LLM agents can communicate without words, and now without sharing their entire context.

TL;DR: What an agent sends should depend on what the next agent needs. CacheBack lets agents share a selected subset of their internal state. With Qwen3-8B on FanOutQA, it achieves 3.2× faster median task completion and 14.7 percentage points higher accuracy than same-size text communication. It’s training-free, with improvements across multiple architectures and benchmarks. https://reddit.com/link/1wz8hta/video/pemxyo4sqvth1/player Hi everyone! We’ve been working on making latent communication scalable and practical when agents read large, separate contexts. We’re excited about the results and wanted to share the paper, demos, and code with you. Check out our new paper, Receiver-Conditioned Latent Communication gives 94% CacheBack. Multi-agent systems let us parallelise computation and split large contexts across agents. These agents usually communicate through text messages, which take time to generate and can leave out evidence the receiving agent needs. Work such as Cache-to-Cache, LatentMAS, and KVComm explores communication through internal model representations. We focus on a setting where agents read large, separate contexts and one receiver combines their findings. In this fan-in setting, methods that retain every sender position bring those contexts back together at the receiver, undoing the benefit of splitting them across agents. In our Qwen3-8B FanOutQA setup, full-cache transfer leaves insufficient context for receiver generation on every task. Our idea is simple: what an agent sends should depend on what the receiving agent needs \-- we call this receiver conditioned communication. The sender uses a query from the receiver to select which parts of its internal state to share. CacheBack is our simple, training-free implementation. It uses attention to the receiver’s request to select from state the sender has already computed. https://preview.redd.it/mtvbcinhpvth1.png?width=1460&format=png&auto=… On FanOutQA, our selected operating points improve strict accuracy by 7.3–20.7 percentage points, with 1.3–8.0× faster median task completion than same-size text agents. We see improvements across four model families, including dense Transformers, Mamba-attention hybrids, and sliding-window attention. We also see gains when agents work in sequence on LongBench v2 Easy. At 16× compression, CacheBack removes approximately 94% of sender positions while improving accuracy and latency over same-size text across every tested family and topology. This is a separate setting from the Qwen3-8B result in the TL;DR, which uses 4× compression. Each benchmark evaluates 50 tasks. Completion times include queueing under concurrent load on eight H100s. More aggressive compression can discard useful evidence and reduce accuracy. Here is a quick demo on seven Qwen3-8B workers helping a coordinator fix a Django bug. With CacheBack, the task takes 26 seconds instead of 113, a 4.41× speedup. Both runs produce the same patch and pass all 88 tests. https://reddit.com/link/1wz8hta/video/ix08pucmqvth1/player This is one recorded case, separate from the benchmarks. The video reconstructs separate runs with varied playback speed; startup and test grading are excluded. The code is open source, with runnable examples. The current package supports matching dense Qwen3 models through Hugging Face and vLLM. Check it out. Website and demos: https://agentcacheback.github.io/ Paper: https://arxiv.org/abs/2609.32046 Code: https://github.com/agentcacheback/cacheback Happy to discuss the method, implementation, and tradeoffs. I’d be particularly interested in other workflows where agents need to combine evidence from large, separate contexts.