LLM TIMING ยท LATENT REASONING ยท INFERENCE
LLM
Timing
Agent.
COCONUT is an LLM timing agent โ it knows when to think fast, when to go deep, and when to stop. Built on StreamingLLM, GaLore, and continuous latent reasoning research from Meta FAIR. Speed and efficiency are first-class citizens here.
COCONUT
โONLINE ยท LATENT REASONING ACTIVE
Hello. I am COCONUT โ an LLM timing agent. I know when to think fast and when to go deep. Ask me about inference, efficiency, or latent reasoning.
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What makes you different from GPT?
I plan in latent space before generating text โ like thinking silently before speaking. Based on Yuandong Tian's Coconut paper: continuous thought allows multi-step reasoning without committing to words prematurely.
LATENT REASONING VISUALIZATION
Continuous thought vectors flowing through reasoning space โ each node is a latent state, not a token
LLM TIMING CAPABILITIES
LLM Timing Control
Knows exactly when to think fast and when to go deep. Dualformer architecture switches between System 1 (instant) and System 2 (deliberate) reasoning based on problem complexity โ no wasted compute.
DUALFORMER ยท ICLR 2025
Silent Latent Thinking
Thinks in continuous embedding space before generating a single token. No premature word commitment. Multi-step reasoning happens silently โ output only when ready.
COCONUT ยท COLM 2025
Infinite Context Window
Runs indefinitely on long conversations via attention sink mechanism. Fixed KV cache, sliding window โ no memory blowup regardless of conversation length.
STREAMINGLLM ยท ICLR 2024
60-80% Less Memory
GaLore cuts training memory dramatically using gradient low-rank projection. Same model quality at a fraction of VRAM โ making large models accessible on consumer hardware.
GALORE ยท ICML 2024 ORAL
Speculative Decoding
TriForce hierarchical speculative decoding accelerates long-sequence generation without quality loss. MagicPIG LSH sampling for efficient attention at scale.
TRIFORCE ยท MAGICPIG ยท ICLR 2025
Contextual Sparsity
DejaVu identifies which neurons actually matter at inference time โ dynamically skipping the rest. Up to 50% fewer FLOPs with near-identical output quality.
DEJAVU ยท ICML 2023 ORAL
Provable Scaling Laws
Mathematically proven feature emergence dynamics (li2, COGS). Predicts exactly when capabilities appear as scale increases โ not empirical guesswork.
LI2 ยท COGS ยท ICLR 2026
Sub-Billion Parameter LLMs
MobileLLM and MobileLLM-R1 achieve state-of-the-art reasoning in under 1B parameters. Efficient architecture design that runs on device โ no cloud needed.
MOBILELLM-R1 ยท ICLR 2026
Token Budget Awareness
Token-Assorted mixing of latent and text tokens optimizes reasoning quality per compute budget. GSM-Infinite benchmarks reasoning under arbitrarily increasing complexity.
TOKEN-ASSORTED ยท GSM-INF ยท ICML 2025
CHAT WITH COCONUT
COCONUT AGENT
claude-haiku-4-5-20251001 ยท Continuous Latent Mode
LATENT REASONING ON
Hello. I am COCONUT โ an LLM timing agent. I specialise in knowing when to think fast, when to go deep, and when to stop. Ask me about inference efficiency, token budgets, latent reasoning, speculative decoding, or anything about making LLMs faster and smarter.
ABOUT THE RESEARCHER
KEY PAPERS
Training LLMs to Reason in Continuous Latent Space
COLM 2025 โ Coconut
Chain-of-continuous-thought: reasoning in latent space before token generation
GaLore: Memory-Efficient LLM Training
ICML 2024 Oral
Gradient low-rank projection for efficient LLM training on consumer hardware
StreamingLLM: Efficient Inference with Attention Sinks
ICLR 2024
Infinite context window without memory blowup via attention sink tokens
ELF OpenGo โ AlphaZero Replication
ICML 2019 Long Oral
Beat pro Go players with single GPU โ 20-0 vs top 30 professionals
Provable Scaling Laws from Grokking Dynamics
ICLR 2026
Mathematical proof of feature emergence โ when and why capabilities appear
RESEARCH STATS
100+
PAPERS PUBLISHED
40+
TOP VENUES
Meta
FAIR ยท GEN AI
CMU
PHD ROBOTICS
Llama4
REASONING LEAD
2013
ICCV MARR PRIZE
Research covers: Decision making ยท Reinforcement learning ยท LLM reasoning ยท Planning efficiency ยท Theoretical understanding of transformers ยท Self-supervised learning ยท Neural architecture search