REVIEW 7 cited by
Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies Ahead
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Inference-time scaling can enhance the reasoning capabilities of large language models (LLMs) on complex problems that benefit from step-by-step problem solving. Although lengthening generated scratchpads has proven effective for mathematical tasks, the broader impact of this approach on other tasks remains less clear. In this work, we investigate the benefits and limitations of scaling methods across nine state-of-the-art models and eight challenging tasks, including math and STEM reasoning, calendar planning, NP-hard problems, navigation, and spatial reasoning. We compare conventional models (e.g., GPT-4o) with models fine-tuned for inference-time scaling (e.g., o1) through evaluation protocols that involve repeated model calls, either independently or sequentially with feedback. These evaluations approximate lower and upper performance bounds and potential for future performance improvements for each model, whether through enhanced training or multi-model inference systems. Our extensive empirical analysis reveals that the advantages of inference-time scaling vary across tasks and diminish as problem complexity increases. In addition, simply using more tokens does not necessarily translate to higher accuracy in these challenging regimes. Results from multiple independent runs with conventional models using perfect verifiers show that, for some tasks, these models can achieve performance close to the average performance of today's most advanced reasoning models. However, for other tasks, a significant performance gap remains, even in very high scaling regimes. Encouragingly, all models demonstrate significant gains when inference is further scaled with perfect verifiers or strong feedback, suggesting ample potential for future improvements.
Forward citations
Cited by 7 Pith papers
-
ThinkRetrieve: Retrieval-Augmented Reasoning Traces for Test-Time Scaling
Per-step retrieval of solved exemplars injected into the reasoning trace improves test-time scaling accuracy, with up to 13.4 absolute points gained on AIME 2025.
-
Accurate structural modeling of chemically diverse molecular interfaces with Vilya-2
Vilya-2 predicts bound structures of chemically diverse peptides and small molecules at state-of-the-art accuracy using an all-atom diffusion transformer, recovering 59.1% of peptide interfaces to sub-2 Å backbone RMSD.
-
Efficient Chain-of-Modality Reasoning via Progressive Compression for Spoken Language Models
Spoken math models that emit a 40%-compressed reasoning trace between question and answer beat full-reasoning baselines by ~3 accuracy points while using roughly one third of the text tokens.
-
NEURA: A Unified and Retargetable Compilation Framework for Coarse-Grained Reconfigurable Architectures
NEURA flattens CGRA control flow into a pure predicated dataflow IR and reports 2.20× kernel and up to 2.71× application speedups over high-performance SOTA baselines.
-
Rethinking Test-Time Scaling for Medical AI: Model and Task-Aware Strategies for LLMs and VLMs
Test-time scaling helps only certain medical AI models and only on hard questions, with parallel sampling best for short-reasoning models and sequential revision best for deep-reasoning models.
-
AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up
The framework trains small models on synthetic AI-generated data to produce PFD/PID text, then validates two examples by manual DWSIM setup, leaving the industrial-viability claim unproven.
-
CEC-Zero: Chinese Error Correction Solution Based on LLM
The authors claim that reinforcement learning with an embedding-clustering reward improves Chinese spelling correction and cross-domain generalization, but the evidence is missing key baselines and reproducibility artifacts.
Discussion (0). Continue with ORCID to comment.