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Latest Digest

2026-03-30
## The Big Picture AI’s economic story this week is a tale of two deployment regimes. In narrow, productionized settings, generative systems move the needle on core business metrics: generative retrieval raises clicks and conversions, enterprise stacks deliver near-frontier parity at a fraction of t
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Top Papers

Top papers from the latest pipeline run, ranked by relevance, evidence strength, methods rigor, and supported claims.

1
AI code reviewers currently catch only a fraction of issues human experts find — top models detect 15–31% of flagged problems — and giving them more file-level context often makes performance worse, suggesting attention dilution in long prompts.
SWE-PRBench: Benchmarking AI Code Review Quality Against Pull Request Feedback · Deepak Kumar
high evidence high rigor relevance 7/10 10 strong claims descriptive
2
A co-regulating metacognitive layer helps LLM design agents escape fixation and produce better battery-pack designs. The CRDAL architecture outperforms both a plain loop and a self-assessing loop on design quality and design-space exploration, with no significant increase in computational cost.
Supervising Ralph Wiggum: Exploring a Metacognitive Co-Regulation Agentic AI Loop for Engineering Design · Zeda Xu, Nikolas Martelaro, Christopher McComb
medium evidence medium rigor relevance 7/10 8 strong claims quasi experimental
3
Making an AI transparent doesn't automatically win more data: transparency raises stated willingness to share only for users who already trust AI, while immediate sharing decisions remain high and unchanged across human, white-box, and black-box scenarios.
Understanding Data-Sharing with AI Systems: The Roles of Transparency, Trust, and the Processing Entity · Julian Rosenberger, Sophie Kuhlemann, Verena Tiefenbeck, Mat...
medium evidence medium rigor relevance 7/10 6 strong claims rct
4
China's AI collaboration network grows through triangle-closing and 'rich-get-richer' dynamics and is driven more by universities and research institutes than by firms; notably, organizations seek complementary technological expertise rather than similar tech, while geographic, cultural and institutional closeness still helps form ties.
The evolutionary mechanism of artificial intelligence industry collaboration networks: evidence from China · Jianlin Lyu, Bin Hu, Wenrong Lyu
medium evidence medium rigor relevance 7/10 6 strong claims correlational
5
Reconstructed sentiment from sparse AI news reliably precedes stock-price moves by roughly three weeks across multiple pipeline designs; stable, market-relevant sentiment indices require careful aggregation and causal reconstruction rather than only better classifiers.
Causal Reconstruction of Sentiment Signals from Sparse News Data · Stefania Anca Stan, Marzio Lunghi, Vito Vargetto, Cláudio Ri...
medium evidence medium rigor relevance 7/10 8 strong claims descriptive

Recent Papers

Agentic AI could halve investment-management costs and threaten large-scale displacement among financial planners; the authors propose mandatory AI literacy, specialized degree tracks, federal retraining and income support, and regulatory incentives to steer a 5–15 year workforce transition.
STRENGTHENING FINANCIAL WORKFORCE COMPETITIVENESS: A CURRICULAR FRAMEWORK FOR INTEGRATING AGENTIC AI INTO FINANCIAL PLANNING EDUCATION · Satyadhar Joshi · Mar 30
semantic_scholar low
Countries speak the same AI ethics language but govern very differently: a comparative analysis of 24 policy texts finds five distinct models of AI governance—from Europe’s rights-first approach to China’s state-centric model—driven by underlying political-economic institutions rather than convergent ethical principles.
Artificial intelligence governance and social policy divergence: a comparative political economy perspective on global AI regulation · Deepak Kumar, Chinmaya Kumar Sahu · Mar 30
semantic_scholar low
Agentic AI is only beginning to surface in U.S. finance firms' annual reports—0.4% of firms in 2024 and 1.6% in 2025—but those that mention agentic systems tend to surround them with denser governance and safety language, implying deployments are currently concentrated where governance is more mature.
Measuring agentic AI adoption and control frameworks in finance · Atta ul Mustafa, A. Aysan · Mar 30
semantic_scholar n/a
A model warns that consumer demand switching to AI services can widen wage gaps in non-traded sectors, but Indian transport data show no clear post-2007 spike in inequality — uptake of AI ride services stayed below 0.7%, muting measurable effects.
Artificial Intelligence, Demand Switching and Sectoral Wage Gap · Shreya Roy, Bibek Ray Chaudhuri · Mar 30
semantic_scholar low
Smarter search can hurt consumers: more informative AI-assisted search may reduce learning and consumer surplus and soften price competition unless markets themselves learn from agent-consumer interactions; by contrast, cheaper search unambiguously benefits consumers.
Agentic Markets: Equilibrium Effects of Improving Consumer Search · Brendan Lucier, Nicole Immorlica, Markus Mobius, Aleksandrs ... · Mar 30
arxiv n/a
Closed AI models often undermine scientific inference by hiding construction and deployment details, making results hard to interpret, replicate, or generalize; researchers should explicitly document threats to inference, the mitigation steps taken, and why a given (closed or open) model was chosen.
How Open Must Language Models be to Enable Reliable Scientific Inference? · James A. Michaelov, Catherine Arnett, Tyler A. Chang, Pamela... · Mar 30
arxiv n/a
An ASR system trained on field recordings of Ikema achieves around 15% character error rate and materially speeds up human transcription, cutting labor time and reducing cognitive load; this suggests practical AI-assisted pathways for scaling documentation of severely endangered languages.
Automatic Speech Recognition for Documenting Endangered Languages: Case Study of Ikema Miyakoan · Chihiro Taguchi, Yukinori Takubo, David Chiang · Mar 30
arxiv medium
A co-regulating metacognitive layer helps LLM design agents escape fixation and produce better battery-pack designs. The CRDAL architecture outperforms both a plain loop and a self-assessing loop on design quality and design-space exploration, with no significant increase in computational cost.
Supervising Ralph Wiggum: Exploring a Metacognitive Co-Regulation Agentic AI Loop for Engineering Design · Zeda Xu, Nikolas Martelaro, Christopher McComb · Mar 30
arxiv medium
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Recent Claims

mixed At equilibrium prices in symmetric markets, consumer surplus is improved by chea...
Agentic Markets: Equilibrium Effects of Improving ...
mixed The market (in the model) tracks indications of fit for searched products and in...
Agentic Markets: Equilibrium Effects of Improving ...
negative More informative search can degrade both learning and consumer surplus unless th...
Agentic Markets: Equilibrium Effects of Improving ...
positive Cheaper search improves learning and consumer surplus.
Agentic Markets: Equilibrium Effects of Improving ...
neutral Evaluation is carried out under three frozen context configurations (diff only: ...
SWE-PRBench: Benchmarking AI Code Review Quality A...

Contested Findings

Claims from different papers that reach opposing conclusions, ranked by evidence weight.

negative
Prevailing reskilling strategies assume access to stable employment, time and funds for training, certification systems,...
Who Loses to Automation? AI-Driven Labour Displace... (w=0.07)
vs.
positive
Personalized LLM coaching produced a statistically significant increase in alignment with the normative empathic taxonom...
Practicing with Language Models Cultivates Human E... (w=1.0)
Same outcome category, opposite direction (auto-detected, may differ in population/context)
negative
Relying on secondary literature limits the paper's ability to make causal inferences and constrains empirical generaliza...
Who Loses to Automation? AI-Driven Labour Displace... (w=0.12)
vs.
null result
The Lend an Ear platform collected a large conversational corpus: 33,938 messages across 2,904 conversations with 968 pa...
Practicing with Language Models Cultivates Human E... (w=1.0)
Same outcome category, opposite direction (auto-detected, may differ in population/context)
negative
Relying on secondary literature limits the paper's ability to make causal inferences and constrains empirical generaliza...
Who Loses to Automation? AI-Driven Labour Displace... (w=0.12)
vs.
null result
A data-driven taxonomy was derived mapping common idiomatic empathic moves (e.g., validation, perspective-taking, emotio...
Practicing with Language Models Cultivates Human E... (w=1.0)
Same outcome category, opposite direction (auto-detected, may differ in population/context)
negative
Relying on secondary literature limits the paper's ability to make causal inferences and constrains empirical generaliza...
Who Loses to Automation? AI-Driven Labour Displace... (w=0.12)
vs.
null result
Outcome measures included alignment to the normative taxonomy (coding/automated), recipient-rated perceptions of being h...
Practicing with Language Models Cultivates Human E... (w=1.0)
Same outcome category, opposite direction (auto-detected, may differ in population/context)
negative
Relying on secondary literature limits the paper's ability to make causal inferences and constrains empirical generaliza...
Who Loses to Automation? AI-Driven Labour Displace... (w=0.12)
vs.
null result
Results are from role-play contexts and short-term interventions; economic estimates of benefit require validation in fi...
Practicing with Language Models Cultivates Human E... (w=1.0)
Same outcome category, opposite direction (auto-detected, may differ in population/context)
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Source Health

arxiv Mar 30, 09:22
openalex Mar 30, 09:22
semantic_scholar Mar 30, 09:22
603
Total Papers
83
This Week
7156
Claims
100.0%
Assessed
3
Active Sources
Last updated Mar 30, 2026 at 09:22 UTC