Intent Fidelity Score: Framework & Metrics

• Intent Fidelity Score (IFS) is a family of metrics that assesses how well systems preserve user intent across various semantic dimensions and formal frameworks.
• The framework includes approaches like dimension-level evaluation, structured prompt ablation, and contract-based scoring to quantify deviations from intended content.
• IFS highlights the structural-fidelity split, emphasizing that systems must retain the correct underlying intent rather than just matching surface-level features.

Searching arXiv for papers on Intent Fidelity Score and closely related formalizations. Searching for “Intent Fidelity Score”, “intent fidelity”, “Constraint Score intent hallucination”, and related terms. Intent Fidelity Score (IFS) is not a single universally standardized metric in the arXiv literature. The term appears in multiple, non-equivalent senses, and several papers explicitly state that they do not define a metric literally called “Intent Fidelity Score,” while nonetheless providing closely related constructs for measuring whether a system preserves a user’s intended content, goal, or semantic constraints (Peng, 24 May 2026, Peng, 14 May 2026, Chen et al., 6 Apr 2026, Ferrao et al., 25 Jun 2026). Across these works, the common theme is a shift away from holistic adequacy, executability, surface relevance, or prompt-response plausibility, toward explicit evaluation of whether an output, action, classifier decision, or generated artifact preserves the intended meaning encoded in an instruction, rationale, intent schema, or formal contract (Peng, 24 May 2026, Hao et al., 6 Jun 2025, Song et al., 10 May 2026). The resulting family of metrics and frameworks includes weighted dimension-level fidelity aggregates, intent-conditioned reward scores, structured constraint-adherence scores, and PDE-grounded contract comparison, each tailored to a distinct application domain (Peng, 14 May 2026, Chen et al., 6 Apr 2026, Hao et al., 6 Jun 2025, Song et al., 10 May 2026).

1. Terminological scope and competing definitions

The term “Intent Fidelity Score” is used inconsistently across the literature. Several papers state directly that they do not use the exact term IFS, but provide the nearest formal equivalent within their own notation (Peng, 24 May 2026, Peng, 14 May 2026, Chen et al., 6 Apr 2026, Ferrao et al., 25 Jun 2026). In Intent Signal Theory, the closest scalar equivalent is the weighted fidelity recovery aggregate $f\text{-ICM}_W$, built from dimension-level fidelity recovery scores $f_i$ and task-conditioned weights $W_i$ (Peng, 24 May 2026). In the structured prompt-ablation framework for LLMs, the corresponding construct is likewise $f\text{-}ICM_w$, described as Fidelity Intent Coverage, weighted, which measures whether each semantic dimension matches the FULL prompt specification rather than merely being structurally present (Peng, 14 May 2026). In computer-use agents, IntentScore is not presented as an IFS by name, but functions as an intent-conditioned step-level scalar that scores candidate GUI actions with respect to current state, candidate rationale, and per-step correctness (Chen et al., 6 Apr 2026). In safety classification, the nearest analogue is the intent-faithfulness judge outcome or the scalar reward $R_{\text{intent}}$ used in GRPO, where good_match, decent_match, and bad_match are mapped to $1.0$, $0.5$, and $0.1$ respectively (Ferrao et al., 25 Jun 2026).

A distinct and explicit use of the term appears in PDE-grounded verification for LLM-generated multiphysics simulation code, where Intent Fidelity Score is formally defined as a structural metric over a physics contract $\mathcal{P}$, covering governing terms, BCs, ICs, coefficients, time scheme, and domain specification (Song et al., 10 May 2026). In that setting, IFS is not an inferred alignment proxy but a normalized contract discrepancy complement: $$\mathrm{IFS}(\mathcal{P}_{\mathrm{ref}}, \mathcal{P}_{\mathrm{cand}})=1-\Delta_{\mathrm{phys}}(\mathcal{P}_{\mathrm{ref}}, \mathcal{P}_{\mathrm{cand}})$$ with checkpoint failures aggregated by severity weights (Song et al., 10 May 2026).

The literature also contains other unrelated uses of the acronym IFS. In “Becoming self-instruct,” IFS means Instruction Following Score, a ratio of “answer-like” responses to all evaluated prompts, intended to detect instruction-following tone rather than semantic preservation of user-specific intent (AlShikh et al., 2023). In “Adversarial Infidelity Learning for Model Interpretation,” IFS means Instance-wise Feature Selection, not Intent Fidelity Score (Liang et al., 2020). This terminological collision is itself central to the concept’s current status: “Intent Fidelity Score” is better understood as a family resemblance across several formal frameworks than as a settled, single metric.

2. Core conceptual structure

Across papers that treat intent fidelity explicitly, the central distinction is between recovering the form of a request and recovering its intended content. The dimension-level evaluation paper states that holistic scores can report an output as fully aligned even when the model has lost the user’s intended content on one or more dimensions; this is termed the structural-fidelity split (Peng, 14 May 2026). The associated discussion summarizes the distinction as: the structural layer asks whether a slot is present, while the fidelity layer asks whether the filled content is the correct one (Peng, 14 May 2026). Intent Signal Theory makes the same distinction via structural recovery scores $f_i$0 and fidelity recovery scores $f_i$1, aggregated respectively as $f_i$2 and $f_i$3 (Peng, 24 May 2026).

This distinction is formalized in IST through the four-object chain

$f_i$4

where $f_i$5 is latent source intent, $f_i$6 is an observable intent proxy, $f_i$7 is the encoded carrier or prompt, and $f_i$8 is the model output (Peng, 24 May 2026). The key move is that fidelity should be evaluated relative to intended content encoded in $f_i$9, not merely prompt surface adequacy (Peng, 24 May 2026). The theory further defines encoding masks $W_i$0 and observable encoding loss

$W_i$1

to quantify how much task-weighted intent is absent from the prompt (Peng, 24 May 2026). In this framework, fidelity is fundamentally constrained by information availability, not only by model capability. The Theorem of Irreversible Intent Loss states that if an omitted intended value $W_i$2 is private and

$W_i$3

then no decoder can recover it beyond generic substitution, since

$W_i$4

by the data processing inequality (Peng, 24 May 2026). This gives a precise information-theoretic account of why fidelity can remain low even when a model produces structurally plausible outputs.

A related but query-centric formulation appears in the FAITHQA paper on intent hallucination. There, user intent is decomposed into atomic intent constraints through

$W_i$5

with mandatory, important, and optional subsets (Hao et al., 6 Jun 2025). The paper defines intent hallucination as the deviation between an ideal response generated from $W_i$6 and a hallucinated response generated from a distorted constraint set $W_i$7 (Hao et al., 6 Jun 2025). This formulation aligns closely with the broader IFS notion: fidelity is high when the model conditions on the correct intent constraint set, and low when it omits or invents constraints.

3. Formal metrics and scoring schemes

Several distinct metric families instantiate intent fidelity.

In IST, per-dimension fidelity recovery scores $W_i$8 are aggregated as

$W_i$9

with structural recovery defined in parallel as

$f\text{-}ICM_w$0

and intent drift as

$f\text{-}ICM_w$1

(Peng, 24 May 2026). The weights satisfy $f\text{-}ICM_w$2, so $f\text{-}ICM_w$3 lies in $f\text{-}ICM_w$4 under the standard weighted-sum interpretation (Peng, 24 May 2026). The paper explicitly states that if one must reconstruct an “IFS” from its formalism, the exact mapping is

$f\text{-}ICM_w$5

(Peng, 24 May 2026).

The large-scale prompt-ablation study uses the same conceptual architecture with slightly different notation. It defines $f\text{-}ICM_w$6 against the FULL prompt specification as gold reference, aggregates these with task weights into $f\text{-}ICM_w$7, and contrasts them with structural scores $f\text{-}ICM_w$8 aggregated as $f\text{-}ICM_w$9 (Peng, 14 May 2026). The paper describes $R_{\text{intent}}$0 as the closest operational equivalent of an Intent Fidelity Score (Peng, 14 May 2026).

FAITHQA’s automatic metric, Constraint Score, operationalizes intent fidelity as importance-weighted satisfaction of intent constraints. With group weights $R_{\text{intent}}$1, total weight

$R_{\text{intent}}$2

satisfied weight

$R_{\text{intent}}$3

and final score

$R_{\text{intent}}$4

the metric evaluates whether a response satisfies the query’s extracted constraints (Hao et al., 6 Jun 2025). The appendix specifies the default weights as $R_{\text{intent}}$5, $R_{\text{intent}}$6, $R_{\text{intent}}$7 (Hao et al., 6 Jun 2025). Although the paper does not rename Constraint Score as IFS, it explicitly frames the metric as an automatic evaluation method for intent hallucination, which is the negative of intent fidelity in that framework (Hao et al., 6 Jun 2025).

In GUI action selection, IntentScore uses a plan-aware dual encoder and computes a temperature-scaled cosine score

$R_{\text{intent}}$8

between a state embedding and an intent-conditioned action embedding (Chen et al., 6 Apr 2026). Candidate selection is defined as

$R_{\text{intent}}$9

where the candidate rationale $1.0$0 is deliberately placed in the action encoder so that actions with similar surface form but different intentions receive different scores (Chen et al., 6 Apr 2026). This is an IFS-like metric in the narrower sense of step-level action fidelity to plan plus correctness, rather than dimension-level semantic preservation.

The PDE-grounded IFS paper is the most explicit. A physics contract is represented as

$1.0$1

and the discrepancy between a reference contract and a candidate contract is

$1.0$2

with

$1.0$3

(Song et al., 10 May 2026). This score is normalized to $1.0$4, grants partial credit, and is sensitive to severity weights, including $1.0$5 for missing or wrong time derivative, $1.0$6 for wrong dominant operator, $1.0$7 for missing coupling term or wrong BC/IC type, $1.0$8 for value-level change with correct operator, $1.0$9 for source/forcing term missing, and $0.5$0 for non-physics metadata issues (Song et al., 10 May 2026).

4. Empirical evidence and validation

A central empirical result across the dimension-level LLM papers is that holistic evaluation systematically misses intent deficits. In the structured prompt-ablation study, among complete paired-score outputs, $0.5$1 of Chinese-language records and $0.5$2 of English-language records fell into a split zone with

$0.5$3

(Peng, 14 May 2026). Human validation found that for 25 split-zone outputs, the LLM judge assigned $0.5$4, while human raters assigned a mean $0.5$5, indicating that the dimensional fidelity deficits corresponded to genuine quality failures rather than harmless analytical artifacts (Peng, 14 May 2026).

The same paper reports that structural recovery is widespread while fidelity recovery is exceptional. Structural support rates were $0.5$6 in Chinese, $0.5$7 in English, and $0.5$8 in Japanese, while fidelity support rates were $0.5$9, $0.1$0, and $0.1$1 respectively (Peng, 14 May 2026). In 19 out of 20 matched analysis cells, $0.1$2, reinforcing the structural-fidelity split (Peng, 14 May 2026). Human–LLM correlation for $0.1$3 was $0.1$4, compared with $0.1$5 for holistic GA; inter-rater agreement for $0.1$6 was $0.1$7, while GA agreement was $0.1$8 (Peng, 14 May 2026).

IST reports the same broad empirical pattern. The supplementary note describes a human evaluation on $0.1$9 outputs with two independent raters, giving human–LLM agreement on $\mathcal{P}$0 of $\mathcal{P}$1 and agreement on holistic GA of $\mathcal{P}$2 (Peng, 24 May 2026). The measurement study identifies a split zone using $\mathcal{P}$3 and $\mathcal{P}$4, with $\mathcal{P}$5 of Chinese outputs and $\mathcal{P}$6 of English outputs falling into it (Peng, 24 May 2026). The framework also reports public/private asymmetry: $\mathcal{P}$7 of ablation cells were classified as public-regime and $\mathcal{P}$8 as private-regime, supporting the claim that omitted private intent dimensions strongly depress fidelity (Peng, 24 May 2026).

IntentScore provides a different validation regime. Offline, the final model achieves $\mathcal{P}$9 Hard adjacent-step discrimination and $$\mathrm{IFS}(\mathcal{P}_{\mathrm{ref}}, \mathcal{P}_{\mathrm{cand}})=1-\Delta_{\mathrm{phys}}(\mathcal{P}_{\mathrm{ref}}, \mathcal{P}_{\mathrm{cand}})$$0 Real Incorrect detection on 2,000 held-out test pairs (Chen et al., 6 Apr 2026). The paper’s ablations show that alignment-only training assigns nearly identical mean scores to correct and incorrect actions, with a gap of only $$\mathrm{IFS}(\mathcal{P}_{\mathrm{ref}}, \mathcal{P}_{\mathrm{cand}})=1-\Delta_{\mathrm{phys}}(\mathcal{P}_{\mathrm{ref}}, \mathcal{P}_{\mathrm{cand}})$$1, whereas adding margin ranking increases the gap to $$\mathrm{IFS}(\mathcal{P}_{\mathrm{ref}}, \mathcal{P}_{\mathrm{cand}})=1-\Delta_{\mathrm{phys}}(\mathcal{P}_{\mathrm{ref}}, \mathcal{P}_{\mathrm{cand}})$$2 (Chen et al., 6 Apr 2026). The intention-aware action encoder yields the largest single gain in hard adjacent-step discrimination, from $$\mathrm{IFS}(\mathcal{P}_{\mathrm{ref}}, \mathcal{P}_{\mathrm{cand}})=1-\Delta_{\mathrm{phys}}(\mathcal{P}_{\mathrm{ref}}, \mathcal{P}_{\mathrm{cand}})$$3 to $$\mathrm{IFS}(\mathcal{P}_{\mathrm{ref}}, \mathcal{P}_{\mathrm{cand}})=1-\Delta_{\mathrm{phys}}(\mathcal{P}_{\mathrm{ref}}, \mathcal{P}_{\mathrm{cand}})$$4 (Chen et al., 6 Apr 2026). Deployed as a reranker for Agent S3 on OSWorld, IntentScore improves task success from $$\mathrm{IFS}(\mathcal{P}_{\mathrm{ref}}, \mathcal{P}_{\mathrm{cand}})=1-\Delta_{\mathrm{phys}}(\mathcal{P}_{\mathrm{ref}}, \mathcal{P}_{\mathrm{cand}})$$5 to $$\mathrm{IFS}(\mathcal{P}_{\mathrm{ref}}, \mathcal{P}_{\mathrm{cand}})=1-\Delta_{\mathrm{phys}}(\mathcal{P}_{\mathrm{ref}}, \mathcal{P}_{\mathrm{cand}})$$6, a gain of $$\mathrm{IFS}(\mathcal{P}_{\mathrm{ref}}, \mathcal{P}_{\mathrm{cand}})=1-\Delta_{\mathrm{phys}}(\mathcal{P}_{\mathrm{ref}}, \mathcal{P}_{\mathrm{cand}})$$7 points (Chen et al., 6 Apr 2026).

In safety classification, AIMS-based intent-aware training yields competitive harmful-class F1 across several regimes. The paper reports that intent-faithfulness DPO improves over SFT, and GRPO with explicit intent reward improves average F1 from $$\mathrm{IFS}(\mathcal{P}_{\mathrm{ref}}, \mathcal{P}_{\mathrm{cand}})=1-\Delta_{\mathrm{phys}}(\mathcal{P}_{\mathrm{ref}}, \mathcal{P}_{\mathrm{cand}})$$8 to $$\mathrm{IFS}(\mathcal{P}_{\mathrm{ref}}, \mathcal{P}_{\mathrm{cand}})=1-\Delta_{\mathrm{phys}}(\mathcal{P}_{\mathrm{ref}}, \mathcal{P}_{\mathrm{cand}})$$9 (Ferrao et al., 25 Jun 2026). This is not a standalone IFS evaluation, but it constitutes causal evidence that optimizing intent faithfulness improves downstream decision quality (Ferrao et al., 25 Jun 2026).

FAITHQA validates Constraint Score against human judgment. On 1,000 sampled responses, Constraint Score achieves mean squared error $f_i$00 against human evaluation, compared with $f_i$01 for a holistic baseline LLM-as-judge method (Hao et al., 6 Jun 2025). The paper reports that $f_i$02 of Constraint Score outputs fall within one standard deviation of human scores, and weight ablations show the original $f_i$03 scheme gives Pearson $f_i$04 and Spearman $f_i$05 with human judgments, outperforming equal, extreme, moderate, or inverted weightings (Hao et al., 6 Jun 2025).

The PDE-grounded IFS paper evaluates on MooseBench, a 220-case benchmark. Mean IFS improves from $f_i$06 to $f_i$07 for Claude Sonnet 4.6, from $f_i$08 to $f_i$09 for GPT-5.4, and from $f_i$10 to $f_i$11 for DeepSeek V4 Flash when moving from direct generation to PDE-grounded refinement (Song et al., 10 May 2026). On the subset where direct generation falls below IFS $f_i$12, refinement yields gains of $f_i$13, $f_i$14, $f_i$15, and $f_i$16 depending on model and evaluation slice (Song et al., 10 May 2026). Most strikingly, execution-only repair leaves $f_i$17 to $f_i$18 of all 220 cases runnable but still below the fidelity threshold, demonstrating that executability and intent fidelity are separable failure modes (Song et al., 10 May 2026).

5. Application domains and methodological variants

Intent fidelity has been operationalized in markedly different domains.

In general LLM output evaluation, the dominant pattern is dimension-level semantic decomposition. Both IST and the structured prompt-ablation framework use 5W3H/PPS-style semantic dimensions such as What, Why, Who, When, Where, How-to-do, How-much, and How-feel, score these dimensions separately, and then aggregate with task-conditioned weights (Peng, 24 May 2026, Peng, 14 May 2026). These frameworks are best suited to prompts whose intent can be rendered as a small, semantically meaningful schema.

In query-constrained generation, FAITHQA treats intent as a set of explicit, decomposable constraints over subject, action, time, location, qualifiers, and quantity (Hao et al., 6 Jun 2025). This makes it naturally suited to multi-condition questions, creative writing prompts with explicit requirements, and RAG settings where missing-source handling is part of intent fidelity (Hao et al., 6 Jun 2025).

In computer-use agents, the unit of fidelity is not a semantic dimension but an action candidate conditioned on a rationale. Here the relevant question is whether a single GUI action is faithful to the current state and to the candidate’s own plan (Chen et al., 6 Apr 2026). This is closer to plan-aware process reward modeling than to semantic schema matching.

In intent-based networking, LEAD-Drift defines an intent drift risk score rather than a direct fidelity score. It predicts whether a failure will occur within a future horizon $f_i$19, with labels

$f_i$20

and smooths raw risk scores $f_i$21 via

$f_i$22

(Hossain et al., 14 Feb 2026). The paper states that LEAD-Drift is not a direct IFS in the present-state sense, but a forward-looking risk score for impending loss of intent compliance (Hossain et al., 14 Feb 2026). This suggests a predictive rather than descriptive branch of the intent-fidelity family.

In scientific code generation, intent fidelity is defined over a formal physics contract rather than over natural-language semantics. The reference object is a mathematically structured PDE specification, and fidelity is measured by deterministic reconstruction and comparison against encoded code semantics (Song et al., 10 May 2026). This is the strongest extensional form of IFS presently described in the literature, because the score is computed against a domain-specific formal contract rather than a judge’s semantic impression.

In broad intent understanding, IntentGrasp does not define IFS but provides a benchmarked subset of the problem: instance-level F1 over semantically contextualized intent labels across 49 source corpora and 12 domains (Yin et al., 7 May 2026). The benchmark reports that all tested models score below $f_i$23 on the All Set and below $f_i$24 on the Gem Set, with 17 of 20 tested models performing worse than a random-guess baseline of $f_i$25 on Gem Set, while estimated human performance is about $f_i$26 (Yin et al., 7 May 2026). This supports the view that robust intent understanding remains a weak capability even before full fidelity-to-action or fidelity-to-output is considered.

6. Misconceptions, limitations, and open structure of the concept

A recurring misconception is that high holistic quality, high execution success, or high task success implies high intent fidelity. The literature argues against each of these equivalences. The structural-fidelity split shows that outputs can be structurally complete yet semantically wrong with respect to user-specific dimensions (Peng, 14 May 2026, Peng, 24 May 2026). FAITHQA shows that responses can be factually accurate while still omitting required constraints or acting on invented ones (Hao et al., 6 Jun 2025). The PDE-grounded code paper shows that simulation inputs can mesh, run, and converge while solving the wrong governing equations (Song et al., 10 May 2026). LEAD-Drift shows that present-state deviation and future-risk semantics are distinct, so predictive risk should not be conflated with current fidelity (Hossain et al., 14 Feb 2026).

A second misconception is that “intent fidelity” already names a settled metric. The evidence points in the opposite direction. Some papers explicitly disclaim the term while offering nearby constructs (Peng, 24 May 2026, Peng, 14 May 2026, Chen et al., 6 Apr 2026, Ferrao et al., 25 Jun 2026). Others use IFS for different concepts entirely, such as instruction-following tone (AlShikh et al., 2023) or instance-wise feature selection (Liang et al., 2020). The concept is therefore better understood as an emerging evaluation family centered on preservation of intended content, with domain-specific realizations.

The limitations are equally clear. Dimension-level frameworks require a structured intent schema and an annotation or judging rubric for per-dimension scores; the exact rubric for assigning numerical $f_i$27 and $f_i$28 is not fully specified in IST (Peng, 24 May 2026). The prompt-ablation study notes limitations including only 30 tasks, threshold circularity in public/private classification, and human evaluation on only 60 samples with two raters (Peng, 14 May 2026). FAITHQA depends on LLM-based decomposition and does not formalize a symmetric precision-style penalty for hallucinated intent in the core score (Hao et al., 6 Jun 2025). IntentScore requires candidate generation quality and is strongest when rationale annotations are present and informative; $f_i$29 of AgentNet steps lack intention annotations (Chen et al., 6 Apr 2026). AIMS-based intent faithfulness depends on an LLM judge and remains evaluated primarily through harmful-class F1 rather than a standalone intent metric (Ferrao et al., 25 Jun 2026). PDE-grounded IFS is structural rather than a full physical validity certificate; it does not assess mesh adequacy, discretization error, solver behavior, or coefficients hidden in complex material chains (Song et al., 10 May 2026).

These limitations suggest that intent fidelity is not reducible to a single universal scalar without strong assumptions about representation. A plausible implication is that the field is converging on a layered view: intent fidelity requires an explicit intermediate representation of intent, a comparison target richer than holistic adequacy, and a domain-appropriate mechanism for checking whether outputs preserve that target. The specific metric then depends on whether intent is represented as semantic dimensions, atomic constraints, plans, safety-relevant summaries, risk horizons, or formal contracts (Peng, 24 May 2026, Hao et al., 6 Jun 2025, Chen et al., 6 Apr 2026, Ferrao et al., 25 Jun 2026, Hossain et al., 14 Feb 2026, Song et al., 10 May 2026).

7. Historical and conceptual significance

The broader significance of Intent Fidelity Score lies in the way it reframes evaluation. Earlier work on instruction following, intent classification, or recommendation often treated intent indirectly through response quality, label accuracy, or recommendation relevance (AlShikh et al., 2023, Bhattacharya et al., 2017, Yin et al., 7 May 2026). The recent literature increasingly treats intent as a distinct computational object that must be represented, preserved, and evaluated on its own terms (Peng, 24 May 2026, Peng, 14 May 2026, Ferrao et al., 25 Jun 2026). This shift parallels a move from prompt-centric to intent-centric modeling, from holistic assessment to dimension-level or contract-level comparison, and from execution or plausibility to structural preservation of what the user actually meant (Peng, 24 May 2026, Song et al., 10 May 2026).

Within this emerging landscape, the clearest unifying principle is that fidelity is not the same as fluency, relevance, or even correctness in a narrow task sense. High-fidelity systems must preserve the right content, not just produce the right shape. In LLM outputs, that means reproducing user-specific values rather than generic defaults (Peng, 14 May 2026). In safety classification, it means modeling the user’s safety-relevant goal rather than only the final harmful/safe label (Ferrao et al., 25 Jun 2026). In GUI agents, it means selecting actions that are faithful to plan and state rather than merely plausible (Chen et al., 6 Apr 2026). In scientific code generation, it means encoding the intended PDE rather than merely producing executable code (Song et al., 10 May 2026).

For that reason, “Intent Fidelity Score” now designates less a single formula than a research program: the attempt to make latent user intent computationally visible, compare it to outputs at the right representational level, and measure the gap with enough structure to detect failures that traditional end metrics collapse away (Peng, 24 May 2026, Peng, 14 May 2026, Hao et al., 6 Jun 2025).