GenAI4ELab/papercli-papers huggingface dataset:
CroPe: Cross-Modal Semantic Compensation Adaptation for All Adverse Scene Understanding
Authors: Qin Xu; Qihang Wu; Lu Hongtao; Xiaoxia Cheng; Bo Jiang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Scene understanding in adverse conditions, such as fog, snow, and night, is challenging due to the visual appearance degeneration. In this context, we propose a Cross-modal Semantic Compensation Adaptation method (CroPe) for scene understanding. Distinct from the existing methods, which only use the visual information to learn the domain-invariant features, CroPe establishes a visual-textual paradigm which provides textual semantic compensation for visual features, enabling the model to learn more consistent representations. We propose the Complementary Perceptual Text Generation (CPTG) module which generates a set of multi-level complementary-perceptive text embeddings incorporating both generalization and domain awareness. To achieve cross-modal semantic compensation, the Reverse Chain Text-Visual Fusion (RCTVF) module is developed. By the unified attention and reverse decoding chain, compensation information is successively fused to the visual features from the deep (semantic dense) to shallow (semantic sparse) features, maximizing compensation gain. CroPe yields competitive results under all adverse conditions and significantly improves the state-of-the-art performance by 6.5 mIoU for ACDC-Night dataset and 1.2 mIoU for ACDC-All dataset, respectively.
Unlocking SLM Potential for Data Analysis Code Generation via Non-Parametric Knowledge Distillation
Authors: Jinyang Li; Jack Williams; Nick McKenna; Arian Askari; Nicholas Wilson; Reynold Cheng
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Knowledge distillation from Large Language Models (LLMs) to locally hosted Small Language Models (SLMs) provides advantages for Data Analysis Code Generation (DACG) such as privacy protection. However, achieving effective distillation without resource-intensive training is challenging. This paper investigates whether LLMs can distill knowledge to SLMs through In-Context Learning (ICL), a training-free method for rapid task adaptation. We present the DarGO: Distillation and Adaptive Reasoning-Guided Orchestration framework, which facilitates automatic knowledge distillation from LLMs to SLMs. DarGO consists of three phases: exploration through an Model Orchestration Interface (MOI), Memory Collection of successful trajectories, and Knoweldge-driven Inference. We evaluate DarGO on three challenging DACG benchmarks (WikiTQ, TabMWP, and Bird-SQL), each with in-domain training sets that enable detailed analysis of knowledge distillation effectiveness. DarGO demonstrates a substantial relative performance improvement of 27.5\% on average for the student SLMs. To further observe generalization capabilities, we evaluate the \method across different teacher-student model combinations, knowledge transfer scenarios, and unified memory approaches for more advanced, test-only data analysis tasks. Our findings contribute a novel perspective on distillation methods that enhance high performance for SLMs while avoiding intensive fine-tuning.
Event-based HDR Structured Light
Authors: Jiacheng Fu; Yue Li; Xin Dong; Wenming Weng; Yueyi Zhang; Zhiwei Xiong
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Event-based structured light (SL) systems have attracted increasing attention for their potential in high-performance 3D measurement. Despite the inherent HDR capability of event cameras, reflective and absorptive surfaces still cause event cluttering and absence, which produce overexposed and underexposed regions that degrade the reconstruction quality. In this work, we present the first HDR 3D measurement framework specifically designed for event-based SL systems. First, we introduce a multi-contrast HDR coding strategy that facilitates imaging of areas with different reflectance. Second, to alleviate inter-frame interference caused by overexposed and underexposed areas, we propose a universal confidence-driven stereo matching strategy. Specifically, we estimate a confidence map as the fusion weight for features via an energy-guided confidence estimation. Further, we propose the confidence propagation volume, an innovative cost volume that offers both effective suppression of inter-frame interference and strong representation capability. Third, we contribute an event-based SL simulator and propose the first event-based HDR SL dataset. We also collect a real-world benchmarking dataset with ground truth. We validate the effectiveness of our method with the proposed confidence-driven strategy on both synthetic and real-world datasets. Experimental results demonstrate that our proposed HDR framework enables accurate 3D measurement even under extreme conditions.
HypoBootstrap: A Bootstrapping Framework for Inductive Reasoning
Authors: Si Chen; Yifei Li; Richong Zhang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Inductive reasoning infers general rules from observed evidence, which is one of the most critical intelligence abilities.
Previous works have succeeded in formal languages but suffer from onerous and error-prone conversions between a particular formal language and the working language.
As large language models (LLMs) have emerged, direct reasoning with various kinds of languages, especially natural languages, without formal language involvement has become feasible.
However, existing LLM-based inductive reasoning usually relies on LLM's intrinsic generation ability, which is prone to LLM's hallucination and lacks systematic guidance according to the nature of inductive reasoning.
To this end, we propose HypoBootstrap, an integrated framework for inductive reasoning that generates and confirms hypotheses both in a bootstrapping manner.
Regarding hypothesis generation, we propose a novel bootstrapping generation strategy, bootstrapping object hypotheses, relational hypotheses, and functional hypotheses successively, which assists LLM in observing the evidence from trivial patterns to non-trivial patterns.
Regarding hypothesis confirmation, we utilize Glymour's theory of bootstrap confirmation, a hypothesis confirmation theory from the philosophy of science that can confirm a set of hypotheses.
We use its principles to confirm the object hypotheses, relational hypotheses, and functional hypotheses.
Empirical studies on four inductive reasoning scenarios of different natures, involving causal induction, concept learning, grammar learning, and abstract reasoning, demonstrate that HypoBootstrap significantly outperforms existing methods.
LaM-SLidE: Latent Space Modeling of Spatial Dynamical Systems via Linked Entities
Authors: Florian Sestak; Artur P. Toshev; Andreas Fürst; Günter Klambauer; Andreas Mayr; Johannes Brandstetter
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Generative models are spearheading recent progress in deep learning, showcasing strong promise for trajectory sampling in dynamical systems as well. However, whereas latent space modeling paradigms have transformed image and video generation, similar approaches are more difficult for most dynamical systems. Such systems -- from chemical molecule structures to collective human behavior -- are described by interactions of entities, making them inherently linked to connectivity patterns, entity conservation, and the traceability of entities over time. Our approach, LaM-SLidE (Latent Space Modeling of Spatial Dynamical Systems via Linked Entities), bridges the gap between: (1) keeping the traceability of individual entities in a latent system representation, and (2) leveraging the efficiency and scalability of recent advances in image and video generation, where pre-trained encoder and decoder enable generative modeling directly in latent space. The core idea of LaM-SLidE is the introduction of identifier representations (IDs) that enable the retrieval of entity properties and entity composition from latent system representations, thus fostering traceability. Experimentally, across different domains, we show that LaM-SLidE performs favorably in terms of speed, accuracy, and generalizability. Code is available at https://github.com/ml-jku/LaM-SLidE.
VIKING: Deep variational inference with stochastic projections
Authors: Samuel G. Fadel; Hrittik Roy; Nicholas Krämer; Yevgen Zainchkovskyy; Stas Syrota; Alejandro Valverde Mahou; Carl Henrik Ek; Søren Hauberg
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Variational mean field approximations tend to struggle with contemporary overparametrized deep neural networks. Where a Bayesian treatment is usually associated with high-quality predictions and uncertainties, the practical reality has been the opposite, with unstable training, poor predictive power, and subpar calibration. Building upon recent work on reparametrizations of neural networks, we propose a simple variational family that considers two independent linear subspaces of the parameter space. These represent functional changes inside and outside the support of training data. This allows us to build a fully-correlated approximate posterior reflecting the overparametrization that tunes easy-to-interpret hyperparameters. We develop scalable numerical routines that maximize the associated evidence lower bound (ELBO) and sample from the approximate posterior. Empirically, we observe state-of-the-art performance across tasks, models, and datasets compared to a wide array of baseline methods. Our results show that approximate Bayesian inference applied to deep neural networks is far from a lost cause when constructing inference mechanisms that reflect the geometry of reparametrizations.
SegMASt3R: Geometry Grounded Segment Matching
Authors: Rohit Jayanti; Swayam Agrawal; Vansh Garg; Siddharth Tourani; Muhammad Haris Khan; Sourav Garg; Madhava Krishna
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Segment matching is an important intermediate task in computer vision that establishes correspondences between semantically or geometrically coherent regions across images. Unlike keypoint matching, which focuses on localized features, segment matching captures structured regions, offering greater robustness to occlusions, lighting variations, and viewpoint changes. In this paper, we leverage the spatial understanding of 3D foundation models to tackle wide-baseline segment matching, a challenging setting involving extreme viewpoint shifts. We propose an architecture that uses the inductive bias of these 3D foundation models to match segments across image pairs with up to $180^\circ$ rotation. Extensive experiments show that our approach outperforms state-of-the-art methods, including the SAM2 video propagator and local feature matching methods, by up to 30\% on the AUPRC metric, on ScanNet++ and Replica datasets. We further demonstrate benefits of the proposed model on relevant downstream tasks, including 3D instance mapping and object-relative navigation.
Among Us: A Sandbox for Measuring and Detecting Agentic Deception
Authors: Satvik Golechha; Adrià Garriga-Alonso
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Prior studies on deception in language-based AI agents typically assess whether the agent produces a false statement about a topic, or makes a binary choice prompted by a goal, rather than allowing open-ended deceptive behavior to emerge in pursuit of a longer-term goal.
To fix this, we introduce $\textit{Among Us}$, a sandbox social deception game where LLM-agents exhibit long-term, open-ended deception as a consequence of the game objectives. While most benchmarks saturate quickly, $\textit{Among Us}$ can be expected to last much longer, because it is a multi-player game far from equilibrium.
Using the sandbox, we evaluate $18$ proprietary and open-weight LLMs and uncover a general trend:
models trained with RL are comparatively much better at producing deception than detecting it.
We evaluate the effectiveness of methods to detect lying and deception: logistic regression on the activations and sparse autoencoders (SAEs). We find that probes trained on a dataset of ``pretend you're a dishonest model: $\dots$'' generalize extremely well out-of-distribution, consistently obtaining AUROCs over 95% even when evaluated just on the deceptive statement, without the chain of thought. We also find two SAE features that work well at deception detection but are unable to steer the model to lie less.
We hope our open-sourced sandbox, game logs, and probes serve to anticipate and mitigate deceptive behavior and capabilities in language-based agents.
SWE-SQL: Illuminating LLM Pathways to Solve User SQL Issues in Real-World Applications
Authors: Jinyang Li; Xiaolong Li; Ge Qu; Per Jacobsson; Bowen Qin; Binyuan Hui; Shuzheng Si; Nan Huo; Xiaohan Xu; Yue Zhang; Ziwei Tang; Yuanshuai Li; Florensia Widjaja; Xintong Zhu; Feige Zhou; Yongfeng Huang; Yannis Papakonstantinou; Fatma Ozcan; Chenhao Ma; Reynold Cheng
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Resolution of complex SQL issues persists as a significant bottleneck in real-world database applications. Current Large Language Models (LLMs), while adept at text-to-SQL translation, have not been rigorously evaluated on the more challenging task of debugging on SQL issues. In order to address this gap, we introduce **BIRD-CRITIC**, a new SQL issue debugging benchmark comprising 530 carefully curated PostgreSQL tasks (**BIRD-CRITIC-PG**) and 570 multi-dialect tasks (**BIRD-CRITIC-Multi**), which are distilled from authentic user issues and replayed within new environments to facilitate rigorous and contamination-free evaluation. Baseline evaluations on BIRD-CRITIC underscore the task's complexity, with the leading reasoning model **O3-Mini** achieving only 38.87% success rate on **BIRD-CRITIC-PG** and 33.33% on **BIRD-CRITIC-Multi**. Meanwhile, realizing open-source models for database tasks is crucial which can empower local development while safeguarding data privacy. Therefore, we present **Six-Gym** (**S**ql-f**IX**-Gym), a training environment for elevating the capabilities of open-source models specifically for SQL issue debugging. This environment leverages **SQL-Rewind** strategy, which automatically generates executable issue-solution datasets by reverse-engineering issues from verified SQLs. However, popular trajectory-based fine-tuning methods do not explore substantial supervisory signals. We further propose *f*-Plan Boosting, which extracts high-level debugging plans automatically from SQL solutions, enabling the teacher LLMs to harvest and produce 73.7% more successful trajectories for training. We integrate these components into an open-source agent, **BIRD-Fixer**. Based on Qwen-2.5-Coder-14B, **BIRD-Fixer** raises its success rate to 38.11% on **BIRD-CRITIC-PG** and 29.65% on **BIRD-CRITIC-Multi**, surpassing many leading proprietary models such as Claude-3.7-Sonnet and GPT-4.1, marking a significant step toward democratizing sophisticated SQL-debugging capabilities for both research and industry.
How Ensembles of Distilled Policies Improve Generalisation in Reinforcement Learning
Authors: Max Weltevrede; Moritz Akiya Zanger; Matthijs T. J. Spaan; Wendelin Boehmer
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: In the zero-shot policy transfer setting in reinforcement learning, the goal is to train an agent on a fixed set of training environments so that it can generalise to similar, but unseen, testing environments. Previous work has shown that policy distillation after training can sometimes produce a policy that outperforms the original in the testing environments. However, it is not yet entirely clear why that is, or what data should be used to distil the policy. In this paper, we prove, under certain assumptions, a generalisation bound for policy distillation after training. The theory provides two practical insights: for improved generalisation, you should 1) train an ensemble of distilled policies, and 2) distil it on as much data from the training environments as possible. We empirically verify that these insights hold in more general settings, when the assumptions required for the theory no longer hold. Finally, we demonstrate that an ensemble of policies distilled on a diverse dataset can generalise significantly better than the original agent.
AutoToM: Scaling Model-based Mental Inference via Automated Agent Modeling
Authors: Zhining Zhang; Chuanyang Jin; Mung Yao Jia; Shunchi Zhang; Tianmin Shu
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Theory of Mind (ToM), the ability to understand people's minds based on their behavior, is key to developing socially intelligent agents. Current approaches to ToM reasoning either rely on prompting Large Language Models (LLMs), which are prone to systematic errors, or use handcrafted, rigid agent models for model-based inference, which are more robust but fail to generalize across domains. In this work, we introduce *AutoToM*, an automated agent modeling method for scalable, robust, and interpretable mental inference. Given a ToM problem, *AutoToM* first proposes an initial agent model and then performs automated Bayesian inverse planning based on this model, leveraging an LLM backend. Guided by inference uncertainty, it iteratively refines the model by introducing additional mental variables and/or incorporating more timesteps in the context. Across five diverse benchmarks, *AutoToM* outperforms existing ToM methods and even large reasoning models. Additionally, we show that *AutoToM* can produce human‐like confidence estimates and enable online mental inference for embodied decision-making.
Towards Interpretable and Efficient Attention: Compressing All by Contracting a Few
Authors: Qishuai Wen; Zhiyuan Huang; Chun-Guang Li
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Attention mechanisms have achieved significant empirical success in multiple fields, but their underlying optimization objectives remain unclear yet. Moreover, the quadratic complexity of self-attention has become increasingly prohibitive. Although interpretability and efficiency are two mutually reinforcing pursuits, prior work typically investigates them separately. In this paper, we propose a unified optimization objective that derives inherently interpretable and efficient attention mechanisms through algorithm unrolling. Precisely, we construct a gradient step of the proposed objective with a set of forward-pass operations of our \emph{Contract-and-Broadcast Self-Attention} (CBSA), which compresses input tokens towards low-dimensional structures by contracting a few representatives of them. This novel mechanism can not only scale linearly by fixing the number of representatives, but also covers the instantiations of varied attention mechanisms when using different sets of representatives. We conduct extensive experiments to demonstrate comparable performance and superior advantages over black-box attention mechanisms on visual tasks. Our work sheds light on the integration of interpretability and efficiency, as well as the unified formula of attention mechanisms. Code is available at \href{https://github.com/QishuaiWen/CBSA}{this https URL}.
ParamMute: Suppressing Knowledge-Critical FFNs for Faithful Retrieval-Augmented Generation
Authors: Pengcheng Huang; Zhenghao Liu; Yukun Yan; Haiyan Zhao; Xiaoyuan Yi; Hao Chen; Zhiyuan Liu; Maosong Sun; Tong Xiao; Ge Yu; Chenyan Xiong
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Large language models (LLMs) integrated with retrieval-augmented generation (RAG) have improved factuality by grounding outputs in external evidence. However, they remain susceptible to unfaithful generation, where outputs contradict retrieved context despite its relevance and accuracy. Existing approaches aiming to improve faithfulness primarily focus on enhancing the utilization of external context, but often overlook the persistent influence of internal parametric knowledge during generation. In this work, we investigate the internal mechanisms behind unfaithful generation and identify a subset of mid-to-deep feed-forward networks (FFNs) that are disproportionately activated in such cases. Building on this insight, we propose Parametric Knowledge Muting through FFN Suppression (ParamMute), a framework that improves contextual faithfulness by suppressing the activation of unfaithfulness-associated FFNs and calibrating the model toward retrieved knowledge. To evaluate our approach, we introduce CoFaithfulQA, a benchmark specifically designed to evaluate faithfulness in scenarios where internal knowledge conflicts with accurate external evidence. Experimental results show that ParamMute significantly enhances faithfulness across both CoFaithfulQA and the established ConFiQA benchmark, achieving substantial reductions in reliance on parametric memory. These findings underscore the importance of mitigating internal knowledge dominance and provide a new direction for improving LLM trustworthiness in RAG. All codes are available at https://github.com/OpenBMB/ParamMute.
PANGEA: Projection-Based Augmentation with Non-Relevant General Data for Enhanced Domain Adaptation in LLMs
Authors: Seungyoo Lee; Giung Nam; Moonseok Choi; Hyungi Lee; Juho Lee
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Modern large language models (LLMs) achieve competitive performance across a wide range of natural language processing tasks through zero-shot or few-shot prompting. However, domain-specific tasks often still require fine-tuning, which is frequently hindered by data scarcity, i.e., collecting sufficient domain-specific data remains a practical challenge. A widely adopted solution is to generate synthetic data using LLMs by augmenting a small set of available domain-specific examples. In this work, we first identify fundamental limitations of such approach in terms of both data diversity and quality, particularly when relying on only a handful of domain-specific examples. We then propose our method, PANGEA, which leverages large-scale, publicly available general-purpose data---entirely unrelated to the target domain---to generate more diverse and higher-quality synthetic data. Our extensive experiments on domain-specific benchmarks, including GSM8K, MedQA, and FinQA, as well as a custom domain-specific language task, validate the effectiveness of our approach.