All papers

The paper introduces a decoding framework that dynamically selects sampling temperature based on geometric representations of conversational scenarios.

The paper proposes an agent architecture that dynamically switches between latent reasoning and text interaction while maintaining external tool access.

The paper introduces a training-free contrastive decoding method where a small model guides a larger model toward shorter reasoning chains.

The paper presents a dataset and framework with dense progressive rewards to train language agents on long-horizon tasks.

The paper introduces action chunking for agentic robotics to execute pre-planned primitive routines without redundant model reasoning at each step.

This paper presents an annotated real estate dataset and evaluates language models for extracting structured attributes from unstructured text.

This paper introduces a multimodal federated learning framework that combines spectrograms and network metrics for on-device wireless jamming detection.

This paper presents an ensemble machine learning framework for tracking student knowledge and recommending multimodal content in tutoring systems.

The paper proposes an interpretable framework using brain imaging signals to monitor emotional states in children during robot-assisted therapy.

The paper introduces a multimodal dataset combining physiological sensor signals and video recordings to study human social stress.

The paper presents a multimodal deep learning method using PET and CT scans to predict brain metastasis in non-small cell lung cancer.

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The paper reviews retrieval-augmented generation paradigms and examines frameworks across retrieval, generation, and modular components.

The paper presents an 8-bit weight and activation post-training quantization technique that migrates activation outliers to weights.

The paper introduces a post-training mixed-precision quantization method using GPU-compatible 2D weight tiles for efficient inference.

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Proposes an agent memory architecture that uses biologically inspired adaptive decay functions to selectively retain and forget context.

Proposes a distillation framework and structural loss to speed up diffusion-based 3D LiDAR scene completion for autonomous driving perception.

Uses coarse CTC decoding to filter relevant hotwords into LLM prompts for improved contextual speech recognition on rare words.

The paper introduces a zero-shot LLM pipeline to generate counterfactual examples for stress-testing NLP models.

The paper investigates token pruning strategies to accelerate Vision Transformer inference for object detection and instance segmentation.

Introduces robotic action models that supervise policy representations via action-conditioned future-state prediction while avoiding future-generation inference costs.

Presents a deep reinforcement learning adaptive bitrate scheme for cloud gaming that prioritizes perceptual object quality using semantic segmentation.

Examines the disparity between reachability and self-reported parameters in an LLM-driven audio interface for timbre generation.

The paper proposes a streaming video super-resolution framework reusing intermediate latent diffusion features across frames via cross-step attention.

The paper proposes predicting pretrained DINO representations directly to guide flow matching models, avoiding a secondary ODE denoising process.

The paper introduces Skill-V, a verifiable agent skill library that treats skills as falsifiable contracts tested against environment execution outcomes.

The paper proposes PAVG, a dual-branch video generator modeling distinct solid-gas dynamics and cross-attention interactions for physically grounded motion.

The paper proposes a lightweight pose-graph tracking module using relative pose constraints and geometric uncertainty for real-time 6D object tracking.

The paper introduces FreeInpaint, a feed-forward framework performing 3D scene inpainting directly from unposed multi-view images using masked attention propagation.

The paper proposes Relative Patch Response Learning to improve AI-generated image detection by mitigating self-attention-induced contextual shifts across mixed patches.

The paper presents CELL, learning pose-informative correspondence confidence end-to-end via differentiable probabilistic PnP to localize event cameras in LiDAR maps.

The paper proposes EUA-Cal, regularizing late training against early-stage predictive uncertainty to prevent models from developing overconfidence.

The paper proposes a compact optical-flow representation to classify tactile object rotation across Earth, Mars, Moon, and orbital gravity domains.

The paper proposes data-space iteration, a few-step generative method removing flow discretization by directly refining noise predictions in data space.

Uses diffusion inpainting to erase lesions from mammograms, creating synthetic healthy counterfactuals to augment breast cancer classification training.

Proposes a training framework reconnecting backward gradient paths across autoregressive chunks to improve long video generation consistency.

Introduces an end-to-end diffusion model operating directly in radar space for few-step probabilistic precipitation nowcasting.

Presents a semi-automatic stride-independent image patching method guided by user input to train deep learning segmentation models.

Proposes a set-associative spatial memory retrofit for streaming 3D reconstruction to preserve distinct viewpoints at shared spatial locations.

Introduces an adaptive tokenization method allocating variable compute budgets to image diffusion based on visual complexity and spatial layout.

Develops a framework incorporating character animation principles like exaggeration into generative motion models via adaptation and guidance.

Proposes representing robot policies entirely in deterministic stateful code instead of querying vision-language models during run-time execution loops.

Presents a framework linking game engines with a neural renderer to simulate interactive virtual environments for training embodied agents.

Proposes a decoupled 4D reward framework separating static scenes from dynamic objects to improve consistency in generative video models.