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Notes on large language models
160 notes, page 3 of 7
Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks
Researchers have developed an open-source framework for evaluating the effectiveness of AI agents powered by large, locally deployable language models (LLMs) in data preparation tasks, specifically focused on…
Beyond Sycophancy: Structured Resistance and Compliance in LLM Moral Reasoning
The authors investigate how large language models can learn from others without merely conforming to them, focusing on the distinction between incorporating others' perspectives and maintaining a well-grounded moral…
Euclid-MCP: A Model Context Protocol Server for Deterministic Logical Reasoning via Prolog
The paper introduces Euclid-MCP, an open-source MCP server that provides deterministic logical reasoning via SWI-Prolog. It addresses the issue of unreliable multi-step logical reasoning in Large Language Models (LLMs)…
MIRROR: Learning from the Other View for Multi-Modal Reasoning
Researchers have created a new approach called Modality-Informed Reciprocal Reasoning Optimization (MIRROR) to improve the reasoning abilities of both large language models and vision-language models. The approach uses…
Scaling Up Formal Representation of Clinical Trial Protocols in Ensemble Logic Using LLMs: A Preliminary Study
Researchers present CT-TEL workflow, a scalable pipeline that uses Large Language Models (LLMs) to convert clinical trial protocols from unstructured free text into Temporal Ensemble Logic (TEL) formulas. The study…
Thinkink: 2D Spatial Ink-native Interaction with LLMs
The researchers proposed a new tool called Thinkink that integrates large language models (LLMs) into handwritten note-taking and sketching practices. The tool allows users to handwrite or draw on a shared canvas, with…
Thinkink: 2D Spatial Ink-native Interaction with LLMs
Researchers propose Thinkink, a tool that allows users to interact with large language models (LLMs) using handwritten notes and sketches on a shared digital canvas. The tool was designed through a series of studies to…
HijackKV: New Threat in Position-Independent KV Cache Reuse
Researchers have identified a new threat called KV Cache Hijacking in position-independent KV cache reuse, which is used to reduce inference latency in large language models. This threat allows an attacker to silently…
Agents in the Wild: Where Research Meets Deployment
This tutorial paper discusses the transition of large language model (LLM) based agentic systems from research to practical use in various fields like software engineering, scientific discovery, and finance. It…
CASE: Causal Alignment and Structural Enforcement for Improving Chain-of-Thought Faithfulness
Researchers propose a new framework called CASE to improve the faithfulness of chain-of-thought (CoT) reasoning in large language models (LLMs). The goal is to ensure that the generated reasoning supports the final…
Harness Engineering for LLM-Driven GPU Kernel Generation
This research presents a system that uses large language models (LLMs) to optimize GPU kernels. The system separates an evaluation harness from an optimization controller, with human-authored skills and code agents…
LLMs and Agentic AI Systems for Smart Grids: A Tutorial on Architectures and Applications
The paper discusses how large language models (LLMs) and agentic AI systems are being used in smart grids for tasks such as forecasting, optimization, and control. The authors present a design principle that ensures…
LLMs and Agentic AI Systems for Smart Grids: A Tutorial on Architectures and Applications
The paper discusses the use of Large Language Models (LLMs) and Agentic AI systems in smart grids for tasks like forecasting, optimization, and control. It presents a design principle to ensure numerical results come…
Natural Language Access to Domain-Specific Metadata: A Reusable Framework for LLM Query Generation
Researchers have developed a system called Natural Language Knowledge Graph Query (NLKGQ) that allows them to ask ad-hoc questions about the contents of domain-specific archives using natural language, without…
PEARL: Auditable Repair for Scientific Reasoning Graph Extraction
The paper proposes a new framework called PEARL for extracting reliable scientific reasoning graphs from Large Language Models (LLMs). The LLM outputs often contain errors such as malformed syntax, incorrectly oriented…
SGA: Plug&Play Geometric Verification for Educational Video Synthesis
Researchers propose a new module called Symbolic Geometric Agent (SGA) that works with existing animation pipelines to improve spatial correctness and visual legibility for educational videos. The SGA intercepts code…
SGA: Plug&Play Geometric Verification for Educational Video Synthesis
The authors propose the Symbolic Geometric Agent (SGA), a tool for improving the spatial correctness and visual legibility of educational animations generated by Large Language Models (LLMs) using libraries like Manim…
Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents
Researchers present a multi-agent framework using Large Language Models (LLMs) to simulate and audit political coalition formation in elections. The framework combines Supervised Fine-Tuning (SFT), Direct Preference…
Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents
Researchers have developed a multi-agent framework using Large Language Models (LLMs) to simulate coalition formation in political elections. The framework combines Supervised Fine-Tuning, Direct Preference…
SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration
The paper introduces SearchOS, a system designed to improve the efficiency of open-domain information-seeking agents by making their search progress more explicit and persistent. It aims to prevent repetitive loops…
Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation
Researchers have proposed a new framework to improve the quality of annotations in depression-related datasets for artificial intelligence systems in mental health research. The framework uses a large language model…
Automatic Ordinary Differential Equations Discovery For Biological Systems Using Large Language Model Powered Agentic System
The MEDA system is a new framework that uses large language models and symbolic regression to automatically discover ordinary differential equation (ODE) models for biological systems. The system retrieves background…
Deep Interaction: An Efficient Human-AI Interaction Method for Large Reasoning Models
Researchers propose a new method called Deep Interaction to correct errors made by large language models (LLMs) during complex, multi-step tasks. The method allows for direct editing of the original response, improving…
Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents
The paper presents a new framework called Memory as a Controlled Process (MemCon) for large language model (LLM) agents. MemCon aims to address the limitation of existing memory systems that rely on fixed…
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