Qualitative case study examining legal professionals' perceptions on AI governance, regulatory gaps, and institutional readiness in Nigeria.
CritBench: evaluation framework for cybersecurity capabilities of LLM agents in operational technology (OT) environments like IEC 61850 digital substations.
Multi-stage validation framework for trustworthy clinical information extraction using LLMs at scale without annotation-intensive reference standards.
Evaluation of LLM personality simulation using psychometric profiles and life story generation, comparing model outputs against real human psychological data.
Framework using graph priors to improve structural coherence in part-based image synthesis by modeling spatial and semantic relationships.
Method using dual self-consistency reinforcement learning to synthesize TikZ graphics code from images, addressing precision challenges in multimodal LLM code generation.
Framework modeling paraphrasing as affine transformations in transformer embedding spaces to improve interpretability of language model latent spaces.
Research on how social dynamics in multi-agent LLM systems (conformity, expertise perception, dominance) undermine objective decision-making by representative agents.
Research paper LLM4CodeRE uses domain-adapted LLMs for malware decompilation analysis and reverse engineering of obfuscated code.
Research paper on lightweight multimodal VLM adaptation for thermal drone imagery species recognition and habitat analysis via projector alignment.
Research paper on Gym-Anything, a framework converting any software into agent environments for training computer-use agents on complex, long-horizon tasks.
Research paper introducing Polynomial Mixer (PoM), a linear-time token mixing mechanism replacing self-attention in transformers with preserved universality.
Shot-based quantum encoding distributes quantum resources for efficient data loading in quantum neural networks.
Synthetic pipeline generates doctor-patient conversations for training and evaluating long-form audio summarization models.
MIGT taxonomy addresses governance of machine identities and automated agents in enterprise and geopolitical contexts.
Analyzes multi-token prediction's gradient inductive bias for developing coherent world models compared to next-token prediction.
MMEmb-R1 incorporates chain-of-thought reasoning into multimodal embeddings with pair-aware selection and adaptive control mechanisms.
Diffusion model approach for converting low dynamic range video to HDR through scene radiance estimation.
Test-time training method updates LLM fast weights at inference to adapt dynamically to new information streams.
UserCentrix is a hybrid agentic orchestration framework for smart spaces combining memory augmentation with multi-agent coordination.
ARIEL framework pairs expert-vetted biomedical tasks with LLMs for evaluation and optimization of AI research assistants.
Fine-tunes open-source LLMs for smartphone app control by learning action semantics rather than syntax, reducing API costs.
URSA framework enables LLMs to conduct autonomous research through complex reasoning, planning, coding, and multi-agent collaboration.
MedGemma is a medical vision-language foundation model collection designed for healthcare AI tasks with privacy preservation.
Agent-based model framework for simulating cascading climate risks in supply chains with adaptive firm behavior and economic network effects.
Extends Nash learning from human feedback to multiplayer setting, addressing non-transitive and heterogeneous preference capture in LLM alignment.
DeepSearch applies Monte Carlo Tree Search to overcome training plateaus in reinforcement learning from verifiable rewards for language model reasoning.
Introduces Supervised Multi-Dimensional Scaling to analyze and compare feature manifold hypotheses in language models' latent spaces.
TS-Agent enables LLMs to reason over raw time series data directly without converting to text/images, reducing hallucination and knowledge leakage.
DRIFT method automates mathematical theorem formalization for LLMs by decomposing statements and retrieving prerequisite knowledge in formal languages.
Critiques rule-based and reward-based approaches in RL ethics, proposes virtue ethics framework for more robust machine ethics.
Information-theoretic analysis extending Gödel's incompleteness to AI security and alignment, establishing fundamental limitations for robust AI systems.
Framework enabling GUI agents to build actionable memory from past tasks via self-exploration with critic guidance, improving generalization and reducing errors.
Asynchronous reinforcement learning framework for vision-language-action model training, enabling flexible post-training optimization for embodied agents.
Study demonstrating that introspection mechanisms in LLMs are content-agnostic, detecting anomalies without understanding their semantic meaning.
Framework adapting hindsight experience replay to recover training signal from failed LLM agent trajectories, addressing low real-world task success rates.
Diffusion-based surgical video restoration framework using physics and semantics-guided reinforcement learning to remove surgical smoke.
Two-phase training framework jointly optimizing LLMs for reasoning and self-refinement using group relative policy optimization on correctness rewards.
High-fidelity benchmark with rubrics-based evaluation assessing LLMs on expert-level complex open-ended tasks across multiple domains.
LLM-based peer review system that verifies claims by checking related work and executing code, improving review quality beyond manuscript-only analysis.
Theoretical framework for evaluating cyclic non-transitive interactions between LLM-based agents using equilibrium concepts instead of linear rankings.
Framework proposing that ambient AI systems transition from modeling to constituting users' cognitive functions through sustained causal coupling.
Molecular discovery framework combining LLMs with diffusion models to improve generation of chemically valid molecules by relaxing autoregressive constraints.
Memory system for deep research agents that improves trajectory retrieval and memory evolution to enhance LLM reasoning and autonomous learning.
Unsupervised fine-tuning method to improve adversarial robustness and semantic quality of vision-language models through siamese contrastive learning.
LLM-based code translation agent using execution alignment to improve cross-language code generation without parallel training data.
Multimodal LLM fine-tuned for image forgery detection and localization with interpretable visual reasoning capabilities.
Divide-and-conquer proof synthesis approach using LLMs to automate formal verification in proof assistants like Coq, improving software quality verification.
Systematic analysis of challenges in transitioning foundation model systems from demos to production, covering reliability, cost, scalability, and compliance issues.
Edge-cloud collaborative VQA system using aligned vector quantization to split vision-language model computation between edge and cloud devices, reducing bandwidth and utilizing edge resources.