Singapore

Large language models face significant cost challenges in long-sequence inference. To address this, reusing historical Key-Value (KV) Cache for improved inference efficiency has become a mainstream approach. Recent advances further enhance throughput by sparse attention mechanisms to select the most relevant KV Cache, thereby reducing sequence length. However, such techniques are limited to single-context scenarios, where historical KV Cache is computed sequentially with causal-attention dependencies. In retrieval-augmented generation (RAG) scenarios, where retrieved documents as context are unknown beforehand, each document’s KV Cache is computed and stored independently (termed multiple-context KV Cache), lacking cross-attention between contexts. This renders existing methods ineffective. Although prior work partially recomputes multiple-context KV Cache to mitigate accuracy loss from missing cross-attention, it requires retaining all KV Cache throughout, failing to reduce memory overhead. This paper presents SamKV, the first exploration of attention sparsification for multiple-context KV Cache. Specifically, SamKV takes into account the complementary information of other contexts when sparsifying one context, and then locally recomputes the sparsified information. Experiments demonstrate that our method compresses sequence length to 15% without accuracy degradation compared with full-recomputation baselines, significantly boosting throughput in multi-context RAG scenarios.

AAAI 2026

Sparse Attention Across Multiple-Context KV Cache

(large) language models

question answering

generation

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

[![](https://assets.underline.io/markdown_image/1/image/b087f8a4dc5816d6e1a6514e59c59ac3.png)](https://drive.google.com/file/d/1T96GzPqObXrMln2BMByCSXTSizjTg69P/view?usp=sharing)

You need to log in with the email address you registered with. Access credentials have been sent to your email. 

Please be sure to check your spam and other email folders if you do not see an email confirmation right away.

Please log in to explore this event.

To access NAACL 2025 event page you are required to register. Please follow [**this link**](https://2025.naacl.org/registration/registration/) to register. Access will depend on your registration type.

Please register!

NAACL 2025

Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

poster

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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-26 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.

To access this event page, you need to log in with the **email address you registered with**. Access credentials will be sent to your email from Underline - subject line "Welcome to AAAI 2026". Please be sure to check your spam email folder if you do not see an email confirmation right away.

Please log in

To access this event page, you are required to register.
Please complete your registration to continue.

We recommend reading [**the registration information**](https://aaai.org/conference/aaai/aaai-26/registration/) first.

**Online Registration Form**: https://aaai.getregistered.net/conference-2026 

Registration Required

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Accurate prediction of driving intention is key to enhancing the safety and interactive efficiency of human-machine co-driving systems. It serves as a cornerstone for achieving high-level autonomous driving. However, current approaches remain inadequate for accurately modeling the complex spatiotemporal interdependencies and the unpredictable variability of human driving behavior. To address these challenges, we propose CaTFormer, a causal Temporal Transformer that explicitly models causal interactions between driver behavior and environmental context for robust intention prediction. Specifically, CaTFormer introduces a novel Reciprocal Delayed Fusion (RDF) mechanism for precise temporal alignment of internal and external feature streams, a Counterfactual Residual Encoding (CRE) module that systematically eliminates spurious correlations to reveal authentic causal dependencies, and an innovative Feature Synthesis Network (FSN) that adaptively synthesizes these purified representations into coherent temporal representations. We evaluate the proposed CaTFormer on the public Brain4Cars dataset, and it achieves state-of-the-art performance. It effectively captures complex causal temporal dependencies and enhances both the accuracy and transparency of driving intention prediction.

CaTFormer: Causal Temporal Transformer with Dynamic Contextual Fusion for Driving Intention Prediction

Spiking neural networks (SNNs) offer a promising path toward energy-efficient speech command recognition (SCR) by leveraging their event-driven processing paradigm. However, existing SNN-based SCR methods often struggle to capture rich temporal dependencies and contextual information from speech due to limited temporal modeling and binary spike-based representations. To address these challenges, we first introduce the **m**ulti-view **s**piking **t**emporal-**a**ware **s**elf-**a**ttention (**MSTASA**) module, which combines effective spiking temporal-aware attention with a multi-view learning framework to model complementary temporal dependencies in speech commands. Building on MSTASA, we further propose **SpikCommander**, a fully spike-driven transformer architecture that integrates MSTASA with a **s**piking **c**ontextual **r**efinement channel MLP (**SCR-MLP**) to jointly enhance temporal context modeling and channel-wise feature integration. We evaluate our method on three benchmark datasets: the Spiking Heidelberg Dataset (SHD), the Spiking Speech Commands (SSC), and the Google Speech Commands V2 (GSC). Extensive experiments demonstrate that SpikCommander consistently outperforms state-of-the-art (SOTA) SNN approaches with fewer parameters under comparable time steps, highlighting its effectiveness and efficiency for robust speech command recognition.

SpikCommander: A High-performance Spiking Transformer with Multi-view Learning for Efficient Speech Command Recognition

Class-incremental learning (CIL) has recently gained great attention in the field of time series classification.
Existing methods based on knowledge distillation exhibit impressive ability to preserve prior knowledge and overcome catastrophic forgetting, however, their effectiveness faces a major challenge posed by time series data.
Since temporal data are more susceptible to sensor errors and electronic noise,
the distillation process may be negatively affected by noisy knowledge transfer.
To address this issue, we propose a novel confidence-guided mask distillation (CMD) framework,
to prevent the noisy inheritance during distillation. 
The core of CMD lies in a dynamic masking mechanism guided by prediction confidence, 
capable of allocating higher weights to high-confidence time series and substantially suppressing
the influence of low-confidence ones.
Additionally, different from prior work passing a set of feature prototypes to the classifier simply,
we develop prototype-guided contrastive learning (PCL) to alleviate the classifier bias on new
classes, through extra contrastive constraints to push away the feature distributions of old feature prototypes from those of new classes features.
Extensive experiments on three time-series datasets demonstrate that our CDMD method significantly outperforms other replay-free CIL approaches in raising average accuracy, as well as decreasing forgetting rate.

Time Series Class-Incremental Learning via Confidence-guided Mask Distillation and Prototype-guided Contrastive Learning

Real-world control systems require policies that are not only high-performing but also interpretable and robust. A promising direction toward this goal is model-based control, which learns system dynamics and cost functions from historical data and then uses these models to inform decision-making. Building on this paradigm, we introduce DiffOP, a novel framework for learning optimization-based control policies defined implicitly through optimization control problems.
Without relying on value function approximation, DiffOP jointly learns the cost and dynamics models and directly optimizes the actual control costs using policy gradients.
To enable this, we derive analytical policy gradients by applying implicit differentiation to the underlying optimization problem and integrating it with the standard policy gradient framework.
Under standard regularity conditions, we establish that DiffOP converges to an $\epsilon$-stationary point within $\mathcal{O}(\epsilon^{-1})$ iterations.
We demonstrate the effectiveness of DiffOP through experiments on nonlinear control tasks and power system voltage control with constraints.

DiffOP: Reinforcement Learning of Optimization-Based Control Policies via Implicit Policy Gradients

Composed Image Retrieval (CIR) combines the reference image with text to retrieve the intended target image. Recently, zero-shot CIR has gained significant attention by eliminating the need for labeled triplets required in supervised CIR. However, it inevitably demands additional training corpus, storage, and computational resources, limiting its applicability in real-world scenarios. Inspired by advancements in Test-Time Adaptation (TTA), we propose a Test-Time CIR setting named TT-CIR, which aims to efficiently adapt models to unlabeled test samples while reducing resource consumption. Within the TT-CIR setting, we identify that naively introducing existing TTA methods (e.g., reward-based) into CIR faces two vital challenges: 1) Modification-restricted reward pool, which limits the exploration of semantically relevant candidate rewards; 2) Conservative knowledge feedback, which inhibits the adaptability of rewards to the current data distribution. To address these challenges, we propose a test-time reinforcement learning framework that integrates a Counterfactual-guided Multinomial Sampling (CMS) strategy and a Duplex Rewards Modeling (DRM) module. The CMS explores a candidate reward pool that is both visually similar and semantically relevant to the given query, while the DRM generates stable and adaptive duplex rewards to guide model adaptation. Extensive experiments demonstrate the superiority and adaptability of our method over existing approaches. Code is available in the supplementary materials.

Duplex Rewards Optimization for Test-Time Composed Image Retrieval

Segment Anything Model (SAM) exhibits remarkable zero-shot segmentation capability; however, its prohibitive computational costs make edge deployment challenging. Although post-training quantization (PTQ) offers a promising compression solution, existing methods yield unsatisfactory results when applied to SAM, owing to its specialized model components and promptable workflow: 
(i) The mask decoder's attention exhibits extreme activation outliers, and we find that aggressive clipping (even 100$\times$), without smoothing or isolation, is effective in suppressing outliers while maintaining performance. Unfortunately, traditional distribution-based metrics (e.g., MSE) fail to provide such large-scale clipping. 
(ii) Existing quantization reconstruction methods neglect semantic interactivity of SAM, leading to misalignment between image feature and prompt intention.
To address the above issues, we propose SAQ-SAM in this paper, which boosts PTQ for SAM from the perspective of semantic alignment.
Specifically, we propose Perceptual-Consistency Clipping, which exploits attention focus overlap to promote aggressive clipping while preserving semantic capabilities. 
Furthermore, we propose Prompt-Aware Reconstruction, which incorporates image-prompt interactions by leveraging cross-attention in mask decoder, thus facilitating alignment in both distribution and semantic. 
Moreover, to ensure the interaction efficiency, we design a layer-skipping strategy for image tokens in encoder.
Extensive experiments are conducted on various SAM sizes and tasks, including instance segmentation, oriented object detection, and semantic segmentation, and the results show that our method consistently exhibits advantages.
For example, when quantizing SAM-B to 4-bit, SAQ-SAM achieves 11.7\% higher mAP than the baseline in instance segmentation task.

SAQ-SAM: Semantically-Aligned Quantization for Segment Anything Model

Video Camouflaged Object Detection (VCOD) poses significant challenges due to the subtle appearance of camouflaged objects, especially under dynamic motion and occlusion. Existing methods predominantly rely on optical flow or black-box features for motion modeling, which often entail substantial computational costs and suffer from limited interpretability. Inspired by the human strategy of identifying abnormal movements between frames and the principle of event camera image formation, we propose an eventstream-inspired dual-branch framework for VCOD. Specifically, we design an eventstream-like data extraction module to capture pixel-level motion variations, effectively distinguishing object motion from background dynamics. This event-based representation is integrated into SAM2 through a dual-branch memory-augmented framework, consisting of Time Bridge Attention and Visual Bridge Attention, enabling joint modeling of motion and appearance cues. In addition, we introduce a Prompt Embedding Generator to eliminate the need for human-provided interactive prompts, facilitating fully automatic VCOD. Extensive experiments on MoCA-Mask and CAD2016 demonstrate that our approach significantly outperforms state-of-the-art methods, achieving both superior segmentation accuracy and interpretable motion modeling. To the best of our knowledge, this is the first work to incorporate eventstream-inspired representations into the VCOD task. Code and related resources will be released.

Towards Explainable Video Camouflaged Object Detection: SAM2 with Eventstream-Inspired Data

In this paper, we propose new randomized algorithms for estimating the two-to-infinity and one-to-two norms in a matrix-free setting, using only matrix-vector multiplications. Our methods are based on appropriate modifications of Hutchinson's diagonal estimator and its Hutch++ version. We provide oracle complexity bounds for both modifications. We further illustrate the practical utility of our algorithms for Jacobian-based regularization in deep neural network training on image classification tasks. We also demonstrate that our methodology can be applied to mitigate the effect of adversarial attacks in the domain of recommender systems.

Matrix-Free Two-to-Infinity and One-to-Two Norms Estimation

Open-vocabulary 3D scene understanding (OV-3D) aims to localize and classify novel objects beyond the closed set of object classes. However, existing approaches and benchmarks primarily focus on the open vocabulary problem within the context of object classes, which is insufficient in providing a holistic evaluation to what extent a model understands the 3D scene. In this paper, we introduce a more challenging task called Generalized Open-Vocabulary 3D Scene Understanding (GOV-3D) to explore the open vocabulary problem beyond object classes. It encompasses an open and diverse set of generalized knowledge, expressed as linguistic queries of fine-grained and object-specific attributes. To this end, we contribute a new benchmark named OpenScan, which consists of 3D object attributes across eight representative linguistic aspects, including affordance, property, and material. We further evaluate state-of-the-art OV-3D methods on our OpenScan benchmark and discover that these methods struggle to comprehend the abstract vocabularies of the GOV-3D task, a challenge that cannot be addressed simply by scaling up object classes during training. We highlight the limitations of existing methodologies and explore promising directions to overcome the identified shortcomings.

OpenScan: A Benchmark for Generalized Open-Vocabulary 3D Scene Understanding

Currently, pretrained models are rapidly scaling in size, which substantially increases the cost of fine-tuning them for downstream tasks. To address this challenge, parameter-efficient fine-tuning (PEFT) methods have been developed to optimize a minimal set of parameters for adaptation. While current PEFT approaches predominantly employ an "additive'' strategy, introducing learnable modules into inputs or architectures, neglect the inherent knowledge embedded within pretrained models, which may be redundant or even conflict with downstream tasks. This limitation leads to increased inference latency and suboptimal transfer performance, particularly in scenarios with significant domain gaps. In this paper, we propose a Subtractive Fine-tuning Paradigm(SFP), which converts multiple redundant operations within the original module into a linear transformation to enhance inference speed and model performance. Specifically, we introduce a compact filter block to replace specific module with interference and redundancy in the original structure to reduce model conflicts. By using a pseudo inverse matrix to construct filter block, ensuring that it can inherit the knowledge of the replacement module, and then freezing the rest of the model, only fine-tuning the filter block is performed to eliminate interference and redundant knowledge, thereby enhancing the model’s adaptability to downstream tasks. Experimental results demonstrate that our SFP outperforms existing PEFT methods in accuracy while decreasing the overall model parameters by 12%. Compared to full fine-tuning, the accuracy has increased by 8.47%(74.04% vs. 65.57%, VTAB).

Content not yet available

Next from AAAI 2026

CaTFormer: Causal Temporal Transformer with Dynamic Contextual Fusion for Driving Intention Prediction

Similar lecture

MEDA: Dynamic KV Cache Allocation for Efficient Multimodal Long-Context Inference