GRASP: Gradient Realignment via Active Shared Perception for Multi-Agent Collaborative Optimization
Multi-agent optimization framework addressing non-stationarity through active shared perception of agent policies.
Multi-agent optimization framework addressing non-stationarity through active shared perception of agent policies.
Memory-efficient LLM training via truncated SVD factorization of weight matrices on consumer hardware.
Token pruning framework for vision-language models using attention dual form perspective without retraining.
Security analysis of backdoor attacks on language models using continuous latent reasoning without token output.
LLM pretraining at exascale using Aurora supercomputer with Mula-1B model and Optimus training library.
Bayesian inference framework for multi-dimensional emotion understanding accounting for dependencies among emotions.
Language agents with learnable adaptation policies that optimize test-time learning instead of using fixed hand-crafted policies.
Mixture-of-Experts architecture for actor-level stance detection in geopolitical text classification.
PixelPrune: adaptive visual token reduction for vision-language models using predictive coding for document and GUI tasks.
Dataset and analysis of autonomous coding agents' contributions in real-world projects, examining code quality and team dynamics over time.
Training-free canonical correlation analysis method for improving efficiency of pretrained image encoder representations.
WARP: method for repairing adversarial vulnerabilities in transformer NLP models with provable inner-layer repair guarantees.
Reinforcement learning with flow-based policies and distributional RL for trajectory optimization in multi-solution problems.
Dignified Peer framework addressing evasive and sycophantic behavior in aligned LLMs through anti-sycophancy and empathy.
MyPhoneBench: evaluation framework measuring privacy-compliant behavior in mobile phone-use agents during task execution.
Egocentric world simulator generating interaction videos with persistent 3D scene state updates for embodied AI.
Query-conditioned keyframe sampling approach for long-form video understanding with multimodal LLMs using evidential reasoning.
OrgAgent: hierarchical multi-agent framework organizing LLM-based agents into governance, execution, and compliance layers for complex reasoning.
Transfer learning algorithms for nonparametric Bayesian network structure learning under limited data.
Method for fast probing of LLM downstream performance during training using metrics correlated with performance beyond training loss.
Controlled decomposition of multi-LLM revision pipelines to separate gains into re-solving, scaffold, and content components across benchmarks.
Automated framework to evaluate and harden LLM system instructions against encoding-based attacks to prevent credential and policy leakage.
Security framework to detect and prevent vulnerabilities in AI-generated code through systematic verification of code safety gates.
Training-free detection method for partial audio deepfakes using speech foundation models without frame-level annotations.
Analysis of how LLMs use induction heads to track and retrieve information from context, revealing serial-recall patterns in in-context learning.
Study on how students' trust in AI assistants affects their reliance and critical evaluation of AI-generated output in educational settings.
PaperRecon evaluation framework for assessing quality and hallucination risks in AI-generated research papers from coding agents.
Brainstacks modular architecture for continual multi-domain LLM fine-tuning using MoE-LoRA stacks composing frozen adapters for domain expertise.
ORCA framework for test-time calibration of LLM reasoning using conformal prediction, improving efficiency of sampling-based scaling methods.
Multiscreen architecture introducing explicit query-key relevance rejection mechanism in attention, improving LLM discrimination of irrelevant information.
ROS 2 middleware integration for Florence-2 vision-language model in robotics systems, enabling local inference for robotic perception.
ORBIT dataset with 20K reasoning-intensive queries for training search agents combining LMs and web search, using verifiable generation methodology.
Agentic evolutionary framework for scientific algorithm discovery combining LLM-guided search with structured theory and code co-evolution.
Benchmark for evaluating LLM agents on long-term planning over one-year startup simulation with hundreds of turns, testing strategic coherence under uncertainty.
Mathematical framework analyzing AI weather prediction pipelines, emphasizing training methodology and data diversity over architecture choices.
Method for unsupervised code correctness evaluation using LLMs through code comprehension before auditing, eliminating need for reference implementations.
Survey of agentic RAG systems combining LLMs with real-time retrieval to address static training data limitations and improve contextual accuracy.
Research on fine-tuning LLMs as agentic systems to handle exceptions and improve decision-making in complex real-world contexts.
Study on mitigating reasoning biases in LLMs through activation steering at inference time to improve logical validity discrimination.
Research evaluating LLM reasoning capabilities on real-world site selection tasks, testing if models like o1 and DeepSeek-R1 generalize beyond math/code domains.
Benchmark and framework for training hierarchical multi-agent LLM systems with master-coordinator and specialized sub-agents for e-commerce applications.
Approach using LLMs to automate formulation of dynamic programming models for operations research, addressing stochastic transitions and data scarcity.
Retrieval-of-Thought method that reuses reasoning steps across problems via thought graphs to improve inference efficiency and reduce latency/cost.
Research on self-replication risks in LLM agents driven by objective misalignment, moving from theoretical concern to practical reality assessment.
Genesis: framework evolving attack strategies for red-teaming LLM web agents using behavioral pattern learning.
EHRStruct: benchmark framework evaluating LLM performance on structured electronic health record tasks with standardized metrics.
Alphacast: agentic reasoning framework for time series forecasting using iterative multi-step reasoning with domain knowledge integration.
DR-LoRA: parameter-efficient fine-tuning method for MoE LLMs using dynamic rank allocation based on expert specialization.
ReasonMa: semantic-guided watermarking technique for reasoning LLMs that preserves logical coherence while protecting models.
arXiv paper on Lexpop framework using deep RL to train finite-state controllers for solving POMDPs robustly.