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Public story · 2026-03-15 · source-backed
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
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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...
Counterintuitive finding: 36.4% of multi-turn prompts are fully self-contained. Removing prior assistant responses yields up to 10x context length reduction with minimal quality loss. Models over-condition on previous responses, introducing errors. Directly actionable for mult...
arXiv 2607.22807 controlled for problem difficulty across Python, Java, Rust and OCaml and found stark, model-consistent variation in tokens burned per task. The behavioral explanations are the useful part: agents produce non-compiling solutions more often in unfamiliar langua...
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.
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 2603.17368 proposes evaluating safety policy before chain-of-thought generation rather than after. Models that reason first and apply safety second can be manipulated through the reasoning trace itself. Reordering substantially improves alignment without degrading benchm...
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