Change Detection
Venue: arXiv cs.CV 2026
Year: 2026

Remote sensing change detection (RSCD) aims to identify the spatio-temporal changes of land cover, providing critical support for multi-disciplinary applications (e.g., environmental monitoring, disaster assessment, and climate change studies). Existing methods focus either on extracting features from localized patches, or pursue processing entire images holistically, which leads to the cross temporal feature matching deviation and exhibiting sensitivity to radiometric and geometric noise. Following the above issues, we propose a dual-module collaboration guided hierarchical adaptive aggregation framework, namely HA2F, which consists of dynamic hierarchical feature calibration module (DHFCM) and noise-adaptive feature refinement module (NAFRM). The former dynamically fuses adjacent-level features through perceptual feature selection, suppressing irrelevant discrepancies to address multi-temporal feature alignment deviations. The NAFRM utilizes the dual feature selection mechanism to highlight the change sensitive regions and generate spatial masks, suppressing the interference of irrelevant regions or shadows. Extensive experiments verify the effectiveness of the proposed HA2F, which achieves state-of-the-art performance on LEVIR-CD, WHU-CD, and SYSU-CD datasets, surpassing existing comparative methods in terms of both precision metrics and computational efficiency. In addition, ablation experiments show that DHFCM and NAFRM are effective. \href{https://huggingface.co/InPeerReview/RemoteSensingChangeDetection-RSCD.HA2F}{HA2F Official Code is Available Here!}
X-Aligner: Composed Visual Retrieval without the Bells and Whistles
Venue: arXiv cs.CV 2026
Year: 2026

Composed Video Retrieval (CoVR) facilitates video retrieval by combining visual and textual queries. However, existing CoVR frameworks typically fuse multimodal inputs in a single stage, achieving only marginal gains over initial baseline. To address this, we propose a novel CoVR framework that leverages the representational power of Vision Language Models (VLMs). Our framework incorporates a novel cross-attention module X-Aligner, composed of cross-attention layers that progressively fuse visual and textual inputs and align their multimodal representation with that of the target video. To further enhance the representation of the multimodal query, we incorporate the caption of the visual query as an additional input. The framework is trained in two stages to preserve the pretrained VLM representation. In the first stage, only the newly introduced module is trained, while in the second stage, the textual query encoder is also fine-tuned. We implement our framework on top of BLIP-family architecture, namely BLIP and BLIP-2, and train it on the Webvid-CoVR data set. In addition to in-domain evaluation on Webvid-CoVR-Test, we perform zero-shot evaluations on the Composed Image Retrieval (CIR) data sets CIRCO and Fashion-IQ. Our framework achieves state-of-the-art performance on CoVR obtaining a Recall@1 of 63.93% on Webvid-CoVR-Test, and demonstrates strong zero-shot generalization on CIR tasks.
SCHIGAND: A Synthetic Facial Generation Mode Pipeline
Venue: arXiv cs.CV 2026
Year: 2026

The growing demand for diverse and high-quality facial datasets for training and testing biometric systems is challenged by privacy regulations, data scarcity, and ethical concerns. Synthetic facial images offer a potential solution, yet existing generative models often struggle to balance realism, diversity, and identity preservation. This paper presents SCHIGAND, a novel synthetic face generation pipeline integrating StyleCLIP, HyperStyle, InterfaceGAN, and Diffusion models to produce highly realistic and controllable facial datasets. SCHIGAND enhances identity preservation while generating realistic intra-class variations and maintaining inter-class distinctiveness, making it suitable for biometric testing. The generated datasets were evaluated using ArcFace, a leading facial verification model, to assess their effectiveness in comparison to real-world facial datasets. Experimental results demonstrate that SCHIGAND achieves a balance between image quality and diversity, addressing key limitations of prior generative models. This research highlights the potential of SCHIGAND to supplement and, in some cases, replace real data for facial biometric applications, paving the way for privacy-compliant and scalable solutions in synthetic dataset generation.
Sycophancy Hides Linearly in the Attention Heads
Venue: arXiv cs.CL 2026
Year: 2026

We find that correct-to-incorrect sycophancy signals are most linearly separable within multi-head attention activations. Motivated by the linear representation hypothesis, we train linear probes across the residual stream, multilayer perceptron (MLP), and attention layers to analyze where these signals emerge. Although separability appears in the residual stream and MLPs, steering using these probes is most effective in a sparse subset of middle-layer attention heads. Using TruthfulQA as the base dataset, we find that probes trained on it transfer effectively to other factual QA benchmarks. Furthermore, comparing our discovered direction to previously identified "truthful" directions reveals limited overlap, suggesting that factual accuracy, and deference resistance, arise from related but distinct mechanisms. Attention-pattern analysis further indicates that the influential heads attend disproportionately to expressions of user doubt, contributing to sycophantic shifts. Overall, these findings suggest that sycophancy can be mitigated through simple, targeted linear interventions that exploit the internal geometry of attention activations.
Revisiting the Role of Natural Language Code Comments in Code Translation
Venue: arXiv cs.SE 2026
Year: 2026

The advent of large language models (LLMs) has ushered in a new era in automated code translation across programming languages. Since most code-specific LLMs are pretrained on well-commented code from large repositories like GitHub, it is reasonable to hypothesize that natural language code comments could aid in improving translation quality. Despite their potential relevance, comments are largely absent from existing code translation benchmarks, rendering their impact on translation quality inadequately characterised. In this paper, we present a large-scale empirical study evaluating the impact of comments on translation performance. Our analysis involves more than $80,000$ translations, with and without comments, of $1100+$ code samples from two distinct benchmarks covering pairwise translations between five different programming languages: C, C++, Go, Java, and Python. Our results provide strong evidence that code comments, particularly those that describe the overall purpose of the code rather than line-by-line functionality, significantly enhance translation accuracy. Based on these findings, we propose COMMENTRA, a code translation approach, and demonstrate that it can potentially double the performance of LLM-based code translation. To the best of our knowledge, our study is the first in terms of its comprehensiveness, scale, and language coverage on how to improve code translation accuracy using code comments.
The Green Side of the Lua
Venue: arXiv cs.SE 2026
Year: 2026

The United Nations' 2030 Agenda for Sustainable Development highlights the importance of energy-efficient software to reduce the global carbon footprint. Programming languages and execution models strongly influence software energy consumption, with interpreted languages generally being less efficient than compiled ones. Lua illustrates this trade-off: despite its popularity, it is less energy-efficient than greener and faster languages such as C. This paper presents an empirical study of Lua's runtime performance and energy efficiency across 25 official interpreter versions and just-in-time (JIT) compilers. Using a comprehensive benchmark suite, we measure execution time and energy consumption to analyze Lua's evolution, the impact of JIT compilation, and comparisons with other languages. Results show that all LuaJIT compilers significantly outperform standard Lua interpreters. The most efficient LuaJIT consumes about seven times less energy and runs seven times faster than the best Lua interpreter. Moreover, LuaJIT approaches C's efficiency, using roughly six times more energy and running about eight times slower, demonstrating the substantial benefits of JIT compilation for improving both performance and energy efficiency in interpreted languages.
I Guess That's Why They Call it the Blues: Causal Analysis for Audio Classifiers
Venue: arXiv cs.SD 2026
Year: 2026

It is well-known that audio classifiers often rely on non-musically relevant features and spurious correlations to classify audio. Hence audio classifiers are easy to manipulate or confuse, resulting in wrong classifications. While inducing a misclassification is not hard, until now the set of features that the classifiers rely on was not well understood. In this paper we introduce a new method that uses causal reasoning to discover features of the frequency space that are sufficient and necessary for a given classification. We describe an implementation of this algorithm in the tool FreqReX and provide experimental results on a number of standard benchmark datasets. Our experiments show that causally sufficient and necessary subsets allow us to manipulate the outputs of the models in a variety of ways by changing the input very slightly. Namely, a change to one out of 240,000 frequencies results in a change in classification 58% of the time, and the change can be so small that it is practically inaudible. These results show that causal analysis is useful for understanding the reasoning process of audio classifiers and can be used to successfully manipulate their outputs.
Stable Source Coding
Venue: arXiv cs.IT 2026
Year: 2026

A source encoder is stable if a small change in the source sequence (e.g., changing a few symbols) results in a small (or bounded) change in the output codeword. By this definition, the common technique of random binning is unstable; because the mapping is random, two nearly identical source sequences can be assigned to completely unrelated bin indices. We study compression rates of stable lossless source codes. Using combinatorial arguments, we derive information-theoretic limits on the achievable rate as a function of the stability parameters.
EMemBench: Interactive Benchmarking of Episodic Memory for VLM Agents
Venue: arXiv cs.CL 2026
Year: 2026

We introduce EMemBench, a programmatic benchmark for evaluating long-term memory of agents through interactive games. Rather than using a fixed set of questions, EMemBench generates questions from each agent's own trajectory, covering both text and visual game environments. Each template computes verifiable ground truth from underlying game signals, with controlled answerability and balanced coverage over memory skills: single/multi-hop recall, induction, temporal, spatial, logical, and adversarial. We evaluate memory agents with strong LMs/VLMs as backbones, using in-context prompting as baselines. Across 15 text games and multiple visual seeds, results are far from saturated: induction and spatial reasoning are persistent bottlenecks, especially in visual setting. Persistent memory yields clear gains for open backbones on text games, but improvements are less consistent for VLM agents, suggesting that visually grounded episodic memory remains an open challenge. A human study further confirms the difficulty of EMemBench.
Fine-grained quantum advantage beyond double-logarithmic space
Venue: arXiv cs.CC 2026
Year: 2026

Polynomial-time quantum Turing machines are provably superior to their classical counterparts within a common space bound in $o(\log \log n)$. For $Ω(\log \log n)$ space, the only known quantum advantage result has been the fact $\mathsf{BPTISP}(2^{O(n)},o(\log n))\subsetneq \mathsf{BQTISP}(2^{O(n)},o(\log n))$, proven by exhibiting an exponential-time quantum finite automaton (2QCFA) that recognizes $L_{pal}$, the language of palindromes, which is an impossible task for sublogarithmic-space probabilistic Turing machines. No subexponential-time quantum algorithm can recognize $L_{pal}$ in sublogarithmic space. We initiate the study of quantum advantage under simultaneous subexponential time and $Ω(\log \log n) \cap o(\log n)$ space bounds. We exhibit an infinite family $\mathcal{F}$ of functions in $(\log n)^{ω(1)}\cap n^{o(1)}$ such that for every $f_i\in\mathcal{F}$, there exists another function $f_{i+1}\in\mathcal{F}$ such that $f_{i+1}(n) \in o(f_{i}(n))$, and each such $f_i$ corresponds to a different quantum advantage statement, i.e. a proper inclusion of the form $\mathsf{BPTISP}(2^{O(f_i(n))},o(\log f_i(n)))\subsetneq \mathsf{BQTISP}(2^{O(f_i(n))},o(\log f_i(n)))$ for a different pair of subexponential time and sublogarithmic space bounds. Our results depend on a technique enabling polynomial-time quantum finite automata to control padding functions with very fine asymptotic granularity.
Supporting Stakeholder Requirements Expression with LLM Revisions: An Empirical Evaluation
Venue: arXiv cs.SE 2026
Year: 2026

Stakeholders often struggle to accurately express their requirements due to articulation barriers arising from limited domain knowledge or from cognitive constraints. This can cause misalignment between expressed and intended requirements, complicating elicitation and validation. Traditional elicitation techniques, such as interviews and follow-up sessions, are time-consuming and risk distorting stakeholders' original intent across iterations. Large Language Models (LLMs) can infer user intentions from context, suggesting potential for assisting stakeholders in expressing their needs. This raises the questions of (i) how effectively LLMs can support requirement expression and (ii) whether such support benefits stakeholders with limited domain expertise. We conducted a study with 26 participants who produced 130 requirement statements. Each participant first expressed requirements unaided, then evaluated LLM-generated revisions tailored to their context. Participants rated LLM revisions significantly higher than their original statements across all dimensions-alignment with intent, readability, reasoning, and unambiguity. Qualitative feedback further showed that LLM revisions often surfaced tacit details stakeholders considered important and helped them better understand their own requirements. We present and evaluate a stakeholder-centered approach that leverages LLMs as articulation aids in requirements elicitation and validation. Our results show that LLM-assisted reformulation improves perceived completeness, clarity, and alignment of requirements. By keeping stakeholders in the validation loop, this approach promotes responsible and trustworthy use of AI in Requirements Engineering.