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arXiv:2607. 20064v1 Announce Type: new Abstract: Long-horizon tasks require sustained perception, reasoning, and exploration, and are a persistent challenge for large language model (LLM) agents.
ContextPipe is a database-inspired framework for assembling context in long-horizon large language model agents. It treats context assembly like relational query execution, using a five-phase pipeline—Plan, Bind, Optimize, Execute, Feedback—backed by a structured catalog, deterministic cache-aware optimizer, and EXPLAIN ANALYZE tracing. In a preliminary evaluation on the SWE-bench Pro Qutebrowser subset, ContextPipe reduced token volume by 31%, LLM calls by 23%, and response time by 9% compared to an append-only policy, though it lowered KV cache-hit ratio.
arXiv:2605. 18421v2 Announce Type: replace-cross Abstract: Recent benchmarks for Large Language Model (LLM) agents mainly evaluate reasoning, planning, and execution.
arXiv:2609.08318v1 Announce Type: cross Abstract: The transition from human-centric assistance to Autonomous Software Engineering (ASE) agents has enabled the resolution of complex real-world SE task...
arXiv:2609.40118v1 Announce Type: new Abstract: As LLM capabilities advance, agents are tackling increasingly complex tasks over longer horizons. Their growing interaction histories make memory compa...
The paper introduces Gated-Memory Routing, a method for efficient collaboration in multi‑agent large language model systems. It uses a learned execution memory with write and retrieval gates to keep only non‑redundant reasoning steps, and an adaptive halting controller to stop execution when enough evidence is gathered. Experiments on five reasoning and code‑generation benchmarks show the approach achieves higher accuracy and reduces inference cost by 31.9% compared to the strongest baseline.