pwc-archive/papers-with-abstracts huggingface dataset:
Rosetta-PL: Propositional Logic as a Benchmark for Large Language Model Reasoning
Authors: Shaun Baek, Shaun Esua-Mensah, Cyrus Tsui, Sejan Vigneswaralingam, Abdullah Alali, Michael Lu, Vasu Sharma, Sean O'Brien, Kevin Zhu
Date: 2025-03-25 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Logical Reasoning, Translation

Large Language Models (LLMs) are primarily trained on high-resource natural languages, limiting their effectiveness in low-resource settings and in tasks requiring deep logical reasoning. This research introduces Rosetta-PL, a benchmark designed to evaluate LLMs' logical reasoning and generalization capabilities in a controlled environment. We construct Rosetta-PL by translating a dataset of logical propositions from Lean into a custom logical language, which is then used to fine-tune an LLM (e.g., GPT-4o). Our experiments analyze the impact of the size of the dataset and the translation methodology on the performance of the model. Our results indicate that preserving logical relationships in the translation process significantly boosts precision, with accuracy plateauing beyond roughly 20,000 training samples. These insights provide valuable guidelines for optimizing LLM training in formal reasoning tasks and improving performance in various low-resource language applications.
Generative QoE Modeling: A Lightweight Approach for Telecom Networks
Authors: Vinti Nayar, Kanica Sachdev, Brejesh lall
Date: 2025-04-30 00:00:00
Tasks: Computational Efficiency, Quantization

Quality of Experience (QoE) prediction plays a crucial role in optimizing resource management and enhancing user satisfaction across both telecommunication and OTT services. While recent advances predominantly rely on deep learning models, this study introduces a lightweight generative modeling framework that balances computational efficiency, interpretability, and predictive accuracy. By validating the use of Vector Quantization (VQ) as a preprocessing technique, continuous network features are effectively transformed into discrete categorical symbols, enabling integration with a Hidden Markov Model (HMM) for temporal sequence modeling. This VQ-HMM pipeline enhances the model's capacity to capture dynamic QoE patterns while supporting probabilistic inference on new and unseen data. Experimental results on publicly available time-series datasets incorporating both objective indicators and subjective QoE scores demonstrate the viability of this approach in real-time and resource-constrained environments, where inference latency is also critical. The framework offers a scalable alternative to complex deep learning methods, particularly in scenarios with limited computational resources or where latency constraints are critical.
Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks
Authors: Vishnu Sarukkai, Zhiqiang Xie, Kayvon Fatahalian
Date: 2025-05-01 00:00:00
Tasks: Decision Making, Large Language Model, Sequential Decision Making

Improving Large Language Model (LLM) agents for sequential decision-making tasks typically requires extensive task-specific knowledge engineering--custom prompts, curated examples, and specialized observation/action spaces. We investigate a different approach where agents automatically improve by learning from their own successful experiences without human intervention. Our method constructs and refines a database of self-generated trajectories that serve as in-context examples for future tasks. Even naive accumulation of successful trajectories yields substantial performance gains across three diverse benchmarks: ALFWorld (73% to 89%), Wordcraft (55% to 64%), and InterCode-SQL (75% to 79%). These improvements exceed those achieved by upgrading from gpt-4o-mini to gpt-4o and match the performance of allowing multiple attempts per task. We further enhance this approach with two innovations: database-level curation using population-based training to propagate high-performing example collections, and exemplar-level curation that selectively retains trajectories based on their empirical utility as in-context examples. With these enhancements, our method achieves 93% success on ALFWorld--surpassing approaches that use more powerful LLMs and hand-crafted components. Our trajectory bootstrapping technique demonstrates that agents can autonomously improve through experience, offering a scalable alternative to labor-intensive knowledge engineering.
Transformer-Empowered Actor-Critic Reinforcement Learning for Sequence-Aware Service Function Chain Partitioning
Authors: Cyril Shih-Huan Hsu, Anestis Dalgkitsis, Chrysa Papagianni, Paola Grosso
Date: 2025-04-26 00:00:00
Tasks: Computational Efficiency

In the forthcoming era of 6G networks, characterized by unprecedented data rates, ultra-low latency, and extensive connectivity, effective management of Virtualized Network Functions (VNFs) is essential. VNFs are software-based counterparts of traditional hardware devices that facilitate flexible and scalable service provisioning. Service Function Chains (SFCs), structured as ordered sequences of VNFs, are pivotal in orchestrating complex network services. Nevertheless, partitioning SFCs across multi-domain network infrastructures presents substantial challenges due to stringent latency constraints and limited resource availability. Conventional optimization-based methods typically exhibit low scalability, whereas existing data-driven approaches often fail to adequately balance computational efficiency with the capability to effectively account for dependencies inherent in SFCs. To overcome these limitations, we introduce a Transformer-empowered actor-critic framework specifically designed for sequence-aware SFC partitioning. By utilizing the self-attention mechanism, our approach effectively models complex inter-dependencies among VNFs, facilitating coordinated and parallelized decision-making processes. Additionally, we enhance training stability and convergence using $\epsilon$-LoPe exploration strategy as well as Asymptotic Return Normalization. Comprehensive simulation results demonstrate that the proposed methodology outperforms existing state-of-the-art solutions in terms of long-term acceptance rates, resource utilization efficiency, and scalability, while achieving rapid inference. This study not only advances intelligent network orchestration by delivering a scalable and robust solution for SFC partitioning within emerging 6G environments, but also bridging recent advancements in Large Language Models (LLMs) with the optimization of next-generation networks.
SymPlanner: Deliberate Planning in Language Models with Symbolic Representation
Authors: Siheng Xiong, Jieyu Zhou, Zhangding Liu, Yusen Su
Date: 2025-05-02 00:00:00
Tasks: valid

Planning remains a core challenge for language models (LMs), particularly in domains that require coherent multi-step action sequences grounded in external constraints. We introduce SymPlanner, a novel framework that equips LMs with structured planning capabilities by interfacing them with a symbolic environment that serves as an explicit world model. Rather than relying purely on natural language reasoning, SymPlanner grounds the planning process in a symbolic state space, where a policy model proposes actions and a symbolic environment deterministically executes and verifies their effects. To enhance exploration and improve robustness, we introduce Iterative Correction (IC), which refines previously proposed actions by leveraging feedback from the symbolic environment to eliminate invalid decisions and guide the model toward valid alternatives. Additionally, Contrastive Ranking (CR) enables fine-grained comparison of candidate plans by evaluating them jointly. We evaluate SymPlanner on PlanBench, demonstrating that it produces more coherent, diverse, and verifiable plans than pure natural language baselines.
LLM-hRIC: LLM-empowered Hierarchical RAN Intelligent Control for O-RAN
Authors: Lingyan Bao, Sinwoong Yun, Jemin Lee, Tony Q. S. Quek
Date: 2025-04-25 00:00:00
Tasks: Management, Reinforcement Learning (RL)

Despite recent advances in applying large language models (LLMs) and machine learning (ML) techniques to open radio access network (O-RAN), critical challenges remain, such as insufficient cooperation between radio access network (RAN) intelligent controllers (RICs), high computational demands hindering real-time decisions, and the lack of domain-specific finetuning. Therefore, this article introduces the LLM-empowered hierarchical RIC (LLM-hRIC) framework to improve the collaboration between RICs in O-RAN. The LLM-empowered non-real-time RIC (non-RT RIC) acts as a guider, offering a strategic guidance to the near-real-time RIC (near-RT RIC) using global network information. The RL-empowered near-RT RIC acts as an implementer, combining this guidance with local real-time data to make near-RT decisions. We evaluate the feasibility and performance of the LLM-hRIC framework in an integrated access and backhaul (IAB) network setting, and finally, discuss the open challenges of the LLM-hRIC framework for O-RAN.
TRAVELER: A Benchmark for Evaluating Temporal Reasoning across Vague, Implicit and Explicit References
Authors: Svenja Kenneweg, Jörg Deigmöller, Philipp Cimiano, Julian Eggert
Date: 2025-05-02 00:00:00
Tasks: Natural Language Understanding, Question Answering

Understanding and resolving temporal references is essential in Natural Language Understanding as we often refer to the past or future in daily communication. Although existing benchmarks address a system's ability to reason about and resolve temporal references, systematic evaluation of specific temporal references remains limited. Towards closing this gap, we introduce TRAVELER, a novel synthetic benchmark dataset that follows a Question Answering paradigm and consists of questions involving temporal references with the corresponding correct answers. TRAVELER assesses models' abilities to resolve explicit, implicit relative to speech time, and vague temporal references. Beyond investigating the performance of state-of-the-art LLMs depending on the type of temporal reference, our benchmark also allows evaluation of performance in relation to the length of the set of events. For the category of vague temporal references, ground-truth answers were established via human surveys on Prolific, following a procedure similar to the one from Kenneweg et al. To demonstrate the benchmark's applicability, we evaluate four state-of-the-art LLMs using a question-answering task encompassing 3,300 questions. Our findings show that while the benchmarked LLMs can answer questions over event sets with a handful of events and explicit temporal references successfully, performance clearly deteriorates with larger event set length and when temporal references get less explicit. Notably, the vague question category exhibits the lowest performance across all models. The benchmark is publicly available at: https://gitlab.ub.uni-bielefeld.de/s.kenneweg/TRAVELER
A Transformer-based Neural Architecture Search Method
Authors: Shang Wang, Huanrong Tang, Jianquan Ouyang
Date: 2025-05-02 00:00:00
Tasks: Decoder, Neural Architecture Search

This paper presents a neural architecture search method based on Transformer architecture, searching cross multihead attention computation ways for different number of encoder and decoder combinations. In order to search for neural network structures with better translation results, we considered perplexity as an auxiliary evaluation metric for the algorithm in addition to BLEU scores and iteratively improved each individual neural network within the population by a multi-objective genetic algorithm. Experimental results show that the neural network structures searched by the algorithm outperform all the baseline models, and that the introduction of the auxiliary evaluation metric can find better models than considering only the BLEU score as an evaluation metric.
A Factorized Probabilistic Model of the Semantics of Vague Temporal Adverbials Relative to Different Event Types
Authors: Svenja Kenneweg, Jörg Deigmöller, Julian Eggert, Philipp Cimiano
Date: 2025-05-02 00:00:00

Vague temporal adverbials, such as recently, just, and a long time ago, describe the temporal distance between a past event and the utterance time but leave the exact duration underspecified. In this paper, we introduce a factorized model that captures the semantics of these adverbials as probabilistic distributions. These distributions are composed with event-specific distributions to yield a contextualized meaning for an adverbial applied to a specific event. We fit the model's parameters using existing data capturing judgments of native speakers regarding the applicability of these vague temporal adverbials to events that took place a given time ago. Comparing our approach to a non-factorized model based on a single Gaussian distribution for each pair of event and temporal adverbial, we find that while both models have similar predictive power, our model is preferable in terms of Occam's razor, as it is simpler and has better extendability.
EvalxNLP: A Framework for Benchmarking Post-Hoc Explainability Methods on NLP Models
Authors: Mahdi Dhaini, Kafaite Zahra Hussain, Efstratios Zaradoukas, Gjergji Kasneci
Date: 2025-05-02 00:00:00
Tasks: Benchmarking

As Natural Language Processing (NLP) models continue to evolve and become integral to high-stakes applications, ensuring their interpretability remains a critical challenge. Given the growing variety of explainability methods and diverse stakeholder requirements, frameworks that help stakeholders select appropriate explanations tailored to their specific use cases are increasingly important. To address this need, we introduce EvalxNLP, a Python framework for benchmarking state-of-the-art feature attribution methods for transformer-based NLP models. EvalxNLP integrates eight widely recognized explainability techniques from the Explainable AI (XAI) literature, enabling users to generate and evaluate explanations based on key properties such as faithfulness, plausibility, and complexity. Our framework also provides interactive, LLM-based textual explanations, facilitating user understanding of the generated explanations and evaluation outcomes. Human evaluation results indicate high user satisfaction with EvalxNLP, suggesting it is a promising framework for benchmarking explanation methods across diverse user groups. By offering a user-friendly and extensible platform, EvalxNLP aims at democratizing explainability tools and supporting the systematic comparison and advancement of XAI techniques in NLP.
Gender Bias in Explainability: Investigating Performance Disparity in Post-hoc Methods
Authors: Mahdi Dhaini, Ege Erdogan, Nils Feldhus, Gjergji Kasneci
Date: 2025-05-02 00:00:00
Tasks: Fairness

While research on applications and evaluations of explanation methods continues to expand, fairness of the explanation methods concerning disparities in their performance across subgroups remains an often overlooked aspect. In this paper, we address this gap by showing that, across three tasks and five language models, widely used post-hoc feature attribution methods exhibit significant gender disparity with respect to their faithfulness, robustness, and complexity. These disparities persist even when the models are pre-trained or fine-tuned on particularly unbiased datasets, indicating that the disparities we observe are not merely consequences of biased training data. Our results highlight the importance of addressing disparities in explanations when developing and applying explainability methods, as these can lead to biased outcomes against certain subgroups, with particularly critical implications in high-stakes contexts. Furthermore, our findings underscore the importance of incorporating the fairness of explanations, alongside overall model fairness and explainability, as a requirement in regulatory frameworks.
Planet as a Brain: Towards Internet of AgentSites based on AIOS Server
Authors: Xiang Zhang, Yongfeng Zhang
Date: 2025-04-19 00:00:00
Tasks: AI Agent

The internet is undergoing a historical transformation from the "Internet of Websites" to the "Internet of AgentSites." While traditional Websites served as the foundation for information hosting and dissemination, a new frontier is emerging where AgentSites serve as the hubs of the internet, where each AgentSite hosts one or more AI agents that receive tasks, address them, and deliver actionable solutions, marking a significant shift in the digital landscape and representing the next generation of online ecosystems. Under this vision, AIOS, the AI Agent Operating System, serves as the server for the development, deployment and execution of AI agents, which is a fundamental infrastructure for the Internet of Agentsites. In this paper, we introduce AIOS Server, a runtime framework to host agents and enable global-scale collaboration among decentralized agents. AIOS Server provides a communication protocol leveraging the Model Context Protocol (MCP) and JSON-RPC to enable agent-agent or human-agent interactions. Each AIOS node operates as a server to host and execute agents, while supporting peer-to-peer coordination without reliance on centralized orchestration. Based on AIOS Server, we further present the world's first practically deployed Internet of Agentsites (AIOS-IoA), including AgentHub for agent registration and discovery and AgentChat for interactive communication, at https://planet.aios.foundation. The agent discovery mechanism based on Distributed Hash Tables (DHT) and a Gossip protocol serves as the search engine for the internet of agentsites. This work provides a practical foundation for building the Internet of Agentsites-a new paradigm where autonomous agents become first-class citizens of the web. The implementation is available at https://github.com/agiresearch/AIOS.Server and is integrated into the AIOS main branch at https://github.com/agiresearch/AIOS.
Sparks of Tabular Reasoning via Text2SQL Reinforcement Learning
Authors: Josefa Lia Stoisser, Marc Boubnovski Martell, Julien Fauqueur
Date: 2025-04-23 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Text to SQL, Text-To-SQL

This work reframes the Text-to-SQL task as a pathway for teaching large language models (LLMs) to reason over and manipulate tabular data--moving beyond the traditional focus on query generation. We propose a two-stage framework that leverages SQL supervision to develop transferable table reasoning capabilities. First, we synthesize detailed chain-of-thought (CoT) traces from real-world SQL queries, providing step-by-step, clause-level supervision that teaches the model how to traverse, filter, and aggregate table fields. Second, we introduce a Group Relative Policy Optimization (GRPO) reinforcement learning objective that connects SQL execution accuracy to generalizable reasoning by encouraging steps that extend beyond task-specific syntax and transfer across datasets. Empirically, our approach improves performance on standard Text-to-SQL benchmarks and achieves substantial gains on reasoning-intensive datasets such as BIRD and CRT-QA, demonstrating enhanced generalization and interpretability. Specifically, the distilled-quantized LLaMA model achieved a relative 33.9\% increase in accuracy when trained on Text-to-SQL tasks, while Qwen achieved a relative 14.5\% increase. These results suggest that SQL can serve not only as a target formalism but also as an effective scaffold for learning robust, transferable reasoning over structured data.
Research on Cloud Platform Network Traffic Monitoring and Anomaly Detection System based on Large Language Models
Authors: Ze Yang, Yihong Jin, Juntian Liu, Xinhe Xu, Yihan Zhang, Shuyang Ji
Date: 2025-04-22 00:00:00
Tasks: Anomaly Detection, Computational Efficiency, Language Modeling, Language Modelling, Large Language Model, Transfer Learning

The rapidly evolving cloud platforms and the escalating complexity of network traffic demand proper network traffic monitoring and anomaly detection to ensure network security and performance. This paper introduces a large language model (LLM)-based network traffic monitoring and anomaly detection system. In addition to existing models such as autoencoders and decision trees, we harness the power of large language models for processing sequence data from network traffic, which allows us a better capture of underlying complex patterns, as well as slight fluctuations in the dataset. We show for a given detection task, the need for a hybrid model that incorporates the attention mechanism of the transformer architecture into a supervised learning framework in order to achieve better accuracy. A pre-trained large language model analyzes and predicts the probable network traffic, and an anomaly detection layer that considers temporality and context is added. Moreover, we present a novel transfer learning-based methodology to enhance the model's effectiveness to quickly adapt to unknown network structures and adversarial conditions without requiring extensive labeled datasets. Actual results show that the designed model outperforms traditional methods in detection accuracy and computational efficiency, effectively identify various network anomalies such as zero-day attacks and traffic congestion pattern, and significantly reduce the false positive rate.
On the Limitations of Steering in Language Model Alignment
Authors: Chebrolu Niranjan, Kokil Jaidka, Gerard Christopher Yeo
Date: 2025-05-02 00:00:00
Tasks: Language Modeling, Language Modelling

Steering vectors are a promising approach to aligning language model behavior at inference time. In this paper, we propose a framework to assess the limitations of steering vectors as alignment mechanisms. Using a framework of transformer hook interventions and antonym-based function vectors, we evaluate the role of prompt structure and context complexity in steering effectiveness. Our findings indicate that steering vectors are promising for specific alignment tasks, such as value alignment, but may not provide a robust foundation for general-purpose alignment in LLMs, particularly in complex scenarios. We establish a methodological foundation for future investigations into steering capabilities of reasoning models.
State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination
Authors: Qingyuan He, Chang Liu, Juecen Zhan, Weiqiang Huang, Ran Hao
Date: 2025-04-22 00:00:00
Tasks: Scheduling

This paper addresses the challenge of energy efficiency management faced by intelligent IoT devices in complex application environments. A novel optimization method is proposed, combining Deep Q-Network (DQN) with an edge collaboration mechanism. The method builds a state-action-reward interaction model and introduces edge nodes as intermediaries for state aggregation and policy scheduling. This enables dynamic resource coordination and task allocation among multiple devices. During the modeling process, device status, task load, and network resources are jointly incorporated into the state space. The DQN is used to approximate and learn the optimal scheduling strategy. To enhance the model's ability to perceive inter-device relationships, a collaborative graph structure is introduced to model the multi-device environment and assist in decision optimization. Experiments are conducted using real-world IoT data collected from the FastBee platform. Several comparative and validation tests are performed, including energy efficiency comparisons across different scheduling strategies, robustness analysis under varying task loads, and evaluation of state dimension impacts on policy convergence speed. The results show that the proposed method outperforms existing baseline approaches in terms of average energy consumption, processing latency, and resource utilization. This confirms its effectiveness and practicality in intelligent IoT scenarios.