All papers

The paper models individual scientific taste using criterion assessments and introduces privileged hindsight distillation to align proposals with researcher preferences.

The paper introduces prompt optimization and recovery measurement tools to evaluate the limits of transmitting concepts between scaling language models.

This paper introduces a large-scale underwater acoustic recording dataset paired with vessel tracking data for ship recognition tasks.

This paper introduces a task and benchmark for tracking candidate sets of referent objects across ambiguous interactive dialogue turns.

This paper presents a training-free contrastive decoding framework using attention-guided perturbations to reduce hallucinations in video language models.

This paper analyzes three distinct failure modes where reinforcement learning agents misgeneralize goals despite competent behavior.

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

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

This paper evaluates how reliably typed decision models switch to fallback options and remain consistent under equivalent decision rules.

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 investigates whether machine translation systems preserve or neutralize media bias across languages.

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

The paper presents an empirical framework measuring neural network backdoor stealthiness across input, feature, and parameter spaces.

The paper introduces a benchmark to study why frontier models prioritize protecting subjects over preventing larger physical property damage.

The paper proposes a federated learning framework with risk-aware aggregation to prevent malicious updates from compromising global model safety.

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

The paper identifies an activation steering vector that controls whether language models rely on internal memory or retrieved external context.

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 proposes a synthetic data pipeline that extracts atomic facts from source texts to guide factual pretraining for language models.

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

The paper describes an automated system using language models to verify corporate loan applications against internal bank lending policies.

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

The paper uses in-context neurofeedback sessions to evaluate whether language models can intentionally control their internal activations.

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 providing analytical bounds for counterfactual reasoning in sequential decision processes under statistical and causal uncertainty.

The paper introduces a reasoning-guided translation framework to scale multilingual safety and reasoning benchmarks while preserving original task meanings.

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

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

The paper studies Byzantine robustness in fair federated learning and proposes an update screening method using client trajectory consistency.

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

The paper detects backdoor triggers in multimodal language models by tracking output evolution trajectories across internal hidden layers.

The paper estimates reasoning confidence by segmenting chain-of-thought traces into argument graphs evaluated through natural language inference.

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

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

The paper retargets object motion rather than human hands from video demonstrations to generate post-training data for various robot embodiments.

The paper presents a method to consolidate context gains into parametric model weights using prospective queries and low-rank feed-forward updates.

The paper analyzes when first-moment momentum accelerates reinforcement learning with verifiable rewards under exact and sampled policy gradients.

The paper introduces an adaptive perturbation method for sharpness-aware minimization that modulates perturbation radii using local loss curvature.

The paper evaluates failure modes and confidence calibration across frontier and lightweight language models during entity extraction from cyber threat intelligence reports.