Difference between revisions of "AI research trends"
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| + | =System 2 Reasoning= | ||
| + | See: [[AI_Agents#Increasing_AI_Agent_Intelligence|Increasing AI Agent Intelligence]] | ||
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| + | =Episodic Memory= | ||
| + | * 2024-03: [https://arxiv.org/abs/2403.11901 Larimar: Large Language Models with Episodic Memory Control] | ||
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=Neural (non-token) Latent Representation= | =Neural (non-token) Latent Representation= | ||
* 2024-11: Microsoft: [https://arxiv.org/abs/2411.02820 DroidSpeak: KV Cache Sharing for Cross-LLM Communication and Multi-LLM Serving]: LLMs invent their own inter-communication language | * 2024-11: Microsoft: [https://arxiv.org/abs/2411.02820 DroidSpeak: KV Cache Sharing for Cross-LLM Communication and Multi-LLM Serving]: LLMs invent their own inter-communication language | ||
Revision as of 09:04, 24 December 2024
System 2 Reasoning
See: Increasing AI Agent Intelligence
Episodic Memory
Neural (non-token) Latent Representation
- 2024-11: Microsoft: DroidSpeak: KV Cache Sharing for Cross-LLM Communication and Multi-LLM Serving: LLMs invent their own inter-communication language
- 2024-12: Meta: Training Large Language Models to Reason in a Continuous Latent Space: feeding the latent representation directly back into the model, instead of tokenizing intermediate thoughts (Chain of Continuous Thought, a.k.a. Coconut)
- 2024-12: Meta: Large Concept Models: Language Modeling in a Sentence Representation Space: train a model that operates at a higher level of abstraction than typical word/token LLMs; model operates in a space of concept embeddings (more akin to full sentences than individual words)
- 2024-12: Meta: Byte Latent Transformer: Patches Scale Better Than Tokens: Instead of tokenization, dynamically convert input byte-stream into patches, yielding gains in compute efficiency, with minimal loss in performance
- 2024-12: Google DeepMind: Deliberation in Latent Space via Differentiable Cache Augmentation
- 2024-12: LANG-JEPA: Learning to Think in Latent Space
