GenAI4ELab/papercli-papers huggingface dataset:
SemCoT: Accelerating Chain-of-Thought Reasoning through Semantically-Aligned Implicit Tokens
Authors: Yinhan He; Wendy Zheng; Yaochen Zhu; Zaiyi Zheng; Lin Su; Sriram Vasudevan; Qi Guo; Liangjie Hong; Jundong Li
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Chain-of-Thought (CoT) enhances the performance of Large Language Models (LLMs) on reasoning tasks by encouraging step-by-step solutions. However, the verbosity of CoT reasoning hinders its mass deployment in efficiency-critical applications. Recently, implicit CoT approaches have emerged, which encode reasoning steps within LLM's hidden embeddings (termed ``implicit reasoning'') rather than explicit tokens. This approach accelerates CoT reasoning by reducing the reasoning length and bypassing some LLM components. However, existing implicit CoT methods face two significant challenges: (1) they fail to preserve the semantic alignment between the implicit reasoning (when transformed to natural language) and the ground-truth reasoning, resulting in a significant CoT performance degradation, and (2) they focus on reducing the length of the implicit reasoning; however, they neglect the considerable time cost for an LLM to generate one individual implicit reasoning token. To tackle these challenges, we propose a novel semantically-aligned implicit CoT framework termed **SemCoT**. In particular, for the first challenge, we design a contrastively trained sentence transformer that evaluates semantic alignment between implicit and explicit reasoning, which is used to enforce semantic preservation during implicit reasoning optimization. To address the second challenge, we introduce an efficient implicit reasoning generator by finetuning a lightweight language model using knowledge distillation. This generator is guided by our sentence transformer to distill ground-truth reasoning into semantically aligned implicit reasoning, while also optimizing for accuracy. SemCoT is the first approach that enhances CoT efficiency by jointly optimizing token-level generation speed and preserving semantic alignment with ground-truth reasoning. Extensive experiments demonstrate the superior performance of SemCoT compared to state-of-the-art methods in both efficiency and effectiveness. Our code can be found at https://github.com/YinhanHe123/SemCoT/.
In-Context Learning Strategies Emerge Rationally
Authors: Daniel Wurgaft; Ekdeep Singh Lubana; Core Francisco Park; Hidenori Tanaka; Gautam Reddy; Noah Goodman
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Recent work analyzing in-context learning (ICL) has identified a broad set of
strategies that describe model behavior in different experimental conditions. We
aim to unify these findings by asking why a model learns these disparate strategies in
the first place. Specifically, we start with the observation that when trained to learn a
mixture of tasks, as is popular in the literature, the strategies learned by a model for
performing ICL can be captured by a family of Bayesian predictors: a memorizing
predictor, which assumes a discrete prior on the set of seen tasks, and a generalizing
predictor, where the prior matches the underlying task distribution. Adopting the
normative lens of rational analysis, where a learner’s behavior is explained as
an optimal adaptation to data given computational constraints, we develop a
hierarchical Bayesian framework that almost perfectly predicts Transformer next-
token predictions throughout training—without assuming access to its weights.
Under this framework, pretraining is viewed as a process of updating the posterior
probability of different strategies, and inference-time behavior as a posterior-
weighted average over these strategies’ predictions. Our framework draws on
common assumptions about neural network learning dynamics, which make explicit
a tradeoff between loss and complexity among candidate strategies: beyond how
well it explains the data, a model’s preference towards implementing a strategy
is dictated by its complexity. This helps explain well-known ICL phenomena,
while offering novel predictions: e.g., we show a superlinear trend in the timescale
for transitioning from generalization to memorization as task diversity increases.
Overall, our work advances an explanatory and predictive account of ICL grounded
in tradeoffs between strategy loss and complexity.
Optimality and NP-Hardness of Transformers in Learning  Markovian Dynamical Functions
Authors: Yanna Ding; Songtao Lu; Yingdong Lu; Tomasz J Nowicki; Jianxi Gao
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Transformer architectures can solve unseen tasks based on input-output pairs in a given prompt due to in-context learning (ICL). Existing theoretical studies on ICL have mainly focused on linear regression tasks, often with i.i.d. inputs. To understand how transformers express in-context learning when modeling dynamics-driven functions, we investigate Markovian function learning through a structured ICL setup, where we characterize the loss landscape to reveal underlying optimization behaviors. Specifically, we (1)   provide the closed-form expression of the global minimizer (in an enlarged parameter space) for a single-layer linear self-attention (LSA) model; (2)  prove that recovering transformer parameters that realize the optimal solution is NP-hard in general, revealing a fundamental limitation of one-layer LSA in representing structured dynamical functions; and (3)  supply a novel interpretation of a multilayer LSA as performing preconditioned gradient descent to optimize multiple objectives beyond the square loss. These theoretical results are numerically validated using simplified transformers.
Thinking vs. Doing: Improving Agent Reasoning by  Scaling Test-Time Interaction
Authors: Junhong Shen; Hao Bai; Lunjun Zhang; Yifei Zhou; Amrith Setlur; Shengbang Tong; Diego Caples; Nan Jiang; Tong Zhang; Ameet Talwalkar; Aviral Kumar
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Test-time scaling in agentic tasks often relies on generating long reasoning traces ("think" more) before acting, but this does not allow agents to acquire new information from the environment or adapt behavior over time. In this work, we propose scaling test-time interaction, an untapped dimension for test-time scaling that increases the agent's interaction horizon to enable rich behaviors such as exploration, backtracking, and dynamic re-planning within a single rollout. To demonstrate the promise of this scaling dimension, we situate our study in the domain of web agents. We first show that even prompting-based interaction scaling can improve task success on web benchmarks non-trivially. Building on this, we introduce TTI, a curriculum-based online reinforcement learning (RL) approach that trains agents by adaptively adjusting their interaction lengths during rollout. Using a Gemma 3 12B model, TTI sets a new state-of-the-art among open-source agents trained on public data on WebVoyager and WebArena. Case studies further reveal that TTI enables agents to balance exploration and exploitation adaptively. Our results establish interaction scaling as a powerful, complementary axis to scaling per-action compute, offering new avenues for training robust and adaptive agents.
Time Reversal Symmetry for Efficient Robotic Manipulations in Deep Reinforcement Learning
Authors: Yunpeng Jiang; Jianshu Hu; Paul Weng; Yutong Ban
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Symmetry is pervasive in robotics and has been widely exploited to improve sample efficiency in deep reinforcement learning (DRL). However, existing approaches primarily focus on spatial symmetries—such as reflection, rotation, and translation—while largely neglecting temporal symmetries. To address this gap, we explore time reversal symmetry, a form of temporal symmetry commonly found in robotics tasks such as door opening and closing. We propose Time Reversal symmetry enhanced Deep Reinforcement Learning (TR-DRL), a framework that combines trajectory reversal augmentation and time reversal guided reward shaping to efficiently solve temporally symmetric tasks. Our method generates reversed transitions from fully reversible transitions, identified by a proposed dynamics-consistent filter, to augment the training data. For partially reversible transitions, we apply reward shaping to guide learning, according to successful trajectories from the reversed task. Extensive experiments on the Robosuite and MetaWorld benchmarks demonstrate that TR-DRL is effective in both single-task and multi-task settings, achieving higher sample efficiency and stronger final performance compared to baseline methods.
On the Sample Complexity Bounds of Bilevel Reinforcement Learning
Authors: Mudit Gaur; Utsav Singh; Amrit Singh Bedi; Raghu Pasupathy; Vaneet Aggarwal
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Bilevel reinforcement learning (BRL) has emerged as a powerful framework for aligning generative models, yet its theoretical foundations, especially sample complexity bounds, remain relatively underexplored. In this work, we present the first sample complexity bound for BRL, establishing a rate of $\tilde{\mathcal{O}}(\epsilon^{-3})$ in continuous state-action spaces. Traditional MDP analysis techniques do not extend to BRL due to its nested structure and non-convex lower-level problems. We overcome these challenges by leveraging the Polyak-Łojasiewicz (PL) condition and the MDP structure to obtain closed-form gradients, enabling tight sample complexity analysis. Our analysis also extends to general bi-level optimization settings with non-convex lower levels, where we achieve state-of-the-art sample complexity results of $\tilde{\mathcal{O}}(\epsilon^{-3})$ improving upon existing bounds of  $\tilde{\mathcal{O}}(\epsilon^{-6})$. Additionally, we address the computational bottleneck of hypergradient estimation by proposing a fully first-order, Hessian-free algorithm suitable for large-scale problems.
QuARI: Query Adaptive Retrieval Improvement
Authors: Eric Xing; Abby Stylianou; Robert Pless; Nathan Jacobs
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Massive-scale pretraining has made vision-language models increasingly popular for image-to-image and text-to-image retrieval across a broad collection of domains. However, these models do not perform well when used for challenging retrieval tasks, such as instance retrieval in very large-scale image collections. Recent work has shown that linear transformations of VLM features trained for instance retrieval can improve performance by emphasizing subspaces that relate to the domain of interest. In this paper, we explore a more extreme version of this specialization by learning to map a given query to a query-specific feature space transformation.  Because this transformation is linear, it can be applied with minimal computational cost to millions of image embeddings, making it effective for large-scale retrieval or re-ranking.  Results show that this method consistently outperforms state-of-the-art alternatives, including those that require many orders of magnitude more computation at query time.
Correlated Low-Rank Adaptation for ConvNets
Authors: Wu Ran; Weijia Zhang; ShuYang Pang; Qi Zhu; Jinfan Liu; JingSheng Liu; Xin Cao; Qiang Li; Yichao Yan; Chao Ma
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Low-Rank Adaptation (LoRA) methods have demonstrated considerable success in achieving parameter-efficient fine-tuning (PEFT) for Transformer-based foundation models. These methods typically fine-tune individual Transformer layers using independent LoRA adaptations. However, directly applying existing LoRA techniques to convolutional networks (ConvNets) yields unsatisfactory results due to the high correlation between the stacked sequential layers of ConvNets. To overcome this challenge, we introduce a novel framework called Correlated Low-Rank Adaptation (CoLoRA), which explicitly utilizes correlated low-rank matrices to model the inter-layer dependencies among convolutional layers. Additionally, to enhance tuning efficiency, we propose a parameter-free filtering method that enlarges the receptive field of LoRA, thus minimizing interference from non-informative local regions. Comprehensive experiments conducted across various mainstream vision tasks, including image classification, semantic segmentation, and object detection, illustrate that CoLoRA significantly advances the state-of-the-art PEFT approaches. Notably, our CoLoRA achieves superior performance with only 5\% of trainable parameters, surpassing full fine-tuning in the image classification task on the VTAB-1k dataset using ConvNeXt-S. Code is available at [https://github.com/VISION-SJTU/CoLoRA](https://github.com/VISION-SJTU/CoLoRA).
PRIMT: Preference-based Reinforcement Learning with Multimodal Feedback and Trajectory Synthesis from Foundation Models
Authors: Ruiqi Wang; Dezhong Zhao; Ziqin Yuan; Tianyu Shao; Guohua Chen; Dominic Kao; Sungeun Hong; Byung-Cheol Min
Venue: NeurIPS 2025 (NeurIPS 2025 oral)

Abstract: Preference-based reinforcement learning (PbRL) has emerged as a promising paradigm for teaching robots complex behaviors without reward engineering. However, its effectiveness is often limited by two critical challenges: the reliance on extensive human input and the inherent difficulties in resolving query ambiguity and credit assignment during reward learning. In this paper, we introduce PRIMT, a PbRL framework designed to overcome these challenges by leveraging foundation models (FMs) for multimodal synthetic feedback and trajectory synthesis. Unlike prior approaches that rely on single-modality FM evaluations, PRIMT employs a hierarchical neuro-symbolic fusion strategy, integrating the complementary strengths of vision-language models (VLMs) and large language models (LLMs) in evaluating robot behaviors for more reliable and comprehensive feedback. PRIMT also incorporates foresight trajectory generation to warm-start the trajectory buffer with bootstrapped samples, reducing early-stage query ambiguity, and hindsight trajectory augmentation for counterfactual reasoning with a causal auxiliary loss to improve credit assignment. We evaluate PRIMT on 2 locomotion and 6 manipulation tasks on various benchmarks, demonstrating superior performance over FM-based and scripted baselines. Website at https://primt25.github.io/.
Beyond Prediction: Managing the Repercussions of Machine Learning Applications
Authors: Aline Weber; Blossom Metevier; Yuriy Brun; Philip S. Thomas; Bruno Castro da Silva
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Machine learning models are often designed to maximize a primary goal, such as accuracy. However, as these models are increasingly used to inform decisions that affect people's lives or well-being, it is often unclear what the real-world repercussions of their deployment might be—making it crucial to understand and manage such repercussions effectively. Models maximizing user engagement on social media platforms, e.g., may inadvertently contribute to the spread of misinformation and content that deepens political polarization. This issue is not limited to social media—it extends to other applications where machine learning-informed decisions can have real-world repercussions, such as education, employment, and lending. Existing methods addressing this issue require prior knowledge or estimates of analytical models describing the relationship between a classifier's predictions and their corresponding repercussions. We introduce Theia, a novel classification algorithm capable of optimizing a primary objective, such as accuracy, while providing high-confidence guarantees about its potential repercussions. Importantly, Theia solves the open problem of providing such guarantees based solely on existing data with observations of previous repercussions. We prove that it satisfies constraints on a model's repercussions with high confidence and that it is guaranteed to identify a solution, if one exists, given sufficient data. We empirically demonstrate, using real-life data, that Theia can identify models that achieve high accuracy while ensuring, with high confidence, that constraints on their repercussions are satisfied.
Offline RL by Reward-Weighted Fine-Tuning for Conversation Optimization
Authors: Subhojyoti Mukherjee; Viet Dac Lai; Raghavendra Addanki; Ryan A. Rossi; Seunghyun Yoon; Trung Bui; Anup Rao; Jayakumar Subramanian; Branislav Kveton
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Offline reinforcement learning (RL) is a variant of RL where the policy is learned from a previously collected dataset of trajectories and rewards. In our work, we propose a practical approach to offline RL with large language models (LLMs). We recast the problem as reward-weighted fine-tuning, which can be solved using similar techniques to supervised fine-tuning (SFT). To showcase the value of our approach, we apply it to learning short-horizon question-answering policies of a fixed length, where the agent reasons about potential answers or asks clarifying questions. Our work stands in a stark contrast to state-of-the-art methods in this domain, based on SFT and direct preference optimization, which have additional hyper-parameters and do not directly optimize for rewards. We compare to them empirically, and report major gains in both optimized rewards and language quality.
CAT: Content-Adaptive Image Tokenization
Authors: Junhong Shen; Kushal Tirumala; Michihiro Yasunaga; Ishan Misra; Luke Zettlemoyer; LILI YU; Chunting Zhou
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Most existing image tokenizers encode images into a fixed number of tokens or patches, overlooking the inherent variability in image complexity and introducing unnecessary computate overhead for   simpler images. To address this, we propose Content-Adaptive Tokenizer (CAT), which dynamically adjusts representation capacity based on the image content and encodes simpler images into fewer tokens. We design (1) a caption-based evaluation system that leverages LLMs to predict content complexity and determine the optimal compression ratio for an image, and (2) a novel nested VAE architecture that performs variable-rate compression in a single model.
Trained on images with  varying   complexity, CAT achieves an average of 15% reduction in rFID across seven detail-rich datasets containing text, humans, and complex textures. On natural image datasets like ImageNet and COCO, it  reduces token   usage by 18% while maintaining high-fidelity reconstructions.   We further evaluate CAT on two downstream tasks.  For image classification, CAT consistently improves top-1 accuracy across  five datasets spanning diverse domains. For image generation, it boosts training throughput by 23% on ImageNet, leading to more efficient learning and  improved FIDs over fixed-token baselines.
H3D-DGS: Exploring Heterogeneous 3D Motion Representation for Deformable 3D Gaussian Splatting
Authors: Bing He; Yunuo Chen; Guo Lu; Qi Wang; Qunshan Gu; Rong Xie; Li Song; Wenjun Zhang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Dynamic scene reconstruction poses a persistent challenge in 3D vision. Deformable 3D Gaussian Splatting has emerged as an effective method for this task, offering real-time rendering and high visual fidelity.
This approach decomposes a dynamic scene into a static representation in a canonical space and time-varying scene motion.
Scene motion is defined as the collective movement of all Gaussian points, and for compactness, existing approaches commonly adopt implicit neural fields or sparse control points. 
However, these methods predominantly rely on gradient-based optimization for all motion information. Due to the high degree of freedom, they struggle to converge on real-world datasets exhibiting complex motion.
To preserve the compactness of motion representation and address convergence challenges, this paper proposes heterogeneous 3D control points, termed \textbf{H3D control points}, whose attributes are obtained using a hybrid strategy combining optical flow back-projection and gradient-based methods. 
This design decouples directly observable motion components from those that are geometrically occluded.
Specifically, components of 3D motion that project onto the image plane are directly acquired via optical flow back projection, while unobservable portions are refined through gradient-based optimization.
Experiments on the Neu3DV and CMU-Panoptic datasets demonstrate that our method achieves superior performance over state-of-the-art deformable 3D Gaussian splatting techniques. Remarkably, our method converges within just 100 iterations and achieves a per-frame processing speed of 2 seconds on a single NVIDIA RTX 4070 GPU.
Keeping an Eye on LLM Unlearning: The Hidden Risk and Remedy
Authors: Jie Ren; Zhenwei DAI; Xianfeng Tang; Yue Xing; Shenglai Zeng; Hui Liu; Jingying Zeng; Qiankun Peng; Samarth Varshney; Suhang Wang; Qi He; Charu C. Aggarwal; Hui Liu
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Although Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of tasks, growing concerns have emerged over the misuse of sensitive, copyrighted, or harmful data during training. To address these concerns, unlearning techniques have been developed to remove the influence of specific data without retraining from scratch. However, this paper reveals a critical vulnerability in fine-tuning-based unlearning: a malicious user can craft a manipulated forgetting request that stealthily degrades the model’s utility for benign users. We demonstrate this risk through a red-teaming Stealthy Attack (SA), which is inspired by two key limitations of existing unlearning—the inability to constrain the scope of unlearning effect and the failure to distinguish benign tokens from unlearning signals. Prior work has shown that unlearned models tend to memorize forgetting data as unlearning signals, and respond with hallucinations or feigned ignorance when unlearning signals appear in the input. By subtly increasing the presence of common benign tokens in the forgetting data, SA enhances the connection between benign tokens and unlearning signals. As a result, when normal users include such tokens in their prompts, the model exhibits unlearning behaviors, leading to unintended utility degradation. To address this vulnerability, we propose Scope-aware Unlearning (SU), a lightweight enhancement that introduces a scope term into the unlearning objective, encouraging the model to localize the forgetting effect. Our method requires no additional data processing, integrates seamlessly with existing fine-tuning frameworks, and significantly improves robustness against SA. Extensive experiments validate the effectiveness of both SA and SU.
Sketch-Augmented Features Improve Learning Long-Range Dependencies in Graph Neural Networks
Authors: Ryien Hosseini; Filippo Simini; Venkatram Vishwanath; Rebecca Willett; Henry Hoffmann
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Graph Neural Networks learn on graph-structured data by iteratively aggregating local neighborhood information. While this local message passing paradigm imparts a powerful inductive bias and exploits graph sparsity, it also yields three key challenges: (i) oversquashing of long-range information, (ii) oversmoothing of node representations, and (iii) limited expressive power. In this work we inject randomized global embeddings of node features, which we term Sketched Random Features, into standard GNNs, enabling them to efficiently capture long-range dependencies. The embeddings are unique, distance-sensitive, and topology-agnostic---properties which we analytically and empirically show alleviate the aforementioned limitations when injected into GNNs. Experimental results on real-world graph learning tasks confirm that this strategy consistently improves performance over baseline GNNs, offering both a standalone solution and a complementary enhancement to existing techniques such as graph positional encodings.