All papers

This paper establishes the exact cut complexity required for equal-length proportional cake cutting among multiple agents.

This paper introduces an efficient encoder-only protein language model incorporating modern architectural updates to lower training costs.

This paper investigates using Classical Chinese as a compact intermediate reasoning register to reduce token length in Chinese language models.

This paper introduces a multimodal benchmark evaluating AI models on evidence-grounded decision making in chemistry and materials laboratories.

This paper presents a dual-branch latent diffusion method to generate LiDAR point clouds from multi-view camera images.

This paper presents a decentralized active inference controller for multi-zone building heating and cooling systems on edge hardware.

This paper pairs language models with video diffusion to maintain persistent abstract states across dynamic video world interactions.

This paper trains language models using reinforcement learning to generate explicit perception-to-action rules for theory-of-mind reasoning tasks.

This paper introduces a benchmark evaluating whether coding agents can reimplement and train biological AI tools from published scientific papers.

This paper presents a compact spatio-temporal forecasting model designed to generalize across shifting spatial domains and varying observation availability.

This paper proposes an on-policy distillation method that aligns student agents using semantic actions rather than token-level supervision.

This paper presents a distillation method where a teacher model minimally edits a student web agent's reasoning traces at failure points.

This paper introduces an architecture that stores and reuses specialized language agents as temporally extended options for complex coding tasks.

This paper presents a memory-efficient subspace optimization framework using residual correction to stabilize language model training.

This paper proposes an agentic video framework combining change-aware memory and targeted replay for streaming video understanding.

This paper introduces a multi-agent mathematical discovery system that organizes proofs, assumptions, and failed attempts into structured research graphs.

This paper proposes a self-play reinforcement learning framework that automatically generates tasks and intermediate verifiable rewards for long-horizon agent training.

This paper presents a flow matching method to distill diverse continuous latent reasoning distributions from multiple chain-of-thought rationales.

The paper presents a continuous trajectory inference method that regularizes population dynamics using neural splines and Wasserstein smoothness.

The paper proposes weighting majority voting responses using a length-conditioned prior adapted at test time without model fine-tuning.

The paper introduces a deep learning framework to estimate voxel-wise brain tissue fractions directly from magnetic resonance fingerprinting signals.

The paper proposes a sample-based neural framework to learn fixed-size embedding representations for higher-order objects like distributions and graphs.

The paper describes a vision-language model using task-specific low-rank adapters for multimodal medical image parsing.

The paper introduces a sparse transformer testbed with interpretable neurons to evaluate mechanistic interpretability methods.

The paper proposes a modular manipulation framework that sequences verified robot capability units for long-horizon tasks.

The paper proposes an energy-based learning method that optimizes acceptance rates for finite-step discrete graph sampling.

The paper proposes a tree search method that optimizes agent skills through contrastive atomic rule editing and execution memory.

The paper empirically analyzes echo state network variants on synthetic sequence-to-sequence pattern tasks.

The paper analyzes the minimum context size required for transformer models to perform in-context reinforcement learning without parameter updates.

The paper provides a theoretical analysis of stability and convergence for semi-gradient temporal-difference learning using mean-field neural networks.

The paper proposes a contextual parameterization framework that dynamically adjusts robot policy parameters as interaction contexts change.

The paper introduces a benchmark to evaluate how personal assistants handle interdependent personal and procedural memories over long trajectories.

The paper introduces an automated pipeline that builds repository-level software engineering benchmarks from test execution footprints.

The paper introduces a data selection strategy that prioritizes training examples where reasoning performance improves with additional inference compute.

The paper proposes a vision-language-action model that dynamically crops high-resolution regions to manipulate objects without using a wrist camera.

The paper investigates the loss surface of deep networks using a specific polynomial activation function without standard normalization layers.

The paper studies the impact of separating prediction horizons from state update intervals during sampling in generative flow models.

The paper compares three textual interface designs for optimizing prompts in multimodal audio-visual question answering systems.

The paper presents a framework to evaluate whether language model agents can build lower-cost benchmark tests that match reference evaluations.

The paper analyzes the mathematical implicit bias of practical Muon optimization compared to gradient descent on linearly separable classification problems.

The paper presents a network to decouple acoustic and semantic representations when speech tone conflicts with transcript text.

The paper presents a deterministic context compaction method designed to preserve factual history and commitments in lightweight coding agents.

The paper presents a framework adapting video generation priors to improve 3D human body and hand motion reconstruction from video.

The paper introduces a benchmark to evaluate conversational floor control, turn completion, and interruptions in multi-party spoken dialogue systems.

The paper proposes a value-guided repulsion method for offline reinforcement learning that aligns behavior regularization with action value estimates.

The paper introduces a framework that progressively constructs and streams executable training tasks from code repositories for software engineering agents.

The paper presents a benchmark evaluating whether conversational agents agree on verifiable plans that satisfy both parties' private conditions.

The paper evaluates how open and closed language models reach consensus when presented with competing payoff incentives.

The paper analyzes implicit communication conventions between humans and AI agents in the cooperative card game Hanabi.

The paper explores reinforcement learning design configurations for post-legalization placement refinement in chip design.

The paper introduces a dataset and framework pairing synchronized point-of-view and panoramic video to evaluate multiplayer action-conditioned world models.

The paper proposes a retrieval framework that selects suitable anomaly detection models for time-series data using similarity search over existing datasets.

The paper investigates an asynchronous training recipe combining the Muon optimizer, token-averaged loss, and penalties for long-horizon agent tasks.

The paper introduces a benchmark and evaluation framework assessing context-sensitive code-switching by language models in creative writing tasks.

The paper presents an intervention method using Shapley values to assign credit to individual actions within multi-action agentic retrieval sets.

The paper introduces a brain-inspired reflection framework that diagnoses failure points to guide self-evolving agents in embodied spatial tasks.

The paper proposes a framework where an agent policy navigates a global search tree by alternating between reflection and execution roles.

The paper presents a benchmark dataset evaluating unified page-level document parsing alongside alternative text descriptions for visual elements.

The paper introduces a benchmark evaluating how well language models engineer isolated execution harnesses for coding agents using specification documents.

The paper provides finite-sample convergence guarantees for average-reward option Q-learning algorithms under inter- and intra-option settings.