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Cache-to-Cache: Direct Semantic Communication Between LLMs (2025)
- cubefox 14d agoSo the models will not only be using more and more Neuralese in their CoT (like GPT-6), but different agents will also be able to communicate with each other in Neuralese. It's not looking good for monitorability.
- sparky_twofort 14d agoIs Neuralese in no way decodable into a human-interpretable system? Genuine question -- I don't know the answer.
- Y_Y 14d agoDefinitely decodable, that's what's being done now
- cubefox 14d agoIt's not.
- deleted 14d ago[deleted]
- TeMPOraL 13d agoProbably not without a sufficiently powerful LLM from same family, or something equivalent, to act as a translation layer, which includes the risk of the translator lying to you.
- jephs 14d agoThis is from like a year ago.
- gavinray 14d agoA few months ago I asked why semantic representation rather than text wasn't used, since natural language seems quite a lossy representation for semantic concepts: https://news.ycombinator.com/item?id=47195212 https://news.ycombinator.com/item?id=47195212 I wouldn't have thought to use it for LLM-to-LLM communication, though
- msdz 14d agoMy guess is that if you go with something other than readable text as the “thought layer”, observability becomes impossible. Which is not necessarily something the humans training a model would want re/ alignment.
- TeMPOraL 13d agoIndeed this is one of the key problems here. If models start doing CoT in "neuralese", or use it when talking with each other, we lose what little observability we have.
- foota 14d agoI feel like multimodal models that can read images should work differently than they do. My understanding is that multimodal models basically first generate an image embedding and then the model is trained to interpret that embedding, but in the same way that text is lossy, it seems like the embedding would be as well. Why don't multimodal models learn to interpret images themselves without an embedding? Or e.g., by passing some "prompt" to the embedding model?
- thfuran 14d agoWhat does interpreting images mean in practice if you exclude the possibility of feature extraction or any other sort of implicit embedding?
- foota 14d agoI'm not an ML expert, but I was thinking of a sort of "guided" embedding. E.g., give the image model some prompt for what it's trying to do? I don't understand why multimodal models generate an embedding that doesn't understand what the model is trying to "figure out". I think this is similar to how Gemma 4 12B is implemented, but even then I don't think the single layer image embedding is "aware" of the context.
- seanhunter 13d agoYou already do give the image model a prompt to tell it what to do. That’s not something the embedding can use independently of how the model is already using it. In general an embedding doesn’t have intent or awareness in the way you’re looking for. “Embedding” just means one mathematical structure stuffed inside another. So for example the real number line is embedded into the Cartesian plane as each axis- that’s an embedding. Now in this case specifically, the embeddings in any kind of transformer model encode the meaning of the thing they represent into vectors (which is what the model itself actually operates on). You can train the embedding to be more useful for a particular task at inference time, which already happens.
- 12d ago
- ACCount39 14d agoIt's an old paper (from 2025, so, a decade ago in AI years), but the concept is still fascinating. And I'm yet to see it show up in any production models. If multiple models can use cache representations for this kind of enrichment, the KV cache representations of different models must be somewhat compatible. What stops us then from going a step further, and producing a model family where all models are "KV aligned", and each model can utilize the KV cache of other models directly? So, an "expensive" reasoning model can use its full faculties to plan, but "delegate" simple subgoals to a smaller model. That smaller model can access the large model's intent directly, as rich KV cache representations - with no prefill recompute and no associated "handover" latency. Or, likewise, a "cheap" small model can generate a diminished but highly compact KV cache that the "expensive" model can then operate on - for example, for skimming a large file for shallow patterns.
- refibrillator 14d agoYou might like The Universal Weight Subspace Hypothesis: https://arxiv.org/abs/2512.05117 https://arxiv.org/abs/2512.05117 Curious to know if anyone is aware of research trying what parent suggested?
- ayewo 14d agoSounds similar to Ramp's Latent Briefing for multi-agent coordination. https://x.com/RampLabs/status/2042672773747589588 https://x.com/RampLabs/status/2042672773747589588
- movedx01 14d agoKind of reminds me of this more recent paper: https://arxiv.org/html/2602.02459v1 https://arxiv.org/html/2602.02459v1 Different use case and implementation, but a similar idea. In this case applied to sharing last state(not the whole KV cache) from big brain model running in the cloud with a smaller/dumber model running on-device in a robot, in a latancy-aware way.
- ACCount39 13d agoThanks for the link. This is the kind of "edge AI" adaptation that I expect to see more of in the future. A "local 8B VLA" that runs at high frequency can be sufficient for things like sensor fusion, plan execution and manipulation dynamics, but insufficient for advanced problem solving and in-depth "exception handling". So being able to ask a larger model for steering cues is going to be valuable for AI robots. And that does introduce latency and interface boundaries. Not sure if the exact approach described there is the way to go, but I do expect something similar to show up in deployments eventually. If nothing else, having this one described does provide a point of comparison.
- Transformanshen 14d ago[dead]
- paidx 14d ago[flagged]
- lemonleks 14d ago[dead]
- A1Lab-tech 14d ago[flagged]
- djoldman 13d agoSeems like fundamentally a cool idea but: KV is not context. if the KV cache gets evicted, you'd have to rerun the translation. Seems like it could still help but also feels like one of those things where it becomes vastly more complex and difficult to debug.