~ / reading
##Reading
The psychology the name comes from.
### Docs & ecosystem 11
How Choice/Score confidence differs from probability, and how to gate act / review / escalate in code. Noul has no separate confidence field.
Official patterns: parallel questions, citation check, RAG passage classification, date extraction, skill suggestion.
Keep control flow in code. Split work into atomic questions, then combine the answers and route on uncertainty.
TypeSafe's launch post: RLCD, parallel sampling, and how this differs from an LLM.
A data-driven survey and analysis of 2,170 public GitHub Jev projects, mapping early ecosystem growth, application domains, and decision-use patterns.
Official definition of the model class and the Choice / Score / Noul primitives.
Community directory indexing about a thousand Jev projects with daily-refreshed stars. Less curated, good for exhaustive search.
Community store for Jev apps, extensions, and tools: a distribution surface with a submit flow, distinct from the firehose directories.
Launch-day writeup on what a decision-only model changes for latency and cost.
Alpha multi-provider surface for the class: ~10 routes from nine publishers (Jev, Solar Decide, Span-01, Kev-4B, Clef, Luna Decisions, d1, Decider, Mercury Decide, Tev1) under one
state+questionsrequest shape. Browse the full decision-model catalog, or check the live usage rankings — Jev is still over 95% of real traffic.Independent hub tracking every System One model, open alternative, community SDK, and example. The place to check when this list lags.
### Jev under the microscope 4
Rao & Callison-Burch (2026). The counterpoint to the cascade story: Jev is 29–325× cheaper and competitive on binary criteria, but LLM judges repeat nearly all of its most confident errors, capping escalation gains at ~1.5 points.
Li, Miao, Krishnan & Padman / CMU (2026). First independent study of Jev as an eval judge: within ~3 points of the strongest LLM judge on ordinary preference at 0.36% of the fee, weaker on derivation-checking. A confidence-gated cascade keeps 99% of the big judge's accuracy at ~57% of the cost.
First-party failure modes: counting, dates, indirection, distractors, generation. Keep arithmetic in code.
Community teardown of Jev's probable architecture; the write-up kev's open models are built from.
### Foundations 10
Christakopoulou, Mourad & Matarić / DeepMind (2024). Splits an agent into a fast conversational Talker (System 1) and a slow planning Reasoner (System 2).
Nish Tahir (2026). The clearest mechanism explainer yet: constrained option tokens and a softmax over answer letters versus autoregressive JSON generation, with an interactive demo, working Qwen code, and an honest note that raw token probabilities are not calibrated confidence.
Yu, Xu, Weston & Kulikov / Meta (2024). Compiles intermediate-thought techniques into single-pass outputs; the research framing closest to what Jev is.
Evans (2008). Review of dual-process theories. System 1 is a family of accounts, not one algorithm.
Tversky & Kahneman (1974). Heuristic judgment under uncertainty.
Masked diffusion language model (NeurIPS 2024): parallel, non-autoregressive generation. The open research line nearest to Jev's parallel-sampler claims.
Gigerenzer & Goldstein (1996). Simple heuristics that work with incomplete information.
Learns when to use fast direct planning and when to search, instead of always picking one.
Weston & Sukhbaatar / Meta (2023). The LLM regenerates the context it should attend to before answering: deliberate filtering on top of fast attention.
Daniel Kahneman. The book TypeSafe cites for the System 1 / System 2 framing.
nothing matches. try fewer words, or clear a filter.