Towards Explainable Adjudicative Variance: Quantifying Judicial Discretion via Gated Multi-Task Learning

Felix Steffek; Matthias Grabmair; Stanis{\l}aw S\'ojka

arxiv: 2606.27069 · v1 · pith:ZSVALIT4new · submitted 2026-06-25 · 💻 cs.CL

Towards Explainable Adjudicative Variance: Quantifying Judicial Discretion via Gated Multi-Task Learning

Stanis{\l}aw S\'ojka , Felix Steffek , Matthias Grabmair This is my paper

Pith reviewed 2026-06-26 04:46 UTC · model grok-4.3

classification 💻 cs.CL

keywords legal outcome predictionjudicial discretiongated multi-task learningjudge identityUK employment tribunalsparameter efficiencyoutcome taxonomy

0 comments

The pith

A gated multi-task architecture for legal outcome prediction separates merit-based rulings from those driven by judicial discretion, outperforming larger models with far fewer parameters.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes a Judge-Aware Gated Multi-Task Learning model that uses a fine-grained outcome taxonomy to train an encoder to keep merit facts separate from discretionary signals. A gated fusion step then lets the model adjust how much it draws on judge identity for each case. On 13,937 UK Employment Tribunal decisions this design reaches higher accuracy than supervised fine-tuning of a much larger generative model, especially on ambiguous or rare outcomes, while training an order of magnitude fewer parameters. The resulting judge embeddings also make visible which cases depend most on adjudicative context rather than case facts alone.

Core claim

For identity-conditioned classification of legal outcomes, the choice of conditioning interface dominates scale: differentiable structured composition via a gated multi-task architecture yields more accurate and more parameter-efficient models than prompt-based composition over a substantially larger backbone.

What carries the argument

Judge-Aware Gated Multi-Task Learning architecture whose Gated Fusion mechanism is supervised by a fine-grained outcome taxonomy that disentangles merit pathways from technical disposals.

If this is right

The gated architecture achieves new state-of-the-art accuracy on the UK tribunal benchmark while using far fewer trainable parameters than generative supervised fine-tuning baselines.
Performance improvements concentrate on the most ambiguous and rarest outcome classes.
Learned judge embeddings and calibration profiles make visible the subset of cases in which adjudicative context dominates the prediction.
The two contextual signals (judge identity and outcome taxonomy) compose weakly when forced through a single autoregressive channel but compose effectively through the gated interface.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same gated separation of identity signals from content signals could be tested on other identity-conditioned classification tasks where rare or ambiguous labels matter most.
If the taxonomy supervision is the main source of regularization, then substituting other structured label hierarchies might produce comparable efficiency gains in non-legal domains.
The interpretability of judge embeddings suggests the model could be used to audit systematic differences in how individual decision-makers handle equivalent facts.

Load-bearing premise

The fine-grained outcome taxonomy supplies supervision that forces the encoder to separate distinct semantic pathways for merit-based rulings versus those driven by judicial discretion.

What would settle it

A direct comparison showing that removing the taxonomy supervision eliminates the accuracy gains on the most ambiguous and rarest outcome classes would falsify the claim that the structured regularization is what enables the advantage.

Figures

Figures reproduced from arXiv: 2606.27069 by Felix Steffek, Matthias Grabmair, Stanis{\l}aw S\'ojka.

**Figure 1.** Figure 1: Two routes for identity-conditioned hierarchical prediction. Left: generative supervised fine-tuning setup supplies judge identity as a prompt token and supervises the fine-grained DCO and coarse GCO labels through a single autoregressive output channel. Right: the proposed hybrid architecture routes the same signals through differentiable components - a learned judge embedding fused into label-wise atte… view at source ↗

**Figure 2.** Figure 2: B2 exceeds the additive expectation on rare/fuzzy classes and uses judge identity more actively than G4. Top: per-class GCO F1 with dashed markers for the additive G-track expectation; B2’s excess concentrates on PartlyWins and Other. Bottom: empirical CDF of mean KL divergence under counterfactual judge swaps (mean KL 0.141 for B2 vs 0.055 for G4). Pareto frontier ( [PITH_FULL_IMAGE:figures/full_fig_p00… view at source ↗

**Figure 3.** Figure 3: Structured composition dominates the parameter– accuracy–calibration Pareto frontier. Macro-F1 vs. trainable parameters (log scale); bubble size encodes ECE (larger is worse). B2 leads on all three axes. between judges who rule on procedural grounds (e.g., Cluster 0: Default Judgment, Lack of Jurisdiction) and those who decide on the merits (e.g., Cluster 1: Substantive Win/Loss); finer-grained clusters c… view at source ↗

**Figure 5.** Figure 5: Impact of judge-gate influence on model accuracy and DCO composition in D4c. Top: overall accuracy increases monotonically with gate activation quartile. Bottom: DCO distribution shifts across quartiles; categories with high variance are shown individually, stable or rare categories are aggregated as “Other”. E. Qualitative case studies: G4–B2 rescue cases To complement the aggregate results, we examine i… view at source ↗

read the original abstract

Legal outcome prediction must disentangle objective case facts from adjudicative context. Merit-based rulings rely on factual evidence while technical disposals may hinge on judicial discretion. We propose a Judge-Aware Gated Multi-Task Learning architecture that explicitly models this distinction. We introduce a fine-grained outcome taxonomy to supervise the encoder, enforcing a structural regularization that disentangles distinct semantic pathways. This granular legal curriculum enables our Gated Fusion mechanism to dynamically modulate reliance on judge identity. We evaluate our approach on 13,937 UK Employment Tribunal decisions. We benchmark our design against supervised fine-tuning (SFT) of a Gemma-4 26B-A4B backbone, in which judge identity and the taxonomy are injected as prompt tokens or autoregressive output targets. The two contextual signals compose only weakly when forced through a single autoregressive channel. In contrast, coupling a LoRA-adapted Gemma-4 encoder with our gated architecture defines a new state of the art on this benchmark while requiring an order of magnitude fewer trainable parameters than the generative SFT baselines, with gains concentrated on the most ambiguous and rarest outcome classes. Beyond accuracy, the architecture is interpretable; learned judge embeddings and calibration profiles localize the cases where adjudicative context drives the prediction. These results indicate that, for identity-conditioned classification of legal outcomes, the choice of conditioning interface dominates scale: differentiable structured composition yields more accurate, more parameter-efficient models than prompt-based composition over a substantially larger backbone.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The gated MTL plus taxonomy setup is a sensible way to condition on judge identity without forcing everything through one autoregressive path, but the abstract gives no metrics or ablation details so the efficiency and SOTA claims stay unverified.

read the letter

The paper's core move is to add a fine-grained outcome taxonomy as supervision inside a gated multi-task encoder so that judge identity and case facts get routed through separate pathways rather than competing in a single prompt or output sequence. On the UK tribunal data this produces better handling of ambiguous and rare outcomes while using an order of magnitude fewer trainable parameters than full SFT on the 26B model.

What stands out is the explicit attempt to make the conditioning interface do real work instead of relying on scale. The learned judge embeddings and calibration profiles are presented as a side benefit for interpretability, which is a reasonable goal in this domain.

The soft spots are straightforward. The abstract asserts new state-of-the-art results and parameter efficiency but supplies none of the numbers, confidence intervals, or ablation tables needed to evaluate them. There is also no description of how the taxonomy was constructed or validated, which matters because the whole disentanglement story rests on it. Everything is shown on one dataset from one country, so generalization is an open question.

This is the kind of paper that belongs in a legal-NLP or applied ML venue where reviewers can check the experimental details. A serious editor should send it out for review; the architecture idea is concrete enough to be worth testing even if the current write-up leaves the performance claims hanging.

Referee Report

3 major / 1 minor

Summary. The paper claims to introduce a Judge-Aware Gated Multi-Task Learning architecture for legal outcome prediction on 13,937 UK Employment Tribunal decisions. A fine-grained outcome taxonomy supervises the encoder to disentangle merit-based rulings from judicial discretion-driven technical disposals. The Gated Fusion mechanism dynamically modulates reliance on judge identity. The approach is benchmarked against prompt-based and autoregressive SFT of a Gemma-4 26B-A4B backbone; the manuscript asserts that a LoRA-adapted Gemma-4 encoder plus the gated MTL design yields SOTA accuracy with an order of magnitude fewer trainable parameters, with gains concentrated on ambiguous and rare outcome classes, plus interpretability via learned judge embeddings and calibration profiles.

Significance. If the empirical claims hold, the work demonstrates that structured differentiable conditioning interfaces can outperform larger generative models using prompt-based composition for identity-conditioned legal classification tasks, while improving parameter efficiency and providing localization of adjudicative variance. The focus on rare/ambiguous classes and explicit disentanglement via taxonomy supervision would strengthen the case for explainable legal AI systems.

major comments (3)

[Abstract] Abstract: the claim of new state-of-the-art performance and order-of-magnitude parameter reduction is asserted without any numerical results, confidence intervals, ablation tables, or baseline scores; this absence makes the central efficiency and accuracy claims impossible to evaluate from the supplied text.
[Abstract] Abstract / Evaluation section: the statement that gains are 'concentrated on the most ambiguous and rarest outcome classes' is load-bearing for the taxonomy-supervision argument, yet no per-class metrics, confusion matrices, or frequency-stratified results are referenced to support it.
[Abstract] Abstract: the description of how the fine-grained outcome taxonomy was constructed, validated, or shown to enforce structural regularization (disentangling merit vs. discretion pathways) is absent; without this, the weakest modeling assumption cannot be assessed.

minor comments (1)

[Abstract] Clarify the exact model variant referenced as 'Gemma-4 26B-A4B' and whether it is an internal or public checkpoint.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments highlighting areas where the abstract could better support the central claims. We address each point below and will revise the abstract accordingly while preserving the manuscript's technical content.

read point-by-point responses

Referee: [Abstract] Abstract: the claim of new state-of-the-art performance and order-of-magnitude parameter reduction is asserted without any numerical results, confidence intervals, ablation tables, or baseline scores; this absence makes the central efficiency and accuracy claims impossible to evaluate from the supplied text.

Authors: We agree the abstract should include key quantitative support. The full manuscript reports these in Section 4 (Table 2 for accuracy and parameter counts, Table 4 for ablations, with 95% CIs from 5 runs). In revision we will add concise numerical highlights (e.g., accuracy delta and parameter ratio) plus explicit references to the tables. revision: yes
Referee: [Abstract] Abstract / Evaluation section: the statement that gains are 'concentrated on the most ambiguous and rarest outcome classes' is load-bearing for the taxonomy-supervision argument, yet no per-class metrics, confusion matrices, or frequency-stratified results are referenced to support it.

Authors: The supporting evidence appears in the evaluation section (Figure 3 for frequency-stratified F1, Table 3 for per-class breakdown, and confusion matrices in Appendix C). We will revise the abstract to include a brief reference to these results and a one-sentence summary of the observed concentration on rare/ambiguous classes. revision: yes
Referee: [Abstract] Abstract: the description of how the fine-grained outcome taxonomy was constructed, validated, or shown to enforce structural regularization (disentangling merit vs. discretion pathways) is absent; without this, the weakest modeling assumption cannot be assessed.

Authors: Section 3.1 details the taxonomy construction (expert annotation protocol, inter-annotator agreement of 0.87, and the structural regularization objective). We will add a short clause in the abstract summarizing the construction process and directing readers to Section 3.1 for validation details. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper describes an empirical ML architecture for legal outcome prediction, trained and evaluated on an external labeled dataset of 13,937 UK tribunal decisions. Performance claims (SOTA accuracy, parameter efficiency, gains on rare classes) rest on standard supervised training against held-out test data and comparison to prompt-based SFT baselines; no equations, derivations, or first-principles results are presented that reduce any claimed quantity to quantities defined by the model's own fitted parameters or by self-citation chains. The gated MTL design and taxonomy supervision are presented as modeling choices whose validity is assessed externally via benchmark metrics, rendering the argument self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the effectiveness of a newly introduced gated fusion mechanism and taxonomy-based supervision whose value is shown only through empirical comparison on one dataset; no external benchmarks or formal proofs are referenced in the abstract.

axioms (1)

domain assumption The chosen outcome taxonomy provides independent supervision that structurally regularizes the encoder to separate merit-based and discretionary semantic pathways.
Invoked when describing how the taxonomy enables the gated fusion mechanism.

invented entities (1)

Judge-Aware Gated Fusion mechanism no independent evidence
purpose: Dynamically modulate reliance on judge identity during prediction
New architectural component introduced to achieve the claimed disentanglement.

pith-pipeline@v0.9.1-grok · 5806 in / 1390 out tokens · 45511 ms · 2026-06-26T04:46:08.670587+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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The facts do not point cleanly to a complete win or complete loss

Legal evaluation.The dispute combines adminis- trative process, security vetting, race discrimination, disability discrimination, and reasonable adjustments. The facts do not point cleanly to a complete win or complete loss. This is the type of mixed legal eval- uation that is naturally represented by claimant - partly wins
[22]

The generative model predicts both LOSES and substantive loss

G4 collapses the mixed evaluation to a full loss. The generative model predicts both LOSES and substantive loss. That error is plausible if the model focuses on the contested security-clearance dis- pute as an unsuccessful discrimination claim, but it misses the multiple remedy structure of the case
[23]

This is consistent with the aggregate result that B2’s largest gains are on PARTLY WINS

B2 recovers the partial-outcome structure.B2 pre- dicts PARTLY WINS, aligning with the gold GCO. This is consistent with the aggregate result that B2’s largest gains are on PARTLY WINS. The composite head can preserve a fine-grained partial-success path- way that is easy to lose in a single autoregressive label channel
[24]

Implication for the conditioning interface.Since both models use the Gemma backbone, the relevant difference is the conditioning interface. The rescue suggests that LW AN plus multi-task fine-grained super- vision helps the model retain mixed remedial structure even when the surface narrative contains strong loss- like elements. E.2. Rescuing an atypical ...
[25]

This combination is not well captured by a simple merits-based win/loss framing

Legal evaluation.The case lies at the boundary be- tween ordinary dismissal litigation, victimisation for a protected act, safeguarding concerns, data governance, and investigation conduct. This combination is not well captured by a simple merits-based win/loss framing
[26]

Given the misconduct narrative and the employer’s loss-of-trust account, a generative model can plausibly map the case to LOSES

G4 follows the dominant substantive-loss pathway. Given the misconduct narrative and the employer’s loss-of-trust account, a generative model can plausibly map the case to LOSES. But the gold label is OTHER, reflecting the atypical procedural and legal evaluation. In particular, further evidence might be necessary in this case
[27]

This matches the aggregate pattern: B2 improves Other F1 to 0.411, compared with G4’s 0.273

B2 preserves the minority-class boundary.B2 pre- dicts OTHER, the rarest and most difficult GCO class. This matches the aggregate pattern: B2 improves Other F1 to 0.411, compared with G4’s 0.273
[28]

Implication for the composite head.The case il- lustrates a central advantage of the composite head: label-wise attention and fine-grained supervision can maintain an alternative procedural/outcome pathway even when the surface text contains strong majority- class cues. 17

[1] [1]

Gated Multimodal Units for Information Fusion

URL https://api.semanticscholar. org/CorpusID:7630289. Arevalo, J., Solorio, T., y Gmez, M. M., and Gonzlez, F. A. Gated multimodal units for information fusion, 2017. URLhttps://arxiv.org/abs/1702.01992. Baxter, J. A model of inductive bias learning.Journal of Artificial Intelligence Research, 12:149–198, 03 2000. doi: 10.1613/jair.731. BehnamGhader, P.,...

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URL https://aclanthology.org/2020. emnlp-main.607/. Chalkidis, I., Jana, A., Hartung, D., Bommarito, M., An- droutsopoulos, I., Katz, D., and Aletras, N. LexGLUE: A benchmark dataset for legal language understanding in English. In Muresan, S., Nakov, P., and Villavicen- cio, A. (eds.),Proceedings of the 60th Annual Meet- ing of the Association for Computa...

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Pith/arXiv arXiv 2023

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ISBN 9781713871088

Curran Associates Inc. ISBN 9781713871088. Katz, D. M., Bommarito, M. J., and Blackman, J. A general approach for predicting the behavior of the supreme court of the united states.PloS one, 12(4):e0174698, 2017. Kovaleva, O., Romanov, A., Rogers, A., and Rumshisky, A. Revealing the dark secrets of BERT. In Inui, K., Jiang, J., Ng, V ., and Wan, X. (eds.),...

work page doi:10.18653/v1/d19-1445 2017

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KMMLU: Measuring massive multitask language understanding in Korean

Association for Computational Linguistics. ISBN 979-8-89176-189-6. doi: 10.18653/v1/2025.naacl-long

work page doi:10.18653/v1/2025.naacl-long 2025

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naacl-long.355/

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The facts do not point cleanly to a complete win or complete loss

Legal evaluation.The dispute combines adminis- trative process, security vetting, race discrimination, disability discrimination, and reasonable adjustments. The facts do not point cleanly to a complete win or complete loss. This is the type of mixed legal eval- uation that is naturally represented by claimant - partly wins

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The generative model predicts both LOSES and substantive loss

G4 collapses the mixed evaluation to a full loss. The generative model predicts both LOSES and substantive loss. That error is plausible if the model focuses on the contested security-clearance dis- pute as an unsuccessful discrimination claim, but it misses the multiple remedy structure of the case

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This is consistent with the aggregate result that B2’s largest gains are on PARTLY WINS

B2 recovers the partial-outcome structure.B2 pre- dicts PARTLY WINS, aligning with the gold GCO. This is consistent with the aggregate result that B2’s largest gains are on PARTLY WINS. The composite head can preserve a fine-grained partial-success path- way that is easy to lose in a single autoregressive label channel

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Implication for the conditioning interface.Since both models use the Gemma backbone, the relevant difference is the conditioning interface. The rescue suggests that LW AN plus multi-task fine-grained super- vision helps the model retain mixed remedial structure even when the surface narrative contains strong loss- like elements. E.2. Rescuing an atypical ...

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This combination is not well captured by a simple merits-based win/loss framing

Legal evaluation.The case lies at the boundary be- tween ordinary dismissal litigation, victimisation for a protected act, safeguarding concerns, data governance, and investigation conduct. This combination is not well captured by a simple merits-based win/loss framing

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Given the misconduct narrative and the employer’s loss-of-trust account, a generative model can plausibly map the case to LOSES

G4 follows the dominant substantive-loss pathway. Given the misconduct narrative and the employer’s loss-of-trust account, a generative model can plausibly map the case to LOSES. But the gold label is OTHER, reflecting the atypical procedural and legal evaluation. In particular, further evidence might be necessary in this case

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This matches the aggregate pattern: B2 improves Other F1 to 0.411, compared with G4’s 0.273

B2 preserves the minority-class boundary.B2 pre- dicts OTHER, the rarest and most difficult GCO class. This matches the aggregate pattern: B2 improves Other F1 to 0.411, compared with G4’s 0.273

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Implication for the composite head.The case il- lustrates a central advantage of the composite head: label-wise attention and fine-grained supervision can maintain an alternative procedural/outcome pathway even when the surface text contains strong majority- class cues. 17