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Notes on artificial intelligence
262 notes, page 9 of 11
Neuron-Aware Active Few-Shot Learning for LLMs
The research presents NeuFS, a new approach for Active Few-Shot Learning (AFSL) that focuses on using a model's internal dynamics to identify valuable unlabeled samples for annotation. This method is designed to reduce…
Neuron-Aware Data Selection for Annotation-Free LLM Self-Distillation
The paper proposes a new method called Neuron On-Policy Self-Distillation (Neuron-OPSD) for annotation-free self-distillation of large language models. This method uses the internal neuron activations to guide data…
Reasoning effort, not tool access, buys first-try reliability in agentic code generation: an observational study
A study found that the level of reasoning effort in agentic code generation, rather than the availability of additional capabilities like testing tools and design prompts, significantly affects the reliability of…
Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas
Researchers present two contributions aimed at improving speaker recognition in long-form TV dramas. They create a large-scale benchmark called DramaSR-532K with over 900 characters and propose using a large reasoning…
Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas
The paper introduces a new benchmark, DramaSR-532K, for speaker recognition in long-form TV dramas. They also propose a new approach called DramaSR-LRM which uses a large reasoning model (LRM) to aggregate contextual…
Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas
The researchers introduce a large-scale benchmark called DramaSR-532K for speaker recognition in long-form TV dramas and propose a new method, DramaSR-LRM, built on a large reasoning model (LRM). This method aggregates…
ReContext: Recursive Evidence Replay as LLM Harness for Long-Context Reasoning
Researchers propose a new method called Recursive Evidence Replay (RECONTEXT) to improve long-context reasoning in large language models without requiring additional training, memory, or context pruning. RECONTEXT…
TestEvo-Bench: An Executable and Live Benchmark for Test and Code Co-Evolution
The paper introduces TestEvo-Bench, a new benchmark for testing software that evolves with its code. This benchmark is designed to evaluate how well a test automation agent understands the impact of a code change on…
Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments
Researchers propose a new framework, SPG-Layout, to generate physically plausible indoor scenes in complex non-Manhattan environments. The framework uses statistical priors of object distributions and a hierarchical…
What Types of Human-AI Teams Exist?
The authors analyze 53 papers about human-AI teams and categorize them into five main clusters based on psychological taxonomies of teaming.
World Wide Models: Literary Tools for Cultural AI
The article discusses the use of literary tools for developing culturally literate Artificial Intelligence (AI) in light of the monolingual nature of Large Language Models (LLMs).
Are Performance-Optimization Benchmarks Reliably Measuring Coding Agents?
The paper evaluates three repository-level performance optimization benchmarks (GSO, SWE-Perf, and SWE-efficiency) used to measure coding agents. The authors find issues with the stability of runtime for these…
DART-VLN: Test-Time Memory Decay and Anti-Loop Regularization for Discrete Vision-Language Navigation
The researchers present DART-VLN, a test-time control framework for vision-language navigation agents that aims to improve their performance by addressing two common issues: stale historical evidence at memory readout…
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…
MemSyco-Bench: Benchmarking Sycophancy in Agent Memory
The authors propose MemSyco-Bench, a new benchmark to evaluate how memory affects decision-making in language models (LLMs) used in AI agents. The benchmark aims to assess if the agents can handle issues of sycophancy…
SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Multi-Faceted Bug Reproduction Tests
Researchers propose a new software agent called SWE-Doctor that uses multiple bug reproduction tests (BRTs) to generate more effective patches for resolving software issues. The agent first generates multi-faceted…
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
The paper presents ProtoPilot, a self-evolving multi-agent system designed to automate the generation and execution of biological protocols. The system achieved high success rates in various tasks compared to…
An Agentic AI Framework to Accelerate Scientific Discovery in Plant Phenotyping
The text discusses an AI framework designed to expedite the discovery process in plant phenotyping at Oak Ridge National Laboratory's Advanced Plant Phenotyping Laboratory (APPL). The framework aims to automate the…
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…
FARS: A Fully Automated Research System Deployed at Scale
The paper introduces FARS, a fully automated AI research system that can generate, execute, and write complete research papers on various AI/ML topics. In its first public deployment, FARS produced 166 papers while…
MECoBench: A Systematic Study of Multimodal Agent Collaboration in Embodied Environments
The research paper introduces MECoBench, a benchmark for evaluating the collaboration abilities of multimodal agents in visually grounded environments. The study explores three key findings: collaboration generally…
TreeAgent: A Generalizable Multi-Agent Framework for Automated Bias Labeling in Forestry via Compiled Expert Rules and Vision-Language Models
Researchers have proposed a multi-agent system that uses expert decision trees and Vision-Language Models (VLMs) to automate the bias classification in tree height in forestry remote sensing. The system mitigates VLM…
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