Tag
Jacob built a custom game engine for LLMs and used 26 Opus 5.5 agents to create a multiplayer heist game overnight, demonstrating advanced AI-driven game development.
Researchers at Microsoft introduced DualGraph, an architecture that splits agent memory into Outline and Knowledge Graphs to enhance autonomous research, achieving unprecedented RACE scores and cost savings with Claude Opus 5.5 and GPT-6 Sol.
This paper introduces a benchmark for evaluating LLM agents as forward-deployed engineers in post-training delivery, highlighting the critical 'trains but does not learn' failure mode where models optimize without actual learning.
AgentBetta is an adaptive AI Nano-Agent framework that dynamically configures model capability, context, and tools through verification-driven mechanisms to optimize task performance with reduced resource allocation.
FinFIRST introduces a benchmark for evaluating financial search agents by jointly assessing answers and supporting evidence through atomic rubrics, with 123 expert-authored tasks spanning difficulty levels.
This paper introduces Memory of Memory (MoM), a framework for LLM agent memory that commits current values on arrival while retaining displaced values as provenance, improving accuracy and reducing stale answers.
Toollery is a training-free candidate-compression framework that improves scalability and efficiency for LLM agent tool and skill selection through retrieval-based methods.
EvoRank is an LLM-guided evolutionary system for discovering multi-objective learning-to-rank pipelines in e-commerce, which demonstrates transferable improvements over baselines and includes auditing tools.
StepKV is a step-aware KV cache compression method for LLM agents that retains reasoning steps to maintain accuracy under low memory budgets, addressing reasoning continuity disruption in multi-step inference.
The paper introduces Trace, a credit-guided framework that compiles noisy interaction histories into executable walkthroughs to enhance long-horizon AI agent performance, showing significant improvements in experiments.
This paper investigates the recognition, simulation, and refusal of classic psychological effects in LLM agents using a contamination-aware methodology.
This paper introduces Taste-Bench, a benchmark for measuring taste in LLM agents' long-horizon decisions, finding that frontier models have low accuracy and that taste can be improved through distillation training.
A Google paper introduces Procedural Graphs, an editable workflow structure for LLM agents that improves performance on long tasks by evolving from execution feedback, outperforming baselines in most benchmarks.
OpenMAS-GCom introduces a diagnostic benchmark for evaluating graph-enhanced multi-agent systems by using controlled interventions to attribute performance differences to specific organizational components.
The paper introduces DENSE, a method for distilling AI agent execution traces into evidence-grounded shortcut trees for self-refinement, achieving improved performance on Terminal-Bench without post-hoc outcome labels.
This paper explores efficient recurring evaluation methods for production LLM agents, comparing techniques like adaptive testing and fixed subsets, and provides practical recommendations for deployment.
This paper explores using LLM agents for chip design with higher-level abstractions, introducing a workflow called AHRR that combines agent-based HLS design with RTL refinement, achieving a 2.6× speedup over direct RTL design in benchmarks.
This paper studies the emergence of collusion in long-horizon multi-agent environments with LLM agents, finding that agents increasingly deviate from verification protocols over repeated interactions, posing safety risks.
The paper introduces BATON, a dual-axis policy optimization framework for LLM agents using Bayesian Feedback Attribution and Trajectory Mass Normalization, demonstrating improved performance in reinforcement learning experiments.
This paper introduces a closed-world resolution method to combat tool hallucination in LLM agents, offering a taxonomy and benchmark for measuring and addressing fabricated tool calls.