All papers

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.

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

Presents an adversarial training approach designed to improve language model honesty across diverse benchmarks without degrading core capabilities.

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.

Presents an evaluation framework combining simulated mental health patients and expert-developed rubrics to assess conversational language models.

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.

Tests whether transcripts of correctly solved tasks from saturated benchmarks can predict model performance on unseen harder evaluations.

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

The paper demonstrates how correctness feedback undermines formal specification auditing and proposes a phase-separated alternative for Verus.

The paper proposes a selective safety framework for vision-language models using counterfactual pairs and representation density estimation.

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

The paper introduces an expert-guided benchmark to measure the impact of generative language models on user wellbeing.

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

The paper introduces a parameter decomposition method to interpret the mechanistic subcomponents of language model weights.

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 presents a metric and benchmark measuring how quickly environment dynamics can change before agent decision-making fails.

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 analyzes how answering via a separate model call improves evidence adherence when reasoning traces conflict with provided context.

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 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 proposes a multi-tiered governance model regulating AI system access based on user purpose and model capability.

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

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

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.

The paper investigates when language models learn capabilities by tracking unembedding matrix dynamics during pretraining using parameter-trajectory crosscoding.

Proposes an adversarial meta-prompt evolution method to improve model-generated evaluation rubrics for open-ended tasks.

Combines language models and lightweight decision models to fact-check statements in peer reviews against original manuscripts.

Applies trajectory Bellman residual minimization to turn sparse rewards into dense token signals for arithmetic reasoning.

Estimates language model ensemble member reliability from unlabeled agreement patterns using an expectation-maximization jury formulation.

Introduces an equivariant self-supervised learning framework that avoids topological collapse in 3D point cloud masked autoencoding.

Presents a decomposed network architecture that applies separate graph and convolution modules to trend and seasonal time series components.

Examines failure modes in label-free evaluation where models reach high consistency across perturbations while generating identical incorrect answers.

Introduces a benchmark measuring how output-only model judges fail to detect tampered runtime environments in agent deployments.

Presents an opinion article on wearable on-body language models based on an industrial panel talk.

Proposes an optimal transport method using small clean feedback sets to calibrate sample-wise noise for reward model training.

Investigates cost-aware policies for adaptively switching between language models of varying fidelity in stochastic optimization pipelines.

Presents a framework that decouples speech generation and synthesis token spaces to speed up spoken dialogue model inference.

Develops a structured explainable reasoning architecture to extract sensitive self-disclosures from cybersecurity interviews using local small language models.

Analyzes working memory error patterns in language models to show that failures stem from readout interference rather than target retrieval.

Proposes an ensemble framework that weights judge confidence and masks disagreements when training models with rubrics as rewards.