Field notes
1,406 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.
1,406 notes and 240 topics.
- Question answered
- Source note
- Topic
- Shared notes
All notes, newest first
1,406 notes, page 58 of 59
Dynamic multi-agent deep reinforcement learning-based pricing and incentivization approach in multimodal transportation networks
A research paper proposes a multi-agent deep reinforcement learning framework to address challenges in multimodal transportation systems, particularly regarding shared mobility services (SMSs) demand concentration and…
EHR-Complex: Benchmarking Medical Agents for Complex Clinical Reasoning
Researchers have introduced a new benchmark called EHR-Complex to test the performance of clinical agents that interact with electronic health records (EHRs). The benchmark is built on a large dataset from MIMIC-IV and…
Exposing the Illusion of Erasure in Knowledge Editing for LLMs
This research paper explores the reliability and mechanisms of Knowledge Editing (KE) in Language Models (LLMs), revealing that edited knowledge is not always fully erased but can continue to surface even across…
GIF: Locally Sound Geometric Information Flow Control for LLMs
Researchers present a new framework called Geometric Information Flow (GIF) for tracking information flow from input tokens to outputs in large language models. This framework uses the Jacobian and local output…
GRINQH: Graded Input-based Quantization Hierarchy for Efficient LLM Generation
The paper presents a method called GRINQH (GRaded INput-based Quantization Hierarchy) to accelerate the decoding process of language models during inference, particularly beneficial for edge-computing settings. The…
HOLMES: Evaluating Higher-Order Logical Reasoning in LLMs
The paper introduces HOLMES, a new benchmark for evaluating higher-order symbolic reasoning in Large Language Models (LLMs). This benchmark is designed to test the models' ability to reason over rules, predicates…
HyperQuant: A Rate-Distortion-Optimal Quantization Pipeline for Large Language and Diffusion Models
Researchers present HyperQuant, a quantization pipeline for large language and diffusion models, which outperforms other methods in terms of compression rate and quality across various experiments. The method combines…
Kamera: Unified Position-Invariant Multimodal KV Cache for Training-Free Reuse
The paper presents Kamera, a unified position-invariant multimodal KV cache that allows for training-free reuse of cached data in a way that avoids the need to recompute every look-back from scratch. The proposed…
Learning Process Rewards via Success Visitation Matching for Efficient RL
Researchers propose a method to make reinforcement learning (RL) more effective when dealing with sparse rewards, which are typically given only when a task is completed. The method incentivizes the RL-learned policy…
Litmus: Zero-Label, Code-Driven Metric Specification for Evaluating AI Systems
The paper presents Litmus, a system that creates evaluation metrics for AI pipelines by understanding the purpose of the code and asking questions about it. It was tested on three real AI pipelines and outperformed…
Measuring & Mitigating Over-Alignment for LLMs in Multilingual Criminal Law Courts
The authors present TF-RefusalBench, a multilingual benchmark for assessing over-alignment in legal language models used by the Swiss Federal Supreme Court. They find that over-alignment is complex and influenced by…
Memory Contagion: Cross-Temporal Propagation of Evaluator Bias via Agent Memory
The study identifies a new phenomenon called Memory Contagion, where biased evaluators' influence can affect the experiences of language model agents, causing their memories to become biased as well. This bias is…
MuPPET: A Benchmark for Contextual Privacy of LLM Assistants in Multi-Party Conversations
The researchers introduce a new benchmark called MuPPET to measure contextual privacy in multi-party conversations involving LLM agents handling sensitive personal data for multiple users. They found that models leak…
How Telcos Build Autonomous Networks with Agentic AI
NVIDIA describes how telecommunications companies are implementing agentic AI across network operations and customer workflows to build autonomous network management systems.
Open Problem: Is AdamW Effective Under Heavy-Tailed Noise?
The authors question if AdamW, a commonly used optimizer for training large language models, is effective under heavy-tailed noise as its theory only applies to finite-variance regimes. They mention that sign-based…
POTracker: Optimizing Large Language Models for Standard-Compliant Power Outage Report Generation
Researchers have developed POTracker, an optimized large language model (LLM) for generating power outage reports that follow strict formatting and structural rules required by energy-sector regulatory bodies in the…
PsyBridge: A Hybrid Intelligent Framework for Multi-Dimensional Mental Health Assessment and Decision Support
The study proposes a new framework called PsyBridge, designed to improve mental health assessment by integrating multiple dimensions, including clinically validated screening tools, cognitive evaluation, and…
ReasoningLens: Hierarchical Visualization and Diagnostic Auditing for Large Reasoning Models
The paper introduces ReasoningLens, an open-source framework designed to address the transparency issue in Large Reasoning Models by creating hierarchical visualizations and diagnostic auditing tools for complex…
RECALL: Recovery Experience Collection for Active Lifelong Learning in Vision-Language-Action Models
The paper proposes a new approach for training Vision-Language-Action (VLA) models through active, continual learning. This method aims to address the inefficiencies of traditional passive imitation learning by…
RS-Gen: A Multi-Stage Agentic Framework for Reasoning and Search-Augmented Image Generation
The paper introduces RS-Gen, a multi-stage image agent framework that aims to improve the performance of existing image generation and editing models by introducing a 'Questioning-and-Solving' closed-loop mechanism for…
Scaling Linear Mode Connectivity and Merging to Billion Parameter Pretrained Transformers
The authors propose a method for scaling Linear Mode Connectivity (LMC) to allow the merging of billion-parameter pretrained transformers, improving interpolation performance. The approach involves optimizing weight…
Semantic Browsing: Controllable Diversity for Image Generation
Researchers have developed a new approach for generating diverse images by controlling the diversity at the text level instead of relying on stochastic variation within the text-to-image model. This method allows users…
SPIRAL: Learning to Search and Aggregate
Researchers introduced SPIRAL, a framework for improving language model reasoning at test time. The framework uses three primitives - sequential reasoning within a trace, independently sampled parallel traces, and…
Teaching LLMs String Matching, Backtracking, and Error Recovery to Deduce Bases and Truth Tables for the Combinatorially Exploding Bit Manipulation Puzzles
The paper presents a new approach to solve the NVIDIA Nemotron Model Reasoning Challenge's Bit Manipulation Puzzles, where the goal is to discover hidden rules for transforming binary strings. The approach avoids…
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.
