Article
CommunityJev Benchmarks & Evaluations
RouteAlign: Aligning Task Requirements with Model Profiles for LLM Service Selection
arXiv paper presenting RouteAlign, a method that uses task requirements to structure model performance measurement and LLM service selection. Reports quality gains over open Jev-style decision backends at lower generation cost.
- Format
- Article
- Reading time
- 2 min
- Published by
- Community
- Added
- Nov 2026
Highlights
- Uses task requirements describing capability demand and difficulty to align with model performance measurements.
- Historical requirements shape global profiles and local capability estimates; incoming requirements inform the current decision.
- A shared scorer integrates the evidence, and a generation-cost preference controls selection among eligible services.
- Reports consistent quality gains over open Jev-style decision backends at lower generation cost than their strongest tested configuration.
- Catalog-update experiments show the fitted scorer can be reused with refreshed model measurements.
Questions about this entry
Short answers drawn from the linked project or page.
It addresses LLM service selection, where aggregate scores obscure differences in capability and difficulty and semantically related examples can involve different combinations of skills.
Task requirements describe capability demand and difficulty, letting the method align measurement of model performance with the demands of the current task. Historical requirements shape global profiles and local capability estimates, while incoming requirements determine how those measurements inform the current decision.
The paper reports consistent quality gains over open Jev-style decision backends, with lower generation cost than their strongest tested configuration.
A shared scorer integrates the evidence, and a generation-cost preference controls selection among eligible services.
Yes. The paper states that ablations support the contribution of local performance evidence to RouteAlign's results.
Catalog-update experiments in the paper demonstrate effective reuse of the fitted scorer with refreshed model measurements.
The provided material describes RouteAlign as an arXiv paper and does not name a code repository, license, or installation method.
Reactions & coverage
Posts, threads, and videos about this entry from around the web.
Facebook: Intake Router keeps diagnosis out of the answer space at the front desk.
FacebookClinicnotes
X: OpenRouter Decision Model Rankings
X: herdr + jev, an agent router
X: A group chat bot with decision room routing
More Jev resources like this
arXiv paper presenting AbductionBench, a unified framework and taxonomy for abductive reasoning tasks, instantiated on 27 datasets across ten domains and used to evaluate eight LLMs from four model families on single-choice explanation selection.
Jev Benchmarks & EvaluationsArticle#arxiv#benchmark#evaluation
Community
Open, deterministic audit benchmark for System 1 decision models, covering calibration under label-preserving perturbations, cross-question interference, and post-hoc attribution faithfulness across five open-weight models and generative baselines.
Jev Benchmarks & EvaluationsArticle#arxiv#evaluation#calibration
Community
arXiv paper evaluating 67 systems, including 47 decision models and 20 LLMs, on 43 Arabic tasks across 9 task families under a shared zero-shot protocol. Jev 1.14.0 scores 69.24 against 68.77 for Jev 1.13.0 and 71.81 for Gemma-4-31B.
Jev Benchmarks & EvaluationsArticle#arxiv#evaluation#benchmark
Community
Multilingual benchmark of nine suites in 122 languages that evaluates typed decision models such as Jev, Clef, and pplx-decider, and tests whether confidence thresholds fitted on English transfer to other languages.
Jev Benchmarks & EvaluationsArticle#arxiv#evaluation#benchmark
Community
Same Facts, Different Remedy: Linguistic Profiling and Arbitrariness in LLM and System One Decisions
arXiv paper auditing three remedy-decision systems on US consumer financial complaints: GPT-4o-mini, GPT-5-mini, and Jev, a probabilistic System One model returning a distribution over remedies. It separates group-scale disparities from individual decision instability.
Jev Benchmarks & EvaluationsArticle#arxiv#evaluation#benchmark
Community
arXiv study of two-tier web attack triage that holds seven attack families out of training, recalibration and thresholds, then compares zero-shot decision models including Jev with supervised detectors, a local language model, the OWASP Core Rule Set and an anomaly baseline.
Jev Benchmarks & EvaluationsArticle#arxiv#security#evaluation
Community
From the Jev guides
Original write-ups on what this entry is for.
From the Jev AI model community
Posts from builders shipping with Jev right now.