OpenRaiser/Intern-Atlas huggingface dataset:
Context-Aware Pragmatic Metacognitive Prompting for Sarcasm Detection
Venue: arXiv cs.CL 2025
Year: 2025

Detecting sarcasm remains a challenging task in the areas of Natural Language Processing (NLP) despite recent advances in neural network approaches. Currently, Pre-trained Language Models (PLMs) and Large Language Models (LLMs) are the preferred approach for sarcasm detection. However, the complexity of sarcastic text, combined with linguistic diversity and cultural variation across communities, has made the task more difficult even for PLMs and LLMs. Beyond that, those models also exhibit unreliable detection of words or tokens that require extra grounding for analysis. Building on a state-of-the-art prompting method in LLMs for sarcasm detection called Pragmatic Metacognitive Prompting (PMP), we introduce a retrieval-aware approach that incorporates retrieved contextual information for each target text. Our pipeline explores two complementary ways to provide context: adding non-parametric knowledge using web-based retrieval when the model lacks necessary background, and eliciting the model's own internal knowledge for a self-knowledge awareness strategy. We evaluated our approach with three datasets, such as Twitter Indonesia Sarcastic, SemEval-2018 Task 3, and MUStARD. Non-parametric retrieval resulted in a significant 9.87% macro-F1 improvement on Twitter Indonesia Sarcastic compared to the original PMP method. Self-knowledge retrieval improves macro-F1 by 3.29% on Semeval and by 4.08% on MUStARD. These findings highlight the importance of context in enhancing LLMs performance in sarcasm detection task, particularly the involvement of culturally specific slang, references, or unknown terms to the LLMs. Future work will focus on optimizing the retrieval of relevant contextual information and examining how retrieval quality affects performance. The experiment code is available at: https://github.com/wllchrst/sarcasm-detection_pmp_knowledge-base.
Identifying Codes Kernelization Limitations
Venue: arXiv cs.CC 2025
Year: 2025

The Identifying Code (IC) problem seeks a vertex subset whose intersection with every vertex's closed neighborhood is unique, enabling fault detection in multiprocessor systems and practical uses in identity verification, environmental monitoring, and dynamic localization. A closely related problem is the Locating-Dominating Set (LD), which requires each non-dominating vertex to be uniquely identified by its intersection with the set. Cappelle, Gomes, and Santos (2021) proved that LD is W-hard for minimum clique cover and lacks polynomial kernels for parameters such as vertex cover, but their methods did not apply to IC. This paper answers their question by showing that IC does not admit a polynomial kernel parameterized by solution size plus vertex cover unless NP is a subset of coNP/poly.
ASR Error Correction in Low-Resource Burmese with Alignment-Enhanced Transformers using Phonetic Features
Venue: arXiv cs.CL 2025
Year: 2025

This paper investigates sequence-to-sequence Transformer models for automatic speech recognition (ASR) error correction in low-resource Burmese, focusing on different feature integration strategies including IPA and alignment information. To our knowledge, this is the first study addressing ASR error correction specifically for Burmese. We evaluate five ASR backbones and show that our ASR Error Correction (AEC) approaches consistently improve word- and character-level accuracy over baseline outputs. The proposed AEC model, combining IPA and alignment features, reduced the average WER of ASR models from 51.56 to 39.82 before augmentation (and 51.56 to 43.59 after augmentation) and improving chrF++ scores from 0.5864 to 0.627, demonstrating consistent gains over the baseline ASR outputs without AEC. Our results highlight the robustness of AEC and the importance of feature design for improving ASR outputs in low-resource settings.
Coco: Corecursion with Compositional Heterogeneous Productivity
Venue: arXiv cs.LO 2025
Year: 2025

Contemporary proof assistants impose restrictive syntactic guardedness conditions that reject many valid corecursive definitions. Existing approaches to overcome these restrictions present a fundamental trade-off between coverage and automation. We present Compositional Heterogeneous Productivity (CHP), a theoretical framework that unifies high automation with extensive coverage for corecursive definitions. CHP introduces heterogeneous productivity applicable to functions with diverse domain and codomain types, including non-coinductive types. Its key innovation is compositionality: the productivity of composite functions is systematically computed from their components, enabling modular reasoning about complex corecursive patterns. Building on CHP, we develop Coco, a corecursion library for Rocq that provides extensive automation for productivity computation and fixed-point generation.
Mortgage Language Model: Domain-Adaptive Pretraining with Residual Instruction, Alignment Tuning, and Task-Specific Routing
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) demonstrate exceptional capabilities across general domains, yet their application to specialized sectors such as mortgage finance requires domain-specific knowledge augmentation while preserving instruction-following fidelity. We present MortgageLLM, a novel domain-specific large language model that addresses this dual challenge. It is developed using a dual-track specialization framework from a single base model (LLaMA-3.1-8B). We opted for this dual-expert approach as a single multi-task model suffers from performance trade-offs, where optimizing for structured tasks (via SFT) degrades conversational fidelity (via DPO). Our dual-track method solves this by creating two specialists, allowing each to be optimally trained for its distinct capability. Our approach applies the instruction residual technique to restore instruction-following capabilities post-domain adaptation without supervised fine-tuning. We contribute: (1) application of this residual technique to the highly specialized mortgage finance domain; (2) a dual-expert architecture combining a conversational Q&A model and a structured task model for classification and summarization; and (3) an intelligent task routing mechanism using few-shot classification performed by one of the expert models itself. We validate our approach on domain-specific benchmarks, where our final model (MLM v2) significantly outperforms the base LLaMA-3.1-8B-Instruct, achieving an LLM-as-a-Judge summarization score of 4.58 (vs. 3.99), a Q&A score of 4.09 (vs. 4.0), and a classification score of 2.6 (vs. 1.2). On semantic similarity, our model achieved a BERTScore of 0.77 for summarization (vs. 0.74), 0.68 for Q&A (vs. 0.58), and 0.75 for classification (vs. 0.73), substantially outperforming baseline approaches.
Dynamic Stratified Contrastive Learning with Upstream Augmentation for MILP Branching
Venue: arXiv cs.LG 2025
Year: 2025

Mixed Integer Linear Programming (MILP) is a fundamental class of NP-hard problems that has garnered significant attention from both academia and industry. The Branch-and-Bound (B\&B) method is the dominant approach for solving MILPs and the branching plays an important role in B\&B methods. Neural-based learning frameworks have recently been developed to enhance branching policies and the efficiency of solving MILPs. However, these methods still struggle with semantic variation across depths, the scarcity of upstream nodes, and the costly collection of strong branching samples. To address these issues, we propose \ours, a Dynamic \underline{\textbf{S}}tratified \underline{\textbf{C}}ontrastive Training Framework for \underline{\textbf{MILP}} Branching. It groups branch-and-bound nodes based on their feature distributions and trains a GCNN-based discriminative model to progressively separate nodes across groups, learning finer-grained node representations throughout the tree. To address data scarcity and imbalance at upstream nodes, we introduce an upstream-augmented MILP derivation procedure that generates both theoretically equivalent and perturbed instances. \ours~effectively models subtle semantic differences between nodes, significantly enhancing branching accuracy and solving efficiency, particularly for upstream nodes. Extensive experiments on standard MILP benchmarks demonstrate that our method enhances branching accuracy, reduces solving time, and generalizes effectively to unseen instances.
Interpretable Fair Clustering
Venue: arXiv cs.LG 2025
Year: 2025

Fair clustering has gained increasing attention in recent years, especially in applications involving socially sensitive attributes. However, existing fair clustering methods often lack interpretability, limiting their applicability in high-stakes scenarios where understanding the rationale behind clustering decisions is essential. In this work, we address this limitation by proposing an interpretable and fair clustering framework, which integrates fairness constraints into the structure of decision trees. Our approach constructs interpretable decision trees that partition the data while ensuring fair treatment across protected groups. To further enhance the practicality of our framework, we also introduce a variant that requires no fairness hyperparameter tuning, achieved through post-pruning a tree constructed without fairness constraints. Extensive experiments on both real-world and synthetic datasets demonstrate that our method not only delivers competitive clustering performance and improved fairness, but also offers additional advantages such as interpretability and the ability to handle multiple sensitive attributes. These strengths enable our method to perform robustly under complex fairness constraints, opening new possibilities for equitable and transparent clustering.
Learning Cell-Aware Hierarchical Multi-Modal Representations for Robust Molecular Modeling
Venue: arXiv cs.LG 2025
Year: 2025

Understanding how chemical perturbations propagate through biological systems is essential for robust molecular property prediction. While most existing methods focus on chemical structures alone, recent advances highlight the crucial role of cellular responses such as morphology and gene expression in shaping drug effects. However, current cell-aware approaches face two key limitations: (1) modality incompleteness in external biological data, and (2) insufficient modeling of hierarchical dependencies across molecular, cellular, and genomic levels. We propose CHMR (Cell-aware Hierarchical Multi-modal Representations), a robust framework that jointly models local-global dependencies between molecules and cellular responses and captures latent biological hierarchies via a novel tree-structured vector quantization module. Evaluated on nine public benchmarks spanning 728 tasks, CHMR outperforms state-of-the-art baselines, yielding average improvements of 3.6% on classification and 17.2% on regression tasks. These results demonstrate the advantage of hierarchy-aware, multimodal learning for reliable and biologically grounded molecular representations, offering a generalizable framework for integrative biomedical modeling. The code is in https://github.com/limengran98/CHMR.
Digital Twin-Driven Secure Access Strategy for SAGIN-Enabled IoT Networks
Venue: arXiv cs.NI 2025
Year: 2025

In space-air-ground integrated networks (SAGIN)-enabled IoT networks, secure access has become a significant challenge due to the increasing risks of eavesdropping attacks. To address these threats to data confidentiality, this paper proposes a Digital Twin (DT)-driven secure access strategy. The strategy leverages a virtual replica of the physical SAGIN environment within the DT framework to continuously assess dynamic eavesdropping risks by quantifying secrecy capacity. Operating within this DT framework, an evolutionary game model dynamically balances the DT-updated secrecy capacity against queuing delay, steering IoT devices toward more secure and efficient access decisions. Furthermore, a novel distributed algorithm, integral to the DT operation, is developed to obtain the equilibrium access strategy for each device in a scalable manner. Simulation results demonstrate that the proposed DT-based approach substantially improves the security of SAGIN-enabled IoT networks. Additionally, it effectively balances system load, prevents overload occurrences, and decreases queuing delay compared to benchmark schemes, thereby comprehensively improving overall network performance.
Tâtonnement Dynamics for Fisher Markets with Chores
Venue: arXiv cs.GT 2025
Year: 2025

In this paper, we initiate the study of tâtonnement dynamics in markets with chores. Tâtonnement is a fundamental market dynamics, capturing how prices evolve when they are adjusted in proportion of their excess demand. While its convergence to a competitive equilibrium (CE) is well understood in goods markets for broad classes of utilities, no analogous results are known for chore markets. Analyzing tâtonnement in the chores market presents new challenges. Several elegant structural properties that facilitate convergence in goods markets-such as convexity of the equilibrium price set and monotonicity of excess demand under the tâtonnement price updates-fail to hold in the chore setting. Consistent with these difficulties, we first show that naïve tâtonnement diverges. To overcome this, we propose a modified process called relative tâtonnement, where prices are updated according to normalized excess demand. We prove its convergence to a CE under suitable step-size choices for a broad class of disutility functions, namely continuous, convex, and 1-homogeneous (CCH) disutilities. This class includes many standard forms such as linear and convex CES disutilities. Our proof proceeds by introducing a nonsmooth, nonconvex yet regular objective function-a generalization of the objective in the Eisenberg-Gale-type dual program introduced by [CKMN24]. For convex CES disutilities, where disutility is the weighted $p$-norm of the individual chore disutilities for $p \in (1, \infty)$, we show that relative tâtonnement converges to an $\varepsilon$-CE in $O(1/\varepsilon^2)$ iterations. This quadratic convergence rate is established by leveraging the polar gauge (or gauge dual) of the disutility function. Finally, following the framework of [AH58], we analyze the stability of CE and provide a complete characterization of local stability.
Towards an Effective Action-Region Tracking Framework for Fine-grained Video Action Recognition
Venue: arXiv cs.CV 2025
Year: 2025

Fine-grained action recognition (FGAR) aims to identify subtle and distinctive differences among fine-grained action categories. However, current recognition methods often capture coarse-grained motion patterns but struggle to identify subtle details in local regions evolving over time. In this work, we introduce the Action-Region Tracking (ART) framework, a novel solution leveraging a query-response mechanism to discover and track the dynamics of distinctive local details, enabling effective distinction of similar actions. Specifically, we propose a region-specific semantic activation module that employs discriminative and text-constrained semantics as queries to capture the most action-related region responses in each video frame, facilitating interaction among spatial and temporal dimensions with corresponding video features. The captured region responses are organized into action tracklets, which characterize region-based action dynamics by linking related responses across video frames in a coherent sequence. The text-constrained queries encode nuanced semantic representations derived from textual descriptions of action labels extracted by language branches within Visual Language Models (VLMs). To optimize the action tracklets, we design a multi-level tracklet contrastive constraint among region responses at spatial and temporal levels, enabling effective discrimination within each frame and correlation between adjacent frames. Additionally, a task-specific fine-tuning mechanism refines textual semantics such that semantic representations encoded by VLMs are preserved while optimized for task preferences. Comprehensive experiments on widely used action recognition benchmarks demonstrate the superiority to previous state-of-the-art baselines.
Towards Computational UIP in Cubical Agda
Venue: arXiv cs.PL 2025
Year: 2025

Some advantages of Cubical Type Theory, as implemented by Cubical Agda, over intensional Martin-Löf Type Theory include Quotient Inductive Types (QITs), which exist as instances of Higher Inductive Types, and functional extensionality, which is provable in Cubical Type Theory. However, HoTT features an infinite hierarchy of equalities that may become unwieldy in formalisations. Fortunately, QITs and functional extensionality are both preserved even if the equality levels of Cubical Type Theory are truncated to only homotopical Sets (h-Sets). In other words, removing the univalence axiom from Cubical Type Theory and instead postulating a conflicting axiom: the Uniqueness of Identity Proofs (UIP) postulate. Since univalence is proved in Cubical Type Theory from the so-called Glue Types, therefore, it is known that one can first remove the Glue Types (thus removing univalence) and then set-truncate all equalities (essentially assuming UIP), à la XTT. The result is a "h-Set Cubical Type Theory" that retains features such as functional extensionality and QITs. However, in Cubical Agda, there are currently only two unsatisfying ways to achieve h-Set Cubical Type Theory. The first is to give up on the canonicity of the theory and simply postulate the UIP axiom, while the second way is to use a standard result stating "type formers preserve h-levels" to manually prove UIP for every defined type. The latter is, however, laborious work best suited for an automatic implementation by the proof assistant. In this project, we analyse formulations of UIP and detail their computation rules for Cubical Agda, and evaluate their suitability for implementation. We also implement a variant of Cubical Agda without Glue, which is already compatible with postulated UIP, in anticipation of a future implementation of UIP in Cubical Agda.
DynamicAdaptiveClimb: Adaptive Cache Replacement with Dynamic Resizing
Venue: arXiv cs.OS 2025
Year: 2025

Efficient cache management is critical for optimizing the system performance, and numerous caching mechanisms have been proposed, each exploring various insertion and eviction strategies. In this paper, we present AdaptiveClimb and its extension, DynamicAdaptiveClimb, two novel cache replacement policies that leverage lightweight, cache adaptation to outperform traditional approaches. Unlike classic Least Recently Used (LRU) and Incremental Rank Progress (CLIMB) policies, AdaptiveClimb dynamically adjusts the promotion distance (jump) of the cached objects based on recent hit and miss patterns, requiring only a single tunable parameter and no per-item statistics. This enables rapid adaptation to changing access distributions while maintaining low overhead. Building on this foundation, DynamicAdaptiveClimb further enhances adaptability by automatically tuning the cache size in response to workload demands. Our comprehensive evaluation across a diverse set of real-world traces, including 1067 traces from 6 different datasets, demonstrates that DynamicAdaptiveClimb consistently achieves substantial speedups and higher hit ratios compared to other state-of-the-art algorithms. In particular, our approach achieves up to a 29% improvement in hit ratio and a substantial reduction in miss penalties compared to the FIFO baseline. Furthermore, it outperforms the next-best contenders, AdaptiveClimb and SIEVE [43], by approximately 10% to 15%, especially in environments characterized by fluctuating working set sizes. These results highlight the effectiveness of our approach in delivering efficient performance, making it well-suited for modern, dynamic caching environments.
Common Knowledge, Sailboats, and Publicity
Venue: arXiv cs.LO 2025
Year: 2025

We revisit a recent puzzle about common knowledge, the ``sailboat" case (Lederman, 2018), and argue that Lewisian common knowledge allows us to reconcile the pre-theoretical intuition that certain facts are ``public" in such situations, while these facts cannot be common knowledge in the classical, iterative sense. The crux of the argument is to understand Lewisian common knowledge as an account of what it means for an event to be public. We first formulate this argument informally to clarify its philosophical commitment and then propose one way to capture it formally in epistemic-plausibility models. Taken together, we take the philosophical and the formal arguments as providing evidence that Lewisian common knowledge is a plausible account of what it means for an event to be public.
Improvement of Collision Avoidance in Cut-In Maneuvers Using Time-to-Collision Metrics
Venue: arXiv cs.RO 2025
Year: 2025

This paper proposes a new strategy for collision avoidance system leveraging Time-to-Collision (TTC) metrics for handling cut-in scenarios, which are particularly challenging for autonomous vehicles (AVs). By integrating a deep learning with TTC calculations, the system predicts potential collisions and determines appropriate evasive actions compared to traditional TTC -based approaches.
HIRE: A Hybrid Learned Index for Robust and Efficient Performance under Mixed Workloads
Venue: arXiv cs.DB 2025
Year: 2025

Indexes are critical for efficient data retrieval and updates in modern databases. Recent advances in machine learning have led to the development of learned indexes, which model the cumulative distribution function of data to predict search positions and accelerate query processing. While learned indexes substantially outperform traditional structures for point lookups, they often suffer from high tail latency, suboptimal range query performance, and inconsistent effectiveness across diverse workloads. To address these challenges, this paper proposes HIRE, a hybrid in-memory index structure designed to deliver efficient performance consistently. HIRE combines the structural and performance robustness of traditional indexes with the predictive power of model-based prediction to reduce search overhead while maintaining worst-case stability. Specifically, it employs (1) hybrid leaf nodes adaptive to varying data distributions and workloads, (2) model-accelerated internal nodes augmented by log-based updates for efficient updates, (3) a nonblocking, cost-driven recalibration mechanism for dynamic data, and (4) an inter-level optimized bulk-loading algorithm accounting for leaf and internal-node errors. Experimental results on multiple real-world datasets demonstrate that HIRE outperforms both state-of-the-art learned indexes and traditional structures in range-query throughput, tail latency, and overall stability. Compared to state-of-the-art learned indexes and traditional indexes, HIRE achieves up to 41.7$\times$ higher throughput under mixed workloads, reduces tail latency by up to 98% across varying scenarios.