All papers

Compares abstract-only versus full-text inputs and few-shot versus fine-tuning approaches for generating biomedical lay summaries.

Introduces a system that generates and compares alternative scene drafts to guide full dramatic script adaptations from prose.

Analyzes how specific nouns and semantic clusters relate to answer selection and user engagement in logistics question-answering forums.

Proposes a compressed representation using Gaussian trellises to quantize conditional memory lookup tables down to two bits.

Introduces a non-autoregressive transformer model that dynamically decides tag generation order at inference time for low-resource sequence labeling.

Presents a training-free framework that tracks changing conversational facts as versioned records to improve agent memory retrieval.

Distills multiple speech attributes from four teacher models into a single shared speech encoder.

Proposes a reinforcement learning credit-assignment method for document parsing models to align rewards with structured page elements.

Introduces a benchmark that structures multi-pull-request code changes into dependency graphs to evaluate long-horizon software engineering agents.

Presents a benchmark that measures long-context reasoning difficulty using evidence scope, dispersion, and reasoning depth.

Studies how using UTF-16 byte order improves boundary prediction and representation efficiency in multilingual byte-level language models.

Introduces a compression-based patching strategy for byte-level language models that allocates compute where upcoming bytes are unlikely to repeat.

Specializes a translation model to a specific indigenous Nawatl variety using a curated dataset and finite-state morphological analysis.

Develops small multimodal models that output structured choices using masked language modeling to reduce inference latency in agent tasks.

Develops a two-stage training approach combining evidence reasoning and reinforcement learning to improve pragmatic social reasoning in conversational models.

Introduces lexical reference frames to interpret intermediate hidden states in language models using the model's own remaining layers.

Proposes an event-centric framework to align audio and video features when generating detailed video descriptions with language models.

Introduces a compact language model trained with distillation and reinforcement learning to manage long-term memory across multiple external frameworks.

Analyzes the overuse and contextual appropriateness of a common corrective phrasing pattern in Mandarin language model text.

Uses diffusion language models to perform multi-target few-shot infilling across shared code context guided by demonstration examples.

Evaluates the ability of language models to predict state changes in discrete spatial grids across sequential operations.

Introduces a set-level reinforcement learning objective to train language models to produce diverse and document-grounded scientific research ideas.

Proposes a visual verifier that decomposes complex prompts into sub-questions and assigns continuous scores to improve multimodal evaluation.

Introduces a benchmark evaluating language model agents in real-time environments and a two-timescale training method to improve execution speed.

The paper introduces a training-free schedule optimization method for accelerating sequential Monte Carlo guidance in discrete diffusion models.

The paper introduces a modular measurement system to evaluate how agent frameworks, tools, and memory impact verifiable reasoning tasks.

The paper introduces an agentic framework combining language model reasoning with symbolic engines for formal inequality proving in Lean4.

The paper presents a framework that uses structural tree edit distance to guide language models in symbolic regression.

The paper introduces a hierarchical benchmark for evaluating visual generative models on multi-step world state prediction tasks.

The paper proposes a federated learning framework that tailors sub-network sizes and architectures to heterogeneous client hardware and data.

The paper identifies an internal activation direction in spoken language models to control and transfer conversational turn-taking styles.

The paper introduces an encoder architecture that processes input once to answer multiple structured classification queries simultaneously.

The paper introduces a reasoning-augmented reconstructive post-training framework that unifies multimodal understanding and image generation tasks.

The paper formalizes three core architectural requirements for artificial intelligence systems operating in open-world settings.

The paper proposes an adaptive skill framework that extracts patterns from trajectories to enable multi-modal agents to self-evolve.

The paper presents an agent that expands its domain-specific language for exact answering without external rewards or judges.

The paper introduces an inference-time guidance method that enforces temporal logic constraints during text-to-video generation.

The paper presents open-weight language models adapted for the Wolof language using instruction tuning, preference alignment, and model merging.

The paper proposes a method to translate reverse reasoning paths into verified forward chains of thought for distillation.

The paper trains reasoning models to perform sequential experimental design by optimizing information-gain rewards during action selection.

The paper introduces a confidence-calibrated controller to optimize inference compute and test-time expansion decisions in reasoning models.

The paper analyzes how vision-language models process negation by comparing model attention mechanisms with human eye-tracking data.

The paper presents a self-play reinforcement learning framework to simulate interactions between vehicles, pedestrians, and cyclists without human demonstrations.

The paper proposes a hybrid reasoning framework combining Answer Set Programming and energy-based models to predict interacting trajectory futures.

The paper analyzes why reinforcement learning with majority-vote rewards provides effective training gradients despite noisy individual labels.

The paper presents a zero-shot vision-language method for generating task-oriented robotic grasps in 6 degrees of freedom.

The paper develops replicable algorithms with sublinear regret bounds for the dueling bandits problem.

The paper evaluates how forecast lead times from AI weather models affect the scheduling of utility crew restoration deployments.

The paper formalizes compositional structure in complex systems using algorithmic information theory and compression concepts.

The paper proposes a non-elitist evolutionary optimization framework that retains and iteratively refines low-reward agent skills using execution feedback.

The paper introduces an action and feedback contract separating agent decisions from deterministic tool execution for constraint-governed engineering tasks.

The paper provides an open platform and environment suite for evaluating and training GUI agents with online reinforcement learning.

The paper presents a decision model that performs deeper reasoning by looping its layers instead of generating output tokens.

The paper studies how semantic layers, orchestration, and compaction impact the performance and behavior of data agents across benchmarks.

The paper proposes a self-play framework to train multimodal foundation models using text proxies instead of paired cross-modal datasets.

The paper presents a hierarchical reinforcement learning framework for non-prehensile 3D manipulation of multi-object stacks using quadruped robots.

The paper trains a small language model to generate and refine GPU kernel optimization scripts using compiler traces.

The paper uses latent eigenvectors from CLIP to select smaller, representative training datasets for diffusion models.

The paper evaluates deep reinforcement learning methods against simple heuristics and random baselines for high-performance computing job scheduling.

The paper introduces a method that turns failed reasoning attempts into tested supervision notes to improve reinforcement learning training.