pwc-archive/papers-with-abstracts huggingface dataset:
Attention-Based Multiscale Temporal Fusion Network for Uncertain-Mode Fault Diagnosis in Multimode Processes
Authors: Guangqiang Li, M. Amine Atoui, Xiangshun Li
Date: 2025-04-07 00:00:00
Tasks: Diagnostic, Fault Diagnosis

Fault diagnosis in multimode processes plays a critical role in ensuring the safe operation of industrial systems across multiple modes. It faces a great challenge yet to be addressed - that is, the significant distributional differences among monitoring data from multiple modes make it difficult for the models to extract shared feature representations related to system health conditions. In response to this problem, this paper introduces a novel method called attention-based multiscale temporal fusion network. The multiscale depthwise convolution and gated recurrent unit are employed to extract multiscale contextual local features and long-short-term features. Instance normalization is applied to suppress mode-specific information. Furthermore, a temporal attention mechanism is designed to focus on critical time points with higher cross-mode shared information, thereby enhancing the accuracy of fault diagnosis. The proposed model is applied to Tennessee Eastman process dataset and three-phase flow facility dataset. The experiments demonstrate that the proposed model achieves superior diagnostic performance and maintains a small model size. The source code will be available on GitHub at https://github.com/GuangqiangLi/AMTFNet.
Structured Preference Optimization for Vision-Language Long-Horizon Task Planning
Authors: Xiwen Liang, Min Lin, Weiqi Ruan, Rongtao Xu, Yuecheng Liu, Jiaqi Chen, Bingqian Lin, Yuzheng Zhuang, Xiaodan Liang
Date: 2025-02-28 00:00:00
Tasks: Task Planning, Visual Grounding

Existing methods for vision-language task planning excel in short-horizon tasks but often fall short in complex, long-horizon planning within dynamic environments. These challenges primarily arise from the difficulty of effectively training models to produce high-quality reasoning processes for long-horizon tasks. To address this, we propose Structured Preference Optimization (SPO), which aims to enhance reasoning and action selection in long-horizon task planning through structured preference evaluation and optimized training strategies. Specifically, SPO introduces: 1) Preference-Based Scoring and Optimization, which systematically evaluates reasoning chains based on task relevance, visual grounding, and historical consistency; and 2) Curriculum-Guided Training, where the model progressively adapts from simple to complex tasks, improving its generalization ability in long-horizon scenarios and enhancing reasoning robustness. To advance research in vision-language long-horizon task planning, we introduce ExtendaBench, a comprehensive benchmark covering 1,509 tasks across VirtualHome and Habitat 2.0, categorized into ultra-short, short, medium, and long tasks. Experimental results demonstrate that SPO significantly improves reasoning quality and final decision accuracy, outperforming prior methods on long-horizon tasks and underscoring the effectiveness of preference-driven optimization in vision-language task planning. Specifically, SPO achieves a +5.98% GCR and +4.68% SR improvement in VirtualHome and a +3.30% GCR and +2.11% SR improvement in Habitat over the best-performing baselines.
Enhancing Compositional Reasoning in Vision-Language Models with Synthetic Preference Data
Authors: Samarth Mishra, Kate Saenko, Venkatesh Saligrama
Date: 2025-04-07 00:00:00
Tasks: Question Answering, Visual Question Answering

Compositionality, or correctly recognizing scenes as compositions of atomic visual concepts, remains difficult for multimodal large language models (MLLMs). Even state of the art MLLMs such as GPT-4o can make mistakes in distinguishing compositions like "dog chasing cat" vs "cat chasing dog". While on Winoground, a benchmark for measuring such reasoning, MLLMs have made significant progress, they are still far from a human's performance. We show that compositional reasoning in these models can be improved by elucidating such concepts via data, where a model is trained to prefer the correct caption for an image over a close but incorrect one. We introduce SCRAMBLe: Synthetic Compositional Reasoning Augmentation of MLLMs with Binary preference Learning, an approach for preference tuning open-weight MLLMs on synthetic preference data generated in a fully automated manner from existing image-caption data. SCRAMBLe holistically improves these MLLMs' compositional reasoning capabilities which we can see through significant improvements across multiple vision language compositionality benchmarks, as well as smaller but significant improvements on general question answering tasks. As a sneak peek, SCRAMBLe tuned Molmo-7B model improves on Winoground from 49.5% to 54.8% (best reported to date), while improving by ~1% on more general visual question answering tasks. Code for SCRAMBLe along with tuned models and our synthetic training dataset is available at https://github.com/samarth4149/SCRAMBLe.
VITED: Video Temporal Evidence Distillation
Authors: Yujie Lu, Yale Song, William Wang, Lorenzo Torresani, Tushar Nagarajan
Date: 2025-03-17 00:00:00
Proceeding: CVPR 2025 1
Tasks: Question Answering, Video Question Answering

We investigate complex video question answering via chain-of-evidence reasoning -- identifying sequences of temporal spans from multiple relevant parts of the video, together with visual evidence within them. Existing models struggle with multi-step reasoning as they uniformly sample a fixed number of frames, which can miss critical evidence distributed nonuniformly throughout the video. Moreover, they lack the ability to temporally localize such evidence in the broader context of the full video, which is required for answering complex questions. We propose a framework to enhance existing VideoQA datasets with evidence reasoning chains, automatically constructed by searching for optimal intervals of interest in the video with supporting evidence, that maximizes the likelihood of answering a given question. We train our model (VITED) to generate these evidence chains directly, enabling it to both localize evidence windows as well as perform multi-step reasoning across them in long-form video content. We show the value of our evidence-distilled models on a suite of long video QA benchmarks where we outperform state-of-the-art approaches that lack evidence reasoning capabilities.
SCALM: Towards Semantic Caching for Automated Chat Services with Large Language Models
Authors: Jiaxing Li, Chi Xu, Feng Wang, Isaac M von Riedemann, Cong Zhang, Jiangchuan Liu
Date: 2024-05-24 00:00:00

Large Language Models (LLMs) have become increasingly popular, transforming a wide range of applications across various domains. However, the real-world effectiveness of their query cache systems has not been thoroughly investigated. In this work, we for the first time conducted an analysis on real-world human-to-LLM interaction data, identifying key challenges in existing caching solutions for LLM-based chat services. Our findings reveal that current caching methods fail to leverage semantic connections, leading to inefficient cache performance and extra token costs. To address these issues, we propose SCALM, a new cache architecture that emphasizes semantic analysis and identifies significant cache entries and patterns. We also detail the implementations of the corresponding cache storage and eviction strategies. Our evaluations show that SCALM increases cache hit ratios and reduces operational costs for LLMChat services. Compared with other state-of-the-art solutions in GPTCache, SCALM shows, on average, a relative increase of 63% in cache hit ratio and a relative improvement of 77% in tokens savings.
The anatomy of a quadruply imaged gravitational lens system
Authors: S. H. Suyu, R. D. Blandford
Date: 2005-06-27 00:00:00
Tasks: Anatomy, valid

The key to using a strong gravitational lens system to measure the Hubble constant is to obtain an accurate model of the lens potential. In this paper, we investigate the properties of gravitational lens B1608+656, a quadruply-imaged lens system with an extended source intensity distribution. Our analysis is valid for generic quadruply-lensed systems. Limit curves and isophotal separatrices are defined for such systems, and we show that the isophotal separatrices must intersect at the critical curves and the satellite isophotes must be tangent to the limit curves. The current model of B1608+656 (Koopmans et al. 2003) satisfies these criteria for some, but not all, of the isophotal separatrices within the observational uncertainty. We study a non-parametric method of potential reconstruction proposed by Blandford, Surpi & Kundic (2001) and demonstrate that although the method works in principle and elucidates image formation, the initial potential only converges to the true model when it is within ~ 1 percent of the true model.
Gravitational Imaging of CDM Substructure
Authors: L. V. E. Koopmans
Date: 2005-01-16 00:00:00

We propose the novel method of ``gravitational imaging'' to detect and quantify luminous and dark-matter substructure in gravitational-lens galaxies. The method utilizes highly-magnified Einstein rings and arcs as sensitive probes of small perturbations in the lens potential (due to the presence of mass substructure), reconstructing the gravitational lens potential non-parametrically. Numerical simulations show that the implemented algorithm can reconstruct the smooth mass distribution of a typical lens galaxy - exhibiting reasonable signal-to-noise Einstein rings - as well as compact substructure with masses as low as M_sub~10^-3 M_lens, if present. ``Gravitational imaging'' of pure dark-matter substructure around massive galaxies can provide a new window on the standard cold-dark-matter paradigm, using very different physics than ground-based direct-detection experiments, and probe the hierarchical structure-formation model which predicts this substructure to exist in great abundance.
Dissecting the Gravitational Lens B1608+656. I. Lens Potential Reconstruction
Authors: S. H. Suyu, P. J. Marshall, R. D. Blandford, C. D. Fassnacht, L. V. E. Koopmans, J. P. McKean, T. Treu
Date: 2008-04-17 00:00:00

Strong gravitational lensing is a powerful technique for probing galaxy mass distributions and for measuring cosmological parameters. We present a pixelated approach to modeling simultaneously the lens potential and source intensity of strong gravitational lens systems with extended source-intensity distributions. For systems with sources of sufficient extent such that the separate lensed images are connected by intensity measurements, the accuracy in the reconstructed potential is solely limited by the quality of the data. We apply this potential reconstruction technique to deep HST observations of B1608+656, a four-image gravitational lens system formed by a pair of interacting lens galaxies. We present a comprehensive Bayesian analysis of the system that takes into account the extended source-intensity distribution, dust extinction, and the interacting lens galaxies. Our approach allows us to compare various models of the components of the lens system, which include the point-spread function (PSF), dust, lens galaxy light, source-intensity distribution, and lens potential. Using optimal combinations of the PSF, dust, and lens galaxy light models, we successfully reconstruct both the lens potential and the extended source-intensity distribution of B1608+656. The resulting reconstruction can be used as the basis of a measurement of the Hubble constant. We use our reconstruction of the gravitational potential to study the relative distribution of mass and light in the lensing galaxies. We find that the mass-to-light ratio for the primary lens galaxy is (2.0+/-0.2)h M_{\sun} L_{B,\sun}^{-1} within the Einstein radius 3.9 h^{-1} kpc, in agreement with what is found for noninteracting lens galaxies at the same scales. (Abridged)
Bayesian Strong Gravitational-Lens Modeling on Adaptive Grids: Objective Detection of Mass Substructure in Galaxies
Authors: S. Vegetti, L. V. E. Koopmans
Date: 2008-05-02 00:00:00

We introduce a new adaptive and fully Bayesian grid-based method to model strong gravitational lenses with extended images. The primary goal of this method is to quantify the level of luminous and dark-mass substructure in massive galaxies, through their effect on highly-magnified arcs and Einstein rings. The method is adaptive on the source plane, where a Delaunay tessellation is defined according to the lens mapping of a regular grid onto the source plane. The Bayesian penalty function allows us to recover the best non-linear potential-model parameters and/or a grid-based potential correction and to objectively quantify the level of regularization for both the source and the potential. In addition, we implement a Nested-Sampling technique to quantify the errors on all non-linear mass model parameters -- ... -- and allow an objective ranking of different potential models in terms of the marginalized evidence. In particular, we are interested in comparing very smooth lens mass models with ones that contain mass-substructures. The algorithm has been tested on a range of simulated data sets, created from a model of a realistic lens system. One of the lens systems is characterized by a smooth potential with a power-law density profile, twelve include a NFW dark-matter substructure of different masses and at different positions and one contains two NFW dark substructures with the same mass but with different positions. Reconstruction of the source and of the lens potential for all of these systems shows the method is able, in a realistic scenario, to identify perturbations with masses >=10^7 solar mass when located on the Einstein ring. For positions both inside and outside of the ring, masses of at least 10^9 solar mass are required (...).
Language Agents Mirror Human Causal Reasoning Biases. How Can We Help Them Think Like Scientists?
Authors: Anthony GX-Chen, Dongyan Lin, Mandana Samiei, Doina Precup, Blake A. Richards, Rob Fergus, Kenneth Marino
Date: 2025-05-14 00:00:00
Tasks: Efficient Exploration

Language model (LM) agents are increasingly used as autonomous decision-makers who need to actively gather information to guide their decisions. A crucial cognitive skill for such agents is the efficient exploration and understanding of the causal structure of the world -- key to robust, scientifically grounded reasoning. Yet, it remains unclear whether LMs possess this capability or exhibit systematic biases leading to erroneous conclusions. In this work, we examine LMs' ability to explore and infer causal relationships, using the well-established "Blicket Test" paradigm from developmental psychology. We find that LMs reliably infer the common, intuitive disjunctive causal relationships but systematically struggle with the unusual, yet equally (or sometimes even more) evidenced conjunctive ones. This "disjunctive bias" persists across model families, sizes, and prompting strategies, and performance further declines as task complexity increases. Interestingly, an analogous bias appears in human adults, suggesting that LMs may have inherited deep-seated reasoning heuristics from their training data. To this end, we quantify similarities between LMs and humans, finding that LMs exhibit adult-like inference profiles (but not children-like). Finally, we propose a test-time sampling method which explicitly samples and eliminates hypotheses about causal relationships from the LM. This scalable approach significantly reduces the disjunctive bias and moves LMs closer to the goal of scientific, causally rigorous reasoning.
Customizing a Large Language Model for VHDL Design of High-Performance Microprocessors
Authors: Nicolas Dupuis, Ravi Nair, Shyam Ramji, Sean McClintock, Nishant Chauhan, Priyanka Nagpal, Bart Blaner, Ken Valk, Leon Stok, Ruchir Puri
Date: 2025-05-14 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

The use of Large Language Models (LLMs) in hardware design has taken off in recent years, principally through its incorporation in tools that increase chip designer productivity. There has been considerable discussion about the use of LLMs in RTL specifications of chip designs, for which the two most popular languages are Verilog and VHDL. LLMs and their use in Verilog design has received significant attention due to the higher popularity of the language, but little attention so far has been given to VHDL despite its continued popularity in the industry. There has also been little discussion about the unique needs of organizations that engage in high-performance processor design, and techniques to deploy AI solutions in these settings. In this paper, we describe our journey in developing a Large Language Model (LLM) specifically for the purpose of explaining VHDL code, a task that has particular importance in an organization with decades of experience and assets in high-performance processor design. We show how we developed test sets specific to our needs and used them for evaluating models as we performed extended pretraining (EPT) of a base LLM. Expert evaluation of the code explanations produced by the EPT model increased to 69% compared to a base model rating of 43%. We further show how we developed an LLM-as-a-judge to gauge models similar to expert evaluators. This led us to deriving and evaluating a host of new models, including an instruction-tuned version of the EPT model with an expected expert evaluator rating of 71%. Our experiments also indicate that with the potential use of newer base models, this rating can be pushed to 85% and beyond. We conclude with a discussion on further improving the quality of hardware design LLMs using exciting new developments in the Generative AI world.
How Hungry is AI? Benchmarking Energy, Water, and Carbon Footprint of LLM Inference
Authors: Nidhal Jegham, Marwen Abdelatti, Lassad Elmoubarki, Abdeltawab Hendawi
Date: 2025-05-14 00:00:00
Tasks: Benchmarking

This paper introduces a novel infrastructure-aware benchmarking framework for quantifying the environmental footprint of LLM inference across 30 state-of-the-art models as deployed in commercial data centers. Our framework combines public API performance data with region-specific environmental multipliers and statistical inference of hardware configurations. We additionally utilize cross-efficiency Data Envelopment Analysis (DEA) to rank models by performance relative to environmental cost. Our results show that o3 and DeepSeek-R1 emerge as the most energy-intensive models, consuming over 33 Wh per long prompt, more than 70 times the consumption of GPT-4.1 nano, and that Claude-3.7 Sonnet ranks highest in eco-efficiency. While a single short GPT-4o query consumes 0.43 Wh, scaling this to 700 million queries/day results in substantial annual environmental impacts. These include electricity use comparable to 35,000 U.S. homes, freshwater evaporation matching the annual drinking needs of 1.2 million people, and carbon emissions requiring a Chicago-sized forest to offset. These findings illustrate a growing paradox: Although AI is becoming cheaper and faster, its global adoption drives disproportionate resource consumption. Our study provides a standardized, empirically grounded methodology for benchmarking the sustainability of LLM deployments, laying a foundation for future environmental accountability in AI development and sustainability standards.
WorldView-Bench: A Benchmark for Evaluating Global Cultural Perspectives in Large Language Models
Authors: Abdullah Mushtaq, Imran Taj, Rafay Naeem, Ibrahim Ghaznavi, Junaid Qadir
Date: 2025-05-14 00:00:00
Tasks: Benchmarking

Large Language Models (LLMs) are predominantly trained and aligned in ways that reinforce Western-centric epistemologies and socio-cultural norms, leading to cultural homogenization and limiting their ability to reflect global civilizational plurality. Existing benchmarking frameworks fail to adequately capture this bias, as they rely on rigid, closed-form assessments that overlook the complexity of cultural inclusivity. To address this, we introduce WorldView-Bench, a benchmark designed to evaluate Global Cultural Inclusivity (GCI) in LLMs by analyzing their ability to accommodate diverse worldviews. Our approach is grounded in the Multiplex Worldview proposed by Senturk et al., which distinguishes between Uniplex models, reinforcing cultural homogenization, and Multiplex models, which integrate diverse perspectives. WorldView-Bench measures Cultural Polarization, the exclusion of alternative perspectives, through free-form generative evaluation rather than conventional categorical benchmarks. We implement applied multiplexity through two intervention strategies: (1) Contextually-Implemented Multiplex LLMs, where system prompts embed multiplexity principles, and (2) Multi-Agent System (MAS)-Implemented Multiplex LLMs, where multiple LLM agents representing distinct cultural perspectives collaboratively generate responses. Our results demonstrate a significant increase in Perspectives Distribution Score (PDS) entropy from 13% at baseline to 94% with MAS-Implemented Multiplex LLMs, alongside a shift toward positive sentiment (67.7%) and enhanced cultural balance. These findings highlight the potential of multiplex-aware AI evaluation in mitigating cultural bias in LLMs, paving the way for more inclusive and ethically aligned AI systems.
Variational Visual Question Answering
Authors: Tobias Jan Wieczorek, Nathalie Daun, Mohammad Emtiyaz Khan, Marcus Rohrbach
Date: 2025-05-14 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Despite remarkable progress in multimodal models for Visual Question Answering (VQA), there remain major reliability concerns because the models can often be overconfident and miscalibrated, especially in out-of-distribution (OOD) settings. Plenty has been done to address such issues for unimodal models, but little work exists for multimodal cases. Here, we address unreliability in multimodal models by proposing a Variational VQA approach. Specifically, instead of fine-tuning vision-language models by using AdamW, we employ a recently proposed variational algorithm called IVON, which yields a posterior distribution over model parameters. Through extensive experiments, we show that our approach improves calibration and abstentions without sacrificing the accuracy of AdamW. For instance, compared to AdamW fine-tuning, we reduce Expected Calibration Error by more than 50% compared to the AdamW baseline and raise Coverage by 4% vs. SOTA (for a fixed risk of 1%). In the presence of distribution shifts, the performance gain is even higher, achieving 8% Coverage (@ 1% risk) improvement vs. SOTA when 50% of test cases are OOD. Overall, we present variational learning as a viable option to enhance the reliability of multimodal models.