6 papers
Reasoning without Gold Standards: A Proxy-Judge Theory of Autoformalization
Lei Xu, Xin Quan, André Freitas
Complex reasoning tasks increasingly require systems to produce outputs whose correctness cannot be judged by exact match against a single reference. Autoformalization (AF) is a re…
LogicReward: Incentivizing LLM Reasoning via Step-Wise Logical Supervision
Jundong Xu, Hao Fei, Huichi Zhou +6
Although LLMs exhibit strong reasoning capabilities, existing training methods largely depend on outcome-based feedback, which can produce correct answers with flawed reasoning. Pr…
Decompose-and-Formalise: Recursively Verifiable Natural Language Inference
Xin Quan, Marco Valentino, Louise A. Dennis +1
Recent work has shown that integrating large language models (LLMs) with theorem provers (TPs) in neuro-symbolic pipelines helps with entailment verification and proof-guided refin…
Inferring Latent Intentions: Attributional Natural Language Inference in LLM Agents
Xin Quan, Jiafeng Xiong, Marco Valentino +1
Attributional inference, the ability to predict latent intentions behind observed actions, is a critical yet underexplored capability for large language models (LLMs) operating in…
Faithful and Robust LLM-Driven Theorem Proving for NLI Explanations
Xin Quan, Marco Valentino, Louise A. Dennis +1
Natural language explanations play a fundamental role in Natural Language Inference (NLI) by revealing how premises logically entail hypotheses. Recent work has shown that the inte…
PEIRCE: Unifying Material and Formal Reasoning via LLM-Driven Neuro-Symbolic Refinement
Xin Quan, Marco Valentino, Danilo S. Carvalho +2
A persistent challenge in AI is the effective integration of material and formal inference - the former concerning the plausibility and contextual relevance of arguments, while the…