AI research trends

Novel Tokenization and/or Sampling

• 2024-04: Better & Faster Large Language Models via Multi-token Prediction
• 2024-10: entropix: Entropy Based Sampling and Parallel CoT Decoding
• 2024-10: softmax is not enough (for sharp out-of-distribution)
• 2024-12: I Don't Know: Explicit Modeling of Uncertainty with an [IDK] Token

System 2 Reasoning

See: Increasing AI Intelligence

Memory

LLM Weights Memory

• 2024-12: Memory Layers at Scale

Context Length

• 2020: Various ideas for scaling context window, including Longformer
• 2023-04-02: Discussion of ideas for how to scale context window
• 2023-05-11: Anthropic announces 100k window
• 2023-06-07: magic.dev claims 5M tokens coming soon
• 2023-07-05: Microsoft describes LongNet, with 1 billion token window
• 2023-07-11: Focused Transformer 256k
• 2023-11-06: GPT-4 turbo 128k
• 2023-11-22: Anthropic Claude 2.1 200k
• 2023-12-13: Mamba alternative
• 2024-01-04: LongLM to extend context window
• 2024-02-15: Gemini 1.5 1M tokens
• 2024-03-04: Anthropic Claude 3 200k
• 2024-03-08: Google claims Gemini 1.5 can scale to 10M
• 2024-04-10: Google preprint demonstrates infinite context length by using compressive memory
• 2024-04-12: Meta et al. demonstrate Megalodon that enables infinite context via a more efficient architecture
• 2024-04-14: Google presents TransformerFAM, which leverages a feedback loop so it attends to its own latent representations, acting as working memory and provides effectively infinite context
• 2025-01Jan-14: MiniMax-01 4M (paper)

Extended Context

• 2025-01: Titans: Learning to Memorize at Test Time

Retrieval beyond RAG

See also: AI tools: Retrieval Augmented Generation (RAG)

• 2024-12: Let your LLM generate a few tokens and you will reduce the need for retrieval
• 2024-12: RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation

Working Memory

• 2024-12: Improving Factuality with Explicit Working Memory

Episodic Memory

• 2024-03: Larimar: Large Language Models with Episodic Memory Control

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: Compressed Chain of Thought: Efficient Reasoning Through Dense Representations
• 2024-12: Google DeepMind: Deliberation in Latent Space via Differentiable Cache Augmentation
• 2024-12: LANG-JEPA: Learning to Think in Latent Space

See Also

• Increasing AI Intelligence