Chained Recursive Language Models for Multi-Iteration Reasoning

2026-08-05 · 17:50 UTC ·Purbesh Mitra, Sennur Ulukus ·1 min read

Brief

Chained Recursive Language Models (Chained RLM) target long-context tasks (extraction, counting, ordering, multi-hop reasoning) by decomposing inference into staged fresh-root calls of the same LLM. Each stage receives the original input plus a concise summary, a blackboard, and durable artifacts that can be inspected or corrected. The paper details the handoff, artifact workspace, and an evaluation protocol, and studies when continuing with fresh-context artifacts improves accuracy relative to single-shot LLM answers (including recursive tool-using baselines).

Why it matters

Introduces Chained Recursive Language Models (Chained RLM) by Purbesh Mitra and Sennur Ulukus (arXiv 2026-08-05): an inference-time architecture that repeatedly invokes the same LLM as a sequence of fresh reasoning roots; each root gets the original problem/context plus a compact plain-text summary, a plain-text blackboard, and durable artifacts from predecessors.

Key details

Specifies system components — a handoff mechanism, artifact workspace, and an evaluation protocol — and empirically studies when fresh-context artifact continuation yields measurable accuracy gains over direct LLM answering, including baselines that use recursive tool-calling.

Abstract

Long context reasoning in large language models (LLMs) is usually constrained by the fact that a single inference trajectory has to simultaneously explore the context, store intermediate state, verify evidence, and produce the final answer. This becomes particularly difficult in tasks that require extraction, counting, ordering, or multi-hop reasoning, where an early mistake can propagate until the final response. In this work, we propose Chained Recursive Language Models (Chained RLM), an inference-time architecture, in which the same underlying model is called repeatedly as a sequence of fresh reasoning roots. Each root receives the original problem and context, but does not inherit the full conversational history. Instead, it receives a compact plain-text summary, a plain-text blackboard, and some durable task-specific artifacts written by predecessor roots. The motivation is to manage the context by chopping into partial tasks rather than one large inference response; in each staged computation, intermediate artifacts can be inspected, corrected, and extended by a later fresh inference by the same model. We describe the system model, handoff mechanism, artifact workspace, and evaluation protocol for this system. We study when fresh-context artifact continuation gives a measurable gain in accuracy over direct LLM answering even with recursive tool-calling.