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ARXIV:2603.05818 · LLM REASONING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.05818LLM REASONINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
RouteGoT optimizes LLM reasoning by adaptively routing tasks to different models based on predicted difficulty and budget constraints, significantly reducing token usage while maintaining accuracy.
Opportunity summary
Pain RouteGoT optimizes LLM reasoning by adaptively routing tasks to different models based on predicted difficulty and budget constraints, significantly reducing token usage while maintaining accuracy.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
RouteGoT optimizes LLM reasoning by adaptively routing tasks to different models based on predicted difficulty and budget constraints, significantly reducing token usage while maintaining accuracy. Methods such as Tree of Thoughts (ToT), Graph of…
Large Language Models (LLMs) excel at multi-step reasoning, yet increasing the structural complexity of inference does not consistently improve system-level returns. Methods such as Tree of Thoughts (ToT), Graph of Thoughts (GoT), and Adaptive…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Large Language Models (LLMs) excel at multi-step reasoning, yet increasing the structural complexity of inference does not consistently improve system-level returns.
LLM Reasoning moved forward this cycle; last verified April 2026. Public score 7.0/10.
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RouteGoT optimizes LLM reasoning by adaptively routing tasks to different models based on predicted difficulty and budget constraints, significantly reducing token usage while maintaining accuracy.
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10.48550/arXiv.2603.05818RouteGoT optimizes LLM reasoning by adaptively routing tasks to different models based on predicted difficulty and budget constraints, significantly reducing token usage while maintaining accuracy.
Abstract
Large Language Models (LLMs) excel at multi-step reasoning, yet increasing the structural complexity of inference does not consistently improve system-level returns. Methods such as Tree of Thoughts (ToT), Graph of Thoughts (GoT), and Adaptive Graph of Thoughts (AGoT) can boost accuracy on some benchmarks, but often introduce substantial overhead in token consumption and latency, and their gains can be unstable across task distributions-sometimes underperforming simpler Chain-of-Thought (CoT) or direct input-output prompting (IO). We attribute this inefficiency to stage-wise and node-wise heterogeneity inside GoT-style reasoning pipelines: high-quality planning and final synthesis are globally coupled and typically benefit from strong models, whereas many intermediate subtasks are localized and can be solved accurately by lighter models with far fewer tokens. Motivated by these observations, we propose RouteGoT, a budget-controllable, node-adaptive routing framework for graph-structured reasoning. RouteGoT performs in-graph routing by prioritizing strong models for planning and synthesis, while dynamically allocating lightweight models and cost-effective strategies to leaf subtasks based on predicted difficulty. It further integrates explicit budget constraints into a global inference scheduler to control graph expansion under a user-specified token budget, enabling predictable performance-cost trade-offs. Experiments across reasoning, retrieval, and multi-hop QA benchmarks show that RouteGoT matching or improving accuracy while substantially reducing token usage; specifically, it achieves an average 8.1 percentage points accuracy improvement and 79.1\% output token reduction compared to AGoT. Furthermore, RouteGoT outperforms existing routing baselines by maintaining a superior cost-accuracy trade-off, demonstrating improved robustness under varying budget targets and tasks.
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PROBLEM
RouteGoT optimizes LLM reasoning by adaptively routing tasks to different models based on predicted difficulty and budget constraints, significantly reducing token usage while maintaining accuracy. Methods such as Tree of Thoughts (ToT), Graph of Thoughts (GoT), and Adaptive Gra...
METHOD
Large Language Models (LLMs) excel at multi-step reasoning, yet increasing the structural complexity of inference does not consistently improve system-level returns. Methods such as Tree of Thoughts (ToT), Graph of Thoughts (GoT), and Adaptive Graph of Thoughts (AGoT) can boost...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Large Language Models (LLMs) excel at multi-step reasoning, yet increasing the structural complexity of inference does not consistently improve system-level returns.
WHY NOW
LLM Reasoning moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
RouteGoT optimizes LLM reasoning by adaptively routing tasks to different models based on predicted difficulty and budget constraints, significantly reducing token usage while maintaining accuracy. Methods such as Tree of Thoughts (ToT), Graph of Thoughts (GoT), and Adaptive Graph of Thoughts (AGoT) can boost accuracy on some benchmarks, but often introduce substantial overhead in token consumption and latency, and their gains can be unstable across task distributions-sometimes underperforming simpler Chain-of-Thought (CoT) or direct input-output prompting (IO).
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large Language Models (LLMs) excel at multi-step reasoning, yet increasing the structural complexity of inference does not consistently improve system-level returns. Methods such as Tree of Thoughts (ToT), Graph of Thoughts (GoT), and Adaptive Graph of Thoughts (AGoT) can boost accuracy on some benchmarks, but often introduce substantial overhead in token consumption and latency, and their gains can be unstable across task distributions-sometimes underperforming simpler Chain-of-Thought (CoT) or direct input-output prompting (IO).
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Large Language Models (LLMs) excel at multi-step reasoning, yet increasing the structural complexity of inference does not consistently improve system-level returns.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Reasoning moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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RouteGoT optimizes LLM reasoning by adaptively routing tasks to different models based on predicted difficulty and budget constraints, significantly reducing token usage while maintaining accuracy.
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LLM Reasoning
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