All papers

This paper introduces a multi-agent retrieval framework that coordinates search across legal statutes and precedent cases for grounded legal reasoning.

The paper introduces confidential error-correcting codes, providing a theoretical framework and concrete construction balancing message privacy with reliable decoding.

This paper presents an ecosystem of benchmark datasets and evaluation frameworks to advance machine learning applications in veterinary medicine.

This paper proposes a progress-aware credit assignment framework for multi-turn reinforcement learning in language model agents without using critic models.

The paper evaluates representation-based defenses against backdoors in federated intrusion detection systems using disjoint server data.

The paper introduces HomogeneityNLI to evaluate whether open-weight language models recognise truth-value gaps in plural predication.

The paper introduces a framework combining guidelines and agentic linking to ground medical concepts across diverse text types and ontologies.

The paper introduces a decision-layer watermarking method for multi-agent routing to protect routing policies against unauthorized cloning.

The paper introduces a large-scale benchmark of synthetic underwater audio records to evaluate audio-language models on acoustic recognition tasks.

The authors propose a memory-driven vision-language navigation system using temporal compression and geometric-semantic representations to support long-horizon trajectory tasks.

This paper introduces a framework combining zero-knowledge proofs and serving certificates to verify model selection and query rewriting in routers.

Surveys methods for compressing large language model benchmarks to reduce evaluation costs while maintaining accurate score estimates.

The authors propose an embodied world model that unifies RGB video, metric depth, and robot actions into a shared dynamic representation.

This paper evaluates whether existing deepfake detection models can identify physical humanoid face robots performing speech-synchronized lip movements.

Proposes a teacher-guided reinforcement learning method using bidirectional constraints to help small language models learn effective tool use.

Evaluates whether framing explanations around unreliable narrator theory enables open-weight models to explain satirical news cost-effectively.

Introduces a low-latency guardrail model that routes incoming agent actions through editable policy trees for safety auditing.

This paper establishes mathematical conditions to guarantee long-term performance and low counterfactual regret for reflective language agents under resource constraints.

Compares human and Llama 3.1 8B facilitation turns across design meeting transcripts using multiple computational behavioral metrics.

The authors introduce a parameter-free conformal martingale method modeled as a universal portfolio to quickly detect distribution shifts at unknown times.

The paper analyzes aesthetic judgments by foundation models on K-pop music videos to measure inter-model consensus across audio and visual modalities.

Presents a multi-agent swing trading framework separating long-term strategy formulation from fast-timescale portfolio risk governance.

The paper analyzes the theoretical detection limits and sum-test efficiency of textGrain watermarking under human text edits.

This paper synthesizes literature on educational language models to evaluate whether tutoring systems measurably improve student learning gains.

This paper introduces LoongSage, an asynchronous reinforcement learning framework designed to run multi-turn agent tasks with reduced system overhead.

The paper proposes an inference-time alignment method that optimizes expected costs while controlling tail risk in diffusion models.

Introduces Agentic-MatriBO, a modular Bayesian optimization framework using LLM agents and deterministic compilation for material and chemical experiment design.

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

This paper establishes authorization models and integrity proofs to prevent narrative model history from becoming unauthorized actions.

The paper presents an instrumented predator-prey simulation framework to evaluate communication, scheduling, and collective dynamics in remote decision-making agents.

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

The paper proposes an embedding-based graph kernel entropy method for computationally efficient hallucination detection in language models.

The paper presents a multi-stage automated labeling framework using multimodal models and agents to annotate short videos for content moderation.

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

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.