Jitskit is an iterative LLM-based synthesis pipeline that generates key-value stores matching spec cards for YCSB workloads, resources, and properties, outperforming SOTA baselines on all 18 tested cases by up to 4.6x.
Context as a tool: Context management for long-horizon swe-agents.arXiv preprint arXiv:2512.22087
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MemDocAgent generates consistent hierarchical repository-level code documentation by combining dependency-aware traversal with memory-guided agent interactions that accumulate work traces.
LEAD lets LLMs solve checkers jumping puzzles up to size 13 by using lookahead to recover from irreversible errors on hard steps that break extreme decomposition.
VISTA supplies LLM agents with a visible proprioceptive dashboard of typed context blocks, enabling untrained self-management that lifts performance on long-horizon tool-use benchmarks across multiple model scales.
SAM is a standalone memory framework for long-horizon LLM agents that creates state-adaptive cues from interactions, preserves raw trajectories for intent-driven recall, and optimizes the module via expert supervision and RL, outperforming baselines on BrowseComp and related benchmarks.
GenericAgent outperforms other LLM agents on long-horizon tasks by maximizing context information density with fewer tokens via minimal tools, on-demand memory, trajectory-to-SOP evolution, and compression.
SWE-MeM introduces adaptive memory management for coding agents via synthesized trajectories and Memory-aware GRPO, reporting 43.4% and 60.2% resolve rates on SWE-Bench Verified for 4B and 30B models while beating baselines on performance and token use.
Longer action horizons bottleneck LLM agent training through instability, but training with reduced horizons stabilizes learning and enables better generalization to longer horizons.
Survey framing LLM agents as model-plus-harness systems, decomposing harness responsibilities, mapping them to tasks, and highlighting open challenges in evaluation, safety, and co-evolution.
On a hotel expense benchmark, pruning LLM agent context to the last 5 tool pairs plus summarization raises completion to 91.6% and cuts tokens by ~63% compared with retaining full conversation history.
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From Question Answering to Task Completion: A Survey on Agent System and Harness Design
Survey framing LLM agents as model-plus-harness systems, decomposing harness responsibilities, mapping them to tasks, and highlighting open challenges in evaluation, safety, and co-evolution.