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ARXIV:2603.12634 · LLM AGENTS · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE
ARXIV:2603.12634LLM AGENTSSUBMITTED 19 MAR · 18:48 UTCFRESHNESS STALEarXiv
Budget-Aware Value Tree (BAVT) optimizes LLM agent performance by intelligently managing resource allocation during multi-hop reasoning.
Opportunity summary
Pain Budget-Aware Value Tree (BAVT) optimizes LLM agent performance by intelligently managing resource allocation during multi-hop reasoning.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
Budget-Aware Value Tree (BAVT) optimizes LLM agent performance by intelligently managing resource allocation during multi-hop reasoning. Existing budget-aware methods either require expensive fine-tuning or rely on coarse, trajectory-level heuristics that cannot intervene mid-execution.
Test-time scaling has become a dominant paradigm for improving LLM agent reliability, yet current approaches treat compute as an abundant resource, allowing agents to exhaust token and tool budgets on redundant steps or dead-end…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive evaluations on four multi-hop QA benchmarks across two model families demonstrate that BAVT consistently outperforms parallel sampling baselines.
LLM Agents moved forward this cycle; last verified April 2026. Public score 7.0/10.
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Budget-Aware Value Tree (BAVT) optimizes LLM agent performance by intelligently managing resource allocation during multi-hop reasoning.
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Paper Pack
10.48550/arXiv.2603.12634Budget-Aware Value Tree (BAVT) optimizes LLM agent performance by intelligently managing resource allocation during multi-hop reasoning.
Abstract
Test-time scaling has become a dominant paradigm for improving LLM agent reliability, yet current approaches treat compute as an abundant resource, allowing agents to exhaust token and tool budgets on redundant steps or dead-end trajectories. Existing budget-aware methods either require expensive fine-tuning or rely on coarse, trajectory-level heuristics that cannot intervene mid-execution. We propose the Budget-Aware Value Tree (BAVT), a training-free inference-time framework that models multi-hop reasoning as a dynamic search tree guided by step-level value estimation within a single LLM backbone. Another key innovation is a budget-conditioned node selection mechanism that uses the remaining resource ratio as a natural scaling exponent over node values, providing a principled, parameter-free transition from broad exploration to greedy exploitation as the budget depletes. To combat the well-known overconfidence of LLM self-evaluation, BAVT employs a residual value predictor that scores relative progress rather than absolute state quality, enabling reliable pruning of uninformative or redundant tool calls. We further provide a theoretical convergence guarantee, proving that BAVT reaches a terminal answer with probability at least $1-ε$ under an explicit finite budget bound. Extensive evaluations on four multi-hop QA benchmarks across two model families demonstrate that BAVT consistently outperforms parallel sampling baselines. Most notably, BAVT under strict low-budget constraints surpasses baseline performance at $4\times$ the resource allocation, establishing that intelligent budget management fundamentally outperforms brute-force compute scaling.
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Dimensions overall score 7.0
PROBLEM
Budget-Aware Value Tree (BAVT) optimizes LLM agent performance by intelligently managing resource allocation during multi-hop reasoning. Existing budget-aware methods either require expensive fine-tuning or rely on coarse, trajectory-level heuristics that cannot intervene mid-ex...
METHOD
Test-time scaling has become a dominant paradigm for improving LLM agent reliability, yet current approaches treat compute as an abundant resource, allowing agents to exhaust token and tool budgets on redundant steps or dead-end trajectories. Existing budget-aware methods either...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive evaluations on four multi-hop QA benchmarks across two model families demonstrate that BAVT consistently outperforms parallel sampling baselines.
WHY NOW
LLM Agents moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Budget-Aware Value Tree (BAVT) optimizes LLM agent performance by intelligently managing resource allocation during multi-hop reasoning. Existing budget-aware methods either require expensive fine-tuning or rely on coarse, trajectory-level heuristics that cannot intervene mid-execution.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Test-time scaling has become a dominant paradigm for improving LLM agent reliability, yet current approaches treat compute as an abundant resource, allowing agents to exhaust token and tool budgets on redundant steps or dead-end trajectories. Existing budget-aware methods either require expensive fine-tuning or rely on coarse, trajectory-level heuristics that cannot intervene mid-execution.
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. Extensive evaluations on four multi-hop QA benchmarks across two model families demonstrate that BAVT consistently outperforms parallel sampling baselines.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Agents 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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Budget-Aware Value Tree (BAVT) optimizes LLM agent performance by intelligently managing resource allocation during multi-hop reasoning.
Segment
LLM Agents
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Commercial read
7.0/10 public viability
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