AI Research

AI Agents

MARS: A New Modular Agent Architecture for AI Research Automation

New research introduces MARS, a modular agent with reflective search capabilities designed to automate AI research tasks through intelligent decomposition and self-correction.

LLM Interpretability

LLM Self-Explanations Can Predict Model Behavior, Study Finds

New research presents evidence that LLM self-explanations can help predict model behavior, offering a positive case for faithfulness in AI interpretability.

LLM Evaluation

PeerRank: A New Framework for Autonomous LLM Evaluation

New research proposes PeerRank, a system where LLMs evaluate each other through web-grounded peer review with built-in bias controls, potentially transforming how we benchmark AI models.

LLM Safety

How Persuasion Spreads Through Networks of AI Agents

New research examines how persuasive content propagates through multi-agent LLM systems, revealing critical insights for AI safety and synthetic influence detection.

AI Research

HumanStudy-Bench: Benchmarking AI Agents as Research Participants

New benchmark evaluates how well AI agents can simulate human research participants, raising important questions about synthetic behavior, authenticity detection, and the future of AI-human interaction studies.

LLM Evaluation

Representation-as-a-Judge: Small Models Beat LLMs at Evaluation

New research reveals smaller language models can outperform large LLMs at evaluation tasks through semantic capacity asymmetry, challenging the dominant LLM-as-a-Judge paradigm.

LLM Reasoning

Why Advanced AI Models Still Fail at Simple Logic Puzzles

New research reveals that even frontier AI models like GPT-4 and Claude struggle with basic reasoning puzzles, exposing fundamental limitations in how large language models process logic.

LLM

Stingy Context: 18:1 Code Compression for Efficient LLM Coding

New hierarchical compression method achieves 18:1 ratio for code context, dramatically expanding what LLMs can process during automated coding tasks while maintaining semantic understanding.

LLM Agents

Counterfactual Intent Generation Improves LLM Agent Control

New research introduces a counterfactual generation framework that helps LLM-based autonomous systems reason about alternative intents, improving decision-making reliability in control applications.

multi-agent systems

Insight Agents: Multi-Agent LLM System Automates Data Analysis

New research introduces Insight Agents, an LLM-powered multi-agent framework that automates complex data analysis workflows through specialized agent collaboration.

LLM Inference

DART Brings Diffusion Concepts to Accelerate LLM Inference

New research introduces DART, a speculative decoding method that borrows denoising concepts from diffusion models to dramatically accelerate large language model inference without sacrificing output quality.

LLM Agents

Cross-Domain RL Training: Reducing the Generalization Tax for LLM

New research explores how reinforcement learning training affects LLM agent generalization across domains, introducing the concept of 'generalization tax' and strategies to minimize performance degradation.