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1,406 notes, page 46 of 59
LLM-Guided ODE Discovery and Parameter Inference from Small-Cohort Aggregate Data
The paper introduces AgentODE, a framework that uses large language models (LLM) to propose ODE structures for complex dynamics modeling in rare diseases. The framework operates on population-level summary statistics…
LLVM-Bench: Benchmarking and Advancing Large Language Models for LLVM Compiler Issue Resolution
The authors present LLVM-Bench, a large-scale benchmark for testing the effectiveness of large language models (LLMs) in resolving issues related to the LLVM compiler. They found that current LLM-based techniques are…
Measuring the Gap Between Human and LLM Research Ideas
The authors explore the gap between ideas generated by Large Language Models (LLMs) and those of human researchers in the field of computer science. They develop an evaluation framework based on high-quality human…
MemSyco-Bench: Benchmarking Sycophancy in Agent Memory
Researchers have proposed a new benchmark called MemSyco-Bench to evaluate how memory in large language models (LLMs) can lead to sycophancy, where agents over-align with the user at the cost of factual accuracy or…
Optimal Resource Utilization for Autonomous Laboratory Orchestrators
The authors propose a two-step method to optimize resource utilization in autonomous laboratories, specifically for metal-organic framework synthesis. The first step involves using constraint programming to find the…
Post-Training Pruning for Diffusion Transformers
The paper proposes DiT-Pruning, a method to improve post-training pruning performance for Diffusion Transformers (DiTs), which are used in image generation but have high computational overhead. The new method uses…
Right in the Right Way: LM Training with Verifiable Rewards and Human Demonstrations
Researchers propose an adversarial generator-discriminator framework to train language models (LM) for tasks with well-defined success metrics, like code generation and mathematical reasoning. The new approach improves…
Self-Evolving Agents with Anytime-Valid Certificates
In this paper, Biswa Sengupta introduces SEA (Self-Evolving Agents), a new architecture that allows for self-modification in AI agents while maintaining certain guarantees and controls. The architecture consists of a…
Self-GC: Self-Governing Context for Long-Horizon LLM Agents
A research paper titled 'Self-GC: Self-Governing Context for Long-Horizon LLM Agents' presents a system called Self-GC that aims to manage the lifecycle of context objects for long-horizon language models (LLMs). The…
SenseWalk: Agent-Based Semantic Trajectory Simulation Powered by Large Language Models in Zoned Environments
The paper proposes SenseWalk, an interactive system that uses large language models (LLMs) to simulate semantic trajectories for human movement analysis. The simulation workflow combines LLMs and the social force model…
Skills Are Not Islands: Measuring Dependency and Risk in Agent Skill Supply Chains
Researchers introduce a new method called Agent Skill Supply Chains (ASSCs) to manage dependencies in Large Language Model agents. They develop SkillDepAnalyzer, which extracts dependency graphs and metadata from…
SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Multi-Faceted Bug Reproduction Tests
A study was conducted to improve software engineering agents that generate patches for software issues. The researchers found that using bug reproduction tests (BRTs) directly to guide patch generation can be…
The State-Prediction Separation Hypothesis
Researchers propose a new model for language modeling called 'state-prediction separation' which aims to improve efficiency by separating the prediction of the next token and the storage of useful state for future…
Theoria: Rewrite-Acceptability Verification over Informal Reasoning States
Theoria is a verification architecture presented by Ben Slivinski and Michael Saldivar that aims to address the issue of trust in AI systems' answers. It rewrites candidate solutions into a sequence of typed state…
Two AI Metrics Diverged: Will it Make All the Difference?
The authors discuss how different metrics for evaluating AI capabilities can lead to varying conclusions about whether advanced AI will remain in the hands of a few powerful entities or become more accessible to…
A Lifecycle and Application-Stack Survey of Large Language Model Vulnerabilities: Attacks, Risks, Defenses, and Open Problems
This research paper discusses the increased role of large language models in various systems beyond just text generation, and how this shift changes the security landscape. The authors systematize the existing…
A Self-Evolving Agentic System for Automated Generation and Execution of Biological Protocols
Researchers have developed a self-evolving multi-agent system called ProtoPilot for automated generation and execution of biological protocols. The system incorporates various components to ensure alignment between…
A Technical Typology of AI Systems in Public Administration
The paper argues that different types of AI systems impact public values like accountability, procedural justice, and non-discrimination differently. The authors propose a typology of five AI system categories…
A Tutorial on Autonomous Fault-Tolerant Control Using Knowledge-Grounded LLM Agents
This research paper proposes a framework for using Large Language Model (LLM) agents to support operators in recovering from faults in process plants. The LLM is used as a constrained supervisory planner, proposing…
ACE: Pluggable Adaptive Context Elasticizer across Agents
Researchers propose a new module called Adaptive Context Elasticizer (ACE) to address the challenges posed by growing trajectory lengths in large language models (LLMs) with fixed context windows. ACE is designed to be…
AdaJEPA: An Adaptive Latent World Model
The paper proposes a new adaptive latent world model called AdaJEPA that can improve planning accuracy by continuously recalibrating the world model during test-time, particularly when dealing with distribution shift…
Adaptive Cluster-First Route-Second Decomposition for Industrial-Scale Vehicle Routing
The researchers propose an adaptive system for solving large-scale capacitated vehicle routing problems (CVRPs). The system uses a large language model (LLM) as a high-level decision maker to analyze the evolving…
An Agentic AI Framework to Accelerate Scientific Discovery in Plant Phenotyping
A new AI framework developed at Oak Ridge National Laboratory's Advanced Plant Phenotyping Laboratory aims to automate the analysis of large plant imaging datasets generated by automated stations. The framework…
Attend, Transform, or Silence: Operator-Level Visual Skipping for Efficient Multimodal LLM Inference
The paper proposes a method for improving the efficiency of multimodal large language models (MLLMs) by selectively skipping redundant attention and FFN operators during visual-token processing. The method aims to…
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