All papers

The authors provide a comprehensive textbook covering reinforcement learning methods, game-playing algorithms, and key historical milestones.

The authors propose a conditional flow-matching generative model to extend the field of view in diffusion brain MRI scans across scanners.

The paper presents a multi-station transformer model that jointly detects phase arrivals, locations, magnitudes, and associations for earthquake monitoring.

The authors introduce a time-frequency diffusion framework that improves the generation of rare extreme events in time-series data.

The authors propose a visual token compression framework that reduces computational costs by retaining only visual tokens with high reconstruction residuals.

The authors present a flight navigation policy for quadrotors that penalizes movement outside the camera view to avoid collisions in cluttered environments.

The paper develops a plug-and-play module for convolutional neural networks that provides optimal rotation and translation invariance based on invariant theory.

The authors formulate colon cancer treatment planning as an interactive reinforcement learning task in settings where actions are only partially verifiable.

The paper proposes using test-time training memory to maintain consistent long-horizon visual scenes in real-time camera-controlled video world models.

The authors present a parallel decoding method that speeds up on-device tool calling for language model agents without requiring model retraining.

The study evaluates scientific hypotheses generated by language models, concluding that they extend existing knowledge rather than starting new research directions.

The paper introduces a training-free technique and benchmark to help vision-language models correctly process and reject negated concepts in text prompts.

The paper studies how much models contribute to multi-model teams, showing individual test scores do not correlate well with collaborative performance.

The authors design an FPGA implementation of spiking neural networks that performs both inference and training on-chip without backpropagation through time.

The paper evaluates seven machine learning and graph models on detecting misinformation within social media noise from conflict-related tweets.

The paper presents an in-context foundation model architecture for next-event prediction across diverse operational event logs.

The paper introduces a framework combining 3D Gaussian Splatting and physical simulation to evaluate robot policies using handheld fisheye cameras.

The paper proposes predicting 3D bounding box trajectories instead of dense video frames as compact representations for world action models.

The paper evaluates the energy efficiency, carbon intensity, and accuracy of ten post-training quantization methods across various language models and GPUs.

The paper proposes a vision-language-action model where action execution avoids inference latency by decoupling from explicit reasoning output at runtime.

The paper presents a convex-analytic framework to select query directions for safety verification in Markov decision processes.

The paper investigates how indexed instruction wording affects the performance of lexical rankers for robot task retrieval in manipulation benchmarks.

The paper presents an efficient video super-resolution method using hybrid attention and reward-yield distribution matching distillation.

The paper benchmarks twenty-three self-supervised 3D medical vision foundation models across segmentation, classification, and regression tasks.

The paper analyzes why dynamic graph neural networks fail on evolving nodes and introduces a change-aware classification framework.

The paper introduces an action-conditioned world model that separates observation representation learning from intervention learning for mobile network energy control.

The paper applies Hilbert geometry to constrain activation shifts in softmax layers for stable optimizer updates in language model heads.

The paper re-evaluates the pretraining compute scaling laws between diffusion and autoregressive language models using modern diffusion training designs.

The paper introduces a dataset and benchmark to evaluate vision-language models on recognizing and answering questions about Philippine cultural heritage sites.

The paper introduces a morphology-aware contrastive learning framework to predict five-year colorectal cancer survival from whole-slide pathology images.

The paper presents a monocular depth estimation method that uses image editing priors to improve geometric consistency under environmental perturbations.

The paper proposes a rubric-based reinforcement learning method combining selective teacher distillation with an adaptive hand-off to standard optimization.

The paper introduces a 4D latent cache to reuse static and dynamic visual representations for efficient conditioning in video generation models.

Introduces RoTA-Bench to evaluate vision-language models on 3D spatial reasoning through interactive object rotation to match target 2D silhouettes.

Introduces DeltaTTG, a method using predicted remaining token count as a progress signal to allocate advantages in multi-turn reinforcement learning.

Proposes PrT-Flow, an invertible flow matching method that enables exact action probability computation for reinforcement learning in robotics.

Introduces Cowork-Gym, a framework synthesizing multi-tool collaborative work environments and user simulations to train enterprise agents.

Introduces EncounterMob, an LLM-based framework that generates human mobility trajectories while preserving both individual routines and multi-person encounter patterns.

Proposes Dynamic K-Head, a question-conditioned budget selector that sets continuous latent reasoning steps based on input difficulty.

Proposes Token Window Pacing, a serverless scheduling framework that regulates request admissions at the token level to reduce inter-token latency.

Proposes MedEvo, an iterative training framework that repairs multimodal medical reasoning trajectories using minimal hints and taxonomy-guided feedback.

Introduces the PocketWatch benchmark and zPocket, an approach training pocket-prediction heads on frozen representations from protein co-folding models.

Presents LeanPRM, a method using the Lean proof assistant to generate step-level correctness labels for training natural-language mathematical proof verifiers.

Studies the trade-off between spatial smoothness and gene-level fidelity when mapping single-cell RNA sequencing profiles to images for neural networks.

Introduces a graph embedding and Procrustes alignment framework to provide a bounded metric for evaluating discovered process models against ground truth.

Develops SCRIM, a method quantifying state criticality in imitation learning by measuring downstream value degradation under bounded action perturbations.

Introduces an adaptive-group process reward framework that partitions GUI trajectories into subgoals to provide intermediate credit assignment for reinforcement learning.

Proposes a recursive self-improvement framework for electrocardiogram analysis models combining verified synthetic waveform edits with targeted intervention selection.

This paper proposes a training framework that triggers supervised fine-tuning specifically on failure states during reinforcement learning to prevent agent forgetting.

This paper proposes a credit-assignment method that uses rubric evaluation outcomes to allocate token-level advantages during reinforcement learning.

This paper presents a reinforcement learning approach that uses in-context dynamic memory during rollouts and self-distillation to accelerate reasoning exploration.

This paper introduces a posterior learning approach that improves multi-sample reasoning coverage by updating models using single posterior draws.

This paper presents a benchmark with an action taxonomy to evaluate the interactive conversation capabilities of full-duplex spoken language models.

This paper proposes an end-to-end framework integrating nonlinear dimension reduction with longitudinal counterfactual prediction for high-dimensional observational data.

This paper presents a coupled evolution framework that co-adapts an executable tool library and a model's policy for scientific equation discovery.

This paper presents a system combining interactive human corrections with iterative policy retraining for long-horizon whole-body mobile robot manipulation.

This paper introduces a framework that quantifies delegation flexibility to analyze the spectrum between centralized and decentralized multi-agent coordination.

This paper introduces an approach and benchmark for evaluating whether language model agents can infer unstated true requirements instead of following incomplete instructions.

This paper presents an autonomous system combining LiDAR SLAM, visual imagery, and thermal sensing to inspect under-panel components of solar farms.

This paper presents an optimization method that trains models to decide when to invoke reasoning tokens, reducing unnecessary thinking overhead.