Hybrid architecture combining classical ML for customer segmentation with RAG-enabled LLMs for compliant content generation in financial services marketing.
AI-ECG system using ECGFounder foundation model for non-invasive hyperkalemia detection with multicenter validation and handheld deployment.
Analysis of attention dispersion in multimodal LLMs during reasoning tasks and mitigation strategies for visual question answering.
Membership inference attack on contrastive pretraining models (CLIP, CLAP) detecting memorized PII using text-only queries without shadow models.
Self-indexing KV cache method predicting sparse attention from compressed keys to reduce memory bottleneck in long-context and large-batch LLM inference.
Empirical study on cognitive engagement of software engineers with agentic coding assistants, examining over-reliance risks and critical thinking degradation in high-stakes development.
QiMeng-CodeV-SVA trains specialized LLMs for SystemVerilog assertion generation via RTL-grounded bidirectional data synthesis to address NL2SVA scarcity.
Q-guided distillation method for flow-matching policies in reinforcement learning, optimizing initial noise distribution to improve inference latency and exploration.
Early-exit strategy for reasoning language models that monitors reasoning path deviation to mitigate overthinking, reducing redundant steps while maintaining performance.
Model editing approach applied to generative recommendation systems to mitigate cold-start collapse where recommendation accuracy drops near zero on unseen items.
Tucker adaptation method for lifelong vision-and-language navigation agents across diverse multi-scene environments without catastrophic forgetting.
Non-intrusive multi-hop attestation system for verifying LLM API outputs match client requests, addressing shadow API divergence and unauthorized endpoint behavior.
Systematic study investigating why generalist multimodal LLMs underperform on medical image interpretation tasks, focusing on visual grounding failures in zero-shot settings.
Benchmark with 6,400+ samples to evaluate step-by-step reasoning in multimodal LLMs for ECG interpretation, assessing whether models perform actual reasoning or rely on visual shortcuts.
AgroNVILA: multi-view agricultural MLLM decoupling perception from reasoning, with 288K image corpus addressing scale confusion.
DeLL framework for autonomous driving using Dirichlet process mixture models and causal adjustment to address catastrophic forgetting.
M²RNN: non-linear RNN architecture with matrix-valued states for language modeling with greater expressive power than Transformers.
AerialVLA: end-to-end vision-language-action model for UAV navigation combining visual interpretation with fuzzy linguistic instructions.
OxyGen system for unified KV cache management in vision-language-action models enabling efficient multi-task parallel inference.
SPARQ framework integrating spiking neural networks, quantization, and early-exit mechanisms for energy-efficient edge AI.
Bilateral decoupled decay method for stabilizing soft clipping in reinforcement learning with verifiable rewards for LLM reasoning.
Extension of minimal pairs evaluation using ordinal surprisal curves to assess linguistic knowledge in LLMs beyond binary judgments.
Method for merging specialized biological multimodal LLMs using embedding space signals to combine modalities.
Study showing questionnaire-based safety assessments of AI agents fail to capture real-world deployment safety concerns.
Modular framework separating planning from retrieval in LLMs to improve reliability on factual QA with explicit tool usage.
Infinite Problem Generator: agentic framework synthesizing physics problems with guaranteed solvability for LLM training data generation.
CangjieBench benchmark for evaluating LLMs on Cangjie, a low-resource general-purpose programming language with contamination-free evaluation.
Trust-region search algorithm for black-box alignment of diffusion and flow models to target rewards at inference time without gradient access.
Vision-Language-Action framework with thinking-with-image reasoning allowing models to revisit visual context during long-horizon embodied tasks.
Benchmark of 12 language models on MALINT, a human-annotated disinformation corpus capturing malicious intent, for improved detection.
End-to-end language-driven agent system for high-energy physics phenomenology workflows, executing tasks from theoretical input to final outputs.
Survey on using machine learning methods for adaptive memory system design in modern computing platforms instead of static heuristics.
Efficient drop-in replacement for dense classification heads in language models, reducing parameter and compute overhead for consumer devices.
Biologically-inspired agentic memory architecture using reward prediction error routing to reduce token costs and write latency in LLM agents.
Loss landscape visualization framework for interpreting reinforcement learning behavior in actor-critic algorithms and control systems.
Policy-aware agent alignment framework using chain-of-thought reasoning to help LLM agents adhere to complex business rules without excessive prompting.
Novel policy gradient method addressing pathological behavior in standard policy gradients through context-aware advantage weighting.
LLM-augmented system for automated change summarization and impact analysis in cloud-native CI/CD pipelines and release management.
Benchmark for evaluating LLMs on low-level code reasoning and formal proof generation using cryptographic library assembly code.
Open-source multi-agent system for literature review assistance using DSPy, Qdrant, and local-first architecture to synthesize papers and draft related work.
Study on compute allocation strategies for LLM-augmented retrieval agents handling reasoning-intensive queries over long horizons with growing memory stores.
Training-free inference-time model steering strategies to improve chain-of-thought reasoning in large audio-language models across multiple benchmarks.
EARCP ensemble architecture dynamically weights heterogeneous expert models based on performance and inter-model coherence for sequential decision making.
VisionCoach uses reinforcement learning with visual-perception prompting to improve spatio-temporal grounding in video reasoning models.
Study on detecting when language models actively conceal knowledge, finding larger models better at deception with gradient-based concealment easier to detect.
AgentTrace provides lightweight causal graph tracing for post-hoc root cause diagnosis in deployed multi-agent workflows with cascading failures.
Multi-agent reasoning framework for automated software system performance optimization beyond local code transformations, reasoning about whole-system interactions.
AdapterTune adds zero-initialized low-rank adapters to frozen Vision Transformers for stable transfer learning with principled capacity guidance.
Privacy-preserving machine translation at inference stage with new benchmark dataset for evaluating local translation without cloud servers.
POLCA framework uses LLMs as optimizers guided by rewards and feedback to automate optimization of prompts and multi-turn agent systems, formalizing it as stochastic generative optimization.