All papers

The paper presents a framework using Wasserstein geometry and Shapley values to audit and decompose intersectional fairness disparities across multiple attributes.

Evaluates DistilHuBERT-based accent classification under speaker-disjoint and cross-corpus conditions to measure generalization to unseen speakers.

Constructs context-matched preference comparisons from sibling rollouts to improve credit assignment during language model agent training.

The paper develops an adaptive computation framework offering conformal uncertainty guarantees for processing large scientific and medical imagery efficiently.

Derives a formula to forecast disagreement between masked text reconstruction models fitted on independent samples from the same population.

Decomposes joint temporal logic constraint violations to allocate credit among agents in cooperative reinforcement learning.

The paper introduces a federated multi-agent framework that guides agent selection and query refinement under dynamic privacy constraints.

Presents a stateless service and library providing isolated, semantics-preserving text perturbations for language model robustness testing.

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

This paper proposes a training-free framework that uses teacher-graded examples and dual memories to improve automated short answer grading.

The paper compares mental health stigma representations in large language models against human survey data across different gender groups.

Presents a multi-agent LLM framework that automatically tunes specific internal algorithm components while preserving overall neural network structures.

The paper distills an LLM-based search relevance judge into smaller multimodal student models for efficient e-commerce search evaluation.

The paper introduces a two-stage reasoning framework to generate personalized, safe, and explainable medication recommendations using language models.

Presents a local agentic framework using smaller language models to automatically synthesize edge-case testing scenarios for autonomous systems.

The paper introduces a benchmark to measure vision-language-action model robustness against hardware, optical, and physical robot setup variations.

Compares sparse, dense, hybrid, and fusion RAG architectures across GPT-4o and LLaMA deployments using the RAGAS evaluation protocol.

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

This systematic review analyzes security vulnerabilities, benchmark shortcomings, and developer oversight challenges in code generated by language models.

Shows that evil persona vectors extracted from low-resource languages transfer poorly and fail to steer model behavior effectively across languages.

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

This paper evaluates proprietary and open-source language models on Hong Kong fire safety code compliance using expert-curated questions.

The paper introduces a test-time wrapper that distorts exposed output probabilities to defend black-box machine learning models from query-based adversarial attacks.

Proposes a multi-agent framework that evolves the data generation space using learner feedback to synthesize agent training trajectories.

The paper presents a Nastaliq Urdu comment corpus and evaluates transformer models with word-level attributions for offensive content detection.

Introduces HelpMeOut, an embodied AI benchmark for diagnosing human execution errors across household activity trajectories motivated by neuropsychology research.

Introduces a benchmark and environment for evaluating coding agents on text-conditioned LEGO design with physical and discrete assembly constraints.

The paper introduces a synthetic parallel translation dataset covering 22 scheduled Indic languages aligned via English pivot sentences.

The paper analyzes forty legal instruments to examine how copyright governance protects scholarly authors whose works train generative AI models.

Presents a top-down skill refinement system for robot self-improvement through structured execution feedback and reusable modular skills.

Surveys dataset design, construction methods, and evaluation protocols for improving spatial reasoning in multimodal language models.

The paper formalizes transparency as role-dependent answerability using information flow networks to evaluate AI-supported decision systems.

Introduces an evidence-grounded multi-agent framework combining multimodal medical data and clinical literature for knee osteoarthritis progression prediction.

Evaluates closed and open models on English and Bengali abstractive summarization using lexical metrics, factuality checks, and automated judges.

The paper proposes an unsupervised drift monitor using internal embedding distances to flag unfamiliar inputs in deployed deepfake speech detectors.

Introduces a hierarchical memory architecture that consolidates nested agent task intents into reusable knowledge across long horizons.

The paper presents a Korean dataset extracted from corporate sustainability reports to support information extraction on environmental, social, and governance topics.

This paper presents an imitation framework that enables agents to identify violated contracts, extract skills, and repair execution failures.

Proposes a self-evolving agent that allocates evaluation resources dynamically across candidate programs during iterative code evolution.

Measures demographic bias related to age, gender, and race based on first names across local language models.

The paper analyzes visual attention sinks in vision-language models and shows they stem from alignment with language text sinks.

Proposes a framework modeling research ideation as modular thinking flows sampled dynamically from a probabilistic supernet.

Reviews and formalizes data selection, weighting, and scheduling policies across the pretraining and post-training lifecycle of language models.

This paper presents an adversarial self-play framework using multi-perceptual views to improve language model detection of obfuscated harmful text.

Presents a program-search method using a curated elite solution table to guide language models in synthesizing executable control policies.

The paper introduces TRACE to monitor harmful response risks during generation, edit token activations selectively, and maintain auditable decision traces.

The paper introduces an open-source library to construct validation suites that stress test the reliability of LLM judges.

The report presents an omni-modal model family trained by scaling reinforcement learning throughput, environments, and agentic reward compute.

The paper evaluates twenty-one language models used as judges across three benchmarks, finding widespread discrepancies between agreement metrics and underlying validity.

The paper develops an attack framework to generate physical adversarial textures that deceive video models across varying trajectories and viewpoints.

The paper proposes an event-centric memory framework that converts dialogue history into query-relevant graph working memory for conversational agents.

This paper proposes a watermarking method embedded during speech synthesis to trace and verify generated audio.

This paper presents a benchmark to evaluate how adversarial attacks affect reasoning security in multi-agent debate systems.

The paper introduces a dual-path parametric memory method that combines pooled history representations with sequential adapter updates.

The authors introduce CharXiv, a benchmark of realistic scientific charts designed to test descriptive and reasoning capabilities in multimodal language models.

The paper introduces a reinforcement learning framework that trains tool-using agents to generate and refine financial alpha factor formulas.

This paper fine-tunes language models to selectively correct speech recognition transcripts using curated candidate pools.

This paper analyzes prediction errors in spectral saliency when deleting singular components to selectively edit language model capabilities.

This paper combines language models with a differentiable traffic equilibrium layer to help non-experts optimize transportation planning problems.

The paper introduces a controlled benchmark evaluating how accurately LLMs determine whether software components match vulnerability advisory criteria.