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CORVUS proposes a new trajectory architecture for LLM coding agents that decouples file-read actions from observations by maintaining a synchronized registry of relevant files, reducing input tokens by 9-50% and reasoning cycles by up to 37% while maintaining comparable pass rates on SWE-bench benchmarks.
SigMap is an open-source grounding layer for AI coding agents that provides a deterministic map of code repositories, reducing wasted context and improving retrieval accuracy and task success rates.
NightFeats is a multi-agent RAG system that won Best Dynamic Evaluation at the MMU-RAGent competition at NeurIPS 2025. It uses a three-phase pipeline (retrieval, curation, composition) and outperforms proprietary baselines on human evaluations.
RAMPART is a Python library that makes LLM context assembly programmable, allowing developers to register named blocks of context for placement before the model's first token. It improves performance by tens of percentage points on various models through block clustering and tool access control.
The article analyzes a 2026 paper by Bai et al. showing that subagents and context bloat cause token costs in long agent runs to be ~1000x higher than chat, and presents three practical fixes (PLAN.md, read budget, out-of-band notes) that reduce token usage by 70-90%.
Fast-Slow Training (FST) interleaves context optimization (via GEPA) with model weight updates via RL, achieving 3× sample efficiency over RL alone on math, code, and physics reasoning while preserving plasticity and enabling continual learning.
This paper introduces a context optimization method that uses active information seeking via Wikipedia search and browser tools, combined with a search-based training procedure, to achieve robust performance improvements across diverse domains without updating model weights.