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2,348 notes and 295 topics.
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TRAJDEBUG: Tracing Error Lifecycle to Identify Critical Failures in Long-Horizon Agent Trajectories
The paper proposes TrajDebug, a framework designed to trace errors in long-term AI agent trajectories. The framework addresses challenges in identifying individual errors in long trajectories and distinguishing…
Tytan: Interactive Neurosymbolic Construction of Analytic Semantic Schemas from Relational Data
The paper presents Tytan, a system that automatically constructs an analytic semantic schema from relational databases to improve data analysis tools. The system combines symbolic analysis and LLM-based semantic…
Tytan: Interactive Neurosymbolic Construction of Analytic Semantic Schemas from Relational Data
Researchers have developed a system called TYTAN that can automatically construct an analytic semantic schema from a relational database, helping to reduce the need for manual knowledge acquisition and potentially…
Tytan: Interactive Neurosymbolic Construction of Analytic Semantic Schemas from Relational Data
The paper presents TYTAN, a system that automatically generates an analytic semantic schema from a relational database and user-provided descriptions. It combines symbolic analysis with LLM-based semantic inference for…
Tytan: Interactive Neurosymbolic Construction of Analytic Semantic Schemas from Relational Data
TYTAN is a system that automatically constructs an analytic semantic schema from a relational database, with user-provided descriptions when available. It uses symbolic analysis and LLM-based semantic inference for…
What Current AI Benchmarks Leave Unmeasured: Modality, Search, Citations, and Implications (for Safety Evaluations)
The study audits the assumptions made in large language model (LLM) benchmark evaluations, focusing on one popular LLM - ChatGPT. The research compares two access modalities (chat UI and API), with and without web…
What Current AI Benchmarks Leave Unmeasured: Modality, Search, Citations, and Implications (for Safety Evaluations)
The research examines the limitations of current benchmarks used to evaluate large language models (LLMs) for safety, reliability, and deployment readiness. The study focuses on a popular LLM, ChatGPT, comparing its…
What Current AI Benchmarks Leave Unmeasured: Modality, Search, Citations, and Implications (for Safety Evaluations)
The study compares two access modalities of a large language model (LLM), ChatGPT's chat UI and OpenAI's API, with and without web search enabled. The research shows that the accuracy of responses varies between the…
What Current AI Benchmarks Leave Unmeasured: Modality, Search, Citations, and Implications (for Safety Evaluations)
The study examines the limitations of current AI benchmarks by focusing on factors such as modality (chat UI vs API), search capabilities, consistency of responses, citation grounding, and abstention behavior using…
A Chain Is Only as Strong as Its Weakest Link: A Scoping Review of System Integration Audits in AI
A study reviews 58 AI audits that focus on system integration, finding that while few measures address integration-specific risks and significant gaps remain in traditional audit expectations, integration can be…
A-SR: Self-Evolving Agentic LLMs for Symbolic Regression via Hierarchical Coordination
Researchers have developed a new framework called A-SR for symbolic regression. This framework uses a self-evolving agentic system that shifts control from expression edits to role-conditioned evidence views. It…
A-SR: Self-Evolving Agentic LLMs for Symbolic Regression via Hierarchical Coordination
A research paper introduces a new framework called A-SR for symbolic regression. A-SR is designed to discover closed-form equations from data and uses a self-evolving agentic system that coordinates formula discovery…
ABSeeker: Training Long-Horizon Search Agents via Answer-Backtracked Credit Assignment
The paper proposes a new method called Answer-Backtracked Credit Assignment (ABC) for training long-horizon search agents that make multiple sequential actions to reach an answer. ABC focuses on rewarding useful…
ABSeeker: Training Long-Horizon Search Agents via Answer-Backtracked Credit Assignment
Researchers have proposed a new method called Answer-Backtracked Credit Assignment (ABC) to train long-horizon search agents more effectively. This method converts sparse trajectory-level outcomes into dense step-level…
ABSeeker: Training Long-Horizon Search Agents via Answer-Backtracked Credit Assignment
Researchers propose a method called Answer-Backtracked Credit Assignment (ABC) to train long-horizon search agents more effectively. This method converts sparse trajectory-level outcomes into dense step-level…
Agentic Reinforcement Learning with Observation-Calibrated Self-Distillation
Researchers propose Observation-Calibrated Self-Distillation (OCSD) to address a confounding issue in On-Policy Self-Distillation (OPSD), a method used to train large language model agents. OCSD aims to attribute…
Agentic Reinforcement Learning with Observation-Calibrated Self-Distillation
Researchers propose a new method called Observation-Calibrated Self-Distillation (OCSD) to train large language models in reinforcement learning. OCSD addresses a confounding issue in previous methods by comparing two…
Argus: A General-Purpose Agentic Runtime for Long-Horizon Reasoning
The paper introduces Argus, a persistent and self-evolving runtime system designed for long-term reasoning. This system separates stable user intent from operational objectives and allows for the addition of various…
Argus: A General-Purpose Agentic Runtime for Long-Horizon Reasoning
The paper presents Argus, a runtime system designed for long-term reasoning by an agent. It allows the system to maintain its approach when evidence supports it and change direction based on measurements that indicate…
Argus: A General-Purpose Agentic Runtime for Long-Horizon Reasoning
The paper introduces Argus, a persistent, self-evolving runtime system designed for long-horizon reasoning. Argus consists of Manager, Planner, Engineer, and Reviewer that execute bounded missions over durable project…
ArtAnno: Annotating Implicit Semantics in Artworks through LLM Agent-Driven Bidirectional Human-AI Augmentation
Researchers have developed a new system called ArtAnno to help with annotating artworks more efficiently. The system uses a bidirectional Human-AI Augmentation (BiHAA) framework, which allows both the AI and human…
ArtAnno: Annotating Implicit Semantics in Artworks through LLM Agent-Driven Bidirectional Human-AI Augmentation
The paper presents ArtAnno, a system that uses a bidirectional human-AI augmentation (BiHAA) framework to improve the efficiency of artwork annotation. This system helps experts by suggesting semantic labels and learns…
Chained Recursive Language Models for Multi-Iteration Reasoning
The paper proposes a new architecture for large language models called Chained Recursive Language Models (Chained RLM). This architecture is designed to handle long context reasoning tasks that require extraction…
Chained Recursive Language Models for Multi-Iteration Reasoning
The paper proposes Chained Recursive Language Models (Chained RLM), an architecture for multi-iteration reasoning in large language models (LLMs). It addresses the difficulty in tasks requiring extraction, counting…
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