All papers

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 SqueezeBench, a saturation-resilient benchmark for formal mathematical reasoning where new accepted proofs dynamically raise the target bounds.

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.

Develops Step-wise Truncation Probing and termination strategies to quantify and mitigate accuracy degradation from overthinking in reasoning models.

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.

Evaluates how pretraining and test-time training computational budgets affect ARC benchmark performance across two distinct reasoning model architectures.

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.

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.

Benchmarks combinations of language models, communication protocols, and agent frameworks on web interaction tasks.

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.

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.

Introduces a controlled Minecraft benchmark to evaluate whether embodied agents acquire persistent, transferrable skills across environment resets.

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.

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

Tests language models on graph-based information retrieval and navigation using the Wikipedia Game across multiple languages and citation datasets.