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Notes on artificial intelligence
262 notes, page 8 of 11
Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26
The researchers present Reward-Adaptive Iterative Discovery (RAID), a new approach for automated game testing in the gaming industry, specifically focusing on the ice hockey game EA SPORTS NHL 26. The goal is to reduce…
RLVP: Penalize the Path, Reward the Outcome
The paper 'RLVP: Penalize the Path, Reward the Outcome' by Bojie Li and Noah Shi proposes a method for training artificial agents to perform tasks effectively in real-world environments. This method addresses two main…
Data Analysis in the Wild: Benchmarking Large Language Models Against Real-World Data Complexities
The paper introduces DataGovBench, a benchmark for evaluating the performance of Large Language Models (LLMs) in practical scenarios, such as complex data analysis and insight discovery from large multi-tabular…
DepthWeave-KV: Token-Adaptive Cross-Layer Residual Factorization for Long-Context KV Cache Compression
The research introduces DepthWeave-KV, a new cache compression method for long-context language models. This method adapts to the needs of different tokens by factorizing key and value states across neighboring…
EcoVision: AI-Powered Drone Imaging for Salt Marsh Vegetation Monitoring and Dominance Mapping
A new AI-powered drone imaging system called EcoVision has been developed for monitoring and mapping the dominance of two types of halophytic grasses in salt marshes. The system uses high-resolution RGB images from…
Harnessing Code Agents for Automatic Software Verification
The authors propose a new method for automatic software verification using large language models (LLMs) in the form of 'code agents'. Instead of wireting a fixed proof strategy, they allow the code agent to choose its…
Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents
The paper presents Information Gain-based Rollout Policy Optimization (IGRPO), a new framework for improving large language model agents in long-horizon search tasks. IGRPO aims to allocate rollout budget more…
RSF-GLLM: Bridging the Semantic Gap in Multi-Hop Knowledge Graph QA via Recurrent Soft-Flow and Decoupled LLM Generation
The authors propose a framework called RSF-GLLM to improve Multi-hop Question Answering over Knowledge Graphs. The framework decouples differentiable graph reasoning from answer generation, allowing the system to learn…
RuBench: A Repository-Level Agentic Coding Benchmark with Natively Authored Russian Task Specifications
The author introduces RuBench 1.0, a benchmark for evaluating coding agents that use native language tasks instead of English. The benchmark includes 25 tasks mined from five open-source repositories and is judged by…
VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection
A study introduces VendorBench-100, a cross-paradigm benchmark for deepfake image detection that evaluates different models using a common protocol. The benchmark includes a diverse set of edge cases and uses the…
When do prophets profit in prediction markets?
Researchers propose a betting strategy that ensures profit when a forecaster's predictions outperform market prices under certain conditions. The strategy is shown to be the only one with such a robust profitability…
Agent Data Injection Attacks are Realistic Threats to AI Agents
A new type of attack called Agent Data Injection (ADI) has been introduced, which injects malicious data disguised as trusted data to make AI agents perform unintended actions. This type of attack can bypass existing…
AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments
The paper presents AgentGym2, a new evaluation framework designed to test language models in realistic, complex scenarios that mimic real-world deployments. It highlights that most existing benchmarks do not fully…
Multiplayer Interactive World Models with Representation Autoencoders
Researchers present a new model for multiplayer interactive worlds, specifically designed for complex environments like Rocket League with fast-paced, coupled dynamics. The model is trained on 10,000 hours of gameplay…
Open Problems in AI Incident Governance
The paper discusses issues with current AI incident governance systems, specifically highlighting inconsistencies in definitions, classification, monitoring, and reporting across various frameworks.
Toward Trustworthy Large Language Model Agents in Healthcare
A study presents CareConnect, a safety-first conversational agent for automating healthcare appointment scheduling using large language models (LLMs), retrieval-augmented generation (RAG), and safety guardrails. The…
TREK: Distill to Explore, Reinforce to Refine
TREK is a procedure that uses distillation for exploration support expansion to improve policy optimization on tasks where the current policy stalls on hard prompts. It identifies low-performing prompts, generates…
Automated grading of Linux/bash examinations using large language models: a four-level cognitive taxonomy approach
The paper evaluates whether four large language models can approximate the judgement of experts when grading Linux/bash command responses from second-year Computer Engineering students. The study uses a four-level…
Criticality-Based Guard Rail Validation for AI Agent Decisions in Autonomous Telecom Networks
The paper proposes a Guard Rail Validation (GRV) framework to ensure autonomous decisions made by AI agents in fully autonomous telecom networks are valid before they cause live network changes. The framework evaluates…
Distributed Attacks in Persistent-State AI Control
Researchers introduce Iterative VibeCoding, a setting for AI control that studies the deployment of autonomous AI coding agents and their potential for distributed attacks. The study finds that gradual attacks, which…
Efficient Waste Sorting for Circular Economy: A Confidence-guided comparison between One-Vs-All and One-Vs-Rest Classification Strategies with Human-in-the-Loop for Automated Waste Sorting
This research evaluates and compares two waste sorting classification strategies, One-Vs-All (OvA) and One-Vs-Rest (OvR), for automated waste sorting in the city of Goslar, Germany. The study considers the performance…
Efficient Waste Sorting for Circular Economy: A Confidence-guided comparison between One-Vs-All and One-Vs-Rest Classification Strategies with Human-in-the-Loop for Automated Waste Sorting
Researchers evaluated and analyzed two waste sorting algorithms, OvA and OvR, using a dataset based on the waste categories and sorting scheme of Goslar, Germany. The goal is to minimize misclassifications while…
Grounded autonomous research: a fault-tolerant LLM pipeline from corpus to manuscript in frontier computational physics
The paper presents an autonomous research pipeline that can conduct end-to-end research from a corpus of 11,083 recent condensed matter physics papers to a publication-grade manuscript. The agent is capable of…
Learning to Move Before Learning to Do: Task-Agnostic pretraining for VLAs
Researchers propose a new framework called Task-Agnostic Pretraining (TAP) for Vision-Language-Action (VLA) models. The TAP aims to address the scarcity of expert demonstrations by separating two learning objectives…
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