pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic LLM Routing and Selection based on User Preferences: Balancing Performance, Cost, and Ethics
Authors: Deepak Babu Piskala, Vijay Raajaa, Sachin Mishra, Bruno Bozza
Date: 2025-02-23 00:00:00
Tasks: Ethics

With the widespread deployment of large language models (LLMs) such as GPT4, BART, and LLaMA, the need for a system that can intelligently select the most suitable model for specific tasks while balancing cost, latency, accuracy, and ethical considerations has become increasingly important. Recognizing that not all tasks necessitate models with over 100 billion parameters, we introduce OptiRoute, an advanced model routing engine designed to dynamically select and route tasks to the optimal LLM based on detailed user-defined requirements. OptiRoute captures both functional (e.g., accuracy, speed, cost) and non-functional (e.g., helpfulness, harmlessness, honesty) criteria, leveraging lightweight task analysis and complexity estimation to efficiently match tasks with the best-fit models from a diverse array of LLMs. By employing a hybrid approach combining k-nearest neighbors (kNN) search and hierarchical filtering, OptiRoute optimizes for user priorities while minimizing computational overhead. This makes it ideal for real-time applications in cloud-based ML platforms, personalized AI services, and regulated industries.
Beyond Release: Access Considerations for Generative AI Systems
Authors: Irene Solaiman, Rishi Bommasani, Dan Hendrycks, Ariel Herbert-Voss, Yacine Jernite, Aviya Skowron, Andrew Trask
Date: 2025-02-23 00:00:00

Generative AI release decisions determine whether system components are made available, but release does not address many other elements that change how users and stakeholders are able to engage with a system. Beyond release, access to system components informs potential risks and benefits. Access refers to practical needs, infrastructurally, technically, and societally, in order to use available components in some way. We deconstruct access along three axes: resourcing, technical usability, and utility. Within each category, a set of variables per system component clarify tradeoffs. For example, resourcing requires access to computing infrastructure to serve model weights. We also compare the accessibility of four high performance language models, two open-weight and two closed-weight, showing similar considerations for all based instead on access variables. Access variables set the foundation for being able to scale or increase access to users; we examine the scale of access and how scale affects ability to manage and intervene on risks. This framework better encompasses the landscape and risk-benefit tradeoffs of system releases to inform system release decisions, research, and policy.
Code Summarization Beyond Function Level
Authors: Vladimir Makharev, Vladimir Ivanov
Date: 2025-02-23 00:00:00
Tasks: Code Summarization, Few-Shot Learning, In-Context Learning, Prompt Engineering, RAG

Code summarization is a critical task in natural language processing and software engineering, which aims to generate concise descriptions of source code. Recent advancements have improved the quality of these summaries, enhancing code readability and maintainability. However, the content of a repository or a class has not been considered in function code summarization. This study investigated the effectiveness of code summarization models beyond the function level, exploring the impact of class and repository contexts on the summary quality. The study involved revising benchmarks for evaluating models at class and repository levels, assessing baseline models, and evaluating LLMs with in-context learning to determine the enhancement of summary quality with additional context. The findings revealed that the fine-tuned state-of-the-art CodeT5+ base model excelled in code summarization, while incorporating few-shot learning and retrieved code chunks from RAG significantly enhanced the performance of LLMs in this task. Notably, the Deepseek Coder 1.3B and Starcoder2 15B models demonstrated substantial improvements in metrics such as BLEURT, METEOR, and BLEU-4 at both class and repository levels. Repository-level summarization exhibited promising potential but necessitates significant computational resources and gains from the inclusion of structured context. Lastly, we employed the recent SIDE code summarization metric in our evaluation. This study contributes to refining strategies for prompt engineering, few-shot learning, and RAG, addressing gaps in benchmarks for code summarization at various levels. Finally, we publish all study details, code, datasets, and results of evaluation in the GitHub repository available at https://github.com/kilimanj4r0/code-summarization-beyond-function-level.
Can ChatGPT Learn to Count Letters?
Authors: Javier Conde, Gonzalo Martínez, Pedro Reviriego, Zhen Gao, Shanshan Liu, Fabrizio Lombardi
Date: 2025-02-23 00:00:00

Large language models (LLMs) struggle on simple tasks such as counting the number of occurrences of a letter in a word. In this paper, we investigate if ChatGPT can learn to count letters and propose an efficient solution.
DISC: DISC: Dynamic Decomposition Improves LLM Inference Scaling
Authors: Jonathan Light, Wei Cheng, Benjamin Riviere, Wu Yue, Masafumi Oyamada, Mengdi Wang, Yisong Yue, Santiago Paternain, Haifeng Chen
Date: 2025-02-23 00:00:00
Tasks: Computational Efficiency, Math, Sentence

Inference scaling methods for large language models often work by breaking problems into steps or groups of tokens, then sampling and selecting the best next steps. However, these steps and their sizes are usually fixed or manually designed based on domain knowledge. We introduce dynamic decomposition, a method that adaptively and automatically breaks down solution and reasoning traces into manageable steps during inference. By allocating compute more effectively - especially by subdividing difficult steps and prioritizing their sampling - dynamic decomposition significantly boosts inference efficiency. Experiments on benchmarks like APPS, MATH, and LiveCodeBench show that dynamic decomposition outperforms fixed strategies such as token-level, sentence-level, and single-step decompositions, reducing the pass@10 error rate by 5.0%, 6.7%, and 10.5% respectively. These results show the promise of dynamic decomposition for improving a broad range of inference scaling techniques.
Reflective Planning: Vision-Language Models for Multi-Stage Long-Horizon Robotic Manipulation
Authors: Yunhai Feng, Jiaming Han, Zhuoran Yang, Xiangyu Yue, Sergey Levine, Jianlan Luo
Date: 2025-02-23 00:00:00

Solving complex long-horizon robotic manipulation problems requires sophisticated high-level planning capabilities, the ability to reason about the physical world, and reactively choose appropriate motor skills. Vision-language models (VLMs) pretrained on Internet data could in principle offer a framework for tackling such problems. However, in their current form, VLMs lack both the nuanced understanding of intricate physics required for robotic manipulation and the ability to reason over long horizons to address error compounding issues. In this paper, we introduce a novel test-time computation framework that enhances VLMs' physical reasoning capabilities for multi-stage manipulation tasks. At its core, our approach iteratively improves a pretrained VLM with a "reflection" mechanism - it uses a generative model to imagine future world states, leverages these predictions to guide action selection, and critically reflects on potential suboptimalities to refine its reasoning. Experimental results demonstrate that our method significantly outperforms several state-of-the-art commercial VLMs as well as other post-training approaches such as Monte Carlo Tree Search (MCTS). Videos are available at https://reflect-vlm.github.io.
Exploring Incremental Unlearning: Techniques, Challenges, and Future Directions
Authors: Sadia Qureshi, Thanveer Shaik, Xiaohui Tao, Haoran Xie, Lin Li, Jianming Yong, Xiaohua Jia
Date: 2025-02-23 00:00:00
Tasks: Machine Unlearning, Privacy Preserving

The growing demand for data privacy in Machine Learning (ML) applications has seen Machine Unlearning (MU) emerge as a critical area of research. As the `right to be forgotten' becomes regulated globally, it is increasingly important to develop mechanisms that delete user data from AI systems while maintaining performance and scalability of these systems. Incremental Unlearning (IU) is a promising MU solution to address the challenges of efficiently removing specific data from ML models without the need for expensive and time-consuming full retraining. This paper presents the various techniques and approaches to IU. It explores the challenges faced in designing and implementing IU mechanisms. Datasets and metrics for evaluating the performance of unlearning techniques are discussed as well. Finally, potential solutions to the IU challenges alongside future research directions are offered. This survey provides valuable insights for researchers and practitioners seeking to understand the current landscape of IU and its potential for enhancing privacy-preserving intelligent systems.
NatSGLD: A Dataset with Speech, Gesture, Logic, and Demonstration for Robot Learning in Natural Human-Robot Interaction
Authors: Snehesh Shrestha, Yantian Zha, Saketh Banagiri, Ge Gao, Yiannis Aloimonos, Cornelia Fermüller
Date: 2025-02-23 00:00:00
Tasks: Instruction Following

Recent advances in multimodal Human-Robot Interaction (HRI) datasets emphasize the integration of speech and gestures, allowing robots to absorb explicit knowledge and tacit understanding. However, existing datasets primarily focus on elementary tasks like object pointing and pushing, limiting their applicability to complex domains. They prioritize simpler human command data but place less emphasis on training robots to correctly interpret tasks and respond appropriately. To address these gaps, we present the NatSGLD dataset, which was collected using a Wizard of Oz (WoZ) method, where participants interacted with a robot they believed to be autonomous. NatSGLD records humans' multimodal commands (speech and gestures), each paired with a demonstration trajectory and a Linear Temporal Logic (LTL) formula that provides a ground-truth interpretation of the commanded tasks. This dataset serves as a foundational resource for research at the intersection of HRI and machine learning. By providing multimodal inputs and detailed annotations, NatSGLD enables exploration in areas such as multimodal instruction following, plan recognition, and human-advisable reinforcement learning from demonstrations. We release the dataset and code under the MIT License at https://www.snehesh.com/natsgld/ to support future HRI research.
Speed and Conversational Large Language Models: Not All Is About Tokens per Second
Authors: Javier Conde, Miguel González, Pedro Reviriego, Zhen Gao, Shanshan Liu, Fabrizio Lombardi
Date: 2025-02-23 00:00:00
Tasks: All

The speed of open-weights large language models (LLMs) and its dependency on the task at hand, when run on GPUs, is studied to present a comparative analysis of the speed of the most popular open LLMs.
Layer-Wise Evolution of Representations in Fine-Tuned Transformers: Insights from Sparse AutoEncoders
Authors: Suneel Nadipalli
Date: 2025-02-23 00:00:00
Tasks: Representation Learning

Fine-tuning pre-trained transformers is a powerful technique for enhancing the performance of base models on specific tasks. From early applications in models like BERT to fine-tuning Large Language Models (LLMs), this approach has been instrumental in adapting general-purpose architectures for specialized downstream tasks. Understanding the fine-tuning process is crucial for uncovering how transformers adapt to specific objectives, retain general representations, and acquire task-specific features. This paper explores the underlying mechanisms of fine-tuning, specifically in the BERT transformer, by analyzing activation similarity, training Sparse AutoEncoders (SAEs), and visualizing token-level activations across different layers. Based on experiments conducted across multiple datasets and BERT layers, we observe a steady progression in how features adapt to the task at hand: early layers primarily retain general representations, middle layers act as a transition between general and task-specific features, and later layers fully specialize in task adaptation. These findings provide key insights into the inner workings of fine-tuning and its impact on representation learning within transformer architectures.
DOSE3 : Diffusion-based Out-of-distribution detection on SE(3) trajectories
Authors: Hongzhe Cheng, Tianyou Zheng, Tianyi Zhang, Matthew Johnson-Roberson, Weiming Zhi
Date: 2025-02-23 00:00:00
Tasks: Out-of-Distribution Detection, Out of Distribution (OOD) Detection

Out-of-Distribution(OOD) detection, a fundamental machine learning task aimed at identifying abnormal samples, traditionally requires model retraining for different inlier distributions. While recent research demonstrates the applicability of diffusion models to OOD detection, existing approaches are limited to Euclidean or latent image spaces. Our work extends OOD detection to trajectories in the Special Euclidean Group in 3D ($\mathbb{SE}(3)$), addressing a critical need in computer vision, robotics, and engineering applications that process object pose sequences in $\mathbb{SE}(3)$. We present $\textbf{D}$iffusion-based $\textbf{O}$ut-of-distribution detection on $\mathbb{SE}(3)$ ($\mathbf{DOSE3}$), a novel OOD framework that extends diffusion to a unified sample space of $\mathbb{SE}(3)$ pose sequences. Through extensive validation on multiple benchmark datasets, we demonstrate $\mathbf{DOSE3}$'s superior performance compared to state-of-the-art OOD detection frameworks.
RapidPen: Fully Automated IP-to-Shell Penetration Testing with LLM-based Agents
Authors: Sho Nakatani
Date: 2025-02-23 00:00:00
Tasks: Task Planning

We present RapidPen, a fully automated penetration testing (pentesting) framework that addresses the challenge of achieving an initial foothold (IP-to-Shell) without human intervention. Unlike prior approaches that focus primarily on post-exploitation or require a human-in-the-loop, RapidPen leverages large language models (LLMs) to autonomously discover and exploit vulnerabilities, starting from a single IP address. By integrating advanced ReAct-style task planning (Re) with retrieval-augmented knowledge bases of successful exploits, along with a command-generation and direct execution feedback loop (Act), RapidPen systematically scans services, identifies viable attack vectors, and executes targeted exploits in a fully automated manner. In our evaluation against a vulnerable target from the Hack The Box platform, RapidPen achieved shell access within 200-400 seconds at a per-run cost of approximately \$0.3-\$0.6, demonstrating a 60\% success rate when reusing prior "success-case" data. These results underscore the potential of truly autonomous pentesting for both security novices and seasoned professionals. Organizations without dedicated security teams can leverage RapidPen to quickly identify critical vulnerabilities, while expert pentesters can offload repetitive tasks and focus on complex challenges. Ultimately, our work aims to make penetration testing more accessible and cost-efficient, thereby enhancing the overall security posture of modern software ecosystems.
DeepSeek reshaping healthcare in China's tertiary hospitals
Authors: Jishizhan Chen, Qingzeng Zhang
Date: 2025-02-23 00:00:00
Tasks: Diagnostic

The rapid integration of artificial intelligence (AI) into healthcare is transforming clinical decision-making and hospital operations. DeepSeek has emerged as a leading AI system, widely deployed across China's tertiary hospitals since January 2025. Initially implemented in Shanghai's major medical institutions, it has since expanded nationwide, enhancing diagnostic accuracy, streamlining workflows, and improving patient management. AI-powered pathology, imaging analysis, and clinical decision support systems have demonstrated significant potential in optimizing medical processes and reducing the cognitive burden on healthcare professionals. However, the widespread adoption of AI in healthcare raises critical regulatory and ethical challenges, particularly regarding accountability in AI-assisted diagnosis and the risk of automation bias. The absence of a well-defined liability framework underscores the need for policies that ensure AI functions as an assistive tool rather than an autonomous decision-maker. With continued technological advancements, AI is expected to integrate multimodal data sources, such as genomics and radiomics, paving the way for precision medicine and personalized treatment strategies. The future of AI in healthcare depends on the development of transparent regulatory structures, industry collaboration, and adaptive governance frameworks that balance innovation with responsibility, ensuring equitable and effective AI-driven medical services.
AUKT: Adaptive Uncertainty-Guided Knowledge Transfer with Conformal Prediction
Authors: Rui Liu, Peng Gao, Yu Shen, Ming Lin, Pratap Tokekar
Date: 2025-02-23 00:00:00
Tasks: Autonomous Driving, Conformal Prediction, image-classification, Image Classification, Prediction, Transfer Learning

Knowledge transfer between teacher and student models has proven effective across various machine learning applications. However, challenges arise when the teacher's predictions are noisy, or the data domain during student training shifts from the teacher's pretraining data. In such scenarios, blindly relying on the teacher's predictions can lead to suboptimal knowledge transfer. To address these challenges, we propose a novel and universal framework, Adaptive Uncertainty-guided Knowledge Transfer ($\textbf{AUKT}$), which leverages Conformal Prediction (CP) to dynamically adjust the student's reliance on the teacher's guidance based on the teacher's prediction uncertainty. CP is a distribution-free, model-agnostic approach that provides reliable prediction sets with statistical coverage guarantees and minimal computational overhead. This adaptive mechanism mitigates the risk of learning undesirable or incorrect knowledge. We validate the proposed framework across diverse applications, including image classification, imitation-guided reinforcement learning, and autonomous driving. Experimental results consistently demonstrate that our approach improves performance, robustness and transferability, offering a promising direction for enhanced knowledge transfer in real-world applications.
Order-Optimal Projection-Free Algorithm for Adversarially Constrained Online Convex Optimization
Authors: Yiyang Lu, Mohammad Pedramfar, Vaneet Aggarwal
Date: 2025-02-23 00:00:00

Projection-based algorithms for constrained Online Convex Optimization (COCO) face scalability challenges in high-dimensional settings due to the computational complexity of projecting iterates onto constraint sets. This paper introduces a projection-free algorithm for COCO that achieves state-of-the-art performance guarantees while eliminating the need for projections. By integrating a separation oracle with adaptive Online Gradient Descent (OGD) and employing a Lyapunov-driven surrogate function, while dynamically adjusting step sizes using gradient norms, our method jointly optimizes the regret and cumulative constraint violation (CCV). We also use a blocked version of OGD that helps achieve tradeoffs betweeen the regret and CCV with the number of calls to the separation oracle. For convex cost functions, our algorithm attains an optimal regret of $\mathcal{O}(\sqrt{T})$ and a CCV of $\mathcal{O}(\sqrt{T} \log T)$, matching the best-known projection-based results, while only using $\tilde{\mathcal{O}}({T})$ calls to the separation oracle. The results also demonstrate a tradeoff where lower calls to the separation oracle increase the regret and the CCV. In the strongly convex setting, we further achieve a regret of $\mathcal{O}(\log T)$ and a CCV of $\mathcal{O}(\sqrt{T\log T} )$, while requiring ${\mathcal{O}}({T}^2)$ calls to the separation oracle. Further, tradeoff with the decreasing oracle calls is studied. These results close the gap between projection-free and projection-based approaches, demonstrating that projection-free methods can achieve performance comparable to projection-based counterparts.
SQLong: Enhanced NL2SQL for Longer Contexts with LLMs
Authors: Dai Quoc Nguyen, Cong Duy Vu Hoang, Duy Vu, Gioacchino Tangari, Thanh Tien Vu, Don Dharmasiri, Yuan-Fang Li, Long Duong
Date: 2025-02-23 00:00:00
Tasks: Data Augmentation

Open-weight large language models (LLMs) have significantly advanced performance in the Natural Language to SQL (NL2SQL) task. However, their effectiveness diminishes when dealing with large database schemas, as the context length increases. To address this limitation, we present SQLong, a novel and efficient data augmentation framework designed to enhance LLM performance in long-context scenarios for the NL2SQL task. SQLong generates augmented datasets by extending existing database schemas with additional synthetic CREATE TABLE commands and corresponding data rows, sampled from diverse schemas in the training data. This approach effectively simulates long-context scenarios during finetuning and evaluation. Through experiments on the Spider and BIRD datasets, we demonstrate that LLMs finetuned with SQLong-augmented data significantly outperform those trained on standard datasets. These imply SQLong's practical implementation and its impact on improving NL2SQL capabilities in real-world settings with complex database schemas.
Towards Reinforcement Learning for Exploration of Speculative Execution Vulnerabilities
Authors: Evan Lai, Wenjie Xiong, Edward Suh, Mohit Tiwari, Mulong Luo
Date: 2025-02-24 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

Speculative attacks such as Spectre can leak secret information without being discovered by the operating system. Speculative execution vulnerabilities are finicky and deep in the sense that to exploit them, it requires intensive manual labor and intimate knowledge of the hardware. In this paper, we introduce SpecRL, a framework that utilizes reinforcement learning to find speculative execution leaks in post-silicon (black box) microprocessors.
LED-Merging: Mitigating Safety-Utility Conflicts in Model Merging with Location-Election-Disjoint
Authors: Qianli Ma, Dongrui Liu, Qian Chen, Linfeng Zhang, Jing Shao
Date: 2025-02-24 00:00:00
Tasks: GSM8K

Fine-tuning pre-trained Large Language Models (LLMs) for specialized tasks incurs substantial computational and data costs. While model merging offers a training-free solution to integrate multiple task-specific models, existing methods suffer from safety-utility conflicts where enhanced general capabilities degrade safety safeguards. We identify two root causes: \textbf{neuron misidentification} due to simplistic parameter magnitude-based selection, and \textbf{cross-task neuron interference} during merging. To address these challenges, we propose \textbf{LED-Merging}, a three-stage framework that \textbf{L}ocates task-specific neurons via gradient-based attribution, dynamically \textbf{E}lects critical neurons through multi-model importance fusion, and \textbf{D}isjoints conflicting updates through parameter isolation. Extensive experiments on Llama-3-8B, Mistral-7B, and Llama2-13B demonstrate that LED-Merging reduces harmful response rates(\emph{e.g.}, a 31.4\% decrease on Llama-3-8B-Instruct on HarmBench) while preserving 95\% of utility performance(\emph{e.g.}, 52.39\% accuracy on GSM8K). LED-Merging resolves safety-utility conflicts and provides a lightweight, training-free paradigm for constructing reliable multi-task LLMs.