2384 papers
This paper introduces v-HUB, a novel benchmark for video humor understanding based on non-verbal short videos with rich annotations, which reveals that current multimodal large language models struggle with visual humor alone but show improved performance when environmental audio is incorporated.
The Latent Speech-Text Transformer (LST) improves the efficiency and performance of auto-regressive speech-text models by aggregating speech tokens into latent patches, which aligns sequence granularity with text, reduces computational costs, and achieves significant accuracy gains across speech and text benchmarks.
AlphaApollo is an agentic reasoning system that enhances foundation models' performance on complex, long-horizon tasks by orchestrating multi-turn agentic reasoning, turn-level reinforcement learning for tool-use optimization, and a propose-judge-update evolution loop with verification.
This paper introduces the NavSpace benchmark to systematically evaluate the spatial intelligence of navigation agents through six task categories and 1,228 trajectory-instruction pairs, revealing limitations in current models and proposing SNav, a new spatially intelligent navigation model that outperforms existing agents on both the benchmark and real robot tests.
The paper introduces RECODE, an agentic framework that enhances visual question answering by reverse-engineering structured visuals into executable code through iterative generation and selection, thereby transforming ambiguous perceptual tasks into verifiable symbolic reasoning problems that significantly outperform existing methods.
This paper introduces REAP, a novel expert pruning method that outperforms existing merging techniques for compressing Mixture-of-Experts models in generative tasks by leveraging router gate-values and activation norms to minimize reconstruction error, achieving near-lossless compression even at 50% expert reduction.
RL-100 is a unified real-world reinforcement learning framework that combines diffusion visuomotor policies with a clipped PPO objective and consistency distillation to achieve 100% success across 1,000 diverse robotic manipulation trials, matching or surpassing human experts while demonstrating robust zero-shot generalization and continuous deployment in dynamic environments.
FALCON addresses the spatial reasoning limitations of existing 2D-based vision-language-action models by leveraging spatial foundation models to inject rich 3D geometric priors directly into the action head, achieving state-of-the-art performance across diverse simulation and real-world tasks without requiring architectural changes or specialized sensors.
This paper introduces SynHLMA, a novel framework that synthesizes hand manipulation sequences for articulated objects by aligning natural language instructions with a discrete human-object interaction representation, thereby enabling robust grasp generation, prediction, and interpolation for applications in embodied AI and robotics.
The paper proposes GraphKeeper, a novel framework for Graph Domain-Incremental Learning that addresses catastrophic forgetting through knowledge disentanglement and deviation-free preservation, achieving state-of-the-art performance across multiple graph domains while remaining compatible with various graph foundation models.
This paper proposes a structured matrix scaling approach for multi-class calibration that leverages theoretical insights from logistic regression, combined with structured regularization and robust optimization, to effectively manage the bias-variance tradeoff and achieve substantial performance gains over existing methods while providing an open-source implementation.
This paper introduces "information capacity," a novel metric that evaluates large language model efficiency by measuring text compression performance relative to computational complexity while accounting for tokenizer efficiency, thereby enabling accurate performance prediction and revealing linguistic biases across diverse models.
This paper proposes LTSV, a lightweight and efficient method for valuing time series data in foundation models by leveraging in-context finetuning and temporal block aggregation to overcome the computational bottlenecks and temporal dependency limitations of traditional approaches.
This paper introduces MediRound, a new task and baseline model for multi-round entity-level reasoning segmentation in medical images, supported by the large-scale MR-MedSeg dataset and a Judgment & Correction Mechanism to mitigate error propagation in multi-turn medical dialogues.
This paper introduces a novel in-context learning approach using Time-Series Foundation Models (TSFMs) to classify bearing health status from vibration data without fine-tuning, enabling scalable, zero-shot maintenance solutions across varying operational conditions.
This paper presents a Google Gemini 2.0 Flash-based chatbot that automates the setup, execution, and post-processing of two-dimensional finite element eddy current simulations using Gmsh and GetDP, thereby significantly reducing the time required to generate electromagnetic models with customizable geometries and analysis routines.
This paper proposes the Adaptive Diversity Cache (ADC), a novel training-free and plug-and-play module that mitigates long-tail bias in Human-Object Interaction detection by dynamically accumulating diverse high-confidence features during inference to enhance rare category performance without additional training.
This paper proposes "Periodic Asynchrony," a framework that accelerates LLM reinforcement learning by decoupling inference and training into a provably on-policy asynchronous pipeline, achieving a 3- to 5-fold throughput improvement on NPU platforms without sacrificing accuracy.
This paper introduces UPA-RFAS, a unified framework that generates universal and transferable physical adversarial patches to effectively attack diverse Vision-Language-Action (VLA) models across unknown architectures, finetuned variants, and sim-to-real shifts by leveraging robust feature alignment, a two-phase min-max optimization, and VLA-specific attention and semantic losses.
This paper proposes a communication-constrained multi-agent reinforcement learning framework that utilizes a generalized model and dual mutual information estimator to distinguish between lossy and lossless messages, thereby quantifying their impact on global rewards to enhance cooperative policy learning in complex, dynamic environments.