ML systems, healthcare, static analysis, and software complexity (authored by agents unless marked 🧑)
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- purpose: find research a systems researcher can test with available tools and data
- original review 2026-10-07 UTC; completion and source checks 2026-10-08 UTC
- proposals are agent recommendations
- novelty and publication potential remain uncertain
- no proposed experiment has been run as part of this study
- source records distinguish full-paper reading from abstract or documentation reading
recommended first experiments
- resumed genomic workflows versus clean runs
- use public inputs and compare scientific answers against declared expectations
- continue only if strongest existing cache controls miss reproducible failures
- checked removal of obsolete code or dependencies
- measure the cost of a later correct change
- fewer lines or warnings alone do not establish improvement
- timed dead-code trials and CodeThread already evaluate later work
- checked bug evidence across repository changes
- compare full regeneration with dependency-aware reuse
- preserve clean conclusions and detect newly reachable bugs as well as old warnings
- classical incremental analysis already establishes sound reuse and fresh-run consistency under its models
- checked numerical reproducibility across inference changes
- identify a precise gap beyond TBIK and Vosti before proof engineering
- cross-TP measurement needs several GPUs; access is unconfirmed
- generic agent-serving routing remains lower priority after the earlier consultation identified Continuum
- these priorities are agent opinions based on access and falsifiability
- significance, novelty, and publication prospects are unconfirmed
- hardware access and clinical partners were not assumed
machine learning systems
- human starting point: WaferLLM notes
- LLM inference, serving, and hardware
- agent systems, training, and ML for systems
- asynchronous MoE serving, StreamEP, and buffer lifetime
healthcare
- human starting point: healthcare topics
- clinical data, care delivery, and federated learning
- biology, genomics, and workflow systems
- cholesterol, diet, and evidence reconciliation
- attribution-limited extension from the earlier worker
- compares study populations, interventions, endpoints, and missing data
static analysis
- human starting point: static-analysis notes
- LLMs combined with static analysis
- classical and incremental static analysis
- practical formal verification and Rust belong to the sibling study
software complexity
- 🧑 human question: “how to avoid the monotonic growth of software complexity”
- from research topics
- causes and interventions
- measurement and empirical evidence
reading and review limits
- each review records source versions, access levels, author claims, and proposed experiments
- source discovery used publisher and conference pages, author PDFs, and an alternate search connector
- the default web-search endpoint failed
- search results sometimes exposed outdated paper versions
- an independent reviewer assessed all eight reviews and this index
- source verification was selective, not exhaustive
- fixes included transformation-specific expected answers and tracking no-warning analysis conclusions
- full reproduction of paper results and proposed pilots remains future work
external consultation
- ChatGPT opinion and checked follow-ups
- browser helper verified Extra High before the successful submission
- earlier consultation favored workflow testing and found Continuum as a close scheduling baseline
- successful cross-topic follow-up identifies additional primary-work constraints
- continuation and independent reviews used Codex agents
- the prior Claude worker had stopped at its usage limit
- the requested Opus/Fable combination was unavailable through this session’s delegation tools
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