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Reasoning Consistency Scanning: A Framework for Auditing Chain-of-Thought Validity in AI Safety Evaluations
The paper introduces a method called Reasoning Consistency Scanning to assess the logical consistency between stated reasoning and answers in AI safety evaluation transcripts. The method can be applied to various AI…
Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26
The authors present Reward-Adaptive Iterative Discovery (RAID), a new approach for automated game testing using Reinforcement Learning (RL) in the gaming industry. The case study is based on EA SPORTS NHL 26's…
Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning
The paper proposes a new method called Single-rollout Asynchronous Optimization (SAO) for Reinforcement Learning (RL) in post-training large language models (LLMs). SAO aims to improve stability and task effectiveness…
SkillCenter: A Large-Scale Source-Grounded Skill Library for Autonomous AI Agents
Researchers have created SkillCenter, a large-scale library of structured skills for autonomous AI agents. The library contains over 216,000 skills across 24 domains, obtained from various sources like peer-reviewed…
SkillCenter: A Large-Scale Source-Grounded Skill Library for Autonomous AI Agents
The paper introduces SkillCenter, a large-scale skill library for autonomous AI agents, containing 216,938 structured skills across 24 domain bundles. The skills are obtained from various sources like peer-reviewed…
An Experimental Design Approach to Evaluating Agentic AI's Autonomous Model Discovery
The researchers propose an experimental design framework to systematically evaluate the autonomous model discovery behavior of large language model coding agents, like Codex and Claude Code. The framework considers…
Bridging Physical Reasoning and Task Generalization via Visual Action Outcome Reasoning Alignment
Researchers propose a new reward system called VAORA to improve the performance of vision-language models in interactive physical reasoning tasks. The proposed system aims to address two main issues: hallucinated…
Data Analysis in the Wild: Benchmarking Large Language Models Against Real-World Data Complexities
The researchers introduce DataGovBench, a new benchmark for evaluating large language models (LLMs) in practical scenarios of real-world data analysis. The benchmark tests the models' ability to answer complex…
Demonstrating TOFFEE: A Learned System for Synthesizing Data Agent Trajectories at Scale
Researchers present TOFFEE, a system that generates high-quality data agent trajectories using Monte Carlo Tree Search with adaptive model selection and cross-task prefix reuse. This system aims to help data agents…
Demonstrating TOFFEE: A Learned System for Synthesizing Data Agent Trajectories at Scale
The paper introduces TOFFEE, a system designed to generate high-quality data agent trajectories using Monte Carlo Tree Search (MCTS) with adaptive model selection and cross-task prefix reuse. This is aimed at…
Finding H. pylori in the Fine Print: Evidence-Linked Multi-Agent Case Finding from Gastric Biopsy Reports
Researchers evaluated a system called nMAS for extracting information about Helicobacter pylori infections from gastric biopsy reports. The system correctly identified the presence of H. pylori and associated…
Finding H. pylori in the Fine Print: Evidence-Linked Multi-Agent Case Finding from Gastric Biopsy Reports
Researchers evaluated a system called nMAS that can automatically extract information about Helicobacter pylori infection from gastric biopsy reports. The system had an accuracy of 98.61% in classifying four binary…
Rethinking Indic AI from a Lens of Cultural Heritage Preservation
The paper 'Rethinking Indic AI from a Lens of Cultural Heritage Preservation' discusses how Artificial Intelligence (AI) impacts India's linguistic and cultural foundations, focusing on the challenges and opportunities…
Task Decomposition-Guided Reranking for Adaptive Agent Skill Retrieval
The authors propose a method called SkillReranker to improve the ability of agent systems to complete complex tasks by accurately selecting skills from large libraries. The method involves semantic decomposition of…
TopoBrick: Agentic Topology Sampling of Exogenous Variables for Zero-Shot Building IoT Forecasting
The paper introduces TopoBrick, a new framework for forecasting IoT data in buildings without requiring training. It uses building knowledge graphs to create a structural skeleton and employs an agentic topology…
UI2App: Benchmarking Visual Interaction Inference in Executable Web Application Generation
Researchers have introduced UI2App, a new benchmark focused on evaluating the ability of large language models to recover application behavior from screenshots alone, without any textual or behavioral guidance. The…
Agent Data Injection Attacks are Realistic Threats to AI Agents
The research paper introduces a new type of attack called Agent Data Injection (ADI), which can manipulate AI agents by injecting malicious data disguised as trusted data. This attack can cause agents to execute…
AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments
The paper presents AgentGym2, a new evaluation framework for language models (LLMs) that are increasingly being used in real-world production environments. Most existing benchmarks evaluate these models in simplified…
ClassicLogic: A Knowledge-Driven Benchmark of Classic Puzzle Games for Evaluating Compositional Generalization
The paper introduces ClassicLogic, a new benchmark suite designed to evaluate an agent's ability to learn and compose problem-solving strategies in four classic logic puzzles: Sudoku, KenKen, Kakuro, and Futoshiki. The…
DSpark: Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generation
DSpark is a new speculative decoding framework for Large Language Models (LLMs) that aims to improve inference speed and maintain draft quality. It does this by using a semi-autoregressive architecture, which combines…
Evaluating and Understanding Model Editing for Medical Vision Language Models
A new clinical benchmark called M3Bench has been introduced to evaluate the effectiveness of model editing in medical vision-language models (VLMs). This benchmark tests whether an edit remains reliable, precise, and…
EvoAgentBench: Benchmarking Agent Self-Evolution via Ability Transfer
Researchers introduce EvoAgentBench, a new benchmark for evaluating agent self-evolution in long-term LLM systems. The benchmark focuses on procedural reuse of experience, which includes searching, debugging, and…
LLM-as-a-Verifier: A General-Purpose Verification Framework
The paper introduces LLM-as-a-Verifier, a general-purpose verification framework that provides fine-grained feedback to improve the accuracy of agentic tasks. The framework scales across multiple dimensions for better…
LLM-as-a-Verifier: A General-Purpose Verification Framework
Researchers introduce a new framework called LLM-as-a-Verifier to improve the ability of Large Language Models (LLMs) to verify the correctness of solutions for various tasks. The method provides fine-grained feedback…
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