All papers

Develops a probabilistic transformer meta-learning architecture to perform zero-shot system identification directly from long training sequence contexts.

Proposes Web$hell, a unified interface combining broad information gathering, browser interactions, and evidence management for web-based agents.

Combines a CNN-Transformer network with a continuous sigmoid label encoding to overcome grid quantization errors in direction-of-arrival estimation.

Evaluates neural embedding and diagnostic strategies to interpret detected anomalies in high-luminosity collider particle physics data.

Proposes Rollout-Time Intervention to convert failed agent trajectories into successful off-policy training signals during reinforcement learning.

Incorporates marginal-cost rewards into multi-agent reinforcement learning to reduce autonomous vehicle travel times without worsening human driver delays.

Presents a textbook providing an overview of the concepts, architectures, and interactions of agentic large language models.

Introduces BranchOPD, which branches student rollouts from shared prefixes at uncertain decision points to improve on-policy distillation efficiency.

Presents a framework alternating multi-agent sessions across context resets to overcome reasoning degradation in long-horizon machine learning engineering.

Presents Explanatory World Models, using language model causal explanations to redistribute reinforcement learning rewards across distant actions.

Proposes PRISM, a multimodal clinical prognostic model combining patient records with retrieved population-level embedding memory banks.

Formalizes a theoretical hierarchy of identifiability criteria for estimating causal effects across equivalence classes of causal graphs.

Proposes Road MOPD, using non-routed teachers as cross-domain critics to resolve gradient conflicts in multi-teacher on-policy distillation.

Introduces an unsupervised method to identify and remove subliminally transferred traits from model parameter updates during knowledge distillation.

Presents MENTOR, a multi-agent framework that intervenes adaptively during failed reasoning rollouts to prevent zero-reward collapse in RLVR.

Proposes AudioTETRIS, a training-free token pruning framework that uses acoustic transition signals to compress audio representations efficiently.

Introduces HOA, a multi-session LLM agent framework coordinating review, wiki knowledge, and verification for complex Olympiad problem solving.

Proposes a model-agnostic method to integrate biological domain knowledge into machine learning models trained on weakly annotated bioimaging.

Presents a constraint acquisition tool that synthesizes size-independent constraint programs from input data and example solutions.

Introduces SelectArena, a benchmark designed to evaluate tabular feature selection methods across real-world domain datasets and contemporary models.

Presents a multi-agent LLM framework that automatically tunes specific internal algorithm components while preserving overall neural network structures.

Proposes a dynamic curriculum for GRPO that re-estimates sample difficulty to progressively transition policy training toward harder problems.

Introduces UPTURN, a framework that trains a small classifier to inject redirection prompts as new user turns during reasoning failures.

Presents Outlier-Aware Attention Linearization, using quadratic approximations to retain outlier dimensions during efficient Transformer attention evaluation.

Introduces Loopless Variance-Reduced Policy Gradient, proving near-optimal sample complexity under weak gradient domination using first-order updates.

Employs a bilevel Stackelberg formulation to jointly optimize classification accuracy and confidence calibration without separate calibration datasets.

Combines load-adaptive hierarchical key-value caching with speculative decoding to accelerate long-context language model serving.

Presents a graph neural process framework to answer counterfactual queries directly from observational data without prior causal graph discovery.

Pre-trains embodied robot policies on human egocentric videos using learned continuous latent action representations.

Proposes an attention mechanism that switches from linear complexity during prefilling to full attention during token decoding.

Decomposes joint temporal logic constraint violations to allocate credit among agents in cooperative reinforcement learning.

Examines error localization and credit assignment failures in critique-conditioned on-policy self-distillation for mathematical reasoning.

Constructs context-matched preference comparisons from sibling rollouts to improve credit assignment during language model agent training.

Introduces a latent sociability prior to mitigate edge-independence issues in discrete graph diffusion models.

Proposes a calibration-free post-training quantization method that jointly optimizes weight and activation scaling for 4-bit model inference.

Replaces frequent state memory updates in state-space models by caching inputs and periodically checkpointing state during decoding.

Demonstrates that discrete graph diffusion models exhibit conditional memorization when reverse trajectories are conditioned on corrupted training graphs.

Describes an industrial framework for evaluating, ablating, and deprecating features in production recommender systems without performance regressions.

Trains vision-language models to perform active spatial reasoning through novel-view generation and simulated mentalization trajectories.

Applies vision-language models and structured annotation to parse and analyze historical comic books and manga at scale.

Presents an automated red-teaming framework that searches for harmful compositional tool-use paths across multi-service environments.

Combines dual-encoder semantic retrieval with a contextual bandit to adaptively select agent tools using execution reliability feedback.

Investigates how changing tasks redistributes attention head influence and alters internal activations in open-weight large language models.

Evaluates regression objective functions and global early stopping limits across neural network backbones and established tabular datasets.

Introduces a model-agnostic, concept-level attribution method for black-box vision models by masking concepts located with vision-language models.