Cover Image

Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Manvi, Rohin, Singh, Anikait, Ermon, Stefano
Format:	Preprint
Published:	2024
Subjects:	Computation and Language Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Large Language Models are Geographically Biased
by: Manvi, Rohin, et al.
Published: (2024)

Zero-Overhead Introspection for Adaptive Test-Time Compute
by: Manvi, Rohin, et al.
Published: (2025)

Even GPT-5.2 Can't Count to Five: The Case for Zero-Error Horizons in Trustworthy LLMs
by: Sato, Ryoma
Published: (2026)

FSPO: Few-Shot Optimization of Synthetic Preferences Personalizes to Real Users
by: Singh, Anikait, et al.
Published: (2025)

How Do LLMs Persuade? Linear Probes Can Uncover Persuasion Dynamics in Multi-Turn Conversations
by: Jaipersaud, Brandon, et al.
Published: (2025)

GeoLLM: Extracting Geospatial Knowledge from Large Language Models
by: Manvi, Rohin, et al.
Published: (2023)

RLAD: Training LLMs to Discover Abstractions for Solving Reasoning Problems
by: Qu, Yuxiao, et al.
Published: (2025)

FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows"
by: Ming, Yifei, et al.
Published: (2024)

Can LLMs Follow Simple Rules?
by: Mu, Norman, et al.
Published: (2023)

Climbing the Ladder of Reasoning: What LLMs Can-and Still Can't-Solve after SFT?
by: Sun, Yiyou, et al.
Published: (2025)

QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks
by: Tseng, Albert, et al.
Published: (2024)

Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate Controllable Controversial Statements
by: Li, Ming, et al.
Published: (2024)

Can LLMs Convert Graphs to Text-Attributed Graphs?
by: Wang, Zehong, et al.
Published: (2024)

Can GRPO Help LLMs Transcend Their Pretraining Origin?
by: Ni, Kangqi, et al.
Published: (2025)

Can Post-Training Transform LLMs into Causal Reasoners?
by: Chen, Junqi, et al.
Published: (2026)

Thoth: Mid-Training Bridges LLMs to Time Series Understanding
by: Lin, Jiafeng, et al.
Published: (2026)

PLDR-LLMs Learn A Generalizable Tensor Operator That Can Replace Its Own Deep Neural Net At Inference
by: Gokden, Burc
Published: (2025)

Language Models Can Predict Their Own Behavior
by: Ashok, Dhananjay, et al.
Published: (2025)

Can Interpretation Predict Behavior on Unseen Data?
by: Li, Victoria R., et al.
Published: (2025)

Enough Coin Flips Can Make LLMs Act Bayesian
by: Gupta, Ritwik, et al.
Published: (2025)

SelfReflect: Can LLMs Communicate Their Internal Answer Distribution?
by: Kirchhof, Michael, et al.
Published: (2025)

Principled RL for Diffusion LLMs Emerges from a Sequence-Level Perspective
by: Ou, Jingyang, et al.
Published: (2025)

Do LLMs Encode Functional Importance of Reasoning Tokens?
by: Singh, Janvijay, et al.
Published: (2026)

LLMs Can Teach Themselves to Better Predict the Future
by: Turtel, Benjamin, et al.
Published: (2025)

Transformers Can Achieve Length Generalization But Not Robustly
by: Zhou, Yongchao, et al.
Published: (2024)

Generation Constraint Scaling Can Mitigate Hallucination
by: Kollias, Georgios, et al.
Published: (2024)

Can AI-Generated Text be Reliably Detected?
by: Sadasivan, Vinu Sankar, et al.
Published: (2023)

Can LLMs Help Uncover Insights about LLMs? A Large-Scale, Evolving Literature Analysis of Frontier LLMs
by: Park, Jungsoo, et al.
Published: (2025)

Can Stories Help LLMs Reason? Curating Information Space Through Narrative
by: Javadi, Vahid Sadiri, et al.
Published: (2024)

RLHF Can Speak Many Languages: Unlocking Multilingual Preference Optimization for LLMs
by: Dang, John, et al.
Published: (2024)

Demystifying Hybrid Thinking: Can LLMs Truly Switch Between Think and No-Think?
by: Wang, Shouren, et al.
Published: (2025)

Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical Machine Reading Comprehension
by: Vatsal, Shubham, et al.
Published: (2024)

Can LLMs Separate Instructions From Data? And What Do We Even Mean By That?
by: Zverev, Egor, et al.
Published: (2024)

Nudging: Inference-time Alignment of LLMs via Guided Decoding
by: Fei, Yu, et al.
Published: (2024)

Can We Count on LLMs? The Fixed-Effect Fallacy and Claims of GPT-4 Capabilities
by: Ball, Thomas, et al.
Published: (2024)

QuestBench: Can LLMs ask the right question to acquire information in reasoning tasks?
by: Li, Belinda Z., et al.
Published: (2025)

Multi-Layer Transformers Gradient Can be Approximated in Almost Linear Time
by: Liang, Yingyu, et al.
Published: (2024)

Can GPT Improve the State of Prior Authorization via Guideline Based Automated Question Answering?
by: Vatsal, Shubham, et al.
Published: (2024)

MIMIC-RD: Can LLMs differentially diagnose rare diseases in real-world clinical settings?
by: AlDin, Zilal Eiz, et al.
Published: (2025)

You Can Generate It Again: Data-to-Text Generation with Verification and Correction Prompting
by: Ren, Xuan, et al.
Published: (2023)