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arxiv: 2606.20661 · v1 · pith:GR547WWGnew · submitted 2026-06-09 · 💻 cs.AI · cs.CL· cs.LG

From Knowing to Acting: Benchmarking Self-Awareness Capability of LLM Agents

Yifan Li , Shengbin Yue , Boyu Feng , Jinhu Qi , Bo Ke , Zixing Song , Hongru Wang , Zhongyu Wei
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Irwin King
This is my paper

Pith reviewed 2026-06-27 13:13 UTC · model grok-4.3

classification 💻 cs.AI cs.CLcs.LG
keywords LLM agentsself-awarenesstool usebenchmarkmetacognitive judgmentcognitive gatingKAware datasetKAPRO framework
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The pith

LLM agents succeed more when they accurately judge whether a problem needs external tools or can be solved from internal knowledge alone.

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

The paper introduces KAPRO, a framework that separates an agent's judgment about needing outside help from its actual behavior when solving tasks. It also builds the KAware dataset, which splits problems into those requiring external resources, those solvable internally, and hybrids. Experiments across many models reveal that this self-awareness strongly tracks overall success yet weakens markedly on internal-knowledge tasks. Open-source models tend to overuse tools through pattern matching, while proprietary models show steadier decisions about when to act.

Core claim

Self-awareness capability, the ability to discern whether a problem requires necessary external resources or can be solved via internal parametric knowledge, is strongly correlated with task success but degrades sharply in internal-capability settings, with open-source and instruction-following models showing stronger tool overuse due to shallow pattern matching and proprietary and reasoning-oriented models demonstrating more reliable cognitive gating.

What carries the argument

KAPRO (Knowing-Acting Quadrant PRObe), a framework that evaluates cognitive-behavioral alignment by decoupling an agent's metacognitive judgment from its spontaneous execution.

If this is right

  • Self-awareness capability correlates positively with task success across agent architectures.
  • Self-awareness drops sharply when tasks fall inside the agent's internal parametric knowledge.
  • Open-source and instruction-following models overuse tools through shallow pattern matching.
  • Proprietary and reasoning-oriented models exhibit more reliable decisions about when to invoke tools.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Training loops that reward accurate self-judgment before execution could reduce wasted tool calls in long-horizon tasks.
  • The same decoupling of judgment from action might be applied to other agent decisions such as when to ask a human for clarification.
  • Hybrid subspaces in the dataset may reveal whether agents can correctly combine internal and external steps rather than defaulting to one mode.

Load-bearing premise

The KAware dataset's partitioning of tasks into external, internal, and hybrid subspaces accurately reflects true epistemic boundaries without selection bias or mislabeling of what counts as solvable via internal parametric knowledge.

What would settle it

A controlled re-labeling of a subset of KAware tasks as internal when they are actually external (or vice versa) followed by re-running the agent evaluations to check whether the reported correlation between self-awareness scores and success rates disappears.

Figures

Figures reproduced from arXiv: 2606.20661 by Bo Ke, Boyu Feng, Hongru Wang, Irwin King, Jinhu Qi, Shengbin Yue, Yifan Li, Zhongyu Wei, Zixing Song.

Figure 1
Figure 1. Figure 1: An illustration of self-awareness capability in [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: An illustration of KAPRO pipeline. (A) Tool Seed construction: Real-world APIs are filtered and annotated into External Function and Internal Function tool seeds. (B) KAware Task Synthesis: we utilize topology-aware sampling (parallel, multi-hop, single) to construct diverse tasks, followed by a rigorous quality recheck of agent trajectories. (C) KAPRO Evaluation: we assess knowing-acting alignment by deco… view at source ↗
Figure 3
Figure 3. Figure 3: KAS performance across logical reasoning types (Single-hop, Parallel, Multi-hop) and models. A [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The negative correlation between KAS and [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Distributional shifts (∆, in percentage points) of the knowing–acting joint distribution under noisy tool environments, relative to the clean baseline. Each cell reports Pnoise(·) − Pbase(·) over the four quadrants Consistent Competence (Kc/Ac), Alignment Issue (Kc/Aw), Behavioral Competence (Kw/Ac), and Cognitive Issue (Kw/Aw). Red denotes an increase, blue a decrease. models show negative DirGap, meaning… view at source ↗
Figure 6
Figure 6. Figure 6: Tool distribution by domain, with parentheses [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
read the original abstract

The integration of external tools has transitioned LLM agents from passive responders to autonomous systems. However, current benchmarks prioritize execution success, neglecting self-awareness capability, the ability to discern whether a problem requires necessary external resources or can be solved via internal parametric knowledge. To address this, we introduce KAPRO (Knowing-Acting Quadrant PRObe), a framework that evaluates cognitive-behavioral alignment by decoupling an agent's metacognitive judgment (Knowing) from its spontaneous execution (Acting). We further construct KAware, a dataset rigorously partitioning tasks into external, internal, and hybrid subspaces to systematically probe these epistemic boundaries. Extensive experiments across diverse agent architectures show that self-awareness capability is strongly correlated with task success but degrades sharply in internal-capability settings. Moreover, open-source and instruction-following models exhibit stronger tool overuse due to shallow pattern matching, while proprietary and reasoning-oriented models demonstrate more reliable cognitive gating. Benchmark and codes are available at https://github.com/AI-Santiago/KAware.

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

2 major / 2 minor

Summary. The paper introduces the KAPRO framework, which decouples metacognitive judgment ('Knowing') from spontaneous execution ('Acting') to probe self-awareness in LLM agents. It constructs the KAware dataset by partitioning tasks into external, internal, and hybrid subspaces, then reports experiments across agent architectures showing that self-awareness correlates strongly with task success, degrades sharply in internal-capability settings, and differs by model type (open-source/instruction-following models show more tool overuse via shallow matching, while proprietary/reasoning models exhibit better cognitive gating).

Significance. If the central claims hold, the work supplies a useful new lens and dataset for evaluating metacognition in tool-using agents, moving beyond pure execution metrics. The public release of the benchmark and code is a clear strength that supports reproducibility.

major comments (2)
  1. [§3] §3 (KAware construction): the partitioning into internal vs. external subspaces is described as 'rigorous' but appears to rely on generic human criteria or zero-shot solvability checks that are not model-specific. This directly undermines the load-bearing claim that observed degradation and 'cognitive gating' differences reflect metacognition rather than inconsistent subspace assignment across proprietary vs. open-source models.
  2. [§4] §4 (experimental results): the reported correlations between self-awareness scores and success, and the model-type differences in tool overuse, rest on the assumption that KAware labels accurately reflect each model's parametric knowledge boundaries. Without per-model validation experiments (e.g., zero-shot accuracy on the 'internal' subset for each evaluated model), these correlations risk being artifacts of labeling rather than evidence of self-awareness.
minor comments (2)
  1. [Abstract / §1] The abstract and introduction use 'self-awareness capability' without a crisp operational definition that distinguishes it from related constructs such as calibration or refusal behavior; a short clarifying paragraph would help.
  2. [Figures] Figure captions and axis labels in the result plots could be expanded to explicitly state the exact metrics used for 'Knowing' and 'Acting' scores.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on the KAware construction and the experimental validation. We address each major comment below.

read point-by-point responses

  1. Referee: [§3] §3 (KAware construction): the partitioning into internal vs. external subspaces is described as 'rigorous' but appears to rely on generic human criteria or zero-shot solvability checks that are not model-specific. This directly undermines the load-bearing claim that observed degradation and 'cognitive gating' differences reflect metacognition rather than inconsistent subspace assignment across proprietary vs. open-source models.

    Authors: The KAware partitioning relies on human expert criteria assessing whether tasks are solvable from parametric knowledge alone, augmented by zero-shot checks using a reference model. This yields a fixed, model-agnostic benchmark intended for cross-architecture comparison. We acknowledge that the labels are not derived from per-model testing and therefore agree that additional validation is warranted to rule out assignment artifacts. revision: yes

  2. Referee: [§4] §4 (experimental results): the reported correlations between self-awareness scores and success, and the model-type differences in tool overuse, rest on the assumption that KAware labels accurately reflect each model's parametric knowledge boundaries. Without per-model validation experiments (e.g., zero-shot accuracy on the 'internal' subset for each evaluated model), these correlations risk being artifacts of labeling rather than evidence of self-awareness.

    Authors: The reported correlations are based on consistent behavioral patterns observed across the tested models. To strengthen the evidence that the subspaces align with each model's actual knowledge boundaries, we will add per-model zero-shot accuracy results on the internal subset in the revised manuscript. revision: yes

Circularity Check

0 steps flagged

No circularity: new benchmark and empirical evaluation are self-contained.

full rationale

The paper constructs a new dataset (KAware) and framework (KAPRO) to measure self-awareness via task partitioning and agent behavior. Claims about correlations, degradation in internal settings, and model-type differences are derived from experiments on this fresh benchmark rather than from any fitted parameter renamed as prediction, self-referential definition, or self-citation chain. No equations or derivations reduce the output to the input by construction; the epistemic-boundary labeling is a methodological assumption open to external validation, not a circular step. This matches the default expectation for benchmark papers.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that tasks can be reliably classified by epistemic requirements and that behavioral alignment measures self-awareness; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Tasks can be partitioned into external, internal, and hybrid categories based on whether they require external resources or can be solved with internal parametric knowledge.
    This partitioning underpins the KAware dataset and all subsequent evaluations of cognitive-behavioral alignment.

pith-pipeline@v0.9.1-grok · 5725 in / 1175 out tokens · 30350 ms · 2026-06-27T13:13:13.920993+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

23 extracted references · 1 linked inside Pith

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    Interaction Scope Assessment Logic:Does the tool alter the state of the world or system? • True:State-mutating operations (e.g., Create/Update/Delete, “Send Email”, “Buy Stock”, “Reboot Server”). • False:Read-only or informational (e.g., “Get Weather”, “Search Docs”, “Read File”). • Unknown:Vague verbs without specify- ing read/write

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    Data Privacy Scope Assessment Logic:Does the tool require restricted data access? • True:Accesses private/sensitive data (e.g., PII, internal DBs, user-auth content). • False:Uses public-domain knowledge (e.g., Wikipedia facts, general reasoning, open web). • Unknown:Data source/permission level not mentioned

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    Modality Scope Assessment Logic:Does the tool handle data formats or media types unsupported by standard LLMs? • True:Unsupported/specialized modali- ties (e.g., video generation, 3D models/- point clouds, CAD files). • False:Standard native modalities (e.g., text, code). • Unknown:Input/output media format not specified

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