PACED: Distillation and On-Policy Self-Distillation at the Frontier of Student Competence
LLM distillation method weighting problems by student competence gradient signal-to-noise ratio for efficient training.
LLM distillation method weighting problems by student competence gradient signal-to-noise ratio for efficient training.
Framework for analyzing autonomous AI agent reasoning behavior through structured behavioral analytics beyond execution traces.
Production agentic system for cloud outage management with real-time updates, knowledge distillation, and conditioned action recommendations.
Domain-scoped inference architecture with explicit domain as computational parameter enabling substrate-independent reasoning.
Memory system for deep research agents enabling efficient evolution and reasoning through intelligent trajectory memory management.
Dual-LLM framework for zero-shot human mobility trajectory synthesis from activity descriptions without historical data.
Lightweight agent benchmark with configurable evaluation metrics addressing environment overhead and task distribution imbalances.
Framework and benchmark for deep research agents using structured knowledge alongside unstructured web content for comprehensive reports.
Multi-model orchestration framework for verifier-free evolutionary inference balancing diversity and computational efficiency.
Query and evidence processing tools (Q+) to improve deep research agents with structured reasoning, reducing redundant exploration.
Multi-agent system for automated industry classification using multimodal data and geographic information without manual annotation.
RL agents using language-conditioned transfer for zero-shot generalization to new tasks via analogical semantic policies.
Data-free meta-learning from pre-trained models without original training data, analyzing robustness and failure modes.
Transfer learning framework for optimizing traffic through real-time driving advisories to human drivers in connected and automated vehicle systems.
Multi-agent reinforcement learning approach using coordination graphs to model higher-order group relationships beyond pairwise agent interactions.
Survey of methods for detecting and characterizing coordinated online behavior in social media, from community dynamics to disinformation campaigns.
Self-supervised learning approach for ECG signal representation using masked modeling from unlabeled medical data.
Investigation of gender bias in Bangla language models with benchmark datasets for sentiment analysis, toxicity detection, hate speech, and sarcasm.
Method for learning disentangled visual concepts in image generation to improve multi-aspect creative generation while reducing concept confusion.
Analysis of polysemanticity in LLMs revealing neurons exhibit multiple semantic meanings, challenging discrete neuron attribution for model interpretation.
Research on large-scale simulation of LLM-driven generative agents for studying human behavior and social dynamics through computational approaches.
Sequential model editing method with editing anchor compression to constrain parameter drift and maintain LLM general abilities during knowledge updates.
Budget-friendly proxy model framework for post-hoc interpretability of LLMs, enabling actionable explanations for prompt engineering and optimization.
Agentic framework for synthetic image data generation and validation addressing data scarcity and label noise in vision tasks like detection and segmentation.
Safety enhancement for medical vision-language models using synthetic demonstrations to improve rejection of harmful clinical queries.
Listener-rewarded thinking approach using reinforcement learning to train robust reward models for generative text-to-image and video models.
Theoretical analysis providing quantitative guarantees for post-training quantization methods OPTQ and Qronos applied to LLMs and neural networks.
Keyframe selection method using visual subtitles for improved long video understanding with multimodal LLMs under context length constraints.
DINOv2-based segmentation framework for plant species and damage detection in herbicide trials, addressing domain drift across real-world conditions.
Investigation of multimodal LLMs for automating usability evaluation of user interfaces by analyzing visual UI context and textual instructions.
Configuration-aware LoRA adaptation for efficient fine-tuning of quantized LLMs on heterogeneous edge devices with privacy preservation.
Monte Carlo Tree Search approach for multi-attribute controllable summarization without per-attribute fine-tuning, enabling flexible constraint satisfaction.
Co-denoising framework for transferring manipulation skills from human videos to robots by bridging morphological differences.
Automated pipeline for scaling reinforcement learning datasets to pretraining scale, addressing data bottleneck in RL for LLM training.
Post-deployment learning framework for Vision-Language-Action policies using retrieved execution memories to improve embodied agent performance.
Data augmentation framework for robotic manipulation using Vision-Language-Action models to improve learning from limited demonstration datasets.
Computational analysis comparing 17,790 articles between Grokipedia (AI-generated) and Wikipedia examining textual and structural biases.
EGMOF: hybrid diffusion-transformer for metal-organic framework generation with inverse design capabilities for materials discovery.
Inference-time optimization using evolutionary algorithms on prompt embeddings for diffusion model control without fine-tuning.
Structured uncertainty framework for LLM agents with tool-calling to generate principled clarifying questions for ambiguous user instructions.
Language-conditioned humanoid robot control using LLM with unified motion vocabulary for free-form command execution and embodied AI.
Bharat Scene Text dataset and benchmark for Indian language scene text recognition addressing script diversity and font variations.
AV-SpeakerBench: multimodal LLM benchmark with 3,212 questions evaluating audiovisual speech understanding and speaker-speech alignment in video.
Research on adversarial perturbations for object detectors using black-box attacks to expose vulnerabilities and understand attack mechanisms.
Research on self-distillation methods for teaching language models to leverage cognitive skills like verification and backtracking without base model exposure.
Research on relational visual similarity in computer vision showing how humans perceive analogical relationships beyond attribute similarity.
Framework combining mechanism design and online learning for sequential mechanism design where principal learns agent beliefs while ensuring truthfulness.
Mechanistic study of self-reflection emergence in RL-trained LLMs, proposing two-stage decision-sampling hypothesis to explain unified optimization producing distinct capabilities.
Analysis showing layer pruning of LLMs degrades generative reasoning tasks beyond surface degradation, causing loss of algorithmic capabilities.
Method addressing prompt misguidance in diffusion-based super-resolution by using tiled prompts for localized semantic guidance.