All papers

Presents a framework allowing language model agents to evolve environment dynamics beliefs through falsifiable hypothesis testing.

Introduces an evolutionary optimization method using multi-turn reflective mutators to improve production data agents and pipelines.

Uses reinforcement learning-based neural architecture search to discover compact models for audio-visual speech enhancement.

Presents an end-to-end joint-embedding predictive architecture that combines world modeling and action flow matching for robot planning and control.

Introduces a counterfactual-guided few-shot distillation framework to train smaller question-answering language models using limited training samples.

Proposes a hierarchy of modularity to identify functional modular organization in neural networks lacking single-neuron specialization.

Proposes formal mathematical postulates and definitions to measure and operationalize computational creativity across domains.

Investigates mappings between generative model latent states and simulator states to enable controllable environment simulation.

Evaluates pull requests from software agents modifying both application code and deployment infrastructure in continuous integration pipelines.

Applies a reward-directed diffusion model to optimize localized ship hull geometry evaluated by automated fluid dynamics simulations.

Trains language models to anticipate user replies and self-revise drafts before responding in multi-turn interactions.

Proposes a sequence-drifting framework to generate full text sequences in a single forward pass from Gaussian noise.

Presents a benchmark using real urban data to evaluate multimodal language models on spatial reasoning and constrained editing.

Introduces an offline goal-conditioned policy optimization method to help vision-language-action models recover from execution deviations.

Proposes a federated pairwise ranking framework for institutions to evaluate decentralized finance participants without sharing transaction data.

Studies the impact of AI shopping assistant mediation on retail product assortment planning and business revenue.

Uses natural language class descriptions as supervision to train neural decoders for brain-computer interfaces across multiple modalities.

Develops a collaborative learning framework that splits large language models into smaller heterogeneous slices to mitigate error propagation.

Presents an uncertainty-guided framework to compress reasoning supervision trajectories and reduce reasoning post-training costs.

Proposes matrix-entropy and kernel alignment criteria to select optimal layer subsets of hidden states for speculative decoding.

Presents a benchmark to evaluate language models on generating mathematical figures and diagram-assisted reasoning across frontier problems.

Introduces a multimodal embodied agent policy learning framework using trajectory construction, distillation, and token-level evidence routing.

Proposes a multimodal agent providing grounded prompts and video clips to guide video editing models in professional sports motions.

Introduces a multi-turn conversational benchmark to evaluate sales agents against resistant, psychologically grounded buyer personas.

Proposes continuous-query generation paradigms to eliminate reachability constraints caused by finite-beam decoding over discrete semantic IDs in recommendation.

Introduces a framework and multimodal dataset of human desktop demonstrations to induce reusable skills for computer-use agents.

Introduces an information-theoretic framework to reweight supervised fine-tuning data based on conditional information gain.

Compares hyperbolic and Euclidean embeddings for unsupervised patent representation to improve cross-category prior-art discovery.

Trains an advisor model on past failures to add dynamic, structured guidance to frozen language model agent prompts.

Presents a scalable method to maximize agent empowerment by guiding agents toward states affording numerous executable skills.

Proposes a generalized f-divergence trust-region framework to unify and stabilize reinforcement learning algorithms for LLM post-training.

Presents a simulation benchmark evaluating how embodied agents continually acquire basic skills and recombine them for complex tasks.

Introduces a benchmark to evaluate how effectively LLMs clarify ambiguous user requirements in repository-level software development tasks.

Proposes spatial-semantic token compression and layer routing to accelerate 3D vision-language model inference across multi-view inputs.

Presents a conversational benchmark testing whether models can edit text under counterfactual changes while preserving unaffected factual details.

Introduces a framework allowing agents to rewind long-horizon environments to earlier states while retaining learned exploratory knowledge.

Proposes an advertising image editing method that identifies and selectively refines visual attributes driving click-through rates.

Presents an agent harness that coevolves evidence and conclusions to improve reasoning across 100-million-token document corpora.

Presents a difficulty-aware framework that allocates reasoning steps across sections to improve efficiency in large audio-language models.

Introduces a recursive self-improvement agent framework that refines symbolic graphics programs using execution traces and runtime state changes.

Proposes a synthetic self-distillation method using a slightly larger aligner to enable continual post-training in small language models.

Designs a modular multi-agent pipeline separating search, verification, and reading for multimodal document question answering.

Introduces an anticipatory generative modeling approach for road networks to improve realistic next-transition selection in trajectory generation.

Presents a framework that dynamically adjusts role activation and multi-agent collaboration based on the model backbone's query-specific competence.

Presents a structural scoring framework to select high-quality coding agent trajectories for supervised fine-tuning based on interaction efficiency.

Proposes a symbolic world modeling method using functional role abstractions to transfer interaction dynamics across environments for LLM agents.

Introduces an adaptive curriculum reinforcement learning method to train multimodal models on long-horizon visual Sudoku reasoning tasks.

Proposes JANUS, using a co-evolving world model to predict, verify, and guide exploration paths in autonomous scientific research.

Investigates whether frozen recurrent networks can provide sufficient memory for partially observable reinforcement learning without end-to-end training.

Introduces AFCE, training vision-language-action policies to predict future physical effects rather than raw action chunks directly.

Introduces ReSFlow, rectifying categorical flows along geodesic trajectories on statistical manifolds to enable one-step discrete generation.

Analyzes tabular foundation models and shows most transformer layers can be replaced with affine translators without sacrificing performance.

Presents Agent4Spec, a multi-agent framework that automatically validates OpenAPI syntax errors and identifies functional API gaps through market analysis.

Proposes BoundaryEvo, dynamically adapting training data around a student model's evolving capability boundary during long-chain reasoning distillation.

Proposes PRIME, an index-side optimization framework using semantic restructuring and deterministic metadata synthesis to improve document retrieval.

Introduces ChemDiffAgent, applying masked diffusion to plan tool use and reasoning trajectories bidirectionally for chemical problem-solving.

Evaluates whether robotic manipulation agents can learn complex multi-step assembly skills purely from sparse binary task rewards.

Introduces ProgramGym, a framework providing verifiable sandbox environments and black-box test feedback for training long-horizon program reproduction agents.

Presents RegulaTutor, a personalized tutoring framework that grounds instructional policies in explicit cognitive and self-regulated learning theories.

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