All papers

The paper uses retrieved in-context exemplars to improve rhetorical structure discourse parsing without requiring model fine-tuning.

The paper demonstrates specification gaming in a simulated cleaning task and tests a constrained reinforcement learning mitigation.

The paper proposes an alternating context optimization and logit steering framework to adapt language model outputs at test time.

The paper develops a multilingual retrieval-augmented tutoring system providing bilingual responses and glossaries for international computing students.

The paper introduces a hierarchical reinforcement learning method designed for agentic tasks that span multiple interaction sessions.

The paper trains feedforward 3D reconstruction models using privileged motion and gravity cues to improve dynamic scene understanding.

The paper introduces a reinforcement learning pipeline enabling humanoid robots to manipulate bulky objects using their arms and torso.

The paper proposes an iterative self-improvement method combining trajectory rewriting and policy refinement for terminal-based coding agents.

The paper introduces a multi-agent system to automate data curation and dataset editing for supervised fine-tuning.

The paper introduces a hierarchical task network framework that separates multi-agent planning from execution scheduling.

The paper examines decentralized multi-agent coordination through sequential market auctions under conditions of imperfect competition.

The paper introduces an adaptive connectivity objective to improve deep learning segmentation of thin, continuous anatomical or physical structures.

The paper proposes a graph neural network method to speed up layout decomposition for advanced semiconductor manufacturing.

The paper introduces a skill-decoupled on-policy distillation method to merge vision-language experts without conflicting gradients or reward hacking.

The paper presents an end-to-end trained multimodal agent that combines visual graphical interface actions and executable code execution.

The paper presents a closed-form, training-free approach to merge specialized fine-tuned models into a single student using forward passes.

The paper develops an unsupervised routing method to escalate tasks from weak language models to stronger models without ground-truth labels.

The paper trains language model agents to reorganize unstructured document collections into structured workspaces to speed up future multi-document queries.

The paper proposes a training-free framework that uses uncertainty signals to escalate computer-use tasks from weak models to strong models.

The paper presents a method for robots to generate targeted preference queries to learn underspecified rewards from humans.

The authors evaluate how developer scaffolding and general prompt instructions affect the performance of environment-agnostic coding agents across diverse benchmarks.

The authors introduce EliteReplay, combining reinforcement learning with quality-diversity search replay to retain solution diversity during post-training.

The authors propose Subspace Tuning, a fine-tuning method restricting weight updates to bottom singular subspaces to mitigate catastrophic forgetting in language models.

The authors present a random projection method to protect sensitive datastore embeddings against extraction attacks in retrieval-augmented generation systems.

The authors propose a learned steering agent that monitors execution context and intervenes to prevent goal drift in tool-using language model systems.

The authors report on a team of autonomous Gemini 4 Argon agents collaborating via chat and shared code to beat Minecraft survival mode.

The authors present a market-based post-training framework that allows decentralized agent swarms to learn both action policies and coordination mechanisms without central orchestration.

The authors propose combining line plots and normalized delay embeddings to provide complementary visual representations for time-series reasoning in language models.

The authors propose ACEdit, a training and distillation scheme that stabilizes image editing models against accumulating errors across multiple editing turns.

The authors introduce LangDiT, an adapted transformer architecture designed specifically for continuous diffusion language models.

The authors propose KnowGPO, a reinforcement learning framework that assigns credit to deep search agents based on acquired knowledge conditions rather than trajectories.

The authors introduce DSP, a framework where robots learn from brief human play and autonomously collect contact-rich data for dexterous manipulation.

The authors introduce 3DCodeVerse, a dataset and multi-agent framework enabling coding agents to generate, reason over, and verify 3D executable code.

The authors introduce GeoState, a view-decoupled 3D world modeling approach separating scene dynamics from camera motion.

The authors propose a post-training method enabling autonomous language model agents to organize experiments and carry out machine learning engineering tasks.

The authors propose MirrorMoE to reconcile an exploration dilemma between fine-grained expert segmentation and shared experts in mixture-of-experts models.

The authors present CoDA, a framework where a simulation data engine and a robot action policy co-evolve using memory of past failures.

The authors propose T2-Z, a method optimizing learnable environment latents at test time to help robotic world models adapt to unobserved physical factors.

The authors introduce an evolutionary framework using large language models to design and optimize resource-efficient quantum circuits.

The authors introduce LIBERO-PeRM, a simulation benchmark for studying how embodied robotic agents learn and follow user preferences in manipulation tasks.

Summary pending (next check).

This survey provides an algorithmic overview and failure mode analysis of reinforcement learning techniques applied to language models.

The paper presents an open-source PyTorch library providing differentiable forward simulation of electron paramagnetic resonance spectra for machine-learning workflows.

The paper introduces a sample complexity metric to analyze binary classification learning under randomly delayed and censored labels.

The paper proves Wong-Zakai convergence and applies Fourier neural operators to learn solution operators for stochastic Volterra integral equations.

The paper proposes a semi-amortized Bayesian inference method that trains neural estimators for parameter blocks and alternates with Markov chain updates.

The paper introduces a physics-aware machine learning framework that interpolates crystal graphs to predict performance in perovskite solar cells.

The paper proposes a revised scaling law for image self-distillation that incorporates sample repetition alongside unique sample counts.

The paper establishes constant regret and convergence guarantees for simultaneous and alternating regret matching variants using previous-utility prediction in matrix games.

The paper establishes minimax-optimal statistical rates for discrete diffusion models generating text and categorical data under sparsity assumptions.

The paper proposes a framework that decouples candidate generation from feasibility assessment using structured sampling for chance-constrained stochastic programs.

The paper introduces a router-free method that adaptively thresholds rank-one LoRA adapter responses to remove cross-talk noise per token.

The paper develops a closed-form index policy for online task assignment with reusable resources, establishing its asymptotic optimality in fluid limits.

The paper compares active inference to standard exploration in reinforcement learning and proposes a square-root calibration to prevent sub-optimal actions.

The paper studies deep generative simulation-based inference to estimate phylogenetic tree posteriors from sequences under complex evolutionary models.

The paper presents a weak regression approach combined with conformal prediction to estimate parameters and uncertainty in epidemic models without numerical derivatives.

The paper introduces a multilevel particle marginal Metropolis-Hastings method for joint Bayesian inference of neural network parameters and prior hyperparameters.

The paper introduces a post-training framework using intermediate token critics to backpropagate verifier rewards through continuous flow language models.

The paper presents a method that weights trajectory peers by context similarity to improve step-level credit assignment for language model agents.

The paper introduces a neural Q-learning framework that clusters treatments by response similarity to improve individualized treatment rules across many options.