All papers

This paper introduces a 240-task benchmark to evaluate recursive self-improvement in agents across diverse real-world office workflows.

This paper introduces an accounting framework to measure recursive self-improvement and audits machine-authored operator generation in a physics runtime.

This paper proposes a curriculum-guided reinforcement learning framework and an agentic reward model for spatial text-to-image generation.

This paper presents an on-device communication agent that uses small language models to generate low-latency mobile network configuration rules.

This paper introduces an adapter that aligns operational numerical weather prediction inputs with ERA5 reference states for pretrained forecasting models.

This paper presents an experience-based learning framework that uses trajectory quality signals rather than final task outcomes to guide agent improvement.

This paper introduces a JAX-native benchmark for evaluating heterogeneous and competitive multi-agent reinforcement learning algorithms.

This paper addresses failure modes in model predictive task sampling for reinforcement learning by using in-context modeling.

This paper presents a framework for discovering and synthesizing terminal-based agent tasks grounded in real-world artifact transitions.

This paper proposes visual counterfactual on-policy distillation to prioritize visually grounded tokens during vision-language reasoning training.

This paper introduces a probabilistic relaxation method with learnable categorical priors for bilevel multi-source data selection.

This paper proposes a causal contrastive reinforcement learning method using counterfactual rollouts to handle unobserved confounding in offline demonstrations.

This paper presents a reinforcement learning framework to train language model agents across tools and servers in the Model Context Protocol ecosystem.

This paper trains language models using reinforcement learning to construct executable financial trading formulas grounded in research literature.

This paper benchmarks language model agents against skilled humans on binary software reverse engineering tasks using interactive tools.

This paper introduces an evaluation benchmark that generates complex, long-horizon agent workflows by composing verifiable business skills.

This paper proposes a steering framework that dynamically personalizes language model responses using low-overhead activation vectors.

This paper introduces a two-layer masked language model approach to generate constrained Thai poetry in a single decoding pass.

This paper investigates failure modes where recurrent algorithmic reasoning models jump ahead prematurely when generalizing to larger graphs.

This paper studies experimentally how the timing of generative AI access impacts team ideation and creative selection.

This paper provides theoretical convergence guarantees for average-reward reinforcement learning algorithms via mixing policy regularization.

This paper proposes an adaptive selection method that identifies the top candidates under uncertainty using minimal scenario evaluations.

This paper introduces parameter-tied recurrent blocks to reduce model size in tabular foundation models without sacrificing performance.

This paper maps the computational complexity landscape of robust decision-making in multi-environment partially observable environments.

This paper analyzes pretraining factors such as masking ratios and token budgets to optimize bidirectional transformer encoders.

This paper fine-tunes a quantized decision model to classify diseases from patient symptom descriptions without free-form text generation.

This paper introduces a benchmark to evaluate continual learning in semi-supervised visual industrial anomaly detection across components.

This paper proposes a tree search method using belief embeddings for planning in partially observable environments.

This paper introduces a benchmark to measure whether autonomous coding agents truly improve their policies from interaction traces.

This paper compares four deep learning segmentation models for detecting structures in dental radiographs under controlled experimental settings.

This paper demonstrates an API and network framework to support split AI inference over 5G mobile networks.

This paper examines how recurrent models generalize and transfer rule-based reasoning across discrete puzzle environments.

This paper introduces a self-evolution framework that trains visual agents to invoke tools using reinforcement learning and unlabeled images.

This paper presents a neuro-symbolic framework using language models and formal argumentation semantics to detect persuasiveness in online discussions.

Introduces a variable-length coding method for post-training neural network quantization that maintains accuracy without retraining.

Analyzes coding-agent trajectories to model execution costs under prompt caching, showing turn count matters far more than observation token length.

Evaluates a compact bidirectional Mamba state-space autoencoder for classifying heart failure disease states from single-nucleus RNA sequencing data.

Introduces an elastic transformer pretraining architecture enabling simultaneous hyperparameter transfer and subnetwork execution within a single model.

Presents an enhanced convolutional neural network architecture for real-time traffic sign classification in autonomous driving applications.

Introduces a benchmark evaluating how and when multimodal language models perceive human social cues from partial audio and video snippets.

Trains chemical retrosynthesis models via reinforcement learning using forward reaction model consistency to credit plausible alternative pathways.

Uses multi-objective evolutionary algorithms to optimize agent execution harnesses across competing metrics like accuracy and operating cost.

Combines historical violation decay with language-model triage of online consumer reviews to dynamically schedule restaurant health inspections.

Fine-tunes a coding model using synthetic tutoring dialogues to provide step-by-step guidance without revealing program fixes prematurely.

Presents a multi-agent framework that decouples research planning, subquestion investigation, and factual verification via a shared semantic workspace.

Proposes a hierarchical evolution framework that allows individual agent software harnesses in multi-agent systems to adapt using feedback.

Probes attention heads in unified vision-language models to study condition processing and introduces an inference-time guidance tuning method.

Introduces a closed-loop benchmark testing language models controlling physical robotic hardware and reacting to real sensor feedback.

Applies an educational measurement framework to prompt engineering to improve the validity and reproducibility of automated essay scoring systems.

Presents a two-stage post-training strategy that improves language model handling of tabular numerical records using auxiliary pretraining and token matching.

Proposes a data-sharing ecosystem and framework to make angle-resolved photoemission spectroscopy data organized and accessible for AI research.

Introduces a medical benchmark of real de-identified patient edge cases pairing imaging with clinical vignettes to test multimodal language models.

The paper introduces Sequent-Prover, training language model agents to perform multi-turn formal reasoning using an external SMT solver scratchpad.

The paper presents RECAP, a post-training quantization method that calibrates reasoning models using dynamic prefixes to reduce error accumulation.

The paper proposes LeHami, incorporating symplectic Hamiltonian dynamics into latent predictive architectures to prevent representation collapse over long rollouts.

The paper presents GeoBridge, an evolutionary framework that searches for reusable, transferable decoder topologies across geometric vision tasks.

The paper presents LENS, a benchmark evaluating language models on North Korean linguistic nuances, sociocultural perspectives, and domain knowledge.

The paper proposes using subordinate color channel intensities in fluorescence microscopy to improve protein crystallization segmentation and classification.

The paper presents Co-DSL, enabling users and language models to co-design reusable spatial concepts to guide layout generation solvers.

The paper presents an ontology-guided retrieval framework combining population and personalized memory for wearable sensor human activity recognition.