Hello. I recently found an interesting preprint which suggests some exciting possibilities with respect to present and near-future R&D into automated reasoning.
This preprint suggests that artificial reasoning could be such so as to occur in a continuous latent space, utilizing high-dimensional vectors, rather than only utilizing tokens, words, or otherwise being bound by language.
Large language models (LLMs) are restricted to reason in the “language space”, where they typically express the reasoning process with a chain-of-thought (CoT) to solve a complex reasoning problem. However, we argue that language space may not always be optimal for reasoning. For example, most word tokens are primarily for textual coherence and not essential for reasoning, while some critical tokens require complex planning and pose huge challenges to LLMs. To explore the potential of LLM reasoning in an unrestricted latent space instead of using natural language, we introduce a new paradigm Coconut (Chain of Continuous Thought). We utilize the last hidden state of the LLM as a representation of the reasoning state (termed “continuous thought”). Rather than decoding this into a word token, we feed it back to the LLM as the subsequent input embedding directly in the continuous space. Experiments show that Coconut can effectively augment the LLM on several reasoning tasks. This novel latent reasoning paradigm leads to emergent advanced reasoning patterns: the continuous thought can encode multiple alternative next reasoning steps, allowing the model to perform a breadth-first search (BFS) to solve the problem, rather than prematurely committing to a single deterministic path like CoT. Coconut outperforms CoT in certain logical reasoning tasks that require substantial backtracking during planning, with fewer thinking tokens during inference. These findings demonstrate the promise of latent reasoning and offer valuable insights for future research.
Here is a related paper from 2023:
We present IMAGEBIND, an approach to learn a joint embedding across six different modalities- images, text, audio, depth, thermal, and IMU data. We show that all combinations of paired data are not necessary to train such a joint embedding, and only image-paired data is sufficient to bind the modalities together. IMAGEBIND can leverage recent large scale vision-language models, and extends their zero-shot capabilities to new modalities just by using their natural pairing with images. It enables novel emergent applications ‘out-of-the-box’ including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection and generation. The emergent capabilities improve with the strength of the image encoder and we set a new state-of-the art on emergent zero-shot recognition tasks across modalities, outperforming specialist supervised models. Finally, we show strong few-shot recognition results outperforming prior work, and that IMAGEBIND serves as a new way to evaluate vision models for visual and non-visual tasks.
What about developer experiences (DX)?
How might developers and engineers best examine and inspect artificial reasoning accompanying and preceding natural-language responses? How might developers and engineers, accustomed to being able to read natural-language and textual representations of artificial reasoning, make sense of and work with one or multiple sequences of high-dimensional vectors?
Would software tools or development environments be required to examine and inspect AI systems’ automated reasoning processes? What might these tools and related experiences resemble?
If there’s any interest here, I’d enjoy discussing any of these or related ideas, (multimodal) reasoning in continuous latent spaces, with you. Thank you.