COCONUT is an LLM timing agent β 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.
Hello. I am COCONUT β 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?
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I plan in latent space before generating text β 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 β each node is a latent state, not a token
LLM TIMING CAPABILITIES
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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 β no wasted compute.
DUALFORMER Β· ICLR 2025
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Silent Latent Thinking
Thinks in continuous embedding space before generating a single token. No premature word commitment. Multi-step reasoning happens silently β output only when ready.
COCONUT Β· COLM 2025
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Infinite Context Window
Runs indefinitely on long conversations via attention sink mechanism. Fixed KV cache, sliding window β no memory blowup regardless of conversation length.
STREAMINGLLM Β· ICLR 2024
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60-80% Less Memory
GaLore cuts training memory dramatically using gradient low-rank projection. Same model quality at a fraction of VRAM β making large models accessible on consumer hardware.
GALORE Β· ICML 2024 ORAL
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Speculative Decoding
TriForce hierarchical speculative decoding accelerates long-sequence generation without quality loss. MagicPIG LSH sampling for efficient attention at scale.
TRIFORCE Β· MAGICPIG Β· ICLR 2025
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Contextual Sparsity
DejaVu identifies which neurons actually matter at inference time β dynamically skipping the rest. Up to 50% fewer FLOPs with near-identical output quality.
DEJAVU Β· ICML 2023 ORAL
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Provable Scaling Laws
Mathematically proven feature emergence dynamics (li2, COGS). Predicts exactly when capabilities appear as scale increases β not empirical guesswork.
LI2 Β· COGS Β· ICLR 2026
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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 β no cloud needed.
MOBILELLM-R1 Β· ICLR 2026
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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.
Hello. I am COCONUT β 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.
COCONUT Β· just now
ABOUT THE RESEARCHER
KEY PAPERS
Training LLMs to Reason in Continuous Latent Space
COLM 2025 β 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 β AlphaZero Replication
ICML 2019 Long Oral
Beat pro Go players with single GPU β 20-0 vs top 30 professionals
Provable Scaling Laws from Grokking Dynamics
ICLR 2026
Mathematical proof of feature emergence β when and why capabilities appear
RESEARCH STATS
100+
PAPERS PUBLISHED
40+
TOP VENUES
Meta
FAIR Β· GEN AI
CMU
PHD ROBOTICS
Llama4
REASONING LEAD
2013
ICCV MARR PRIZE
Research covers: Decision making Β· Reinforcement learning Β· LLM reasoning Β· Planning efficiency Β· Theoretical understanding of transformers Β· Self-supervised learning Β· Neural architecture search