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ARXIV:2604.06155 · LLM TRAINING · SUBMITTED 08 APR · 03:22 UTC · FRESHNESS UNKNOWN
ARXIV:2604.06155LLM TRAININGSUBMITTED 08 APR · 03:22 UTCFRESHNESS UNKNOWNQimin Zhong · Hao Liao · Haiming Qin · Mingyang Zhou · Rui Mao · Wei Chen · +1 at arXiv
A theoretical exploration of multi-token prediction for LLMs, proposing a method to reduce structural hallucinations in latent space representations.
Opportunity summary
Pain A theoretical exploration of multi-token prediction for LLMs, proposing a method to reduce structural hallucinations in latent space representations.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
A theoretical exploration of multi-token prediction for LLMs, proposing a method to reduce structural hallucinations in latent space representations. While conventional Next-Token Prediction (NTP) focuses on one-step-ahead supervision, Multi-Token Prediction (MTP) has shown promise…
Whether Large Language Models (LLMs) develop coherent internal world models remains a core debate. While conventional Next-Token Prediction (NTP) focuses on one-step-ahead supervision, Multi-Token Prediction (MTP) has shown promise in learning more structured representations.
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Experiments on synthetic graphs and real-world Manhattan Taxi Ride show that LSE-MTP effectively bridges the gap between discrete tokens and continuous state representations, enhancing…
LLM Training moved forward this cycle; last verified April 2026. Public score 3.0/10.
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A theoretical exploration of multi-token prediction for LLMs, proposing a method to reduce structural hallucinations in latent space representations.
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10.48550/arXiv.2604.06155A theoretical exploration of multi-token prediction for LLMs, proposing a method to reduce structural hallucinations in latent space representations.
Abstract
Whether Large Language Models (LLMs) develop coherent internal world models remains a core debate. While conventional Next-Token Prediction (NTP) focuses on one-step-ahead supervision, Multi-Token Prediction (MTP) has shown promise in learning more structured representations. In this work, we provide a theoretical perspective analyzing the gradient inductive bias of MTP, supported by empirical evidence, showing that MTP promotes the convergence toward internal belief states by inducing representational contractivity via gradient coupling. However, we reveal that standard MTP often suffers from structural hallucinations, where discrete token supervision encourages illegal shortcuts in latent space that violate environmental constraints. To address this, we propose a novel method Latent Semantic Enhancement MTP (LSE-MTP), which anchors predictions to ground-truth hidden state trajectories. Experiments on synthetic graphs and real-world Manhattan Taxi Ride show that LSE-MTP effectively bridges the gap between discrete tokens and continuous state representations, enhancing representation alignment, reducing structural hallucinations, and improving robustness to perturbations.
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PROBLEM
A theoretical exploration of multi-token prediction for LLMs, proposing a method to reduce structural hallucinations in latent space representations. While conventional Next-Token Prediction (NTP) focuses on one-step-ahead supervision, Multi-Token Prediction (MTP) has shown prom...
METHOD
Whether Large Language Models (LLMs) develop coherent internal world models remains a core debate. While conventional Next-Token Prediction (NTP) focuses on one-step-ahead supervision, Multi-Token Prediction (MTP) has shown promise in learning more structured representations.
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Experiments on synthetic graphs and real-world Manhattan Taxi Ride show that LSE-MTP effectively bridges the gap between discrete tokens and continuous state representations, enhancing representation alig...
WHY NOW
LLM Training moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A theoretical exploration of multi-token prediction for LLMs, proposing a method to reduce structural hallucinations in latent space representations. While conventional Next-Token Prediction (NTP) focuses on one-step-ahead supervision, Multi-Token Prediction (MTP) has shown promise in learning more structured representations.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Whether Large Language Models (LLMs) develop coherent internal world models remains a core debate. While conventional Next-Token Prediction (NTP) focuses on one-step-ahead supervision, Multi-Token Prediction (MTP) has shown promise in learning more structured representations.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Experiments on synthetic graphs and real-world Manhattan Taxi Ride show that LSE-MTP effectively bridges the gap between discrete tokens and continuous state representations, enhancing representation alignment, reducing structural hallucinations, and improving robustness to perturbations.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Training moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A theoretical exploration of multi-token prediction for LLMs, proposing a method to reduce structural hallucinations in latent space representations.
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3.0/10 public viability
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