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ENSEMBITS: an alphabet of protein conformational ensembles

(How) Do Large Language Models Understand High-Level Message Sequence Charts?

Query-Conditioned Test-Time Self-Training for Large Language Models

Constitutional Governance in Metric Spaces

Inducing Overthink: Hierarchical Genetic Algorithm-based DoS Attack on Black-Box Large Language Reasoning Models

LeanSearch v2: Global Premise Retrieval for Lean 4 Theorem Proving

MLGIB: Multi-Label Graph Information Bottleneck for Expressive and Robust Message Passing

Watermarking Should Be Treated as a Monitoring Primitive

Does language matter for spoken word classification? A multilingual generative meta-learning approach

Scaling few-shot spoken word classification with generative meta-continual learning

Context Training with Active Information Seeking

WriteSAE: Sparse Autoencoders for Recurrent State

Learning, Fast and Slow: Towards LLMs That Adapt Continually

Beyond GRPO and On-Policy Distillation: An Empirical Sparse-to-Dense Reward Principle for Language-Model Post-Training

Detecting overfitting in Neural Networks during long-horizon grokking using Random Matrix Theory

A New Technique for AI Explainability using Feature Association Map

TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching

Boosting Omni-Modal Language Models: Staged Post-Training with Visually Debiased Evaluation

GEAR: Granularity-Adaptive Advantage Reweighting for LLM Agents via Self-Distillation

Overcoming Dynamics-Blindness: Training-Free Pace-and-Path Correction for VLA Models

Predictive Maps of Multi-Agent Reasoning: A Successor-Representation Spectrum for LLM Communication Topologies

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale

Prompt-Activation Duality: Improving Activation Steering via Attention-Level Interventions

Pretraining large language models with MXFP4 on Native FP4 Hardware

ShadowMerge: A Novel Poisoning Attack on Graph-Based Agent Memory via Relation-Channel Conflicts

Evolutionary Ensemble of Agents

Geometrically Constrained Stenosis Editing in Coronary Angiography via Entropic Optimal Transport

AgentForesight: Online Auditing for Early Failure Prediction in Multi-Agent Systems

Trapping Attacker in Dilemma: Examining Internal Correlations and External Influences of Trigger for Defending GNN Backdoors

Normalization Equivariance for Arbitrary Backbones, with Application to Image Denoising

Flow-OPD: On-Policy Distillation for Flow Matching Models

One Token Per Frame: Reconsidering Visual Bandwidth in World Models for VLA Policy

Toward Privileged Foundation Models:LUPI for Accelerated and Improved Learning

Predict-then-Diffuse: Adaptive Response Length for Compute-Budgeted Inference in Diffusion LLMs

Motion-Aware Caching for Efficient Autoregressive Video Generation

ClawGym: A Scalable Framework for Building Effective Claw Agents

SIEVES: Selective Prediction Generalizes through Visual Evidence Scoring

Quantifying and Mitigating Self-Preference Bias of LLM Judges

Quotient-Space Diffusion Models

Image Generators are Generalist Vision Learners

RoboWM-Bench: A Benchmark for Evaluating World Models in Robotic Manipulation

Evaluating Adaptive Personalization of Educational Readings with Simulated Learners

UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration

RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies

ECHO: Elastic Speculative Decoding with Sparse Gating for High-Concurrency Scenarios

PinpointQA: A Dataset and Benchmark for Small Object-Centric Spatial Understanding in Indoor Videos

LLMs Should Express Uncertainty Explicitly

A Security Analysis of the OpenClaw AI Agent Framework

OneSearch-V2: The Latent Reasoning Enhanced Self-distillation Generative Search Framework

Procedural Refinement by LLM-driven Algorithmic Debugging for ARC-AGI-2

Residual Stream Duality in Modern Transformer Architectures

M$^2$RNN: Non-Linear RNNs with Matrix-Valued States for Scalable Language Modeling

Reinforcement Learning for Diffusion LLMs with Entropy-Guided Step Selection and Stepwise Advantages

TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning

Adapting Dijkstra for Buffers and Unlimited Transfers

Neural Field Thermal Tomography: A Differentiable Physics Framework for Non-Destructive Evaluation

V2M-Zero: Zero-Pair Time-Aligned Video-to-Music Generation

Gradient Iterated Temporal-Difference Learning

Vibe Code Bench: Evaluating AI Models on End-to-End Web Application Development

Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons

Decoupling Stability and Plasticity for Multi-Modal Test-Time Adaptation

MPU: Towards Secure and Privacy-Preserving Knowledge Unlearning for Large Language Models

Grokking Finite-Dimensional Algebra

CUICurate: A GraphRAG-based Framework for Automated Clinical Concept Curation for NLP applications

MALLVI: A Multi-Agent Framework for Integrated Generalized Robotics Manipulation

Artificial Intelligence Specialization in the European Union: Underexplored Role of the Periphery at NUTS-3 Level

Numerical exploration of the range of shape functionals using neural networks

Finding Interpretable Prompt-Specific Circuits in Language Models

ArGEnT: Arbitrary Geometry-encoded Transformer for Operator Learning

Krause Synchronization Transformers

Proximal Action Replacement for Behavior Cloning Actor-Critic in Offline Reinforcement Learning

VLRS-Bench: A Vision-Language Reasoning Benchmark for Remote Sensing

GhostCite: A Large-Scale Analysis of Citation Validity in the Age of Large Language Models

Boosting LLM Reasoning via Human-Inspired Reward Shaping

PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection

Neural Signals Generate Clinical Notes in the Wild

L2R: Low-Rank and Lipschitz-Controlled Routing for Mixture-of-Experts

OPT-Engine: Benchmarking the Limits of LLMs in Optimization Modeling via Complexity Scaling

Teaching and Evaluating LLMs to Reason About Polymer Design Related Tasks

The Multi-View Paradigm Shift in MRI Radiomics: Predicting MGMT Methylation in Glioblastoma

LangPrecip: Language-Aware Multimodal Precipitation Nowcasting

Achieving Approximate Symmetry Is Exponentially Easier than Exact Symmetry

Why Goal-Conditioned Reinforcement Learning Works: Relation to Dual Control

AI-Driven Optimization under Uncertainty for Mineral Processing Operations

fMRI-LM: Towards a Universal Foundation Model for Language-Aligned fMRI Understanding

A cross-species neural foundation model for end-to-end speech decoding

How Learning Rate Decay Wastes Your Best Data in Curriculum-Based LLM Pretraining

Optimizing PyTorch Inference with LLM-Based Multi-Agent Systems

Chinese Short-Form Creative Content Generation via Explanation-Oriented Multi-Objective Optimization

Descriptor: Distance-Annotated Traffic Perception Question Answering (DTPQA)

Moral Susceptibility and Robustness under Persona Role-Play in Large Language Models

From Ranking to Reasoning: Explainable Web API Recommendation via Semantic Reasoning

Higher-order Linear Attention

A Tutorial on Cognitive Biases in Agentic AI-Driven 6G Autonomous Networks

Seeing Through the Brain: New Insights from Decoding Visual Stimuli with fMRI

AMiD: Knowledge Distillation for LLMs with $\alpha$-mixture Assistant Distribution

VER: Vision Expert Transformer for Robot Learning via Foundation Distillation and Dynamic Routing

TiTok: Transfer Token-level Knowledge via Contrastive Excess to Transplant LoRA

The Pitfalls of KV Cache Compression

AVEX: What Matters for Animal Vocalization Encoding

NutriGen: Personalized Meal Plan Generator Leveraging Large Language Models to Enhance Dietary and Nutritional Adherence

Created by

Haebom

저자

Saman Khamesian, Asiful Arefeen, Stephanie M. Carpenter, Hassan Ghasemzadeh

개요

본 논문은 개인 맞춤형 식단 계획의 어려움(영양학적 복잡성, 시간 제약, 식이 지식 부족)을 해결하기 위해 대규모 언어 모델(LLM) 기반의 새로운 프레임워크 NutriGen을 제시합니다. NutriGen은 사용자 정의 식단 선호도 및 제약 조건을 고려하여 개인화된 식사 계획을 생성합니다. USDA 영양 데이터베이스와 같은 신뢰할 수 있는 영양 정보를 통합하고 프롬프트 엔지니어링을 활용하여 유연성과 사용 편의성을 유지합니다. Llama 3.1 8B와 GPT-3.5 Turbo가 가장 낮은 오차율(각각 1.55%, 3.68%)을 보이며, 사용자 정의 칼로리 목표와의 일치도가 높고 정밀도가 향상된 식사 계획을 생성하는 것을 실험을 통해 보여줍니다. 또한, DeepSeek V3의 성능을 기존 모델과 비교하여 개인 맞춤형 영양 계획의 잠재력을 평가했습니다.