All Articles
17528 articles total
From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents
EvoSOP is a framework that allows LLM agents to self-evolve by synthesizing atomic actions into reusable Standard Operating Procedures (SOPs) to reduce reasoning overhead.
Physics-Audited Agentic Discovery in Scientific Machine Learning
PA-SciML introduces a verification-first workflow for agentic scientific machine learning to ensure discovered surrogate models satisfy fundamental physics requirements.
MIRA-Math: A Benchmark for Minimal Information Requesting and Mathematical Reasoning
MIRA-Math is a benchmark designed to evaluate an AI's ability to identify and request specifically missing atomic facts needed to solve mathematical problems.
Agentic Data Environments
A proposal for 'Agentic Data Environments' reframes data systems from passive stores into active substrates that can amplify agent capabilities while enforcing safety guarantees.
Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode in Tool-Using LLM Agents
Researchers found that tool-using agents often suffer from 'silent policy-violation failures' and propose using deterministic pre-execution gates to prevent these errors.
Ergo: Long Form Philosophy Lectures
A Hacker News thread discussing long-form philosophy lectures.
AgentLens: Production-Assessed Trajectory Reviews for Coding Agent Evaluation
Introduction of AgentLens, an open-source benchmark for evaluating the full trajectory of interactive coding agents beyond simple pass/fail metrics.
When Does In-Context Search Help? A Sampling-Complexity Theory of Reflection-Driven Reasoning
Theoretical analysis of in-context search in LLMs, demonstrating that reflection-driven reasoning can provide exponential improvements in success probability when mistakes are localized.
LLM-powered reasoning in agent-based modeling
Introduction of HALE, a framework that combines Large Language Models with Agent-based Modeling to better predict human decision-making in epidemic simulations.
QANTIS: Hardware-Calibrated Sequential POMDP Belief Updates on IBM Heron
A hardware case study using IBM Heron quantum processors as a belief-update service for sequential POMDPs, focusing on stability and calibration.
Cost-Effective Agent Harnesses for Abstract Reasoning and Generalization on ARC-AGI-1
A study on cost-effective agent harnesses for the ARC-AGI-1 benchmark, achieving high pass rates using an open-weight model without benchmark-specific fine-tuning.
Evaluating SageMath-Augmented LLM Agents for Computational and Experimental Mathematics
Research evaluating the integration of SageMath into LLM agent workflows to improve performance in computational and experimental mathematics.
The Harness Effect: How Orchestration Design Sets the Token Economics of Enterprise Agentic AI
Analysis of how orchestration design ('harnesses') can significantly reduce token consumption and cost in enterprise agentic AI without sacrificing quality.
Grounding Spatial Relations in a Compact World Model: Instruction Leakage and a Goal-Free Dynamics Fix
Identification of 'instruction leakage' in goal-conditioned world models, where models transcribe instructions rather than perceiving relations, and a proposed fix to decouple goals from dynamics.
Large Behavior Model: A Promptable Digital Twin of the Retail Customer
Presentation of a Large Behavior Model (LBM) that learns customer decision-making from retail transaction data to create promptable digital twins of customers.
Benchmarking coding agents on Databricks' multi-million line codebase
Databricks provides a benchmark analysis of how coding agents perform when operating on a massive, multi-million line corporate codebase.
Cargo-nextest: 3x faster than cargo test, per-test isolation, first-class CI
Cargo-nextest is presented as a high-performance Rust test runner that offers significantly faster execution and better isolation than the standard cargo test.
EVA-Net: Subject-Independent EEG Motor Decoding with Video-Derived Motor Priors
EVA-Net is a new framework for EEG motor decoding that uses action videos as semantic priors to improve cross-subject generalization in BCIs.
What Type of Inference is Active Inference?
This paper provides a formal variational characterization of Expected Free Energy (EFE) based planning in active inference, clarifying the role of epistemic and planning corrections.
Reducing the Complexity of Deep Learning Models for EEG Analysis on Wearable Devices
Research on deploying deep learning models for EEG analysis on wearable devices, focusing on the trade-offs between parameter quantization and accuracy for seizure detection.