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ARXIV:2604.04443 · LLM REASONING · SUBMITTED 07 APR · 20:13 UTC · FRESHNESS UNKNOWN
ARXIV:2604.04443LLM REASONINGSUBMITTED 07 APR · 20:13 UTCFRESHNESS UNKNOWNGuangyao Dou · Luis Brena · Akhil Deo · William Jurayj · Jingyu Zhang · Nils Holzenberger · +1 at arXiv
A new benchmark and dataset to evaluate and improve LLM reasoning over complex, real-world rules, with a focus on legal and policy domains.
Opportunity summary
Pain A new benchmark and dataset to evaluate and improve LLM reasoning over complex, real-world rules, with a focus on legal and policy domains.
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A new benchmark and dataset to evaluate and improve LLM reasoning over complex, real-world rules, with a focus on legal and policy domains. In legal and policy settings, this manifests as deontic reasoning: reasoning…
Reasoning with complex, context-specific rules remains challenging for large language models (LLMs). In legal and policy settings, this manifests as deontic reasoning: reasoning about obligations, permissions, and prohibitions under explicit rules.
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Besides free-form chain-of-thought reasoning, DEONTICBENCH enables an optional solver-based workflow in which models translate statutes and case facts into executable Prolog, leading to formal…
LLM Reasoning moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
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A new benchmark and dataset to evaluate and improve LLM reasoning over complex, real-world rules, with a focus on legal and policy domains.
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10.48550/arXiv.2604.04443A new benchmark and dataset to evaluate and improve LLM reasoning over complex, real-world rules, with a focus on legal and policy domains.
Abstract
Reasoning with complex, context-specific rules remains challenging for large language models (LLMs). In legal and policy settings, this manifests as deontic reasoning: reasoning about obligations, permissions, and prohibitions under explicit rules. While many recent benchmarks emphasize short-context mathematical reasoning, fewer focus on long-context, high-stakes deontic reasoning. To address this gap, we introduce DEONTICBENCH, a benchmark of 6,232 tasks across U.S. federal taxes, airline baggage policies, U.S. immigration administration, and U.S. state housing law. These tasks can be approached in multiple ways, including direct reasoning in language or with the aid of symbolic computation. Besides free-form chain-of-thought reasoning, DEONTICBENCH enables an optional solver-based workflow in which models translate statutes and case facts into executable Prolog, leading to formal problem interpretations and an explicit program trace. We release reference Prolog programs for all instances. Across frontier LLMs and coding models, best hard-subset performance reaches only 44.4% on SARA Numeric and 46.6 macro-F1 on Housing. We further study training with supervised fine-tuning and reinforcement learning for symbolic program generation. Although training improves Prolog generation quality, current RL methods still fail to solve these tasks reliably. Overall, DEONTICBENCH provides a benchmark for studying context-grounded rule reasoning in real-world domains under both symbolic and non-symbolic settings.
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PROBLEM
A new benchmark and dataset to evaluate and improve LLM reasoning over complex, real-world rules, with a focus on legal and policy domains. In legal and policy settings, this manifests as deontic reasoning: reasoning about obligations, permissions, and prohibitions under explici...
METHOD
Reasoning with complex, context-specific rules remains challenging for large language models (LLMs). In legal and policy settings, this manifests as deontic reasoning: reasoning about obligations, permissions, and prohibitions under explicit rules.
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Besides free-form chain-of-thought reasoning, DEONTICBENCH enables an optional solver-based workflow in which models translate statutes and case facts into executable Prolog, leading to formal problem int...
WHY NOW
LLM Reasoning moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A new benchmark and dataset to evaluate and improve LLM reasoning over complex, real-world rules, with a focus on legal and policy domains. In legal and policy settings, this manifests as deontic reasoning: reasoning about obligations, permissions, and prohibitions under explicit rules.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Reasoning with complex, context-specific rules remains challenging for large language models (LLMs). In legal and policy settings, this manifests as deontic reasoning: reasoning about obligations, permissions, and prohibitions under explicit rules.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Besides free-form chain-of-thought reasoning, DEONTICBENCH enables an optional solver-based workflow in which models translate statutes and case facts into executable Prolog, leading to formal problem interpretations and an explicit program trace. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Reasoning moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A new benchmark and dataset to evaluate and improve LLM reasoning over complex, real-world rules, with a focus on legal and policy domains.
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LLM Reasoning
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