This paper designed exactly such a training signal -- a verifiable"survival" reward in which both the student's cited authorities and the adversary's counter-authorities are checked by a citation verifier, so that survival is decided on verified grounds rather than rhetoric, and fabricated citations are automatically neutralized.
Abstract
Adversarial self-play is an appealing recipe for legal reasoning: have a student model draft an argument, have an adversary attack it, and reward the student when its argument survives the attack. We designed exactly such a training signal -- a verifiable"survival"reward in which both the student's cited authorities and the adversary's counter-authorities are checked by a citation verifier, so that survival is decided on verified grounds rather than rhetoric, and fabricated citations are automatically neutralized. We then asked a narrow but important question: does the competitive component itself -- the adversary and the survival reward -- add anything on top of an otherwise identical non-competitive training run? Across four independent tests -- a bootstrap comparison, a two-seed replication, a paired per-case adversarial-robustness comparison, and a blinded head-to-head judgment of generated arguments, plus a follow-up pilot with a deliberately strengthened self-play adversary -- the competitive component produced no reliable benefit. The blinded judgment gave a 49% win rate (binomial p approx. 1.000); the strengthened-adversary pilot gave a 50% win rate (32:32, p approx. 1.000). An early apparent +29% advantage reversed and proved to be a small-sample artifact. We report this as an honest negative result. The value of the paper is reproducibility and the sharing of concrete pitfalls: an initially promising metric that inverted on more data, and an adversarial-robustness metric that silently collapsed to plain recall once the adversary stopped citing the same authorities as the gold answer. This null is consistent with, and reconfirms in the legal domain, the conclusion of the companion coding-domain study (Kim, 2026, arXiv:2607.08255) that the value of multi-teacher curricula arises from constructing a verifiable environment rather than from competition itself.
In statutory question answering, every claim must be traceable to evidence, not merely relevant, since unverifiable labour-rights answers carry serious legal consequences. Current systems fall short: single-pass RAG cannot detect insufficient evidence, while multi-agent legal-debate systems treat grounding as a prompti...
Fatema Tuj Johora Faria, Mukaffi Bin Moin, Jubayer Al Mahmud et al.· 0 citations
This work identifies a failure mode not addressed by a stronger model: when the context contains an assertion by a party with an incentive toward optimism - here the sales representative, a witness recorded in the CRM - the model treats the assertion as evidence and clears deals the company's own records deem unaccepta...
Suppose that expert witnesses appearing in litigation are, generically, either honest in their assessment of a fact situation or are advocates who testify in favour of their retaining party, regardless of new information. The type of a witness is known to law firms, who engage with them repeatedly, but not to courts....
Martin Richardson, J. Rodrigues-Neto· Canadian Journal of Economic...· 0 citations
There is a well-established research program that relies on the Condorcet Jury Theorem (CJT) to justify democracy and majority voting from an epistemic perspective. This paper explores how the CJT can be extended beyond its standard epistemic interpretation. I propose a version of the CJT that does not presuppose the...
The framework strategic interactive oversight (SIO), which treats oversight jointly as a verification mechanism and a strategic communication channel, is introduced and the notion of task-admissible latent optimisation is formalised, which entails the pursuit of latent objectives while maintaining a prescribed task per...
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What happens when a legal AI model learns to look like a lawyer instead of reasoning like one? We fine tune Qwen3-8B with Group Relative Policy Optimisation (GRPO) against a proxy built from three surface features: citation count, legalese density, and response length. The model does not learn to reason more effectivel...
Subramanyam Sahoo, Justin C. Shenk· 0 citations
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