A sound judgment applies the law to established facts and weighs the circumstances in which they arose. However, existing methods swing between rigid statute matching and ungrounded discretion, benchmarks score a label or a rubric, and the experience that would supply the balance stays unverified. We formalize legal judgment as a reference-anchored task, whose object is a single decision that stays tied to the statute and to the circumstances at once. We introduce JusticeAxis, 256 real-world criminal cases from 18 countries with audio, image, and text evidence, and three lawyer-written judgments for every case: the recorded one and one for each failure. We further propose JusticeAgent, a harness whose element agents establish the facts and whose judge agent applies the law under skills carrying experience of the circumstances. Skills are distilled from execution trajectories and admitted only under Bayesian credible bounds. Experiments show that failure turns direction with scale: open-weight backbones drift to unsupported grounds, frontier models to the statutory default. We further verify that JusticeAgent, as a simple yet effective plugin, carries a frozen open-weight backbone to commercial level. Project resources are available at https://github.com/beita6969/JusticeAxis.
Figures & tables
Figure 1: Overview: the axis a judgment sits on (a), the two failure modes of existing systems, rigid statute matching (b) and ungrounded discretion (c), and the JusticeAgent framework (d).
Figure 2: The JusticeAgent harness. C1 : an orchestrator edits a shared Element Graph while one agent per offence element calls tools and returns findings until no node is open. C2 : the judge applies the law to the closed graph under contextual skills and grounding constraints. C3 : skills mined from trajectories are verified independently and retained, deferred, refined or pruned on credible bounds, not on judgment scores.
Given per instance
Benchmark
Jur.
Input
Law
Prec.
Bg.
Refs
Fail.
CAIL2018 [ 27 ]
1
T
1
LegalBench [ 10 ]
1
T
1
CrossLex [ 29 ]
3
T
✓
1
Multi-Legal-Bench [ 22 ]
6
T
1
CLAUSE [ 3 ]
1
T
✓
1
LLM
Table 1: Comparison with legal and multimodal benchmarks. A/I/T: audio, image, text; Law, Prec., Bg.: statutes, precedents, background; Refs: references per case; Fail.: who wrote the failure ones.
Table 2: Main results (128 evaluation cases). Acc, Fam, Disp and Sent. are the shares of cases with charge, charge family, disposition and sentence exactly correct, Avg their mean; Acc/Fam is exact charge among family-correct cases, Sent/Disp exact sentence among disposition-correct cases. Nearest anchor is the share landing closest to each reference under ρ , Pol. the signed share of misses toward the rigid pole, (Jrig−Jung)/(Jrig+Jung) . Miss is 100−J∗ . ± are Wilson 95% half-widths; Δ is the gain over the Qwen3.8-27B backbone, RMR the relative reduction of its misses. Higher is better except Jrig , Jung , Miss. † 304B mixture-of-experts.