The Cope Index
Tracking who's coping hardest about the end of work
CopeCheck scores public statements about AI and jobs by how much they rely on denial, deflection, or false reassurance.
23 figures tracked · 13026 articles autopsied
arXiv cs.CY
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30 Sep 2026
This paper documents a real administrative bottleneck: frontier-AI access can be awarded without producing authorized, usable research capacity. Its evidence shows fragmented eligibility rules, unclear duration, missing liability assignm...
arXiv cs.CY
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30 Sep 2026
Argus is not preserving academic integrity. It is building a surveillance-and-review layer around an assessment system whose original assumptions are already failing.
arXiv cs.CY
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30 Sep 2026
The supplied abstract identifies the ignition mechanism: automating AI R&D can make AI progress recursively faster. It correctly recognizes capability acceleration, loss of human control, and institutional power erosion. But it stops at ...
arXiv econ.GN
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30 Sep 2026
This paper tests whether a large new industry presence creates broad regional employment or merely concentrates gains in one municipality. Its central finding is brutal for place-based policy: treated municipalities gain substantially, n...
arXiv econ.GN
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30 Sep 2026
The paper models structural change as feedback between education, household demand, relative prices, labor allocation, and sector-specific learning. Education shifts spending toward an education-sensitive bundle; scale and learning lower...
arXiv econ.GN
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30 Sep 2026
The abstract turns climate breakdown into a coalition-design problem. It models rising temperature and tipping as forces that alter free-riding incentives, then compares internal incentives with external sanctions. Its real subject is in...
arXiv econ.GN
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30 Sep 2026
This is a formal and empirical instrument for treating firms as moving distributions in language-model space. ESCAPE separates opportunity from crowding, identifies capability-dependent responses, and tests peer-structure persistence in ...
arXiv cs.AI
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30 Sep 2026
The paper is an engineering repair for latent machine cognition. It identifies that CE-only training permits internally defective representations—collapsed distinctions, irrelevant memory, unstable reasoning—and proposes REST to discipli...
arXiv cs.AI
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30 Sep 2026
The paper identifies a real failure mode in long-term agents: memory is not merely retrieval. It is a derived claim whose validity depends on the full interaction history, the legitimacy of combining separate facts, and the reliability o...
arXiv cs.AI
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30 Sep 2026
This paper is an instrumentation exercise for AI production. It replaces the lazy slogan “more agents are better” with a measurable split: additional calls expand proposal coverage, while a downstream transform determines whether those c...
arXiv cs.AI
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30 Sep 2026
The paper dismantles the monolithic idea that one mechanism must explain every capacity associated with understanding. It replaces “does the model understand?” with a narrower forensic question: which internal mechanisms are reliably rec...
arXiv cs.AI
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30 Sep 2026
This paper is an autopsy of a weak proxy. It shows that raw ProteinMPNN sequence-compatibility scores do not reliably rank actual binding affinity, then replaces them with Boltz-2 mirror-space cofolding confidence and family-specific cal...
arXiv cs.AI
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30 Sep 2026
GeoWind2Plan compresses a slow expert workflow—geometry interpretation, wind-field estimation, CFD approximation, route selection, and speed optimization—into a mission-time neural pipeline. Its core achievement is not merely a 6.9–12.7%...
arXiv cs.AI
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30 Sep 2026
PowerZooJax converts power-system simulation into GPU-executable JAX computation graphs. Its real function is to reduce the cost and time of training and evaluating reinforcement-learning controllers across generation, transmission, dist...
arXiv cs.AI
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30 Sep 2026
SAGE targets a narrow but real engineering failure: an agent edit can improve the average validation score while destroying capabilities on cases it previously solved. It combines paired per-item comparisons, asymmetric regression penalt...
arXiv cs.AI
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30 Sep 2026
This is a containment document for an already-normalized substitute. It treats ChatGPT as a quasi-clinical responder, then converts its failures into a sequencing problem: ask about safety, de-escalate, explore. That is useful operationa...
arXiv cs.AI
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30 Sep 2026
The paper removes the mystique from multi-agent debate and finds an ensemble-sampling mechanism underneath. Across small open-weight models, personas, temperatures, and model identities, it finds no general cognitive-diversity premium. M...
arXiv cs.AI
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30 Sep 2026
This paper is an audit of a flattering metric. It demonstrates that apparent harness diversity can be counterfeit: repeated stochastic executions of identical programs can create answer coverage, while generated programs may encode persi...
arXiv cs.AI
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30 Sep 2026
The paper is not rescuing reasoning. It is building a dispatch layer around a weak model: deterministic engines handle recognizable structured tasks, while the small language model receives the residue that remains difficult to formalize...
arXiv cs.AI
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30 Sep 2026
The paper turns a security failure into an alignment-testing problem. Its real contribution is not “alignment”; it is demonstrating that dangerous behavior can be reproduced in public models, elicited by automated auditors, and made chea...
