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Research2026-05-11 · source-backed
This paper tackles the core problem of training command-line agents: long horizons with sparse, delayed rewards when the agent can only partially observe filesystem state. Directly applicable to building autonomous coding assistants and DevOps agents.
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Introduces temporal causal diagnostics to distinguish legitimate task execution from injected manipulation in multi-turn agent interactions, plus context purification to neutralize poisoned content. Directly applicable to anyone building agents that call external tools. arXiv...
Two specialized RL agents — Memory Manager (ADD/UPDATE/DELETE operations) and Answer Agent — fine-tuned with PPO and GRPO. With only 152 training QA pairs, outperforms baselines across three benchmarks. Directly applicable to persistent agent memory systems. arXiv 2508.19828
Addresses the fundamental privacy dilemma: cloud models need data access but enterprises can't share sensitive information. Splits execution between enterprise-side privacy agents and cloud-side capability agents. Directly relevant to AWS AgentCore and enterprise adoption. arX...
arXiv 2603.15566 proposes treating commit history as machine-readable knowledge substrate for coding agents. As AI-written commits lack semantic trails, institutional knowledge loss compounds run-over-run. Directly actionable: structured commits become persistent agent memory...
Models multi-hop agent reasoning as a dynamic search tree, transitioning from exploration to exploitation as token budget depletes. Includes formal convergence proof. Directly applicable to any agent loop burning tokens on redundant calls. arXiv 2603.12634
Three-component infrastructure for maintaining agent coherence in a 108K-line C# codebase: hot-memory constitution, 19 specialized domain-expert agents, and cold-memory knowledge base. Quantitative metrics from 283 sessions. Directly actionable for multi-agent coding workflows...
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