What Gets Lost When Memory Becomes Media? Evaluating AI-Generated Oral History Visualization

Anjung Tan; Hyoungchul Park; Jaehyun Koo; Jiyeon Lee; Kwangsuk Park

REVIEW 3 major objections 7 minor 12 references

Scene-planning and narrative preservation conflict in most AI-generated oral-history visualizations, and the strength of the source testimony's narrative structure is what decides which side wins.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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2026-08-02 13:01 UTC pith:DXNKY3FS

load-bearing objection Useful metric framework and honest comparison, but the routing-predictor is a coupling artifact and the LLM judge is unvalidated — treat the trade-off as a hypothesis, not a structural property. the 3 major comments →

arxiv 2607.24756 v1 pith:DXNKY3FS submitted 2026-05-26 cs.HC cs.AI

What Gets Lost When Memory Becomes Media? Evaluating AI-Generated Oral History Visualization

Kwangsuk Park , Jaehyun Koo , Jiyeon Lee , Anjung Tan , Hyoungchul Park This is my paper

classification cs.HC cs.AI

keywords oral historydiaspora memorygenerative AIimage sequence generationevaluation frameworknarrative preservationscene decompositionrouting protocol

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

Generative AI that turns oral-history testimony into image sequences faces an inherent choice between scene-level texture and narrative fidelity, and this paper shows the two values conflict in more than two-thirds of cases, regardless of which of two pipelines is used. A single-summary pipeline that compresses the whole interview into one continuous narrative preserves the macro story arc and speaker identity better; a multi-agent scene-decomposition pipeline that splits testimony into memory episodes produces sharper scene transitions and less generic imagery but damages narrative flow and identity stability. The deciding factor is the narrative-structure strength of the original testimony: when the source story is already tightly structured, decomposition damages it; when the source is loosely structured, decomposition supplies missing structure and improves both values at once. The paper contributes 15 metrics organized around three failure modes, a value-separated evaluation protocol that makes the trade-off visible (aggregate scores hide it), and a routing rule: use the summary pipeline when the source's macro-arc fidelity score is high, the decomposition pipeline when it is low. This matters because AI-generated visualizations of personal and collective memory are spreading without agreed criteria for what a faithful transformation is.

Core claim

The paper's central empirical claim is that, in the majority of text evaluations (59 of 82), a gain in scene-planning metrics is accompanied by a loss in preservation metrics—the trade-off appears in more than two out of three cases, and the authors argue this is a structural property of the task, not a defect of either system. The primary predictor is the narrative-structure strength of the source testimony, operationalized as the single-summary pipeline's macro-arc fidelity score: when that score is high, decomposition tends to damage what is already preserved; when it is low, decomposition supplies missing structure and improves both axes. The conflict is visible only when metrics are rep

What carries the argument

The load-bearing mechanism is the double transformation that defines the task—converting first-person recollection into third-person scenes and shifting from the present interview room to the remembered time and place—together with two pipeline designs that embody opposite strategies: a single summarization pipeline that compresses the whole testimony into one continuous narrative, and a multi-agent scene-decomposition pipeline that splits it into memory episodes with explicit evidence, context, story-generation, and fact-checking stages. The evaluation framework's 15 metrics, grouped into three failure modes (transition dissolution, genericization, preservation damage) and reported separate

Load-bearing premise

Every empirical claim passes through an automated judge's ratings of the 15 metrics; if those ratings do not track what oral-history researchers and community members actually value in testimony, the measured trade-off and the routing rule could be artifacts of the judge rather than structural properties of the task.

What would settle it

Have oral-history researchers and community members score the same outputs on the three failure modes without knowing which pipeline produced them. If, on a majority of cases, the outputs they rate higher on scene texture are also rated higher on narrative fidelity and identity stability, the claim that the conflict is structural collapses. Alternatively, run the routing protocol prospectively on a new set of interviews and check whether routed outputs beat both fixed pipelines on human-judged fidelity.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

A practitioner can run the single-summary pipeline on a new interview, read its macro-arc fidelity score, and route to the appropriate pipeline; the observed decision boundary sits near a score of 3.0 on the 1–5 scale.
For evaluation practice, aggregate scoring hides the trade-off—the two pipelines have nearly identical overall averages—so success must be reported separately by failure mode.
For system design, the image-generation model is the current bottleneck: text-level scene-planning gains shrink to small image-level differences, so intermediate-representation improvements alone have limited payoff.
For ethical oversight, the two pipelines invent similar amounts of detail but in opposite directions—one smooths the narrative, the other fills in sensory specifics—so embellishment must be audited directionally.
The framework provides a first structure for managing the tension between factual accuracy and the subjective form of memory that oral-history scholarship has long described.

Where Pith is reading between the lines

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

A natural next experiment is to test the routing rule prospectively: on a new interview, compute the single-summary macro-arc fidelity score, route, and compare the routed output against each pipeline individually—the paper's retrospective analysis suggests the rule should beat either fixed choice, but that is an inference, not a finding.
The trade-off likely generalizes beyond oral history to any generative transformation that must preserve both global narrative structure and local specificity—documentary video editing, textbook-to-explainer conversion, archival photo restoration—so the failure-mode taxonomy could seed evaluation standards in those areas too.
Because the metrics are entirely judge-based and the paper defers human evaluation, the observed decision boundary (macro-arc fidelity near 3.0) should be recalibrated against expert oral-history judgments; if human raters weight scene texture differently, the routing threshold may shift.
Identity continuity was the clearest image-level cost of scene decomposition; a plausible fix not implemented in the paper is a shared identity-embedding constraint across panels, which could be tested by extending the decomposition pipeline.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Desk Editor's Note

Useful metric framework and honest comparison, but the routing-predictor is a coupling artifact and the LLM judge is unvalidated — treat the trade-off as a hypothesis, not a structural property.

read the letter

Colleague,

Short take: the paper is a transparent workshop contribution with a useful metric framework and a real comparison, but its headline claim—that source-testimony narrative-structure strength predicts the scene-preservation trade-off—is undermined by a mathematical coupling between predictor and outcome. The trade-off observation itself is plausible; the predictor and the routing rule built on it are not.

The useful part: 15 theory-grounded metrics organized around three failure modes (transition dissolution, genericization, preservation damage). The authors run SSP and MAS on 82 real diaspora interviews with full prompts in the appendix, and they report per-metric profiles instead of one aggregate. That is the right way to surface the value conflict. They also flag the missing human evaluation and the safety-filter exclusions explicitly. Honest reporting.

The first problem is load-bearing. In §4.5 they define narrative-structure strength as case-level SSP MAF, and the preservation axis of the trade-off includes MAF. The outcome delta is MAS_MAF − SSP_MAF, so high SSP MAF mechanically drives the delta negative. The right-side concentration in Figure 4 and the MAF≈3.0 threshold are therefore likely regression artifacts, not evidence that source-testimony structure is the primary predictor. The stress-test note holds up. You need a deconfounded predictor or an outcome that excludes MAF before trusting the routing protocol in §5.

Second, all 15 metrics are scored by a single G-Eval judge (Gemini 2.5 Flash) with no human calibration or inter-annotator agreement. That means even the descriptive per-metric profiles are unvalidated measurements. The authors defer human evaluation to future work, which is honest, but it weakens every numeric conclusion.

Third, 16 of 82 interviews are dropped from image evaluation because of safety filters. Minor compared to the first two, but still a selection risk.

The descriptive trade-off in Table 5 may partially survive because the preservation axis also includes ICL, TG, and HAS, but the §4.5 analysis is specifically compromised. No code or data is released; that is a reproducibility gap but not disqualifying for a workshop paper.

Who this is for: researchers working on AI narrative visualization, oral history, and LLM-as-judge methodology. It is a good case study for reading groups on evaluation pitfalls.

Recommendation: send to peer review—the framework deserves referee time—but expect major revision. Require a deconfounded predictor, human validation of the judge, and a clearer separation between descriptive observation and predictive claim.

Referee Report

3 major / 7 minor

Summary. The paper addresses the under-specified task of turning diaspora oral-history interviews into six-panel image sequences. It derives 15 metrics from three oral-history failure modes, implements two systems (SSP: single summarization; MAS: multi-agent scene decomposition), and compares them on 82 RFMI interviews (66 for image evaluation). The headline findings are that scene-planning and preservation conflict in 68.6% of cases, that the source testimony's narrative-structure strength is the primary predictor, and that a routing protocol based on SSP MAF can select between the pipelines. The authors transparently include full prompts, qualitative cases, and acknowledgments that human evaluation and threshold validation remain future work.

Significance. If the results hold, the paper provides a useful value-separated evaluation framework for an ethically important but understudied generation task, and the full prompt appendix supports replication. The empirical trade-off claim and the proposed routing rule are falsifiable and practically relevant. However, two load-bearing pillars are currently unsecured: all quantitative results pass through an unvalidated LLM judge, and the routing predictor is mathematically coupled to the outcome it explains. The paper is honest about the first gap, but acknowledging a limitation does not remove its effect on the central claim. With targeted validation and predictor decoupling, the contribution could be solid; in its present form the 'structural property' and threshold claims are overstatements.

major comments (3)

[§4.5, Fig. 4, §5] The routing predictor is mathematically entangled with the outcome it predicts. 'Narrative-structure strength' is defined as case-level SSP MAF, and the preservation axis of the trade-off classification includes MAF (with ICL, TG, HAS). Thus a 'MAS win' requires MAS_MAF > SSP_MAF, so even with random judge scores, MAS wins will tend to concentrate at low SSP_MAF and trade-offs at high SSP_MAF. The clustering in Fig. 4 and the 'MAF ≈ 3.0' threshold in §5 may therefore be a regression artifact. Moreover, the assumption that SSP 'compresses testimony as-is' is untested; SSP is an LLM summary and can reflect system behavior. Please measure narrative structure from the source transcript independently (e.g., human annotation or a transcript-only metric) and validate the threshold on held-out interviews.
[§3.3, Tables 3–4] All 15 metrics are scored by a single unvalidated LLM judge: grouped G-Eval with Gemini 2.5 Flash. No human calibration, inter-rater reliability, or comparison with expert oral-history assessment is reported; the Discussion defers this to future work. Because every empirical claim—the trade-off rate, the value profiles, the routing threshold—depends on these ratings, the large gaps (e.g., PTC 4.91 vs 1.69; MAF 4.48 vs 3.03) could reflect judge bias rather than artifact quality. At minimum, score a subset of interviews with human raters and report agreement; otherwise the 'structural property of the task' claim is not supported.
[§3.3, §4.2] Image evaluation uses only 66 of 82 interviews because safety filters blocked 16. The paper does not analyze whether the excluded cases differ systematically from included ones. If filtering correlates with testimony content (e.g., violence, sensitive details) or visual outcome, the image-level conclusions (FM2 gains, IC instability, attenuation of text-level effects) may not generalize. Please report which interviews were excluded, compare text-level patterns on the excluded subset, and conduct a robustness check treating filter-blocking as an outcome or selection variable.

minor comments (7)

[§3.2] Typo: 'as shwon at Table 1' should be 'as shown in Table 1'.
[Table 4] The 'Win' percentages are not defined. For metrics like NG (MAS 47%) or TSD (MAS 36%), the mean is higher for MAS but the win rate is below 50%; clarify how ties are counted and what the percentages mean.
[§4.4] The classification of 'trade-off' versus 'MAS win' uses deltas on PTC/VSE and MAF/ICL/TG/HAS, but the paper does not state a threshold for calling a delta nonzero (e.g., any positive delta, majority of metrics, or all). Please specify the exact rule used to generate Table 5.
[§5] The statement 'the boundary is observed near MAF 3.0' is vague; report the exact operationalization, the range of observed SSP MAF, and confidence information.
[Appendix B] Case 38's MAS scene list appears truncated (only five scene beats listed for a six-image output); ensure the appendix is complete and consistent with the pipeline description.
[Abstract] 'spanning from oral interviews to 6-image sequences' is awkward; consider 'converting oral interviews into six-image sequences'.
[§4.2] The claim that MAS shows lower standard deviations in MSS, PRP, and AFC would benefit from a formal variance test; with n=66, the qualitative visual impression may not hold.

Circularity Check

2 steps flagged

The §4.5 'narrative-structure strength' predictor is mathematically coupled to the §4.4 trade-off labels because SSP MAF appears on both sides of the classification, and the §5 routing protocol inherits the coupling.

specific steps

self definitional [Section 4.4–4.5, Figure 4]
"§4.4: 'For each of 82 cases, we compute deltas on the scene-planning axis (PTC, VSE) and the preservation axis (MAF, ICL, TG, HAS) and classify the pattern (Table 5).' §4.5: 'Narrative-structure strength = case-level SSP MAF mean' ... 'Trade-off cases concentrate on the right (high SSP MAF); MAS-win cases concentrate on the left (low SSP MAF).'"

The classification labels in Table 5 are determined by preservation-axis deltas, one of which is ΔMAF = MAS_MAF − SSP_MAF. The predictor is x = SSP_MAF. Since the outcome function contains −SSP_MAF, high SSP_MAF makes a negative ΔMAF (trade-off-like) mechanically more likely and low SSP_MAF makes a positive ΔMAF (MAS-win-like) mechanically more likely, even if MAS scores are pure noise. The right/left separation in Figure 4 is therefore an algebraic consequence of using the same metric on both sides, not independent evidence that narrative-structure strength predicts the conflict.
fitted input called prediction [Section 5, Routing Protocol and Figure 7]
"Given a new testimony, first apply SSP and measure the MAF score of the resulting narrative. This score reflects the narrative-structure strength of the source testimony. If MAF is high, SSP already preserves the narrative structure well; use the SSP result, as MAS is likely to damage it. If MAF is low, SSP has not captured the structure adequately; apply MAS, whose explicit decomposition is likely to supply missing structure. In this dataset, the boundary is observed near MAF 3.0, though generalization of this threshold requires further validation."

The routing rule is the same judge-based SSP MAF that was used as the x-axis predictor while also entering the preservation-axis delta that defined the trade-off/MAS-win labels. The MAF≈3.0 threshold is read off the same 82 cases from which the outcome labels were constructed, with no held-out validation or independent human judgment. Thus the protocol 'predicts' new cases using the very quantity that partly generated the training labels, so it inherits the mathematical coupling rather than providing independent confirmation.

full rationale

The descriptive evaluation part is not circular: SSP and MAS were run on the same 82 interviews and scored by a shared grouped G-Eval judge, and the per-metric tables do not reduce to the prompts by construction. The circularity enters at the conflict-condition analysis. In §4.4 the trade-off / MAS-win / SSP-win labels are computed from deltas on the preservation axis (MAF, ICL, TG, HAS); in §4.5 the predictor 'narrative-structure strength' is defined as case-level SSP MAF and plotted as the x-axis. Because ΔMAF = MAS_MAF − SSP_MAF contains −SSP_MAF, the observed clustering of trade-off cases at high SSP MAF and MAS-win cases at low SSP MAF is partly algebraic. The paper's claim that SSP compresses testimony 'as-is' does not remove this coupling, because the coupling is in the delta construction, not in the interpretation of SSP. The routing protocol inherits the same problem, since the MAF threshold is fitted and evaluated on the same 82 judge-scored cases. The unvalidated G-Eval judge is a validity or correctness risk, not a circularity, and the self-citations to Park & Yang (2025) and Lee et al. (2024) only motivate the MAS architecture and are not load-bearing for the quantitative claims. Overall, the central predictive claim is partially reduced by construction, so the circularity score is 6.

Axiom & Free-Parameter Ledger

4 free parameters · 5 axioms · 0 invented entities

The central empirical result depends on a small set of design choices: the G-Eval judge, the SSP-MAF-as-predictor assumption, the three-minute excerpt, the six-panel target, and the routing threshold. None of these are externally benchmarked. The paper introduces no new physical or mechanistic entities, only a metric framework and an empirical rule.

free parameters (4)

Routing threshold (SSP MAF) = ≈3.0
Section 5: 'the boundary is observed near MAF 3.0' — set from the same 82 cases used to define the trade-off, with no held-out validation.
Key interview segment length = 3 minutes
Section 3.3: each ~1-hour interview is reduced to a 'key three-minute segment'; the selection criteria for that segment are not specified, and the choice may influence narrative-structure scores.
Target panel count = 6
Both pipelines produce 6-image sequences; the number of panels determines how many transition points can be preserved as boundaries.
G-Eval judge model = Gemini 2.5 Flash, 10 runs
All metric scores come from this judge; temperature, version, and prompt sampling details are not reported, and results are not compared with human ratings.

axioms (5)

domain assumption G-Eval ratings by Gemini 2.5 Flash are valid measures of the 15 oral-history metrics.
Section 3.3 uses the LLM judge without human validation; Section 6 defers human evaluation by oral-history researchers and community members to future work.
ad hoc to paper SSP MAF reflects source-testimony narrative strength rather than SSP's own summarization behavior.
Section 4.5: 'Because SSP compresses testimony as-is into a single narrative, its scores reflect the structural properties of the source testimony rather than the system's own capabilities.' This is asserted, not tested.
domain assumption The oral-history theories (Portelli, Hirsch, Abrams) correctly identify the success conditions and failure modes.
Section 3.1 derives FM1-FM3 from these sources; the derived criteria are not independently validated against expert judgment.
domain assumption Whisper transcription is faithful enough for narrative-structure measurement.
Section 2.3 uses Whisper; transcription errors would propagate into both pipelines and into the judge's transcript reference.
domain assumption The shared image-generation model is a neutral bottleneck that does not systematically favor one pipeline.
Section 4.3 attributes output differences to the intermediate representation, but the image model could introduce correlated artifacts that inflate or suppress the measured differences.

pith-pipeline@v1.3.0-alltime-deepseek · 22802 in / 10682 out tokens · 106401 ms · 2026-08-02T13:01:24.281089+00:00 · methodology

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read the original abstract

What gets lost when memory becomes media? Diaspora oral-history interviews require a double transformation; first-person recollection to third-person scene, present interview room to past time and place. When generative AI performs this transformation, no agreed criteria for success exist. We derive success conditions from oral-history theory, design 15 metrics around three failure modes, and compare a Multi-Agent Scene-decomposition pipeline (MAS) with a Single Summarization Pipeline (SSP) across 82 interviews from diaspora communities, spanning from oral interviews to 6-image sequences. Scene-planning and narrative preservation conflict in the majority of cases, and the narrative-structure strength of the source testimony is the primary predictor of this conflict. We propose a failure-mode-based evaluation framework, an empirical analysis of conflict conditions, and a routing protocol for system selection based on narrative-structure strength.

Figures

Figures reproduced from arXiv: 2607.24756 by Anjung Tan, Hyoungchul Park, Jaehyun Koo, Jiyeon Lee, Kwangsuk Park.

**Figure 2.** Figure 2: MAS pipeline. 2. Case Systems Both systems take the same input, a recorded oral-history interview, and produce the same output, a six-panel image sequence. The shared path is: extract text from the interview, reconstruct the first-person account as a third-person period narrative, and sequentially generate six representative images. The difference lies in the structure of the intermediate representation (… view at source ↗

**Figure 3.** Figure 3: Value profiles. Left: text (7 metrics). SSP (blue) and MAS (orange) extend in opposite directions. Right: image (8 metrics). by Hirsch (Hirsch, 2012) are replaced by generic imagery. FM3 metrics monitor whether narrative, identity, and factual anchors are damaged during point-of-view conversion and period reconstruction. AFC separately monitors whether sensory embellishment for image generation stays withi… view at source ↗

**Figure 5.** Figure 5: Case 38 image comparison. Left: MAS. Right: SSP. Compressed story Jorge Quintas recounts his family’s perilous journey through Mexico, facing extortion and cartel kidnapping threats. Saved by his mother’s resourcefulness and DEA intervention, their story underscores the constant danger and corruption migrants navigate while seeking safety in the United States [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗

**Figure 6.** Figure 6: Case 80 image comparison. Left: MAS. Right: SSP. Compressed story Darara Gubo’s journey from an Ethiopian immigrant to a U.S. citizen has fueled her resolve to seek local office. Driven by her faith and past experiences with poverty, she transitioned from a cautious observer to a civic leader. Encouraged by her community, she now aims to serve on the city council, prioritizing grassroots neighborly service… view at source ↗

discussion (0)

Reference graph

Works this paper leans on

12 extracted references · 1 canonical work pages

[1]

Identify the testimony’s macro arc, hard anchors, protagonist identity, era/region context, and scene-level turning points
[2]

Score each condition independently on every metric using the same standard
[3]

Prefer text that is easier to convert into a faithful multi-image visual sequence only for scene-planning metrics
[4]

For preservation metrics, prefer the condition that best preserves chronology, identity context, and hard factual anchors
[5]

Creative connective narration is allowed only if it does not distort the main event, identity, chronology, agency, country, or era
[6]

scores":{

Choose a metric-level winner among SSP, MAS, or tie. Output ONLY JSON: { "scores":{ "SSP":{"metric": 1-5, "...": 1-5}, "MAS":{"metric": 1-5, "...": 1-5} }, "winners":{"metric": "SSP|MAS|tie"}, "rationale": "short evidence-based rationale", "failure flags": ["..."] } User payload: { "independent g eval sample": [sample index], "transcript reference": [tran...

2026
[9]

Read the transcript/source text and identify concrete visual evidence for this sequence position
[10]

Score each image independently on every metric using the same standard
[11]

Choose a metric-level winner among SSP, MAS, or tie
[12]

scores":{

Penalize identity drift, era flattening, generic portrait collapse, evidence-free symbolism, and repeated static mood. Output ONLY JSON: { "scores":{ "SSP":{"metric": 1-5, "...": 1-5}, "MAS":{"metric": 1-5, "...": 1-5} }, "winners":{"metric": "SSP|MAS|tie"}, "rationale": "short evidence-based rationale", "failure flags": ["..."], "visible evidence":{ "SSP...

2026
[2021]

metadata

doi: 10.1093/jrs/feab071. Warren, R. E., Maniscalco, M. P., Schroeder, E. K., Oliver, J. S., Huitt, S., Lambert, D., and Frisch, M. Restoring the human voice to oral history: The audio-video barn website.The Oral History Review, 40(1):107–125, 2013. doi: 10.1093/ohr/oht032. Wu, Q., Bansal, G., Zhang, J., Wu, Y ., Li, B., Zhu, E., Jiang, L., Zhang, X., Zha...

work page doi:10.1093/jrs/feab071 2013
[2025]

com/deepmind-media/Model-Cards/ Gemini-2-5-Flash-Model-Card.pdf

URL https://storage.googleapis. com/deepmind-media/Model-Cards/ Gemini-2-5-Flash-Model-Card.pdf . Ac- cessed: 2026-05-04. Hirsch, M.The Generation of Postmemory: Writing and Visual Culture After the Holocaust. Columbia University Press, New York, 2012. Lee, J., Park, K., and Park, J. Vista: Visual integrated system for tailored automation in math problem ...

Pith/arXiv arXiv 2026

[1] [1]

Identify the testimony’s macro arc, hard anchors, protagonist identity, era/region context, and scene-level turning points

[2] [2]

Score each condition independently on every metric using the same standard

[3] [3]

Prefer text that is easier to convert into a faithful multi-image visual sequence only for scene-planning metrics

[4] [4]

For preservation metrics, prefer the condition that best preserves chronology, identity context, and hard factual anchors

[5] [5]

Creative connective narration is allowed only if it does not distort the main event, identity, chronology, agency, country, or era

[6] [6]

scores":{

Choose a metric-level winner among SSP, MAS, or tie. Output ONLY JSON: { "scores":{ "SSP":{"metric": 1-5, "...": 1-5}, "MAS":{"metric": 1-5, "...": 1-5} }, "winners":{"metric": "SSP|MAS|tie"}, "rationale": "short evidence-based rationale", "failure flags": ["..."] } User payload: { "independent g eval sample": [sample index], "transcript reference": [tran...

2026

[7] [9]

Read the transcript/source text and identify concrete visual evidence for this sequence position

[8] [10]

Score each image independently on every metric using the same standard

[9] [11]

Choose a metric-level winner among SSP, MAS, or tie

[10] [12]

scores":{

Penalize identity drift, era flattening, generic portrait collapse, evidence-free symbolism, and repeated static mood. Output ONLY JSON: { "scores":{ "SSP":{"metric": 1-5, "...": 1-5}, "MAS":{"metric": 1-5, "...": 1-5} }, "winners":{"metric": "SSP|MAS|tie"}, "rationale": "short evidence-based rationale", "failure flags": ["..."], "visible evidence":{ "SSP...

2026

[11] [2021]

metadata

doi: 10.1093/jrs/feab071. Warren, R. E., Maniscalco, M. P., Schroeder, E. K., Oliver, J. S., Huitt, S., Lambert, D., and Frisch, M. Restoring the human voice to oral history: The audio-video barn website.The Oral History Review, 40(1):107–125, 2013. doi: 10.1093/ohr/oht032. Wu, Q., Bansal, G., Zhang, J., Wu, Y ., Li, B., Zhu, E., Jiang, L., Zhang, X., Zha...

work page doi:10.1093/jrs/feab071 2013

[12] [2025]

com/deepmind-media/Model-Cards/ Gemini-2-5-Flash-Model-Card.pdf

URL https://storage.googleapis. com/deepmind-media/Model-Cards/ Gemini-2-5-Flash-Model-Card.pdf . Ac- cessed: 2026-05-04. Hirsch, M.The Generation of Postmemory: Writing and Visual Culture After the Holocaust. Columbia University Press, New York, 2012. Lee, J., Park, K., and Park, J. Vista: Visual integrated system for tailored automation in math problem ...

Pith/arXiv arXiv 2026