ACC: Revolutionizing Long-Context Reasoning for AI Agents

Artificial intelligence agents are evolving, and with them, the need for long-context reasoning capacity in large language models (LLMs). However, training LLMs for this kind of reasoning isn't just a walk in the park. It demands expensive long-document curation or heuristic context synthesis. Enter the Agent Context Compilation (ACC), a novel approach that could change the game.

The ACC Breakthrough

ACC looks at the massive amounts of data agents produce when they solve problems. Think of these agents as digital detectives, gathering clues, invoking tools, and receiving observations across various turns. Traditionally, these valuable pieces of evidence remain scattered and underutilized. Standard agent training masks these tool responses and focuses only on turn-level selection, creating a gaping blind spot.

What ACC does is convert these scattered trajectories into long-context question-answer (QA) pairs. These pairs merge the original question with tool responses and environment observations collected over multiple turns. In essence, ACC trains the model to answer questions without relying on tools, making dependencies between questions and evidence crystal clear.

Impressive Results

ACC isn't just another theoretical approach. It has been validated on challenging tasks like MRCR and GraphWalks. These benchmarks demand cross-turn coreference resolution and graph traversal over extended contexts. The results speak volumes. Training Qwen3-30B-A3B with ACC saw a jump to 68.3 on MRCR (a whopping 18.1-point increase) and 77.5 on GraphWalks (up by 7.6 points). These scores are comparable to much larger models like Qwen3-235B-A22B.

What's more, ACC doesn't compromise on the model's general capabilities. It holds its ground on tasks such as GPQA, MMLU-Pro, AIME, and IFEval. If nobody would play it without the model, the model won't save it, right? But here, ACC shows that it can hold its own against its larger counterparts.

Why ACC Matters

In a world where AI is inching closer to emulating human-like reasoning, ACC offers a scalable way to train LLMs on long-context reasoning. It doesn't just patch the supervision blind spot. It obliterates it, offering a clearer path to understanding and integrating scattered evidence.

But here's the kicker: ACC's approach is simple, yet it's making waves. Why should you care? Because if ACC's approach proves scalable, it could reshape how we train AI agents, not just efficiency but in effective comprehension of complex, long-context questions. Retention curves don't lie. If ACC can maintain or even boost retention and performance, it's worth keeping an eye on.

ACC's potential to restructure attention and specialize tasks within AI models might just be the push we need towards more comprehensive and adaptable AI systems. So, the question remains: Is your AI ready for the ACC revolution?