Field notes
2,343 notes from my AI research vault, mapped by topic. Pick a topic to see its notes, or search.
Click the web, then scroll to zoom
Every note is in the list below. The web of topics draws here once the page has loaded.
458 of 2,343 notes match.
- Question answered
- Source note
- Topic
- Shared notes
Notes on ai
458 notes, page 9 of 20
VoLN: Vision-Only Long-Horizon Navigation---Paradigm, Benchmark, and Method
The text discusses a new navigation system called Vision-Only Long-Horizon Navigation (VoLN) for autonomous agents. In this system, the agent navigates using visual cues instead of relying on route-level instructions…
When Are Reasoning-Based Guardrails Not Efficient? ResponseGuard: A Fast Vision-Language Guard for Real-Time Moderation
A new AI guardrail called ResponseGuard has been developed that can quickly and efficiently detect harmful responses from a vision-language model without needing to go through step-by-step reasoning, which makes…
Co-Evolving LLM Evaluators and Policies via DynamicRubric
The researchers propose DynamicRubric, a method that allows large language models to improve by co-evolving evaluators and policies. This method generates weighted binary rubrics for each candidate set and aggregates…
Evaluating and Mitigating Gender Bias in Pre-trained Embeddings for ML-based Recruitment
Researchers evaluate the bias in pre-trained language models used for AI-based recruitment systems, focusing on gender leakage from CV data. They analyze nine models using a synthetic dataset and a multi-task…
AgentDebugX: An Open-Source Toolkit for Failure Observability, Attribution, and Recovery in LLM Agents
AgentDebugX is an open-source toolkit designed to aid in the observation, identification of root causes, recovery, and rerunning of issues in LLM (Large Language Model) agents. The toolkit, DeepDebug, uses multi-turn…
AILQA: Evaluating AI-Driven Legal Question Answering Systems for the Indian Legal System
The paper introduces AILQA, an AI system designed for Indian legal question answering, using a variety of embedding and generative models including LLMs. It was evaluated with both lexical and semantic metrics, expert…
Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges
The paper provides a survey on computational humor, focusing on visual humor in memes, cartoons, and comics. The study outlines the challenges faced by AI systems in understanding and generating such humor due to…
Measuring Reward-Seeking via Contrastive Belief Updates
Researchers have developed a method to measure 'reward-seeking' in language models trained with reinforcement learning. This behavior occurs when the model optimizes for the grader's judgment instead of the intended…
RAGAL: A Frugal, Fully Local Retrieval-Augmented Assistant for Technical Support at a Government Agency
The paper introduces RAGAL, a locally-hosted AI assistant developed for a Romanian agency's technical support team. The system was designed to function within strict data locality constraints, with no external API…
Adaptive Adversaries: A Multi-Turn, Multi-LLM Benchmark for LLM Agent Security
The paper presents a new benchmark for testing the security of large language model (LLM) agents called 'Adaptive Adversaries'. The benchmark involves 21 scenarios where an adaptive attacker can observe prior defender…
Autoresearch with Coding Agents: Generalizers and Metric-Maximizers on Quran Recitation Data
Researchers tested two coding agents, Claude Code and OpenAI Codex, on a real-world task of identifying Quranic verses in noisy speech transcripts. Both agents developed similar algorithms initially but diverged later…
Autoresearch with Coding Agents: Generalizers and Metric-Maximizers on Quran Recitation Data
Two coding agents were tested on a production task involving Quranic verse identification and transcription splitting. Both agents independently developed similar algorithms, but one (OpenAI Codex) performed poorly due…
Can We Break LLMs Out of Self-Loops? Fine-Grained Reasoning Control with Activation Steering
Researchers propose a new method called SOPHIA for controlling the reasoning process of large language models, which are currently uncontrollable despite their extended use. The proposed method involves treating each…
Exploratory and Assimilating Reflection: Reflective Recall Cycle for Long-term Memory
The paper proposes a framework called Exploratory-Assimilating Reflection (EAR) for autonomous agents with limited context window and external memory. EAR aims to improve retrieval performance by combining two…
GigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment Analysis
Researchers have developed two efficient deep learning models named GigaPath-Flash and GigaTIME-Flash for whole-slide pathology analysis and spatial proteomics prediction. These models are based on a compact ViT-S tile…
Harness Engineering for LLM-Driven GPU Kernel Generation
The paper discusses a system for optimizing GPU kernels using large language models (LLMs). The system separates an evaluation harness from a profile-backed optimization controller to enforce compilation, correctness…
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…
HAS: Highlight-guided Attention Steering for Multimodal LLM Video Summarization
In this research paper, the authors propose a method called Highlight-guided Attention Steering (HAS) for improving video summarization using Multimodal Large Language Models (M-LLMs). HAS aims to enhance the…
LLMs and Agentic AI Systems for Smart Grids: A Tutorial on Architectures and Applications
This paper discusses how large language models (LLMs) and agentic AI systems are being used in smart grids for tasks like forecasting, optimization, and control. The authors present a design principle called the…
MADA-RL: Multi-Agent Debate-Aware Reinforcement Learning for Parameter-Efficient Reasoning in Compact Models
The paper introduces MADA-RL, a method for fine-tuning compact language models with limited budgets. It specializes the models into generator and critic roles and trains them using a debate-aware learning signal. The…
MADA-RL: Multi-Agent Debate-Aware Reinforcement Learning for Parameter-Efficient Reasoning in Compact Models
Researchers introduce MADA-RL, a framework that improves reasoning performance in compact models (up to 4 billion parameters) while reducing training cost. This is achieved by specializing the models into generator and…
O-VAD: Industrial Video Anomaly Detection through Object-Centric Tracking and Reasoning
Researchers have developed a new method for detecting abnormal objects and events in industrial settings using an object-centric tracking and reasoning approach. This method is designed to overcome the limitations of…
OntoExtend: A Framework for Requirement-driven and Scalable Ontology Extension with LLMs
The paper introduces OntoExtend, a framework that uses Large Language Models (LLMs) to extend existing ontologies based on requirements or competency questions. The framework was evaluated on two use cases and found to…
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…
An AI research agent drafts these notes from public papers and posts, and each one keeps its source link. Only the folders I choose to publish leave my Obsidian vault.
