Singapore

Dynamic LiDAR point cloud compression (LPCC) is crucial for the efficient transmission and storage of large-scale three-dimensional data in applications such as autonomous driving. However, many existing methods, which primarily focus on compressing geometric or motion information, face a fundamental limitation: they treat all points as equally important. This approach neglects the semantic priorities of a scene, resulting in inefficient bit allocation and particularly compromising the reconstruction quality of safety-critical regions, such as pedestrians and vehicles, which are vital to downstream perception tasks. To address these limitations, we propose R²D-LPCC, a relevance-ranking framework for region-adaptive LPCC that prioritizes fidelity in semantically important regions. Central to our approach is the Adaptive Relevance Learning (ARL) module, which integrates semantic context with uncertainty to evaluate regional significance and guide compression. We also introduce a Multi-scale Region-Adaptive Transform (MRAT) module to enhance semantic feature modeling and preserve fine-grained details in key areas. Additionally, we develop an adaptive multimodal motion estimation module to improve motion prediction in complex three-dimensional environments. Extensive experiments conducted on the SemanticKITTI benchmark demonstrate that R²D-LPCC significantly surpasses ten recent state-of-the-art methods, achieving a 45.48$\%$ BD-rate gain over the previous leading method, Unicorn, and a 98.58$\%$ gain over the GPCC standard, while ensuring superior reconstruction quality in semantically important regions.

AAAI 2026

R²D-LPCC: Relevance-Ranking Guided Region-Adaptive Dynamic LiDAR Point Cloud Compression

lidar point cloud

relevance ranking

dmkm: data compression

region-adaptive

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

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-24 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.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.

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AAAI 2024

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. 

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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.

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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.

Solving energy-saving distributed heterogeneous flexible job shop scheduling problem (ES-DHFJSP) aims to enhance industrial production efficiency while minimizing energy consumption. State-of-the-art co-evolutionary algorithms have emerged as effective approaches for addressing ES-DHFJSP. However, existing methodologies demonstrate compromised convergence rates and excessive computational overhead when confronted with vast search spaces. In this work, we propose a novel solution space transformation-guided co-evolution algorithm (SSTCE) to overcome this limitation. In SSTCE, we first establish an inter-job similarity metric and incorporate constrained hierarchical clustering with optimal leaf ordering (CHC-OLO) to generate clustered job sets, which are subsequently utilized for population initialization that achieves a favorable balance between convergence and diversity. To enhance search capability in expansive solution spaces, we devise a dynamic solution space transformation mechanism that effectively reduces inefficient searches within the algorithm. Furthermore, we develop tailored local search strategies leveraging domain-specific knowledge of DHFJSP properties. Extensive experimental evaluations across 20 benchmark instances demonstrate that SSTCE significantly outperforms existing evolutionary algorithms in solving ES-DHFJSP.

A Solution Space Transformation-Guided Co-Evolution for Energy-Saving Distributed Heterogeneous Flexible Job Shop Scheduling

Recently, video-language models (VidLMs) have gained widespread attention and adoption. However, existing works primarily focus on terrestrial scenarios, overlooking the highly demanding application needs of underwater observation. To overcome this gap, we introduce UVLM, an under water observation benchmark which is build through a collaborative approach combining human expertise and AI models. To ensure data quality, we have conducted in-depth considerations from multiple perspectives. First, to address the unique challenges of underwater environments, we selected videos that represent typical underwater challenges including light variations, water turbidity, and diverse viewing angles to construct the dataset. Second, to ensure data diversity, the dataset covers a wide range of frame rates, resolutions, 419 classes of marine animals, and various static plants and terrains. Next, for task diversity, we adopted a structured design where observation targets are categorized into two major classes: biological and environmental. Each category includes content observation and change/action observation, totaling 20 subtask types. Finally, we designed several challenging evaluation metrics to enable quantitative comparison and analysis of different methods. Experiments on two representative VidLMs demonstrate that fine-tuning VidLMs on UVLM significantly improves underwater world understanding while also showing potential for slight improvements on existing in-air VidLM benchmarks, such as VideoMME and Perception text. The dataset and prompt engineering will be released publicly.

UVLM: Benchmarking Video Language Model for Underwater World Understanding

Traditional dialogue retrieval aims to select the most appropriate utterance or image from recent dialogue history. However, they often fail to meet users’ actual needs for revisiting semantically coherent content scattered across long-form conversations. To fill this gap, we define the Fine-grained Fragment Retrieval (FFR) task, requiring models to locate query-relevant fragments, comprising both utterances and images, from multimodal long-form dialogues. As a foundation for FFR, we construct MLDR, the longest-turn multimodal dialogue retrieval dataset to date, averaging 25.45 turns per dialogue, with each naturally spanning three distinct topics. To evaluate generalization in real-world scenarios, we curate and annotate a WeChat-based test set comprising real-world multimodal dialogues with an average of 75.38 turns. Building on these resources, we explore existing generation-based Vision-Language Models (VLMs) on FFR and observe that they often retrieve incoherent utterance-image fragments. While optimized for generating responses from visual-textual inputs, these models lack explicit supervision to ensure semantic coherence within retrieved fragments. To address this, we propose F$^2$RVLM, a generative retrieval model trained in a two-stage paradigm: (1) supervised fine-tuning to inject fragment-level retrieval knowledge, and (2) GRPO-based reinforcement learning with multi-objective rewards to encourage outputs with semantic precision, relevance, and contextual coherence. In addition, to account for difficulty variations arising from differences in intra-fragment element distribution, ranging from locally dense to sparsely scattered, we introduce a difficulty-aware curriculum sampling that ranks training instances by predicted difficulty and gradually incorporates harder examples. This strategy enhances the model’s reasoning ability in long, multi-turn dialogue contexts. Experiments on both in-domain and real-domain sets demonstrate that F$^2$RVLM substantially outperforms popular VLMs, achieving superior retrieval performance.

F2RVLM: Boosting Fine-grained Fragment Retrieval for Multi-Modal Long-form Dialogue with Vision Language Model

The low sampling efficiency during the rollout phase poses a significant challenge to scaling reinforcement learning for large language model reasoning. Existing methods attempt to improve efficiency by scheduling problems based on problem difficulties. However, these approaches suffer from unstable and biased estimations of problem difficulty and fail to capture the alignment between model competence and problem difficulty in RL training, leading to suboptimal results. To address these challenges, we introduce $\textbf{C}$ompetence-$\textbf{D}$ifficulty $\textbf{A}$lignment $\textbf{S}$ampling ($\textbf{CDAS}$). This approach allows for accurate and stable estimation of problem difficulties by aggregating historical performance discrepancies across problems. Subsequently, model competence is quantified to adaptively select problems whose difficulties align with the model's current competence using a fixed-point system. Extensive experiments in mathematical RL training show that $\textbf{CDAS}$ consistently outperforms strong baselines, achieving the highest average accuracy of 45.89\%. Furthermore, $\textbf{CDAS}$ reduces the training step time overhead by 57.06\% compared to the widely-used Dynamic Sampling strategy, verifying the efficiency of $\textbf{CDAS}$. Additional experiments on different tasks, model architectures, and model sizes demonstrate the generalization capability of $\textbf{CDAS}$.

Rethinking the Sampling Criteria in Reinforcement Learning for LLM Reasoning: A Competence-Difficulty Alignment Perspective

Depth-wise pruning accelerates LLM inference in resource-constrained scenarios but suffers from performance degradation due to indiscriminate removal of entire Transformer layers. This paper reveals ``Patch-Like'' redundancy across layers via correlation analysis of the outputs of different layers in reproducing kernel Hilbert space, demonstrating consecutive layers exhibit high functional similarity. Building on this observation, this paper proposes Sliding-Window Merging (SWM) - a dynamic compression method that selects consecutive layers from top to bottom using a pre-defined similarity threshold, and compacts patch-redundant layers through a parameter consolidation, thereby simplifying the model structure while maintaining its performance. Extensive experiments on LLMs with various architectures and different parameter scales show that our method outperforms existing pruning techniques in both zero-shot inference performance and retraining recovery quality after pruning. In particular, in the experiment with 35\% pruning on the Vicuna-7B model, our method achieved a 1.654\% improvement in average performance on zero-shot tasks compared to the existing method. Moreover, we further reveal the potential of combining depth pruning with width pruning to enhance the pruning effect.

Sliding-Window Merging for Compacting Patch-Redundant Layers in LLMs

The performance of egocentric AI agents is fundamentally limited by multimodal intent ambiguity. This challenge arises from a combination of underspecified language, imperfect visual data, and deictic gestures, which frequently leads to task failure. Existing monolithic Vision-Language Models (VLMs) struggle to resolve these multimodal ambiguous inputs, often failing silently or hallucinating responses. To address these ambiguities, we introduce the Plug-and-Play Clarifier, a zero-shot and modular framework that decomposes the problem into discrete, solvable sub-tasks. Specifically, our framework consists of three synergistic modules: (1) a text clarifier that uses dialogue-driven reasoning to interactively disambiguate linguistic intent, (2) a vision clarifier that delivers real-time guidance feedback, instructing users to adjust their positioning for improved capture quality, and (3) a cross-modal clarifier with grounding mechanism that robustly interprets 3D pointing gestures and identifies the specific objects users are pointing to. Extensive experiments demonstrate that our framework improves the intent clarification performance of small language models (4--8B) by approximately 30\%, making them competitive with significantly larger counterparts. We also observe consistent gains when applying our framework to these larger models. Furthermore, our vision clarifier increases corrective guidance accuracy by over 20\%, and our cross-modal clarifier improves semantic answer accuracy for referential grounding by 5\%. Overall, our method provides a plug-and-play framework that effectively resolves multimodal ambiguity and significantly enhances user experience in egocentric interaction.

Plug-and-Play Clarifier: A Zero-Shot Multimodal Framework for Egocentric Intent Disambiguation

Reinforcement learning (RL) has shown promise for enhancing code generation capabilities in large language models (LLMs), yet its effectiveness critically depends on high-quality test suites for reliable reward signals.
Current approaches suffer from inadequate test case quantity and quality, leading to false positives (incorrect solutions passing verification) and slow positives (valid but suboptimal implementations), which corrupt RL training dynamics.
We address these challenges through three key contributions:
(1) We systematically analyze how low-quality test suites degrade Code RL performance via reward misalignment;
(2) We propose Themis, an automated framework that transforms test case generation into code synthesis—first extracting problem constraints via template-guided parsing, then generating executable test generators through LLM-powered code synthesis, and finally validating tests through constraint-aware filtering;
(3) We develop an error-guided test case reduction method that preserves error detection efficacy while reducing test set cardinality, thereby enhancing reinforcement learning training efficiency. 
Evaluated on programming competition datasets, Themis achieves 95\% error detection rates, outperforming original test suites in most of the cases.
When integrated into RL pipelines, models trained with Themis-generated tests demonstrate consistent 3-5\% improvements across HumanEval, MBPP, and LiveCodeBench compared to the baseline, matching performance levels achieved with manually curated test suites.
Our constraint-aware test synthesis framework ensures full automation while preserving semantic validity—critical for scaling RL training to complex code generation tasks.
The framework's modular design also enables seamless integration with existing code data synthesis frameworks.

Themis: Automated Constraint-Aware Test Synthesis Framework for Code Reinforcement Learning

The illusion phenomenon of large language models (LLMs) is the core obstacle to their reliable deployment. This article formalizes the large language model as a probabilistic Turing machine by constructing a "computational necessity hierarchy", and for the first time proves the illusions are inevitable on diagonalization, incomputability, and information theory boundaries supported by the new "learner pump lemma". However, we propose two "escape routes": one is to model **Retrieval Enhanced Generations (RAGs)** as oracle machines, proving their absolute escape through "computational jumps", providing the first formal theory for the effectiveness of RAGs; The second is to formalize continuous learning as an "internalized oracle" mechanism and implement this path through a novel neural game theory framework.Finally, this article proposes a feasible new principle for artificial intelligence security - **Computational Class Alignment (CCA)**, which requires strict matching between task complexity and the actual computing power of the system, providing theoretical support for the secure application of artificial intelligence.

Hallucination as a Computational Boundary: A Hierarchy of Inevitability and the Oracle Escape

Aggregated time series are widely used in business and economics, where top-level sequences (e.g., category sales) aggregated from underlying sequences (e.g., individual items) often exhibit clearer trends and are therefore typically the primary focus of forecasting tasks.
However, treating top-level sequences as ordinary multivariate time series is inappropriate in the presence of coupled aggregation constraints.
The core challenge arises in coupled aggregation structures, where a single underlying sequence contributes to multiple top-level sequences, as simple nonnegativity constraints of underlying sequences induce highly complex constraints among top-level sequences.
Existing methods fail to achieve high accuracy while satisfying these constraints.
To address this, we propose ProCAST, a projection-based framework that adjusts forecasts from any multivariate base model to satisfy coupled aggregation constraints.
By introducing virtual underlying sequences and leveraging orthogonal and oblique projection, our method ensures that the top-level forecasts are feasible without explicitly deriving complex constraints.
Theoretically, we prove that the proposed method guarantees improved accuracy under distance-based loss functions.
Experiments on real-world datasets show that our method completely eliminates constraint violations while achieving higher accuracy than current state-of-the-art approaches.

ProCAST: A Projection Framework for Coupled Aggregation Constrained Multivariate Time Series Forecasting

Multimodal large language models (LMMs) have demonstrated remarkable capabilities across diverse vision-language tasks, including image captioning, visual question answering, and text-image retrieval. However, their computational complexity and memory footprint, particularly in the key-value (KV) cache during inference, pose significant challenges for real-time deployment, especially on resource-constrained devices. In this paper, we propose Dynamic KV Cache Quantization, a novel quantization strategy tailored for multimodal LMMs. Our approach applies per-channel quantization to (K) and per-token quantization to (V), leveraging their respective statistical distributions to optimize precision allocation. Additionally, we introduce an adaptive token and channel recording mechanism that dynamically adjusts quantization parameters based on real-time distribution tracking, effectively mitigating the impact of outliers. To further enhance compression efficiency, we implement fine-grained grouping, which partitions KV tensors into localized subgroups, enabling more adaptive quantization. Experimental results on LLaVA-1.5 (7B/13B) and Qwen-VL across multiple multimodal benchmarks demonstrate that our method significantly outperforms existing KV-cache quantization approaches, achieving a superior trade-off between memory efficiency and model accuracy.

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A Solution Space Transformation-Guided Co-Evolution for Energy-Saving Distributed Heterogeneous Flexible Job Shop Scheduling

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