All papers

Proposes a multi-route retrosynthesis planner that evaluates synthesizability and cost across chemical reaction options.

Presents an environment scaling framework that alters dependencies to scale agent benchmark difficulty while preserving task objectives.

Introduces a benchmark evaluating coding agents on evolving web applications across cumulative feature requests.

Applies a causal inference framework to evaluate the impact of robotic social navigation signals in corridor interactions.

Introduces a benchmark of synthetic observability data and incident reports to evaluate automated incident log summarization.

Presents a loop-style parameter-sharing scaling strategy to improve recommendation rankers without increasing parameter counts.

Proposes an adaptive retrieval-augmented generation framework that dynamically expands clinical queries for medication recommendation.

Presents a reference-free evaluation metric that decomposes captions into atomic claims and verifies them against visual evidence.

Introduces a world-action model framework using predictive rehearsal and action perturbations to guide robotic policy learning.

Introduces a benchmark and tool-augmented agent for evaluating multi-hop reasoning in cryptocurrency fund tracing.

Proposes a structured zeroth-order optimization framework using subspace estimation and spectral control for memory-efficient model fine-tuning.

Evaluates a two-stage domain adaptation method for multilingual patent translation and analyzes failure modes in low-resource languages.

Develops an explainability-guided Fourier domain generalization framework to improve sim-to-real reinforcement learning for autonomous driving.

Introduces a benchmark and baseline model for evaluating crystal structure determination from powder X-ray diffraction data.

Introduces a calibration-free latent merging framework to compress Mixture-of-Experts models and reduce memory overhead.

Proposes structural growth pathways to preserve pretrained weights during reinforcement learning and enable transfer across language models.

Presents speculative recursive self-improvement to draft successor agents before full evaluation and optimize verification budgets.

Analyzes how persona construction in generative social simulations causes network segregation and collective political polarization.

Introduces a physics-informed latent variable model combining dimensional analysis and differentiable physics heads for data-scarce multi-physics systems.

Uses analytic hierarchy processes and active learning to rank exploratory data patterns according to individual user preferences efficiently.

Co-evolves the user simulator, environment tools, and rewards during training to improve interactive multi-turn language model agents.

Presents an evaluation benchmark and multi-dimensional protocol for patent machine translation across Chinese, English, Japanese, and Korean.

Distills multi-agent discussions into a single student model by grouping redundant reasoning traces and revisiting unlearned examples.

Accelerates video diffusion models by using closed-loop calibrated caching instead of fixed heuristic thresholds across denoising steps.

Applies rate-distortion theory to structure ordered visual codes so text captions form the initial low-rate representations for images.

Introduces a benchmark of 29 open algebraic combinatorics problems to evaluate algorithm discovery by frontier AI models.

Uses a learned scene-coordinate localization module to maintain consistent 3D spatial context for image-goal robot navigation.

Improves reinforcement learning for reasoning models by sampling continuations from truncated traces to provide more accurate intermediate rewards.

Presents a deep learning framework that integrates heterogeneous surface EEG and intracranial EEG signals for multimodal seizure classification.

Classifies languages at the token level across 111 language-script combinations in mixed-language and code-switched texts.

Dynamically balances relevance and visual diversity per sample to compress visual tokens in vision-language models more effectively.

Proposes a generative adversarial network guided by differentiable segmentation models to edit image colors realistically and with precise control.

Evaluates different agent control granularities for post-route FPGA timing closure to balance execution reliability and frequency gains.

Incorporates human understandability scores directly into feature selection to make tabular machine learning explanations easier for users to interpret.

Combines physical models and state space neural networks in a closed loop to forecast long-term chemically reactive air quality.

Introduces a general visual memory planner that retrieves, creates, and updates visual evidence across shots for long-horizon video generation.

Improves long-context handling in Mamba state space models without retraining by using surprisal scores to guide state routing.

Reconstructs divergence-free 3D fluid velocity fields from sparse, irregular observations in a single forward pass using a set-to-field transformer.

Detects adversarial nodes on evolving graphs without fraud labels by measuring the consistency between node features and aggregated neighborhood contexts.

The paper analyzes the impact of teacher rationales before distilling medical reasoning capabilities into compact language models.

The paper introduces Trace-to-Architecture, a framework that diagnoses LLM agent execution failures to automatically revise agent harness components.

The paper introduces an architecture that incorporates token queue dynamics into wireless communications for foundation model inference services.

The paper proposes VAttnPO, using teacher visual attention to refine token-level policy optimization for multimodal reinforcement learning.

The paper analyzes pre-sink hidden-state geometry to detect and modulate attention sink formation in language models.

The paper presents TabWM, a tabular world model that infers system dynamics from past executions to predict database edits from actions.

The paper proposes GeoVec-VLM, integrating geospatial vector geometry as a native input modality to improve spatial reasoning in vision-language models.

The paper introduces an autonomous multi-agent framework that translates natural language prompts into playable browser games with automated verification.

The paper introduces LIBERO-Industry, a benchmark evaluating robotic manipulation policies under industrial process constraints such as moving material flows and tolerances.

The paper introduces InfoVis, a 9B multimodal model along with a benchmark and training dataset for slide image generation and editing.

The paper investigates progressive depth growth during self-supervised vision transformer pretraining to improve downstream accuracy while reducing computation.

The paper introduces a direction- and topology-aware graph transformer to predict neural network architecture attributes like accuracy and latency.

The paper presents an empirical study evaluating syntactic language models at scales up to 1B parameters across linearizations and masks.

The paper presents multi-objective bandit algorithms to identify a small set of arms that minimizes degradation across multiple objectives.

The paper presents an LLM-based metadata pre-screening method to filter candidate videos before downloading, reducing data curation costs.

The paper introduces a 30B tool-use model and an execution-grounded dataset to scale agentic tool supervision with verifiable environments.

The paper proposes CLIVO, a target-aware method combining semantic clustering and learned validation objectives for differentiable multimodal instruction data mixing.

The paper investigates how observing partner states, sharing knowledge, and predicting partner actions affect coordination in embodied multi-agent teams.

The paper proposes Neural Bayesian Networks, replacing classical probability tables with differentiable tensor operations for scalable probabilistic reasoning on GPUs.

The paper analyzes legal eDiscovery review data to compare generative AI tools against continuous active machine learning and manual human review.

The paper proposes an alignment framework using human feedback signals to personalize search query rewriting without causing intent drift.