In this work, we introduce a new paradigm for exploring a large corpus of small documents by identifying roles at the phrasal and sentence levels, then slice on, reify, group, and/or align the text itself on those roles, with sentences left intact.

These observations underscored the need for an improved UI design for authoring custom aspects, especially for those who are less familiar with the research corpus.

Future work could explore more seamless ways of preserving context, such as allowing users to navigate through every sentence of an abstract directly within the Cross-Sentence Relationship pane, fostering a more cohesive understanding of the content.

Supporting a new corpus would require defining new corpus-appropriate predefined aspects and chunk role labels. Other components would likely be able to remain unchanged.

We posit that our approach can generalize to other domains such as journalism, code synthesis, and social media analytics where visual alignment of text can enable meaningful comparisons of underlying patterns to identify relational clarity.

We consider common sequences of chunk roles to be alignable structures that could be used to support users in identifying structural similarities and differences across sentences in different abstracts, in line with Structure-Mapping Theory [17].

Like prior Structural Mapping Theory (SMT)-informed work in text corpora representation, AbstractExplorer's features have enabled some users to see more of both the overview and the details at the same time, facilitating abstraction without losing context.

Our work demonstrates that designs informed by Structure-Mapping Theory can support users in navigating, making use of, and engaging with variation present in information.

According to SMT, this generalization depends on most documents having some shared implicit structure.

This ordering prioritizes dominant structural patterns (largest groups first) while exposing fine-grained variations (via length-sorted triplets), mirroring how humans compare sentences, if SMT is an accurate description in this domain of comparative close reading.

Structural mappings between objects are part of the cognitive process of comparison according to the Structure-Mapping Theory [17], and juxtaposition can facilitate humans in recognizing particular possible structural mappings between objects [75].

In SMT terminology, rendering and arranging according to corresponding chunks reify "commonalities in structure," while variation within corresponding chunks are "alignable differences" that users are predicted to notice.

The prior SMT-informed tools in Section 2.3 for both code and natural language corpora suggest that the cognitive process of comparing texts may be no exception to the cognitive processes SMT predicts.

SMT posits that visual alignment helps people perceive relational similarities and differences more clearly, thereby improving their ability to make meaningful comparisons and understand underlying patterns [28, 38, 47].

SMT provides a framework for understanding how humans compare two or more objects by finding common structural alignments between objects.

Structural Mapping Theory (SMT) is a long-standing well-vetted theory from Cognitive Science that describes how humans attend to and try to compare objects by finding mental representations of them that can be structurally mapped to each other (analogies).

This SMT-informed approach, which AbstractExplorer shares, tries to give this mental machinery "a leg up," letting users perhaps skip some steps by accepting reified cross-document relationships identified by the computer.

The human perceptual, comparative mental machinery that SMT describes is part of what enables humans to form more abstract structured mental models from concrete examples, among other critical knowledge tasks.

These examples of text-centric lossless techniques do not abstract away or summarize; they strategically re-organize and re-render the existing text to help enhance readers' own perceptual cognition, informed by Structural Mapping Theory (SMT) [17].

Theory (SMT) to facilitate seeing both the overview and the details at the same time, facilitating abstraction without losing context.

We process this data in a three-stage pipeline (Figure 6). In the first stage, Sentence Segmentation and Categorization, abstracts are split into individual sentences using the NLTK package, and each sentence is classified into one of the five pre-defined aspects as listed in Section 4.1.1. Classification is performed by prompting an LLM (see prompt used in Appendix D.1) with the sentence and its full abstract.

Then, we segment sentences within each aspect into grammar-preserving chunks (see prompt used in Appendix D.2). This results in grammatically coherent chunks that are the basis of structure patterns. After identifying chunk boundaries, we again prompt an LLM to generate labels for chunks in a human-in-the-loop approach: starting from an initial set of labels for chunk roles, when a new label is generated, a researcher from the research team examines the new label and merges it with existing labels if appropriate, controlling for the total number of labels.

In this study, we allowed participants to experience views of same-aspect sentences (Section 4.1.1) with different combinations of highlighting, ordering, and alignment (as described in Section 4.1.2 and Section 4.1.4) enabled or not, in order to understand which and/or what combinations most effectively supported users' ability to skim and read laterally across documents.

Inspired by GP-TSM [24], AbstractExplorer first segments sentences into grammar-preserving chunks—segments that respect grammatical boundaries, i.e., an LLM judges that the sentence can be truncated at that chunk boundary without breaking the grammatical integrity of the preceding text. Each chunk is then classified by an LLM as having one of nine pre-defined roles, each of which has its own assigned color.

AbstractExplorer classifies sentences into five pre-defined aspects common in CHI abstracts: Problem Domain, Gaps in Prior Work, Methodology/Contribution, Results/Findings, and Discussion/Conclusion.

We conducted a qualitative analysis of user study transcripts and survey responses using a Grounded Theory approach [8]. First, the lead researcher collected a list of participants' behaviors, approaches, reflections on their experience, and feedback about the interface. The researcher then systematically coded this data, revisiting the data multiples times and refining the codes to ensure consistency and coherence. Through this process, high-level themes were identified and organized using affinity diagramming. Once the thematic structure was finalized, the researcher gathered supporting evidence for each theme and synthesized the findings, which were reviewed by the research team to ensure agreement on the results.

Activity log data, which revealed how participants actually used the interface, echoed the above findings. According to the log data, participants spent most of their reading time (66.31%) with vertical alignment on the second element in structure pairs, followed by alignment on the first element (29.19%), and left-justified alignment (5.13%). Highlighting usage showed a similar preference: 91.13% of time with all chunks highlighted, 8.25% with partial highlighting, and minimal time (0.63%) without highlights.

In this section, we present findings on how AbstractExplorer supports comparative close reading at scale by integrating quantitative survey responses and log data with qualitative analysis of transcripts and open-ended responses. The qualitative analysis process is described in detail in Appendix H.

Throughout the two tasks, we also collected detailed interaction logs including counts of user-defined aspects created, duration of highlighting usage, and time allocation across the three possible alignment options.

Both gaze data and the semi-structured interviews revealed that lower NFC participants were more willing to be guided by the three features and took advantage of them consciously.

Using a two-tailed Mann-Whitney U Test, we found that participants who reported their lowest perceived cognitive load when all three features were enabled had significantly lower NFC than participants who reported their lowest cognitive load level when skimming with no features enabled—in the baseline interface (p=0.03).

The raw NASA-TLX score is the sum of all 6 NASA-TLX questions after reversing the appropriate questions.

To compute a participant's NFC score, we averaged their response to the six questions, each ranging from 1 to 7, after reversing the appropriate questions.

For simplicity of analysis, we denote participants with NFC scores above the overall participants' median NFC of 5.42 (IQR = 0.583) as higher NFC, and lower NFC otherwise.

To contrast participants' gaze patterns in each condition, we used a Tobii Pro Spark eye-tracker placed below the desktop monitor used by all subjects; Tobii Pro Lab software recorded each participant's gaze over time in each condition.

We collected 80 sentences from our abstracts dataset labeled by our system as "Methodology/Contribution." Participants viewed the same 80 sentences in each condition—often with a different subset of sentences initially visible due to ordering changes—but only had two minutes to look at them in each condition.

After obtaining an expanded set of high-level chunk labels, we assign them to each of the sentence chunks by using LLMs in a multiclass classification few-shot learning task, with the initial labels and assignment as examples (see prompt used in Appendix D.3).

Then, we segment sentences within each aspect into grammarpreserving chunks (see prompt used in Appendix D.2). This results in grammatically coherent chunks that are the basis of structure patterns. After identifying chunk boundaries, we again prompt an LLM to generate labels for chunks in a human-in-the-loop approach: starting from an initial set of labels for chunk roles, when a new label is generated, a researcher from the research team examines the new label and merges it with existing labels if appropriate, controlling for the total number of labels.

We process this data in a three-stage pipeline (Figure 6). In the first stage, Sentence Segmentation and Categorization, abstracts are split into individual sentences using the NLTK package, and each sentence is classified into one of the five pre-defined aspects as listed in Section 4.1.1. Classification is performed by prompting an LLM (see prompt used in Appendix D.1) with the sentence and its full abstract.

After the interviews, we analyzed the data using the process described in Appendix B

We conducted a study with 12 blind SR users.

We conducted a collaborative, user-centered design study with a team of scientific researchers.

We conducted quantitative (n=70) and qualitative (n=30) studies with healthcare experts.

We conducted a qualitative study with 16 blind and visually impaired (BI) developers.

We conducted role-playing exercises with 24 US journalists.

We conducted a study with 32 blind SR users.

We conducted a collaborative, user-centered design study with a team of scientific researchers.

We conducted quantitative (N=79) and qualitative (N=93) studies with healthcare experts.

We conducted a qualitative study with 35 blind and visually impaired (VI) Developers.

We conducted a study with 32 blind US users.

We conducted a study with 12 blind SR users.

We conducted a collaborative, user-centered design study with a team of scientific researchers.

We conducted quantitative (n=70) and qualitative (n=30) studies with healthcare experts.

We conducted a qualitative study with 16 blind and visually impaired (BI) developers.

We conducted role-playing exercises with 24 US journalists.

We conducted a study with 32 blind SR users.

We conducted a collaborative, user-centered design study with a team of scientific researchers.

We conducted quantitative (N=79) and qualitative (N=93) studies with healthcare experts.

We conducted a qualitative study with 35 blind and visually impaired (VI) Developers.

We conducted a study with 32 blind US users.

We conducted a study with 12 blind SR users.

We conducted a collaborative, user-centered design study with a team of scientific researchers.

We conducted quantitative (n=70) and qualitative (n=30) studies with healthcare experts.

We conducted a qualitative study with 16 blind and visually impaired (BI) developers.

We conducted role-playing exercises with 24 US journalists.

We conducted a study with 32 blind SR users.

We conducted a collaborative, user-centered design study with a team of scientific researchers.

We conducted quantitative (N=79) and qualitative (N=93) studies with healthcare experts.

We conducted a qualitative study with 35 blind and visually impaired (VI) Developers.

We conducted a study with 32 blind US users.

We conducted a study with 12 blind SR users.

We conducted a collaborative, user-centered design study with a team of scientific researchers.

We conducted quantitative (n=70) and qualitative (n=30) studies with healthcare experts.

We conducted a qualitative study with 16 blind and visually impaired (BI) developers.

We conducted role-playing exercises with 24 US journalists.

Mocha exemplified the application of human cognition and concept learning theories in the interactive machine learning pipeline to support the negotiation of conceptual boundaries for bi-directional human-AI alignment.

This pattern of selective attention suggests that the visual cues provided by Mocha effectively guided participants to focus on more relevant information within the context of unchanged text when making their labeling decisions.

Overall, the incorporation of counterfactuals has generally improved the models' F1 scores, driven largely by the improvements in precision. This suggests that counterfactuals have effectively improved performance without necessitating a significant trade-off between precision and recall.

The inclusion of counterfactuals often resulted in a substantial increase in precision, indicating that the models were better able to correctly classify relevant instances while reducing false positives. This improvement suggests that the counterfactuals provided essential information that helped refine the models' decision boundaries.

By visualizing these consistent pattern rules, users may be better understanding the behavior of the model through inference projection [26]. This can not only boosts the model's performance but also enable participants to validate or correct the model during the interactive training process.

Thus, the integration of both theories enables users to efficiently process and compare variations, leading to more informed decisions and a clearer understanding of the model's behavior.

By helping users see alignable differences, SAT-based rendering helps users focus on key variations that are essential to changing the data item's label, making it easier to interpret the effects of changes and their significance.

We argue that these two theories form a symbiotic relationship (Fig. 6). Variation Theory provides the conceptual basis for generating structurally consistent differences, while Structural Alignment Theory (SAT) enhances the user's ability in recognizing and processing these differences.

Participants were able to efficiently focus on key differences between the original and counterfactual examples, which facilitated more efficient annotations.

The results from our user study suggest that both the participants and the model benefited from the Variation Theory (VT)-based counterfactuals and Structural Alignment Theory (SAT)-based rendering.

Specifically, they underscore the need for co-adaptive systems that can evolve along with users' mental models and definitions of labels.

This requires the development of interfaces and visualizations that demystify the generated data, allowing systematic variation and coverage across the concept space.

By visualizing these consistent pattern rules, users may be better understanding the behavior of the model through inference projection [26].

By incorporating theories such as Structural Alignment Theory and Variation Theory, it aims to support the learning of both the human and the model.

Such studies could determine whether these non-alignable comparisons enhance user performance and elicit deeper insights in human-AI collaborative systems.

Most previous research in counterfactual generation has focused on the model side by either generating counterfactuals to improve the model's performance or explaining its behaviors post hoc.

Variation Theory provides the conceptual basis for generating structurally consistent differences, while Structural Alignment Theory (SAT) enhances the user's ability in recognizing and processing these differences.

While SAT-based rendering supported human sensemaking in both Gero et al. [29] and Mocha, we also show that the combination of VT and SAT support the model's learning.

This finding is consistent with previous work that supports users' sense-making of text, e.g., by modulating text saliency. Specifically, Gu et al. [32] and Gero et al. [29] both found improved reading efficiency and comprehension with saliency-modulating text renderings.

In decision making, SAT argues that people tend to focus on alignable differences—features that can be directly compared—rather than on differences that cannot be easily aligned.

Structural Alignment Theory (SAT) [27] is a cognitive theory that explains how people make sense of concepts by comparing relational structures between two items.

Specifically, we use Variation Theory of learning [44] which states that for learning to occur, some aspects that define the concept being learned must vary while others are held constant.

According to SAT, humans compare two similar entities by trying to find structural alignments between them, and then comparing corresponding elements, with a special focus on differing aligned elements.

VT posits that human learning occurs when learners experience variation across critical and superficial aspects of a concept—through exposure to contrasting examples that systematically vary along different critical and superficial feature dimensions.

To analyze the annotation efficiency, we first conducted a Kruskal-Wallis rank sum test [39] to determine if there were statistically significant differences in annotation time across the three conditions, because our data violated the homogeneity of variances assumption, making non-parametric methods more appropriate.

Correspondence concerning this article should be addressed to Iza Ray Korsmit (iza.korsmit@mail.mcgill.ca).

The experimental protocol was certified for ethical compliance by the McGill University Research Ethics Board II.

IRK was supported by funding from the Prins Bernhard Cultuurfonds (The Netherlands). This project was also funded by a Canadian Social Sciences and Humanities Research Council Insight Grant (435-2021-0224), a Social Sciences and Humanities Research Council Partnership Grant (895-2018-1023), and a Canada Research Chair (950-231872) to SMc.

Part of this research was presented at the Society for Music Perception and Cognition Conference, Portland, Oregon (2022). The authors would like to thank Bennett K. Smith for programming the experimental interface and assisting with the experiment execution on Prolific, and Philippe Macnab-Seguin for creating the chromatic scales for the second experiment.

Iza Ray Korsmit, Marcel Montrey, Alix Yok Tin Wong-Min, & Stephen McAdams McGill University, Montreal, Canada

Grimaud and Eerola (2022) compared instrument ensembles of strings, woodwinds, and brass in a study where participants either rated the emotions they perceived or manipulated musical parameters to produce a certain emotion. They found that strings were associated with increased anger and fear, woodwinds with decreased anger and fear, and brass with decreased fear, in the cases of both emotion perception and production. For the other emotions (joy, sadness, calmness, power, surprise), however, results were less consistent between perception and production, indicating that the emotion-instrument association may also depend on context of the task.

following the constructionist idea that an individual's personality and background will influence the affect they perceive or feel, we considered several sources of individual differences as moderating factors in the effects of instrument family, pitch height, and affect locus.

This research follows a constructionist approach to musical affect (Cespedes-Guevara & Eerola, 2018). That is, although we are interested in the "bottom-up" influence of certain musical features on musical affect, we believe these cannot be adequately evaluated without considering the "top-down" effects of context and individual differences that are present when affects are constructed. The perception or induction of affect does not merely arise in response to a stimulus but is also formed in relation to the individual and the context.

the context of a task (like perception, production, or induction) may change the effect of musical features.

Furthermore, as the method of reporting on perceived and induced affect may influence the construction of an affect (e.g., facilitating categorical perception), we also compare two different affect representations.

This research follows a constructionist approach to musical affect (Cespedes-Guevara & Eerola, 2018). That is, although we are interested in the \'bottom-up\' influence of certain musical features on musical affect, we believe these cannot be adequately evaluated without considering the \'top-down\' effects of context and individual differences that are present when affects are constructed. The perception or induction of affect does not merely arise in response to a stimulus but is also formed in relation to the individual and the context.

Cognitive surrenderA paper that came out this year asked: if you’re working with AI a lot, and you’re using it as a machine to answer all of your questions, what happens with System 1 and System 2?

2. Validator Another basic role for AI is validating your understanding. To do this, you ask it to review your notes for errors or gaps, do basic fact checking, or critique your reasoning. Again, you can do this via the chat interface, but I also experimented with passing my notes in Obsidian using the Copilot plugin and in Emacs using gptel. Example: After reading The Epic of Gilgamesh, I wrote a note in Obsidian summarizing its plot. When I asked ChatGPT to critique my summary, it pointed out that I’d given the central character a redemption arc that isn’t present in the text. I’m so accustomed to the standard hero’s journey, that I projected it onto the book — and an LLM helped me correct this ‘hallucination.’ Suggested prompt: Here are my notes on [WORK]. What important ideas did I miss or underemphasize? Don’t rewrite my notes — just flag the gaps.

For the record, my posts aren’t written or conceived with an LLM, although I know an increasing number of people who use one to write a first draft and then edit. I’m not a fan. The whole point of the web — its beauty — is that it’s unrelentingly human and diverse.

Given the high prevalence of such sounds in everyday life, having misophonia can have large negative effects on one's functioning in personal, academic, and work environments.

Although there are many idiosyncrasies in what may trigger a person with misophonia, the most common triggers are created by other humans, such as the sound of someone chewing, clearing their throat, tapping their foot, or typing on a keyboard.

Misophonia is a psychological disorder that is characterized by severe aversive responses to specific environmental sounds (i.e., triggers).

This indicates that misophonia is not a purely auditory processing disorder but is also influenced by a top-down process of source identification.

an fMRI study found that people with misophonia show increased response in the anterior insular cortex (AIC) in response to misophonic sounds, compared to control participants and other unpleasant or neutral sounds (Kumar et al., 2017).

Both the subjective judgment of aversiveness and the physiological measure of skin conductance response (SCR) increase when people with misophonia are presented with triggers (Edelstein et al., 2013).

The disorder is not yet recognized by the Diagnostic and Statistical Manual − 5th version (DSM-5; American Psychiatric Association, 2013), but there has been an increasing amount of research on the characterization and treatment of misophonia (Vitoratou et al., 2021; see also Brout et al., 2018, for a review).

Composers and music researchers had previously analyzed and annotated 65 movements from the Classical, Romantic, and early Modern repertoire in terms of the Taxonomy of Orchestral Grouping Effects (McAdams et al., 2022).

In a study by McAdams and Goodchild (2018), orchestral simulations created with OrchSim were compared perceptually to commercial recordings and were shown to be of high quality.

These results confirm with orchestral excerpts the findings of studies on isolated tones with dyads or triads of instruments in which the presence of impulsive instruments reduces the perception of blend (Lembke et al., 2019; Reuter, 1996; Tardieu & McAdams, 2012).

This provides additional support for its significant contribution to blend in Fischer et al. (2021).

Lembke et al. (2017) demonstrated that combinations of sustained and impulsive instruments blend less well.

structuring by affecting sequential grouping through the segregation of auditory streams played by different instruments and segmental grouping through timbral contrasts (McAdams et al., 2022).

Several other spectral and spectrotemporal descriptors were found to play a role in blend perception in orchestral works by Fischer et al. (2021). These include spectral flatness and spectral crest (different measures of the degree to which the spectrum is denser or has more emergence of spectral components), and spectral variation (the degree of variation of the spectral shape over time).

Fischer et al. (2021) studied the blends of multi-instrument streams in the context of orchestral stream segregation in predominantly Romantic orchestral excerpts. They found that within-family instrument combinations blended better than between-family combinations. They demonstrated the role played by overlap in timbre correlates of spectral flatness (a measure of the tonalness/noisiness or density of the spectrum), spectral skewness (related to the shape of the spectral envelope), and spectral variation (evolution of the spectral envelope over time), as well as cues derived from the scores such as onset synchrony and the consonance of concurrent pitch relations.

McAdams et al. (2022) distinguish two subcategories of these two types of blend: stable and transforming.

Lembke, S.-A., Parker, K., Narmour, E., & McAdams, S. (2019). Acoustical correlates of perceptual blend in timbre dyads and triads. Musicae Scientiae, 23(2), 250–274.

Several other spectral and spectrotemporal descriptors were found to play a role in blend perception in orchestral works by Fischer et al. (2021).

Lembke and McAdams (2015) found that the degree of spectral overlap between constituent sounds enhanced blend perception.

Tardieu and McAdams (2012) extended this work with combinations of unison sustained and impulsive instruments (including pitched percussion and string pizzicati).

McAdams et al. (2022) introduce other notions related to blend as well.

This in turn creates an effect of perceptual unity (McAdams, 1989).

Four important concurrent grouping cues predict the perceptual fusion of sound events (McAdams, 1984): onset synchrony, harmonicity, coherent frequency behavior, and common spatial origin.

To this, McAdams et al. (2022) have added segmental grouping (chunking into perceptual units).

When the sudden drop to a pianissimo occurred towards the ending of the piece, the perceived arousal responses of CHM and WM dropped slightly but rose again immediately to end on a high arousal. These two groups of listeners appear to have anticipated a return to a loud and majestic close and therefore kept their arousal responses higher than those of the NM.

CHM, who are more experienced with the instruments and compositional techniques used in Chinese orchestral music, might have had an idea of which features figure more prominently in the communication of particular intentions, and therefore would have more information available for their judgments.

The perception of affective intentions in music is influenced by the degree of familiarity listeners have with a musical tradition, the content implicated in the music, and the complex sonic environment created by the composer's creation and the musicians' interpretation.

The version that participants heard was a premier of the work by the Taipei Chinese orchestra.

The communication of emotions or affect takes place when listeners perceive emotional meaning that is expressed by performers in music (Juslin, 2013a, 2013b).

An understanding of a tonal schema with its associations to happiness and sadness has been consistently found to influence listeners who have grown up in a culture of Western music.

Its mirmode function estimates the modal strength of the music in terms of ''majorness'' and ''minorness.''

The perception of consonance also plays an important role in the music listening process—combinations of tones that are consonant are perceived as more positively valenced than dissonant ones (Harrison & Pearce, 2020).

Musical structures such as pitch relations are perceptually salient and provide important information for listeners (e.g., Gabrielsson & Lindstrom, 2010; Krumhansl, 1998; Krumhansl & Kessler, 1982).

Iqa' (plural iqa'at) is used to describe a rhythmic cycle. Iqa'at are made up of two different basic building blocks, the dum and tak, onomatopoeias derived from the sound produced on membranophones such as the darabuka.

H5. Being more culturally bound, musical cues that are learned, such as modal structures, metrical relations, and so on, will exert a greater influence on listeners' perceived valence ratings than on their arousal ratings.

This is a simple PDF file. Fun fun fun.

using SemanticCommit, we recorded all instances of edits, check for conflicts, make change, local, and global resolution actions using telemetry.

A sub-task was considered a failure if the participant was unable to complete it within the time limit.

Finally, we conducted an informal interview about their experience.

After both tasks were completed, participants filled out a final survey to compare the two conditions.

We chose GPT-4o for performance and latency reasons, as it performed optimally against our evals.

We also ran evaluations of model latency and classification performance under varying false positive rates for the following LLMs by OpenAI: GPT-4o, GPT-4o-mini, and o3-mini.

For each task, participants were tasked with integrating three new pieces of information into the memory, one at a time ("sub-tasks").

We ensured each list was 30 items long as our pilot studies suggested this was long enough that manual detection starts to become unwieldy (users need to scroll up and down the document), but short enough that participants could become familiar in a short period.

We adapted two intent specifications from our evals: Mars Game Design Document and Financial Advice AI Agent Memory, as these tasks mapped to the two paradigmatic types covered in Sections 2 and 2.1 (design documents, and AI memory of the user).

We recruited 12 participants (7 female, 5 male) through the mailing lists of two research universities and one multinational technology company.

We chose OpenAI's ChatGPT Canvas as a baseline for five reasons: (i) it is a popular, commercially available tool, hence it is likely familiar to users; (ii) it provides a document editing view, where users can select text and ask GPT to rewrite it, or chat with an AI to make global edits; (iii) it employs a similar class of model (GPT-4o); (iv) it supports similar editing features as SemanticCommit like inline text selection, conflict highlighting, and a diff view, while adding free-form editing; and (v) similar interfaces like Anthropic Artifacts tended to rewrite the specification entirely, and did not offer Canvas's "diff" view to allow for a fair comparison.

With participant consent, we recorded audio and screen-casts, and participants were encouraged to think aloud.

Four coauthors created the evals, and two coauthors manually double-checked all conflicts, a process that took several days.

We ran one pilot study with five users of our card-based interface, and a second with four users of a revised interface.

Our explorations went through substantial iterations and prompt prototyping over a period of eight months, evolving in response to two pilot studies and progressing from a card-based interface to a list of texts.

We iterated on prompts using ChainForge [5] by setting up an evaluation pipeline against our datasets, which allowed us to observe the effects of prompt changes and model choices.

To measure statistical significance, we used Mann–Whitney–Wilcoxon tests and report the p-values.

For qualitative analysis, the first author performed open coding on participant responses and audio transcripts to identify themes, which were used to interpret the qualitative results.

In the post-task surveys, we collected self-reported NASA Task Load Index (TLX) scores, Likert-scale ratings for ease of use, and responses on how well the AI helped participants identify, understand, and resolve semantic conflicts.

Each condition had a time limit of 15 minutes, after which the participant completed a post-task survey.

Before each task, participants received a tutorial on the assigned tool and were given five minutes to explore it using a test document.

Both the order of task assignment and tool assignment were counterbalanced and randomly assigned.

We conducted a controlled within-subjects study with mixed methods, comparing SemanticCommit with a baseline interface.

We run end-to-end on our four eval datasets using GPT-4o and GPT-4o-mini and report the mean ± stddev for accuracy, precision, recall, and F1 scores for the three approaches in Figure 5.

We compare our end-to-end system against two simpler methods: (i) DropAllDocs, which adds all documents to the context for conflict classification; and (ii) InkSync [56] which generates a JSON list of string-replace operations.

In order to minimize relevance assessment issues, we apply a PageRank-based relevance ranking over the KG, akin to HippoRAG [36].

We implement the back-end using a knowledge-graph (KG) RAG architecture [36] consisting of two phases: pre-processing and inference.

Through a within-subjects study with 12 participants comparing SemanticCommit to a chat-with-document baseline (OpenAI Canvas), we find differences in workflow: half of our participants adopted a workflow of impact analysis when using SemanticCommit, where they would first flag conflicts without AI revisions then resolve conflicts locally, despite having access to a global revision feature.

We implement the back-end using a knowledge-graph (KG) RAG architecture [36] consisting of two phases: pre-processing and inference.

In order to minimize relevance assessment issues, we apply a PageRank-based relevance ranking over the KG, akin to HippoRAG [36].

We compare our end-to-end system against two simpler methods: (i) DropAllDocs, which adds all documents to the context for conflict classification; and (ii) InkSync [56] which generates a JSON list of string-replace operations.

We run end-to-end on our four eval datasets using GPT-4o and GPT-4o-mini and report the mean ± stddev for accuracy, precision, recall, and F1 scores for the three approaches in Figure 5.

We conducted a controlled within-subjects study with mixed methods, comparing SemanticCommit with a baseline interface.

Both the order of task assignment and tool assignment were counterbalanced and randomly assigned.

Before each task, participants received a tutorial on the assigned tool and were given five minutes to explore it using a test document.

Each condition had a time limit of 15 minutes, after which the participant completed a post-task survey.

In the post-task surveys, we collected self-reported NASA Task Load Index (TLX) scores, Likert-scale ratings for ease of use, and responses on how well the AI helped participants identify, understand, and resolve semantic conflicts.

For qualitative analysis, the first author performed open coding on participant responses and audio transcripts to identify themes, which were used to interpret the qualitative results.

To measure statistical significance, we used Mann–Whitney–Wilcoxon tests and report the p-values.

We iterated on prompts using ChainForge [5] by setting up an evaluation pipeline against our datasets, which allowed us to observe the effects of prompt changes and model choices.

Our explorations went through substantial iterations and prompt prototyping over a period of eight months, evolving in response to two pilot studies and progressing from a card-based interface to a list of texts.

We ran one pilot study with five users of our card-based interface, and a second with four users of a revised interface.

Four coauthors created the evals, and two coauthors manually double-checked all conflicts, a process that took several days.

With participant consent, we recorded audio and screen-casts, and participants were encouraged to think aloud.

These semantic conflicts require dedicated support to detect, visualize, and resolve. Semantic conflict resolution interfaces must go beyond visualizing what changes were made, to what changes could be made, where they should be made, and what the effects might be. This resembles feedforward: affordances that help the user foresee the impact of an action [67, 93].

Today with LLMs, we are less limited by this constraint, and solutions to the problem of human-machine communication might be better found in cybernetics theory [9] than static formalism.

This reflects the principle of feedforward [67, 93] in communication theory—"a needed prescription or plan for a feedback, to which the actual feedback may or may not confirm" [79]—where a communicator provides "the context of what one was planning to talk about" [64, p. 179-80] in order to "pre-test the impact of [its output]" on the listener [34, p. 65].