Measurement study comparing computational costs of mobile robotic manipulation workloads across onboard, edge, and cloud GPU platforms using foundation models.
Sparse supervised learning framework for monocular 3D object tracking in videos, reducing annotation requirements for autonomous agent perception.
ChoiceEval framework for auditing brand and cultural preference biases in LLMs used as market intermediaries affecting consumer choices.
Neural graph representation method using reinforcement learning to solve approximate subgraph matching, an NP-hard problem in graph analysis.
Reinforcement learning approach combining vision-language models with neuroscience-inspired reward signals for safe autonomous driving without manual reward engineering.
Evaluation framework (VCoT-Bench) measuring LLM reasoning ability for Rust program verification through intermediate verification steps, not just pass/fail outcomes.
Uncertainty quantification method for Vision-Language-Action robotic models that detects safety-critical moments during continuous control rather than averaging uncertainty signals.
PowerFlow: principled RLIF framework for unsupervised LLM capability elicitation via distribution matching instead of heuristic rewards.
Privacy-preserving LLM agent planning via abstractions preventing exposure of local environment data to cloud services.
Token-level Adaptive Routing: inference-time alignment method for freezing LLMs toward structured reasoning without post-training.
Automated hyperparameter optimization framework for sparse attention mechanisms using Bayesian optimization and multi-fidelity search.
Benchmark evaluating large vision-language models on rare skin disease diagnosis with long-context reasoning.
Synthetic data augmentation using generative models for semantic segmentation balancing reliability and diversity.
RL-based adaptive decoder for LLMs that learns task-specific generation policies at test-time for improved output quality.
Sample-efficient reinforcement learning with verifiable rewards for improving LLM reasoning with Bayesian reward estimation.
Research task: automatically extracting and querying structured databases from open web sources for analytical questions.
Hypergraph neural network for medication recommendations leveraging patient relationships and clinical history.
ML research on prostate cancer detection using Vision Transformers on small 162-image dataset with transfer learning.
WASD framework identifies critical neurons as sufficient conditions for explaining and controlling LLM behavior with natural language directives.
Evaluation of vision-language models on inferring human engagement from gameplay video across multiple prompting strategies and games.
Handbook formalizing AI architectures for motor insurance, covering perception, multimodal reasoning, and production infrastructure for risk assessment.
Mechanistic study of how large vision-language models implement counting behavior, combining synthetic benchmarks with interpretability analysis.
ICE-Guard framework detects spurious feature reliance in LLMs for high-stakes decisions through intervention consistency testing on demographic, authority, and framing biases.
Method for scaling vision-language-action robot learning using generative 3D worlds to address sim-to-real gap.
CoDA explores adversarial attacks on medical vision-language models and proposes token-space repair methods.
HiMu hierarchical frame selection method for long video question answering with vision-language models.
Study showing Transformers learn robust in-context regression under distributional uncertainty without restrictive assumptions.
SpecForge: Open-source production framework for training draft models used in speculative decoding to reduce LLM inference latency.
ICE framework evaluates LLM explanation faithfulness using statistical intervention testing with randomization baselines.
Systematic analysis and improvements to Elastic Weight Consolidation for continual learning to better estimate weight importance.
Benchmark comparing PETNN, KAN, and classical deep learning models on myMNIST Burmese handwritten digit recognition dataset.
AutORAN uses LLMs for natural language programming to simplify xApp development in Open Radio Access Networks.
LSE framework trains LLMs to self-improve during inference by iteratively refining context based on problem feedback.
OpenT2M: Million-scale open-source dataset with 2800+ hours of motion data for text-to-motion generation in animation and robotics.
Benchmarking framework for PDF table extraction using LLM-based semantic evaluation on synthetically generated PDFs with LaTeX ground truth.
HISR framework improving multi-turn agentic reinforcement learning through hindsight information modulation and segmental process rewards for complex long-horizon tasks.
CausalRM method for learning reward models from observational user feedback (clicks, upvotes) as scalable alternative to controlled RLHF annotation.
Study measuring confirmation bias in LLM-based security code review systems and its exploitability in software supply-chain attacks.
Ablation study of Group Relative Policy Optimization components for LLM reasoning training, questioning necessity of complex loss functions.
ClawTrap MITM-based red-teaming framework for evaluating security robustness of autonomous web agents like OpenClaw against network-layer threats.
AutoPipe framework for automated configuration of LLM post-training pipelines combining supervised fine-tuning and reinforcement learning under budget constraints.
32B parameter Korean-language LLM optimized for enterprise reasoning, long-context understanding, and agentic workflows with domain-specific capabilities.
Watermarking method for LLM ownership protection using functional subspaces, robust against fine-tuning, quantization, and knowledge distillation.
Vision-language model enhanced with explicit spatial token generation for improved 2D/3D spatial reasoning and fine-grained grounding.
Formal specification for cryptographic admission control governing autonomous agent actions in institutional B2B environments, validating identity and policy compliance.
Study on how AI-mediated video communication affects trust and credibility detection. Social impact of AI, limited technical content.
Case study evaluating LLM-generated lessons in Duolingo for language learning. LLM application assessment with limited technical depth.
MultihopSpatial benchmark for multi-hop spatial reasoning in Vision-Language agents. Evaluation dataset for VLA agents.
Framework proposing readiness metrics for human-AI decision-making teams beyond accuracy. Evaluation methodology for AI collaboration.
PASTE: Pattern-Aware Speculative Tool Execution to reduce latency in LLM agent tool loops. Optimization for agentic workflows.