United States

The emergence of long-context text applications utilizing large language models (LLMs) has presented significant scalability challenges, particularly in memory footprint. The linear growth of the Key-Value (KV) cache—responsible for storing attention keys and values to minimize redundant computations—can lead to substantial increases in memory consumption, potentially causing models to fail to serve with limited memory resources. To address this issue, we propose a novel approach called Cache Sparse Representation (CSR), which converts the KV cache by transforming the dense Key-Value cache tensor into sparse indexes and weights, offering a more memory-efficient representation during LLM inference. Furthermore, we introduce NeuralDict, a novel neural network-based method for automatically generating the dictionary used in our sparse representation. Our extensive experiments demonstrate that CSR achieves performance comparable to state-of-the-art KV cache quantization algorithms while maintaining robust functionality in memory-constrained environments.

AAAI 2025

CSR:Achieving 1 Bit Key-Value Cache via Sparse Representation

snlp

language models

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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ACL 2025

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

poster

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



Guiding the policy of multi-agent reinforcement learning to align with human common sense is a difficult problem, largely due to the complexity of modeling common sense as a reward, especially in complex and long-horizon multi-agent tasks. Recent works have shown the effectiveness of reward shaping, such as potential-based rewards, to enhance policy alignment. The existing works, however, primarily rely on experts to design rule-based rewards, which are often labor-intensive and lack a high-level semantic understanding of common sense. To solve this problem, we propose a hierarchical vision-based reward shaping method. At the bottom layer, a visual-language model (VLM) serves as a generic potential function, guiding the policy to align with human common sense through its intrinsic semantic understanding. To help the policy adapts to uncertainty and changes in long-horizon tasks, the top layer features an adaptive skill selection module based on a visual large language model (vLLM). The module uses instructions, video replays, and training records to dynamically select suitable potential function from a pre-designed pool. Besides, our method is theoretically proven to preserve the optimal policy. Extensive experiments conducted in the Google Research Football environment demonstrate that our method not only achieves a higher win rate but also effectively aligns the policy with human common sense.

Vision-Based Generic Potential Function for Policy Alignment in Multi-Agent Reinforcement Learning

Counterfactual explanations are one of the prominent eXplainable Artificial Intelligence (XAI) techniques, and suggest changes to input data that could alter predictions, leading to more favourable outcomes. Existing counterfactual methods do not readily apply to temporal domains, such as that of process mining, where data take the form of traces of activities that must obey to temporal background knowledge expressing which dynamics are possible and which not. Specifically, counterfactuals generated off-the-shelf may violate the
background knowledge, leading to inconsistent explanations. This work tackles this challenge by introducing a novel approach for generating temporally constrained counterfactuals, guaranteed to comply by design with background knowledge expressed in Linear Temporal Logic on process traces (LTLp). We do so by infusing automata-theoretic techniques for LTLp inside a genetic algorithm for counterfactual generation. The empirical evaluation shows that the generated counterfactuals are temporally meaningful and more interpretable for applications involving temporal dependencies.

Generating Counterfactual Explanations Under Temporal Constraints

Adaptive optimizers such as Adam and RMSProp have gained attraction in complex neural networks, including generative adversarial networks (GANs) and Transformers, thanks to their stable performance and fast convergence compared to non-adaptive optimizers. A frequently overlooked limitation of adaptive optimizers is that adjusting the learning rate of each dimension individually would ignore the knowledge of the whole loss landscape, resulting in slow updates of parameters, invalidating the learning rate adjustment strategy and eventually leading to widespread insufficient convergence of parameters. In this paper, we propose HVAdam, a novel optimizer that associates all dimensions of the parameters to find a new parameter update direction, leading to a refined parameter update strategy for an increased convergence rate.
We validated HVAdam in extensive experiments, showing its faster convergence, higher accuracy, and more stable performance on image classification, image generation, and natural language processing tasks. Particularly, HVAdam achieves a significant improvement on GANs compared with other state-of-the-art methods, especially in Wasserstein-GAN (WGAN) and its improved version with gradient penalty (WGAN-GP).

HVAdam: A Full-Dimension Adaptive Optimizer

We introduce a wearable driving status recognition device and our open-source dataset, along with a new real-time method robust to changes in lighting conditions for identifying driving status from eye observations of drivers. The core of our method is generating event frames from conventional intensity frames, and the other is a newly designed Attention Driving State Network (ADSN). Compared to event cameras, conventional cameras offer complete information and lower hardware costs, enabling captured frames to encode rich spatial information. However, these textures lack temporal information, posing challenges in effectively identifying driving status. DriveGazen addresses this issue from three perspectives. First, we utilize video frames to generate realistic synthetic dynamic vision sensor (DVS) events.Second, we adopt a spiking neural network to decode pertinent temporal information. Lastly, ADSN extracts crucial spatial cues from corresponding intensity frames and conveys spatial attention to convolutional spiking layers during both training and inference through a novel guide attention module to guide the feature learning and feature enhancement of the event frame. We specifically collected the Driving Status (DriveGaze) dataset to demonstrate the effectiveness of our approach. Additionally, we validate the superiority of the DriveGazen on the Single-eye Event-based Emotion (SEE) dataset. To the best of our knowledge, our method is the first to utilize guide attention spiking neural networks and eye-based event frames generated from conventional cameras for driving status recognition.Please refer to our project page for more details: https://github.com/----(please refer to other supplementary materials).

DriveGazen: Event-Based Driving Status Recognition using Conventional Camera

Large Language Models (LLMs) demonstrate impressive capabilities in the domain of program synthesis. This level of performance is not, however, universal across all tasks, all LLMs and all prompting styles. There are many areas where one LLM dominates, one prompting style dominates, or where calling a symbolic solver is a better choice than an LLM. 
A key challenge for the user then, is to identify not only when an LLM is the right choice of solver, and the appropriate LLM to call for a given synthesis task, but also the right way to call it. A non-expert user who makes the wrong choice, incurs a cost both in terms of results (number of benchmarks solved, and the time it takes to solve them) and financial cost, if using a closed-source language model via a commercial API.  In this paper, we frame this choice as an online learning problem. We use a multi-armed bandit algorithm to select which symbolic solver, or LLM and prompt combination to deploy in order to maximise a given reward function (which may prioritize solving time, number of benchmarks solved, or financial cost of solving). We implement an instance of this approach and evaluate it on benchmarks from the literature in ranking function synthesis, from the syntax-guided synthesis competition, and fresh, un-seen benchmarks generated from SMT problems.

Online Prompt Selection for Program Synthesis

Lip reading aims to predict spoken language by analyzing lip movements. Despite advancements in lip reading technologies, performance degrades when models are applied to unseen speakers due to their sensitivity to variations in visual information such as lip appearances. To address this challenge, speaker adaptive lip reading technologies have advanced by focusing on effectively adapting a lip reading model to target speakers in the visual modality. The effectiveness of adapting language information, such as vocabulary choice, of the target speaker has not been explored in the previous works. Moreover, existing datasets for speaker adaptation have limited vocabulary size and pose variations, limiting the validation of previous speaker-adaptive methods in real-world scenarios. To address these issues, we propose a novel speaker-adaptive lip reading method that adapts a pre-trained model to target speakers at both vision and language levels. Specifically, we integrate prompt tuning and the LoRA approach, applying them to a pre-trained lip reading model to effectively adapt the model to target speakers. In addition, to validate its effectiveness in real-world scenarios, we introduce a new dataset, VoxLRS-SA, derived from VoxCeleb2 and LRS3. It contains a vocabulary of approximately 100K words, offers diverse pose variations, and enables the validation of adaptation methods in wild, sentence-level lip reading for the first time. Through various experiments, we demonstrate that the existing speaker-adaptive method also improves performance in the wild at the sentence level. Moreover, with the proposed adaptation method, we show that the proposed method achieves larger improvements when applied to the target speaker, compared to the previous works.

Personalized Lip Reading: Adapting to Your Unique Lip Movements with Vision and Language

Visual Text-to-Speech (VTTS) aims to take the environmental image as the prompt to synthesize the reverberation speech for the spoken content. The challenge of this task lies in understanding the spatial environment from the image. Many attempts have been made to extract global spatial visual information from the RGB space of an image. However, local and depth image information are crucial for understanding the spatial environment, which previous works have ignored. To address the issues, we propose a novel multi-modal and multi-scale spatial environment understanding scheme to achieve immersive VTTS, termed M$^{2}$SE-VTTS. The multi-modal aims to take both the RGB and Depth space to learn more comprehensive spatial information, and the multi-scale seeks to model the local and global spatial knowledge simultaneously.
Specifically, we first split the RGB and Depth image into patch-level and adopt the Gemini-generated environment caption to guide the local spatial understanding. After that, the multi-modal and multi-scale features are integrated by the local-aware global spatial understanding. In this way, M$^{2}$SE-VTTS effectively models the interactions between local and global spatial contexts in the multi-modal spatial environment. Objective and subjective evaluations suggest that our model outperforms the advanced baselines in environmental speech generation. \textcolor[rgb]{0.93,0.0,0.47}{The code and audio samples are available at: {\url{https://github.com/M2SE-VTTS/M2SE-VTTS}}}.

Multi-modal and Multi-scale Spatial Environment Understanding for Immersive Visual Text-to-Speech

Selective state-space models (SSMs) are an emerging alternative to the Transformer, offering the unique advantage of parallel training and sequential inference. While these models have shown promising performance on a variety of tasks, their formal expressiveness and length generalization properties are not sufficiently explored. In this work, we provide insight into the workings of selective SSMs by analyzing their expressiveness and length generalization performance on regular language tasks, i.e. finite-state automaton (FSA) emulation. We address the limitations of modern SSM-based architectures by introducing the Selective Dense State-Space Model (SD-SSM), the first selective SSM that exhibits perfect length generalization on a set of various regular language tasks using a single layer. It utilizes a dictionary of dense transition matrices, a softmax selection mechanism that creates a convex combination of the dictionary matrices at each time step, and a readout consisting of layer normalization followed by a linear map. We then question the expressiveness of variants of diagonal selective SSMs by considering their empirical performance on commutative and non-commutative automata. We explain most of the experimental results with theoretical considerations.

On the Expressiveness and Length Generalization of Selective State Space Models on Regular Languages

The growing trend of sharing short videos on social media platforms, where users capture and share moments from their daily lives, has led to an increase in research efforts focused on micro-video recommendations. However, conventional methods rely on over-simplified graph construction based on watch time or viewing history, failing to reflect fine-grained user-specific features such as possibly preferable items. In this study, we propose a micro-video recommendation method based on graph construction that considers the preferable items. Our empirical experiments showed that the proposed method based on the explicit graph construction of highly positive, less positive, and negative items significantly outperformed conventional methods.

Exploiting Fine-Grained Skip Behaviors for Micro-Video Recommendation

Offline meta-reinforcement learning aims to equip agents with the ability to rapidly adapt to new tasks by training on data from a set of different tasks. Context-based approaches utilize a history of state-action-reward transitions – referred to as the context – to infer a representation of the current task, and then condition the agent, i.e., the policy and value function, on this task representation. Intuitively, the better the task representation captures the underlying tasks, the better the agent can generalize to new tasks. Unfortunately, context-based approaches suffer from distribution mismatch, as the context in the offline data does not match the context at test time, limiting their ability to generalize to the test task. This leads to the task representation overfitting to the offline training data. Intuitively, the task representation should be independent of the behavior policy used to collect the offline data. To address this issue, we approximately minimize the mutual information between the distribution over the task representation and behavior policy by maximizing the entropy of behavior policy conditioned on the task representation. We validate our approach in MuJoCo environments, showing that compared to baselines, our task representation more faithfully represents the underlying tasks, leading to outperforming prior methods in both in-distribution and out-of-distribution tasks.

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