pwc-archive/papers-with-abstracts huggingface dataset:
Using Reinforcement Learning to Train Large Language Models to Explain Human Decisions
Authors: Jian-Qiao Zhu, Hanbo Xie, Dilip Arumugam, Robert C. Wilson, Thomas L. Griffiths
Date: 2025-05-16 00:00:00

A central goal of cognitive modeling is to develop models that not only predict human behavior but also provide insight into the underlying cognitive mechanisms. While neural network models trained on large-scale behavioral data often achieve strong predictive performance, they typically fall short in offering interpretable explanations of the cognitive processes they capture. In this work, we explore the potential of pretrained large language models (LLMs) to serve as dual-purpose cognitive models--capable of both accurate prediction and interpretable explanation in natural language. Specifically, we employ reinforcement learning with outcome-based rewards to guide LLMs toward generating explicit reasoning traces for explaining human risky choices. Our findings demonstrate that this approach produces high-quality explanations alongside strong quantitative predictions of human decisions.
MedGUIDE: Benchmarking Clinical Decision-Making in Large Language Models
Authors: Xiaomin Li, Mingye Gao, Yuexing Hao, Taoran Li, Guangya Wan, Zihan Wang, Yijun Wang
Date: 2025-05-16 00:00:00
Tasks: Benchmarking, Decision Making, Diagnostic, Multiple-choice

Clinical guidelines, typically structured as decision trees, are central to evidence-based medical practice and critical for ensuring safe and accurate diagnostic decision-making. However, it remains unclear whether Large Language Models (LLMs) can reliably follow such structured protocols. In this work, we introduce MedGUIDE, a new benchmark for evaluating LLMs on their ability to make guideline-consistent clinical decisions. MedGUIDE is constructed from 55 curated NCCN decision trees across 17 cancer types and uses clinical scenarios generated by LLMs to create a large pool of multiple-choice diagnostic questions. We apply a two-stage quality selection process, combining expert-labeled reward models and LLM-as-a-judge ensembles across ten clinical and linguistic criteria, to select 7,747 high-quality samples. We evaluate 25 LLMs spanning general-purpose, open-source, and medically specialized models, and find that even domain-specific LLMs often underperform on tasks requiring structured guideline adherence. We also test whether performance can be improved via in-context guideline inclusion or continued pretraining. Our findings underscore the importance of MedGUIDE in assessing whether LLMs can operate safely within the procedural frameworks expected in real-world clinical settings.
Probing the Vulnerability of Large Language Models to Polysemantic Interventions
Authors: Bofan Gong, Shiyang Lai, Dawn Song
Date: 2025-05-16 00:00:00

Polysemanticity -- where individual neurons encode multiple unrelated features -- is a well-known characteristic of large neural networks and remains a central challenge in the interpretability of language models. At the same time, its implications for model safety are also poorly understood. Leveraging recent advances in sparse autoencoders, we investigate the polysemantic structure of two small models (Pythia-70M and GPT-2-Small) and evaluate their vulnerability to targeted, covert interventions at the prompt, feature, token, and neuron levels. Our analysis reveals a consistent polysemantic topology shared across both models. Strikingly, we demonstrate that this structure can be exploited to mount effective interventions on two larger, black-box instruction-tuned models (LLaMA3.1-8B-Instruct and Gemma-2-9B-Instruct). These findings suggest not only the generalizability of the interventions but also point to a stable and transferable polysemantic structure that could potentially persist across architectures and training regimes.
Talk to Your Slides: Language-Driven Agents for Efficient Slide Editing
Authors: Kyudan Jung, Hojun Cho, Jooyeol Yun, Soyoung Yang, Jaehyeok Jang, Jagul Choo
Date: 2025-05-16 00:00:00

Editing presentation slides remains one of the most common and time-consuming tasks faced by millions of users daily, despite significant advances in automated slide generation. Existing approaches have successfully demonstrated slide editing via graphic user interface (GUI)-based agents, offering intuitive visual control. However, such methods often suffer from high computational cost and latency. In this paper, we propose Talk-to-Your-Slides, an LLM-powered agent designed to edit slides %in active PowerPoint sessions by leveraging structured information about slide objects rather than relying on image modality. The key insight of our work is designing the editing process with distinct high-level and low-level layers to facilitate interaction between user commands and slide objects. By providing direct access to application objects rather than screen pixels, our system enables 34.02% faster processing, 34.76% better instruction fidelity, and 87.42% cheaper operation than baselines. To evaluate slide editing capabilities, we introduce TSBench, a human-annotated dataset comprising 379 diverse editing instructions paired with corresponding slide variations in four categories. Our code, benchmark and demos are available at https://anonymous.4open.science/r/Talk-to-Your-Slides-0F4C.
Spectral Policy Optimization: Coloring your Incorrect Reasoning in GRPO
Authors: Peter Chen, Xiaopeng Li, Ziniu Li, Xi Chen, Tianyi Lin
Date: 2025-05-16 00:00:00
Tasks: All, Diversity, Reinforcement Learning (RL)

Reinforcement learning (RL) has demonstrated significant success in enhancing reasoning capabilities in large language models (LLMs). One of the most widely used RL methods is Group Relative Policy Optimization (GRPO)~\cite{Shao-2024-Deepseekmath}, known for its memory efficiency and success in training DeepSeek-R1~\cite{Guo-2025-Deepseek}. However, GRPO stalls when all sampled responses in a group are incorrect -- referred to as an \emph{all-negative-sample} group -- as it fails to update the policy, hindering learning progress. The contributions of this paper are two-fold. First, we propose a simple yet effective framework that introduces response diversity within all-negative-sample groups in GRPO using AI feedback. We also provide a theoretical analysis, via a stylized model, showing how this diversification improves learning dynamics. Second, we empirically validate our approach, showing the improved performance across various model sizes (7B, 14B, 32B) in both offline and online learning settings with 10 benchmarks, including base and distilled variants. Our findings highlight that learning from all-negative-sample groups is not only feasible but beneficial, advancing recent insights from \citet{Xiong-2025-Minimalist}.
ASR-FAIRBENCH: Measuring and Benchmarking Equity Across Speech Recognition Systems
Authors: Anand Rai, Satyam Rahangdale, Utkarsh Anand, Animesh Mukherjee
Date: 2025-05-16 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Benchmarking, Fairness, speech-recognition, Speech Recognition

Automatic Speech Recognition (ASR) systems have become ubiquitous in everyday applications, yet significant disparities in performance across diverse demographic groups persist. In this work, we introduce the ASR-FAIRBENCH leaderboard which is designed to assess both the accuracy and equity of ASR models in real-time. Leveraging the Meta's Fair-Speech dataset, which captures diverse demographic characteristics, we employ a mixed-effects Poisson regression model to derive an overall fairness score. This score is integrated with traditional metrics like Word Error Rate (WER) to compute the Fairness Adjusted ASR Score (FAAS), providing a comprehensive evaluation framework. Our approach reveals significant performance disparities in SOTA ASR models across demographic groups and offers a benchmark to drive the development of more inclusive ASR technologies.
Recombinant dynamical systems
Authors: Saul Kato
Date: 2025-05-19 00:00:00

We describe a connectionist model that attempts to capture a notion of experience-based problem solving or task learning, whereby solutions to newly encountered problems are composed from remembered solutions to prior problems. We apply this model to the computational problem of \emph{efficient sequence generation}, a problem for which there is no obvious gradient descent procedure, and for which not all posable problem instances are solvable. Empirical tests show promising evidence of utility.
Stochastic Orthogonal Regularization for deep projective priors
Authors: Ali Joundi, Yann Traonmilin, Alasdair Newson
Date: 2025-05-19 00:00:00
Tasks: Denoising

Many crucial tasks of image processing and computer vision are formulated as inverse problems. Thus, it is of great importance to design fast and robust algorithms to solve these problems. In this paper, we focus on generalized projected gradient descent (GPGD) algorithms where generalized projections are realized with learned neural networks and provide state-of-the-art results for imaging inverse problems. Indeed, neural networks allow for projections onto unknown low-dimensional sets that model complex data, such as images. We call these projections deep projective priors. In generic settings, when the orthogonal projection onto a lowdimensional model set is used, it has been shown, under a restricted isometry assumption, that the corresponding orthogonal PGD converges with a linear rate, yielding near-optimal convergence (within the class of GPGD methods) in the classical case of sparse recovery. However, for deep projective priors trained with classical mean squared error losses, there is little guarantee that the hypotheses for linear convergence are satisfied. In this paper, we propose a stochastic orthogonal regularization of the training loss for deep projective priors. This regularization is motivated by our theoretical results: a sufficiently good approximation of the orthogonal projection guarantees linear stable recovery with performance close to orthogonal PGD. We show experimentally, using two different deep projective priors (based on autoencoders and on denoising networks), that our stochastic orthogonal regularization yields projections that improve convergence speed and robustness of GPGD in challenging inverse problem settings, in accordance with our theoretical findings.
Hierarchical Representations for Evolving Acyclic Vector Autoregressions (HEAVe)
Authors: Cameron Cornell, Lewis Mitchell, Matthew Roughan
Date: 2025-05-19 00:00:00
Tasks: Econometrics, Time Series

Causal networks offer an intuitive framework to understand influence structures within time series systems. However, the presence of cycles can obscure dynamic relationships and hinder hierarchical analysis. These networks are typically identified through multivariate predictive modelling, but enforcing acyclic constraints significantly increases computational and analytical complexity. Despite recent advances, there remains a lack of simple, flexible approaches that are easily tailorable to specific problem instances. We propose an evolutionary approach to fitting acyclic vector autoregressive processes and introduces a novel hierarchical representation that directly models structural elements within a time series system. On simulated datasets, our model retains most of the predictive accuracy of unconstrained models and outperforms permutation-based alternatives. When applied to a dataset of 100 cryptocurrency return series, our method generates acyclic causal networks capturing key structural properties of the unconstrained model. The acyclic networks are approximately sub-graphs of the unconstrained networks, and most of the removed links originate from low-influence nodes. Given the high levels of feature preservation, we conclude that this cryptocurrency price system functions largely hierarchically. Our findings demonstrate a flexible, intuitive approach for identifying hierarchical causal networks in time series systems, with broad applications to fields like econometrics and social network analysis.
Efficient Heuristics Generation for Solving Combinatorial Optimization Problems Using Large Language Models
Authors: Xuan Wu, Di Wang, Chunguo Wu, Lijie Wen, Chunyan Miao, Yubin Xiao, You Zhou
Date: 2025-05-19 00:00:00
Tasks: Combinatorial Optimization, Semantic Similarity, Semantic Textual Similarity

Recent studies exploited Large Language Models (LLMs) to autonomously generate heuristics for solving Combinatorial Optimization Problems (COPs), by prompting LLMs to first provide search directions and then derive heuristics accordingly. However, the absence of task-specific knowledge in prompts often leads LLMs to provide unspecific search directions, obstructing the derivation of well-performing heuristics. Moreover, evaluating the derived heuristics remains resource-intensive, especially for those semantically equivalent ones, often requiring omissible resource expenditure. To enable LLMs to provide specific search directions, we propose the Hercules algorithm, which leverages our designed Core Abstraction Prompting (CAP) method to abstract the core components from elite heuristics and incorporate them as prior knowledge in prompts. We theoretically prove the effectiveness of CAP in reducing unspecificity and provide empirical results in this work. To reduce computing resources required for evaluating the derived heuristics, we propose few-shot Performance Prediction Prompting (PPP), a first-of-its-kind method for the Heuristic Generation (HG) task. PPP leverages LLMs to predict the fitness values of newly derived heuristics by analyzing their semantic similarity to previously evaluated ones. We further develop two tailored mechanisms for PPP to enhance predictive accuracy and determine unreliable predictions, respectively. The use of PPP makes Hercules more resource-efficient and we name this variant Hercules-P. Extensive experiments across four HG tasks, five COPs, and eight LLMs demonstrate that Hercules outperforms the state-of-the-art LLM-based HG algorithms, while Hercules-P excels at minimizing required computing resources. In addition, we illustrate the effectiveness of CAP, PPP, and the other proposed mechanisms by conducting relevant ablation studies.
Optimizing Interplanetary Trajectories using Hybrid Meta-heuristic
Authors: Amin Abdollahi Dehkordi, Mehdi Neshat
Date: 2025-05-18 00:00:00
Tasks: Metaheuristic Optimization

This paper proposes an advanced hybrid optimization (GMPA) algorithm to effectively address the inherent limitations of the Grey Wolf Optimizer (GWO) when applied to complex optimization scenarios. Specifically, GMPA integrates essential features from the Marine Predators Algorithm (MPA) into the GWO framework, enabling superior performance through enhanced exploration and exploitation balance. The evaluation utilizes the GTOPX benchmark dataset from the European Space Agency (ESA), encompassing highly complex interplanetary trajectory optimization problems characterized by pronounced nonlinearity and multiple conflicting objectives reflective of real-world aerospace scenarios. Central to GMPA's methodology is an elite matrix, borrowed from MPA, designed to preserve and refine high-quality solutions iteratively, thereby promoting solution diversity and minimizing premature convergence. Furthermore, GMPA incorporates a three-phase position updating mechanism combined with L\'evy flights and Brownian motion to significantly bolster exploration capabilities, effectively mitigating the risk of stagnation in local optima. GMPA dynamically retains historical information on promising search areas, leveraging the memory storage features intrinsic to MPA, facilitating targeted exploitation and refinement. Empirical evaluations demonstrate GMPA's superior effectiveness compared to traditional GWO and other advanced metaheuristic algorithms, achieving markedly improved convergence rates and solution quality across GTOPX benchmarks. Consequently, GMPA emerges as a robust, efficient, and adaptive optimization approach particularly suitable for high-dimensional and complex aerospace trajectory optimization, offering significant insights and practical advancements in hybrid metaheuristic optimization techniques.
CALM: Co-evolution of Algorithms and Language Model for Automatic Heuristic Design
Authors: Ziyao Huang, Weiwei Wu, Kui Wu, JianPing Wang, Wei-Bin Lee
Date: 2025-05-18 00:00:00
Tasks: GPU, Language Modeling, Language Modelling, Quantization

Tackling complex optimization problems often relies on expert-designed heuristics, typically crafted through extensive trial and error. Recent advances demonstrate that large language models (LLMs), when integrated into well-designed evolutionary search frameworks, can autonomously discover high-performing heuristics at a fraction of the traditional cost. However, existing approaches predominantly rely on verbal guidance, i.e., manipulating the prompt generation process, to steer the evolution of heuristics, without adapting the underlying LLM. We propose a hybrid framework that combines verbal and numerical guidance, the latter achieved by fine-tuning the LLM via reinforcement learning based on the quality of generated heuristics. This joint optimization allows the LLM to co-evolve with the search process. Our method outperforms state-of-the-art (SOTA) baselines across various optimization tasks, running locally on a single 24GB GPU using a 7B model with INT4 quantization. It surpasses methods that rely solely on verbal guidance, even when those use significantly more powerful API-based models.
Bishop: Sparsified Bundling Spiking Transformers on Heterogeneous Cores with Error-Constrained Pruning
Authors: Boxun Xu, Yuxuan Yin, Vikram Iyer, Peng Li
Date: 2025-05-18 00:00:00

We present Bishop, the first dedicated hardware accelerator architecture and HW/SW co-design framework for spiking transformers that optimally represents, manages, and processes spike-based workloads while exploring spatiotemporal sparsity and data reuse. Specifically, we introduce the concept of Token-Time Bundle (TTB), a container that bundles spiking data of a set of tokens over multiple time points. Our heterogeneous accelerator architecture Bishop concurrently processes workload packed in TTBs and explores intra- and inter-bundle multiple-bit weight reuse to significantly reduce memory access. Bishop utilizes a stratifier, a dense core array, and a sparse core array to process MLP blocks and projection layers. The stratifier routes high-density spiking activation workload to the dense core and low-density counterpart to the sparse core, ensuring optimized processing tailored to the given spatiotemporal sparsity level. To further reduce data access and computation, we introduce a novel Bundle Sparsity-Aware (BSA) training pipeline that enhances not only the overall but also structured TTB-level firing sparsity. Moreover, the processing efficiency of self-attention layers is boosted by the proposed Error-Constrained TTB Pruning (ECP), which trims activities in spiking queries, keys, and values both before and after the computation of spiking attention maps with a well-defined error bound. Finally, we design a reconfigurable TTB spiking attention core to efficiently compute spiking attention maps by executing highly simplified "AND" and "Accumulate" operations. On average, Bishop achieves a 5.91x speedup and 6.11x improvement in energy efficiency over previous SNN accelerators, while delivering higher accuracy across multiple datasets.
Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware
Authors: Zhenhui Chen, Haoran Xu, De Ma
Date: 2025-05-18 00:00:00
Tasks: Distributed Computing

Neuromorphic hardware has been proposed and also been produced for decades. One of the main goals of this hardware is to leverage distributed computing and event-driven circuit design and achieve power-efficient AI system. The name ``neuromorphic'' is derived from its spiking and local computational nature, which mimics the fundamental activity of an animal's nervous system. Neurons as well as distributed computing cores of neuromorphic hardware use single bit data, called a spike, for inter-communication. To construct a spiking model for neuromorphic hardware, the conventional approach is to build spiking neural networks (SNNs). SNN replaces the nonlinearity part of artificial neural networks (ANNs) in the realm of deep learning with spiking neurons, where the spiking neuron mimic the basic behavior of bio-neurons. However, there is still a performance gap between SNN and ANN counterpart. In this paper, we explore a new path from ANN to neuromorphic hardware. The SDANN framework is proposed to directly implement quantized ANN on hardware, eliminating the need for tuning the trainable parameters or any performance degradation. With the power of quantized ANN, our SDANN provides a lower bound of the functionality of neuromorphic hardware. Meanwhile, we have also proposed scaling methods in case of the limited bit-width support in hardware. Spike sparsity methods are also provided for further energy optimization. Experiments on various tasks demonstrate the usefulness of our SDANN framework. Beyond toy examples and software implementation, we successfully deploy the spiking models of SDANN on real neuromorphic hardware, demonstrating the feasibility of the SDANN framework.
Adaptive Gradient Learning for Spiking Neural Networks by Exploiting Membrane Potential Dynamics
Authors: Jiaqiang Jiang, Lei Wang, Runhao Jiang, Jing Fan, Rui Yan
Date: 2025-05-17 00:00:00

Brain-inspired spiking neural networks (SNNs) are recognized as a promising avenue for achieving efficient, low-energy neuromorphic computing. Recent advancements have focused on directly training high-performance SNNs by estimating the approximate gradients of spiking activity through a continuous function with constant sharpness, known as surrogate gradient (SG) learning. However, as spikes propagate among neurons, the distribution of membrane potential dynamics (MPD) will deviate from the gradient-available interval of fixed SG, hindering SNNs from searching the optimal solution space. To maintain the stability of gradient flows, SG needs to align with evolving MPD. Here, we propose adaptive gradient learning for SNNs by exploiting MPD, namely MPD-AGL. It fully accounts for the underlying factors contributing to membrane potential shifts and establishes a dynamic association between SG and MPD at different timesteps to relax gradient estimation, which provides a new degree of freedom for SG learning. Experimental results demonstrate that our method achieves excellent performance at low latency. Moreover, it increases the proportion of neurons that fall into the gradient-available interval compared to fixed SG, effectively mitigating the gradient vanishing problem.
The Geometry of ReLU Networks through the ReLU Transition Graph
Authors: Sahil Rajesh Dhayalkar
Date: 2025-05-16 00:00:00

We develop a novel theoretical framework for analyzing ReLU neural networks through the lens of a combinatorial object we term the ReLU Transition Graph (RTG). In this graph, each node corresponds to a linear region induced by the network's activation patterns, and edges connect regions that differ by a single neuron flip. Building on this structure, we derive a suite of new theoretical results connecting RTG geometry to expressivity, generalization, and robustness. Our contributions include tight combinatorial bounds on RTG size and diameter, a proof of RTG connectivity, and graph-theoretic interpretations of VC-dimension. We also relate entropy and average degree of the RTG to generalization error. Each theoretical result is rigorously validated via carefully controlled experiments across varied network depths, widths, and data regimes. This work provides the first unified treatment of ReLU network structure via graph theory and opens new avenues for compression, regularization, and complexity control rooted in RTG analysis.