GenAI4ELab/papercli-papers huggingface dataset:
QiMeng-CodeV-R1: Reasoning-Enhanced Verilog Generation
Authors: Yaoyu Zhu; Di Huang; Hanqi Lyu; Xiaoyun Zhang; Chongxiao Li; Wenxuan Shi; Yutong Wu; Jianan Mu; Jinghua Wang; Yang zhao; Pengwei Jin; Shuyao Cheng; shengwen Liang; Xishan Zhang; Rui Zhang; Zidong Du; Qi Guo; Xing Hu; Yunji Chen
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Large language models (LLMs) trained via reinforcement learning with verifiable reward (RLVR) have achieved breakthroughs on tasks with explicit, automatable verification, such as software programming and mathematical problems. Extending RLVR to electronic design automation (EDA), especially automatically generating hardware description languages (HDLs) like Verilog from natural-language (NL) specifications, however, poses three key challenges: the lack of automated and accurate verification environments, the scarcity of high-quality NL-code pairs, and the prohibitive computation cost of RLVR. To this end, we introduce CodeV-R1, an RLVR framework for training Verilog generation LLMs. First, we develop a rule-based testbench generator that performs robust equivalence checking against golden references. Second, we propose a round-trip data synthesis method that pairs open-source Verilog snippets with LLM-generated NL descriptions, verifies code–NL–code consistency via the generated testbench, and filters out inequivalent examples to yield a high-quality dataset. Third, we employ a two-stage "distill-then-RL" training pipeline: distillation for the cold start of reasoning abilities, followed by adaptive DAPO, our novel RLVR algorithm that can reduce training cost by adaptively adjusting sampling rate. The resulting model, CodeV-R1-7B, achieves 68.6 \% and 72.9 \% pass@1 on VerilogEval v2 and RTLLM v1.1, respectively, surpassing prior state-of-the-art by 12$\sim$20 \%, while even exceeding the performance of 671B DeepSeek-R1 on RTLLM. We have released our model, training code, and dataset to facilitate research in EDA and LLM communities.
DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented Generation
Authors: Jiashuo Sun; Xianrui Zhong; Sizhe Zhou; Jiawei Han
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Retrieval-augmented generation (RAG) systems combine large language models (LLMs) with external knowledge retrieval, making them highly effective for knowledge-intensive tasks. A crucial but often under-explored component of these systems is the reranker, which refines retrieved documents to enhance generation quality and explainability. The challenge of selecting the optimal number of documents (k) remains unsolved: too few may omit critical information, while too many introduce noise and inefficiencies. Although recent studies have explored LLM-based rerankers, they primarily leverage internal model knowledge and overlook the rich supervisory signals that LLMs can provide, such as using response quality as feedback for optimizing reranking decisions. In this paper, we propose DynamicRAG, a novel RAG framework where the reranker dynamically adjusts both the order and number of retrieved documents based on the query. We model the reranker as an agent optimized through reinforcement learning (RL), using rewards derived from LLM output quality. Across seven knowledge-intensive datasets, DynamicRAG demonstrates superior performance, achieving state-of-the-art results.
Generalizable Domain Adaptation for Sim-and-Real Policy Co-Training
Authors: Shuo Cheng; Liqian Ma; Zhenyang Chen; Ajay Mandlekar; Caelan Reed Garrett; Danfei Xu
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Behavior cloning has shown promise for robot manipulation, but real-world demonstrations are costly to acquire at scale. While simulated data offers a scalable alternative, particularly with advances in automated demonstration generation, transferring policies to the real world is hampered by various simulation and real domain gaps. In this work, we propose a unified sim-and-real co-training framework for learning generalizable manipulation policies that primarily leverages simulation and only requires a few real-world demonstrations. Central to our approach is learning a domain-invariant, task-relevant feature space. Our key insight is that aligning the joint distributions of observations and their corresponding actions across domains provides a richer signal than aligning observations (marginals) alone. We achieve this by embedding an Optimal Transport (OT)-inspired loss within the co-training framework, and extend this to an Unbalanced OT framework to handle the imbalance between abundant simulation data and limited real-world examples. We validate our method on challenging manipulation tasks, showing it can leverage abundant simulation data to achieve up to a 30\% improvement in the real-world success rate and even generalize to scenarios seen only in simulation.
DeltaProduct: Improving State-Tracking in Linear RNNs via Householder Products
Authors: Julien Siems; Timur Carstensen; Arber Zela; Frank Hutter; Massimiliano Pontil; Riccardo Grazzi
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Linear Recurrent Neural Networks (linear RNNs) have emerged as competitive alternatives to Transformers for sequence modeling, offering efficient training and linear-time inference. However, existing architectures face a fundamental trade-off between expressivity and efficiency, dictated by the structure of their state-transition matrices. Diagonal matrices, used in models such as Mamba, GLA, or mLSTM, yield fast runtime but have limited expressivity. To address this, recent architectures such as DeltaNet and RWKV-7 adopted a diagonal plus rank-1 structure, which allows simultaneous token and channel mixing, 
improving associative recall and, as recently shown, state-tracking when
allowing negative eigenvalues in the state-transition matrices.
Building on the interpretation of DeltaNet's recurrence as performing one step of online gradient descent per token on an associative recall loss, we introduce DeltaProduct, which instead takes multiple ($n_h$) steps per token. This naturally leads to diagonal plus rank-$n_h$ state-transition matrices, formed as products of $n_h$ generalized Householder transformations, providing a tunable mechanism to balance expressivity and efficiency. 
We provide a detailed theoretical characterization of the state-tracking capability of DeltaProduct in finite precision and how it improves by increasing $n_h$.
Our extensive experiments demonstrate that DeltaProduct outperforms DeltaNet in both state-tracking and language modeling, while also showing significantly improved length extrapolation capabilities.
A Unified Framework for Variable Selection in Model-Based Clustering with Missing Not at Random
Authors: Binh Huu Ho; Long Nguyen Chi; TrungTin Nguyen; Van Ha Hoang; Binh T. Nguyen; Christopher Drovandi
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Model-based clustering integrated with variable selection is a powerful tool for uncovering latent structures within complex data. However, its effectiveness is often hindered by challenges such as identifying relevant variables that define heterogeneous subgroups and handling data that are missing not at random, a prevalent issue in fields like transcriptomics. While several notable methods have been proposed to address these problems, they typically tackle each issue in isolation, thereby limiting their flexibility and adaptability. This paper introduces a unified framework designed to address these challenges simultaneously. Our approach incorporates a data-driven penalty matrix into penalized clustering to enable more flexible variable selection, along with a mechanism that explicitly models the relationship between missingness and latent class membership. We demonstrate that, under certain regularity conditions, the proposed framework achieves both asymptotic consistency and selection consistency, even in the presence of missing data. This unified strategy significantly enhances the capability and efficiency of model-based clustering, advancing methodologies for identifying informative variables that define homogeneous subgroups in the presence of complex missing data patterns. The performance of the framework, including its computational efficiency, is evaluated through simulations and demonstrated using both synthetic and real-world transcriptomic datasets.
CoT Red-Handed: Stress Testing Chain-of-Thought Monitoring
Authors: Benjamin Arnav; Pablo Bernabeu-Perez; Nathan Helm-Burger; Timothy Kostolansky; Hannes Whittingham; Mary Phuong
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: As AI models are deployed with increasing autonomy, it is important to ensure they do not take harmful actions unnoticed. As a potential mitigation, we investigate Chain-of-Thought (CoT) monitoring, wherein a weaker trusted monitor model continuously oversees the intermediate reasoning steps of a more powerful but untrusted model. We compare CoT monitoring to action-only monitoring, where only final outputs are reviewed, in a red-teaming setup where the untrusted model is instructed to pursue harmful side tasks while completing a coding problem. We find that while CoT monitoring is more effective than overseeing only model outputs in scenarios where action-only monitoring fails to reliably identify sabotage, reasoning traces can contain misleading rationalizations that deceive the CoT monitors, reducing performance in obvious sabotage cases. To address this, we introduce a hybrid protocol that independently scores model reasoning and actions, and combines them using a weighted average. Our hybrid monitor consistently outperforms both CoT and action-only monitors across all tested models and tasks, with detection rates twice higher than action-only monitoring for subtle deception scenarios.
Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations
Authors: Yuhao Yang; Zhi Ji; Zhaopeng Li; YI LI; Zhonglin Mo; Yue Ding; Kai Chen; Zijian Zhang; Jie Li; shuanglong li; LIU LIN
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Generative models have recently gained attention in recommendation systems by directly predicting item identifiers from user interaction sequences. However, existing methods suffer from significant information loss due to the separation of stages such as quantization and sequence modeling, hindering their ability to achieve the modeling precision and accuracy of sequential dense retrieval techniques. Integrating generative and dense retrieval methods remains a critical challenge. To address this, we introduce the Cascaded Organized Bi-Represented generAtive retrieval (COBRA) framework, which innovatively integrates sparse semantic IDs and dense vectors through a cascading process. Our method alternates between generating these representations by first generating sparse IDs, which serve as conditions to aid in the generation of dense vectors. End-to-end training enables dynamic refinement of dense representations, capturing both semantic insights and collaborative signals from user-item interactions. During inference, COBRA employs a coarse-to-fine strategy, starting with sparse ID generation and refining them into dense vectors via the generative model. We further propose BeamFusion, an innovative approach combining beam search with nearest neighbor scores to enhance inference flexibility and recommendation diversity. Extensive experiments on public datasets and offline tests validate our method's robustness. Online A/B tests on a real-world advertising platform with over 200 million daily users demonstrate substantial improvements in key metrics, highlighting COBRA's practical advantages.
Hamiltonian Neural PDE Solvers through Functional Approximation
Authors: Anthony Zhou; Amir Barati Farimani
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Designing neural networks within a Hamiltonian framework offers a principled way to ensure that conservation laws are respected in physical systems. While promising, these capabilities have been largely limited to discrete, analytically solvable systems. In contrast, many physical phenomena are governed by PDEs, which govern infinite-dimensional fields through Hamiltonian functionals and their functional derivatives. Building on prior work, we represent the Hamiltonian functional as a kernel integral parameterized by a neural field, enabling learnable function-to-scalar mappings and the use of automatic differentiation to calculate functional derivatives. This allows for an extension of Hamiltonian mechanics to neural PDE solvers by predicting a functional and learning in the gradient domain. We show that the resulting Hamiltonian Neural Solver (HNS) can be an effective surrogate model through improved stability and conserving energy-like quantities across 1D and 2D PDEs. This ability to respect conservation laws also allows HNS models to better generalize to longer time horizons or unseen initial conditions.
Are Language Models Efficient Reasoners? A Perspective from Logic Programming
Authors: Andreas Opedal; Yanick Zengaffinen; Haruki Shirakami; Clemente Pasti; Mrinmaya Sachan; Abulhair Saparov; Ryan Cotterell; Bernhard Schölkopf
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Modern language models (LMs) exhibit strong deductive reasoning capabilities, yet standard evaluations emphasize correctness while overlooking a key aspect of reasoning: *efficiency*. In real-world reasoning scenarios, much of the available information is irrelevant, and effective deductive inference requires identifying and ignoring such distractions. We propose a framework for assessing LM reasoning efficiency through the lens of logic programming, introducing a simple method to align proofs written in natural language---as generated by an LM---with shortest proofs found by executing the logic program. Efficiency is quantified by measuring how well a model avoids unnecessary inference. Empirically, we construct a dataset of math word problems injected with various number of irrelevant axioms that vary in semantic overlap with the goal theorem. We find that current LMs show marked accuracy declines under such conditions---even with minimal, domain-consistent distractions---and the proofs they generate frequently exhibit detours through irrelevant inferences.
Universal Sequence Preconditioning
Authors: Annie Marsden; Elad Hazan
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: We study the problem of preconditioning in the setting of sequential prediction. From the theoretical lens of linear dynamical systems, we show that applying a convolution to the input sequence translates to applying a polynomial to the unknown transition matrix in the hidden space. With this insight, we develop a novel preconditioning method that convolves the input sequence with the coefficients of the Chebyshev or Legendre polynomials. We formally prove that this improves the regret of a wide family of prediction methods. 

We proceed to apply this preconditioning technique to the method of spectral filtering. This gives the first sublinear regret bound that is also hidden-dimension free (up to logarithmic factors) even when the hidden transition matrix is asymmetric.

From rigorous experiments on synthetic data we show that our simple preconditioning method generalizes to both 1) settings where the data is \emph{not} from a linear dynamical system and 2) a broad range of learning algorithms, including recurrent neural networks.
HybridNorm: Towards Stable and Efficient Transformer Training via Hybrid Normalization
Authors: Zhijian Zhuo; Yutao Zeng; Ya Wang; Sijun Zhang; Xiaoqing Li; Jian Yang; zhou Xun; Jinwen Ma
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Transformers have become the de facto architecture for a wide range of machine learning tasks, particularly in large language models (LLMs). Despite their remarkable performance, many challenges remain in training deep transformer networks, especially regarding the position of the layer normalization. While Pre-Norm structures facilitate more stable training owing to their stronger identity path, they often lead to suboptimal performance compared to Post-Norm.  In this paper, we propose **HybridNorm**, a simple yet effective hybrid normalization strategy that integrates the advantages of both Pre-Norm and Post-Norm. Specifically, HybridNorm employs QKV normalization within the attention mechanism and Post-Norm in the feed-forward network (FFN) of each transformer block. We provide both theoretical insights and empirical evidence to demonstrate that HybridNorm improves the gradient flow and the model robustness. Extensive experiments on large-scale transformer models, including both dense and sparse variants, show that HybridNorm consistently outperforms both Pre-Norm and Post-Norm approaches across multiple benchmarks. These findings highlight the potential of HybridNorm as a more stable and effective technique for improving the training and performance of deep transformer models. Code is available at https://github.com/BryceZhuo/HybridNorm.
Chirality in Action: Time-Aware Video Representation Learning by Latent Straightening
Authors: Piyush Nitin Bagad; Andrew Zisserman
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Our objective is to develop compact video representations that are sensitive to visual change over time. To measure such time-sensitivity, we introduce a new task: chiral action recognition, where one needs to distinguish between a pair of temporally opposite actions, such as “opening vs. closing a door", “approaching vs. moving away from something", “folding vs. unfolding paper", etc. Such actions (i) occur frequently in everyday life, (ii) require understanding of simple visual change over time (in object state, size, spatial position, count . . . ), and (iii) are known to be poorly represented by many video embeddings. Our goal is to build time aware video representations which offer linear separability between these chiral pairs. To that end, we propose a self-supervised adaptation recipe to inject time-sensitivity into a sequence of frozen image features. Our model is based on an auto-encoder with a latent space with inductive bias inspired by perceptual straightening. We show that this results in a compact but time-sensitive video representation for the proposed task across three datasets: Something-Something, EPIC-Kitchens, and Charade. Our method (i) outperforms much larger video models pre-trained on large-scale video datasets, and (ii) leads to an improvement in classification performance on standard benchmarks when combined with these existing models.
Generation as Search Operator for Test-Time Scaling of Diffusion-based Combinatorial Optimization
Authors: Yang Li; Lvda Chen; Haonan Wang; Runzhong Wang; Junchi Yan
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: While diffusion models have shown promise for combinatorial optimization (CO), their inference-time scaling cost-efficiency remains relatively underexplored. Existing methods improve solution quality by increasing denoising steps, but the performance often becomes saturated quickly. This paper proposes GenSCO to systematically scale diffusion solvers by an orthogonal dimension of inference-time computation beyond denoising step expansion, i.e., search-driven generation. GenSCO takes generation as a search operator rather than a complete solving process, where each operator cycle combines solution disruption (via local search operators) and diffusion sampling, enabling iterative exploration of the learned solution space. Rather than over-refining current solutions, this paradigm encourages the model to leave local optima and explore a broader area of the solution space, ensuring a more consistent scaling effect. The search loop is supported by a search-friendly solution-enhancement training procedure that incorporates a rectified flow model learning to establish diffusion trajectories between suboptimal solutions and the optimal ones. The flow model is empowered by a lightweight transformer architecture to learn neural ODEs that linearize solution trajectories, accelerating convergence of the scaling effect with efficiency. The resulting enhanced scaling efficiency and practical scalability lead to synergistic performance improvements. Extensive experiments show that GenSCO delivers performance improvements by orders of magnitude over previous state-of-the-art neural methods. Notably, GenSCO even achieves significant speedups compared to the state-of-the-art classic mathematical solver LKH3, delivering a 141x speedup to reach 0.000% optimality gap on TSP-100, and approximately a 10x speedup to reach 0.02% on TSP-500.
Evaluating LLM-contaminated Crowdsourcing Data Without Ground Truth
Authors: Yichi Zhang; Jinlong Pang; Zhaowei Zhu; Yang Liu
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: The recent success of generative AI highlights the crucial role of high-quality human feedback in building trustworthy AI systems. However, the increasing use of large language models (LLMs) by crowdsourcing workers poses a significant challenge: datasets intended to reflect human input may be compromised by LLM-generated responses. Existing LLM detection approaches often rely on high-dimensional training data such as text, making them unsuitable for structured annotation tasks like multiple-choice labeling. In this work, we investigate the potential of peer prediction --- a mechanism that evaluates the information within workers' responses --- to mitigate LLM-assisted cheating in crowdsourcing with a focus on annotation tasks. Our method quantifies the correlations between worker answers while conditioning on (a subset of) LLM-generated labels available to the requester. Building on prior research, we propose a training-free scoring mechanism with theoretical guarantees under a novel model that accounts for LLM collusion. We establish conditions under which our method is effective and empirically demonstrate its robustness in detecting low-effort cheating on real-world crowdsourcing datasets.
Improved Confidence Regions and Optimal Algorithms for Online and Offline Linear MNL Bandits
Authors: Yuxuan Han; Jose Blanchet; Zhengyuan Zhou
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: In this work, we consider the data-driven assortment optimization problem under the linear multinomial logit(MNL) choice model.
We first establish a improved confidence region for the maximum likelihood estimator (MLE) of the $d$-dimensional linear MNL likelihood function that removes the explicit dependency on a problem-dependent parameter $\kappa^{-1}$ in previous result  (Oh and Iyengar, 2021), which scales exponentially with the radius of the parameter set.
Building on the confidence region result, we investigate the data-driven assortment optimization problem in both offline and online settings. In the offline setting, the previously best-known result scales as $\tilde{O}\left(\sqrt{\frac{d}{\kappa n_{S^\star}}}\right)$, where $n_{S^\star}$ the number of times that optimal assortment $S^\star$ is observed  (Dong et al., 2023). We propose a new pessimistic-based algorithm that, under a burn-in condition, removes the dependency on $d,\kappa^{-1}$ in the leading order bound and works under a more relaxed coverage condition, without requiring the exact observation of $S^\star$. In the online setting, we propose the first algorithm to achieve $\tilde{O}(\sqrt{dT})$ regret without a multiplicative dependency on $\kappa^{-1}$. In both settings, our results nearly achieve the corresponding lower bound when reduced to the canonical $N$-item MNL problem, demonstrating their optimality.