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arxiv: 2605.21516 · v1 · pith:32YL7USPnew · submitted 2026-05-15 · 💻 cs.LG · cs.AI

Harnesses for Inference-Time Alignment over Execution Trajectories

Boyuan Wang , Bochao Li , Minghan Wang , Yuxin Tao , Fang Kong This is my paper

Pith reviewed 2026-05-22 00:59 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords LLM agentsharness engineeringtask decompositionguided executioninference-time alignmentexecution trajectoriespartial workflowsfailure modes
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The pith

Separating task decomposition from guided execution in LLM agent harnesses shows partial guidance often outperforms full workflows.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines harnesses that improve LLM agent performance on long tasks by breaking them into steps and steering actions along the way. It treats these as two distinct parts under an inference-time trajectory alignment lens, so the effects of step count, retry limits, and action changes can be measured separately. This split makes it possible to spot when extra detail starts hurting results through problems like over-decomposition or invented steps. Synthetic tests and real terminal-agent runs back up the measurements. The main finding is that harnesses which only set the first few steps and then let the agent proceed can reach higher success rates than ones that spell out every step.

Core claim

By viewing harness design through inference-time trajectory alignment, the work separates harnesses into task decomposition, which breaks a task into sub-goals, and guided execution, which reweights local actions; this split quantifies how granularity, retry budgets, and reweighting set performance ceilings and isolates failure modes such as over-decomposition, over-pruning, and hallucinated execution, while experiments confirm that partial harnesses specifying only initial steps produce higher pass rates than fully structured workflows.

What carries the argument

Inference-time trajectory alignment that splits harness design into task decomposition and guided execution.

Load-bearing premise

Framing harnesses as separate mechanisms of task decomposition and guided execution enables accurate quantification of performance limits and identification of specific failure modes such as over-decomposition and hallucinated execution.

What would settle it

A controlled run on the same terminal-agent benchmarks in which fully specified step-by-step harnesses produce strictly higher pass rates than partial initial-step versions, without the predicted over-decomposition or hallucination failures, would falsify the central claim.

Figures

Figures reproduced from arXiv: 2605.21516 by Bochao Li, Boyuan Wang, Fang Kong, Minghan Wang, Yuxin Tao.

Figure 1
Figure 1. Figure 1: Alignment Principles for Harnessed Agent Execution [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Three alignment principles for harness design. (a, d) Granularity–capability: pass rate is non-monotonic in subgoal count K and final bias grows with finer decomposition, with each agent peaking at a different K. (b, e) Guidance–evidence: aligned guidance improves pass rate and lowers final error as the action pool grows, while misaligned guidance does the opposite. (c, f) Partial harnessing: pass rate is … view at source ↗
Figure 3
Figure 3. Figure 3: Harness design trade-offs in real and controlled settings. (a) On Terminal-Bench v2, pass [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Partial harnessing on llm-inference-batching-scheduler task. Left: the fully specified workflow over-constrains execution and the agent gets lost in repeated intermediate revisions before reaching the final stages. Right: the partial workflow provides only an initial 3-step harness, after which the agent completes the remaining task through its own planning. uniform selection on the same pool. The benefit … view at source ↗
Figure 5
Figure 5. Figure 5: Retry budget improves recoverability but cannot overcome capability mismatch. Pass rate and final bias both saturate once attempts no longer relax the binding constraint. A.4 Partial Harnessing Experiment This experiment sweeps a progress slice as in Theorem 3. We fix a chunk size c and a number of scaffolded chunks r, defining the harness ∆h = (c, . . . , c | {z } r chunks , G − rc), where r = 0 leaves th… view at source ↗
Figure 6
Figure 6. Figure 6: Tolerance trades recoverability for terminal accuracy. Pass rate rises sharply with ϵ but final bias grows when tolerance exceeds the agent’s natural per-stage variation. (a) Pass rate vs. subgoal count under pruning (b) Final bias vs. subgoal count under pruning [PITH_FULL_IMAGE:figures/full_fig_p018_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Aggressive pruning amplifies granularity mismatch. Removing more action distribu￾tions worsens both pass rate and final bias, with the largest gap appearing under fine decomposi￾tion where local mismatch is most exposed. Aggressive guidance pruning. Starting from a three-distribution pool—Model 1 (µ = 6, σ = 2, [4, 8]), Model 2 (µ = 8, σ = 3, [5, 11]), Model 3 (µ = 10, σ = 6, [4, 14])—we randomly remove m … view at source ↗
Figure 8
Figure 8. Figure 8: Marginal stopping persists across chunk sizes. Both pass rate and final bias remain unimodal in r at c = 10 and c = 25, with peak location shifting as predicted by Theorem 3. B Real World Task Details B.1 Granularity experiment on Terminal-Bench-2 This appendix specifies the full configuration of the granularity sweep referenced in Section 5.2. The experiment varies the workflow step count k ∈ {1, . . . , … view at source ↗
Figure 9
Figure 9. Figure 9: Granularity–capability alignment in a controlled addition task. (a) Pass rate varies with the number of subgoals and depends on the match between harness granularity and agent capability. (b) The mean absolute final bias increases as the number of subgoals grows, showing that overly fine task decomposition can accumulate larger terminal error. task instruction together with a fixed generation prompt that r… view at source ↗
read the original abstract

Harness engineering has emerged as an important inference-time technique for large language model (LLM) agents, aiming to improve long-term performance through task decomposition and guided execution. However, more elaborate harnesses are not uniformly better: increasing decomposition or guidance can sometimes improve execution, but can also reduce final task success. We study harness design through the lens of inference-time trajectory alignment. This perspective separates harness into two mechanisms: task decomposition, which structures a task into sub-goals, and guided execution, which reshapes local action distributions during execution. This decomposition allows us to quantify how workflow granularity, retry budgets, and guidance-induced action reweighting shape the performance limits of harness design. It further reveals concrete failure modes, including over-decomposition, over-pruning, and hallucinated execution. We validate these predictions through controlled synthetic experiments and real terminal agent benchmarks. Inspired by the theory, we further show that effective harnesses can be partial: specifying only the initial steps and leaving the remaining execution to agent can achieve higher pass rate than fully structured workflows.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript frames harness engineering for LLM agents as inference-time trajectory alignment, separating harnesses into task decomposition (structuring into sub-goals) and guided execution (reshaping local action distributions). It quantifies effects of workflow granularity, retry budgets, and guidance on performance limits, identifies failure modes including over-decomposition, over-pruning, and hallucinated execution, validates predictions via controlled synthetic experiments and terminal agent benchmarks, and shows that partial harnesses (specifying only initial steps) can outperform fully structured workflows.

Significance. If the proposed separation of mechanisms is empirically supported and the partial-harness result generalizes, the work offers a useful lens for understanding why more elaborate harnesses are not uniformly superior and provides concrete guidance on harness design. The controlled experiments that test theoretical predictions about granularity and guidance-induced reweighting represent a methodological strength.

major comments (1)
  1. [Validation through controlled synthetic experiments and real terminal agent benchmarks] The central empirical result—that partial harnesses achieve higher pass rates than full workflows—depends on the assumption that task decomposition and guided execution remain separable once the harness is made partial. The synthetic experiments and terminal benchmarks must demonstrate that cross-talk (e.g., initial decomposition reshaping subsequent action distributions via implicit prompting or state carry-over) is negligible; without such evidence, the performance-limit quantification and attribution of gains to 'leaving execution to the agent' risk being artifacts of the prompting regime rather than a general consequence of the framework.
minor comments (2)
  1. [Experimental setup] Clarify the precise operational definitions of 'partial harness' versus 'full workflow' in the experimental setup to allow replication.
  2. [Benchmark results] Add error bars or statistical significance tests to the pass-rate comparisons in the benchmark results.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive review and for acknowledging the methodological value of our controlled experiments. We address the major comment below, clarifying our approach to separability while indicating where we will strengthen the manuscript.

read point-by-point responses

  1. Referee: [Validation through controlled synthetic experiments and real terminal agent benchmarks] The central empirical result—that partial harnesses achieve higher pass rates than full workflows—depends on the assumption that task decomposition and guided execution remain separable once the harness is made partial. The synthetic experiments and terminal benchmarks must demonstrate that cross-talk (e.g., initial decomposition reshaping subsequent action distributions via implicit prompting or state carry-over) is negligible; without such evidence, the performance-limit quantification and attribution of gains to 'leaving execution to the agent' risk being artifacts of the prompting regime rather than a general consequence of the framework.

    Authors: We agree that explicit evidence for negligible cross-talk is necessary to support the separability claim in partial harnesses. Our synthetic experiments isolate this by using a fully observable simulated environment in which we ablate state carry-over (resetting agent memory between steps) and remove any implicit prompting from the initial decomposition. In these controls, the performance advantage of partial harnesses persists and is attributable to the agent’s native execution distribution rather than reweighting from the harness prefix. We have added a new subsection (Section 4.3) that reports these ablations along with quantitative metrics on action-distribution divergence before and after the partial prefix. In the terminal benchmarks, we include a similar post-hoc analysis of execution traces to bound residual cross-talk. These additions directly address the concern while preserving the original experimental design. revision: yes

Circularity Check

0 steps flagged

No significant circularity; framework and empirical claims are self-contained.

full rationale

The paper develops a conceptual separation of harnesses into task decomposition and guided execution mechanisms, then uses this framing to interpret performance limits and failure modes. These are validated via controlled synthetic experiments and terminal benchmarks rather than any closed-form derivation or parameter fitting. No equations, fitted inputs, or self-citation chains are invoked to force the central results; the partial-harness superiority claim follows directly from the reported empirical comparisons. The derivation chain therefore remains independent of its own outputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are specified or implied in the provided abstract; the work appears empirical rather than axiomatic.

pith-pipeline@v0.9.0 · 5717 in / 899 out tokens · 39080 ms · 2026-05-22T00:59:15.197503+00:00 · methodology

discussion (0)

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