Papers with open-ended exploration
RAVR: Reference-Answer-guided Variational Reasoning for Large Language Models (2026.findings-acl)
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| Challenge: | Experiments show that reinforcement learning (RL) can refine the reasoning abilities of large language models (LLMs) but requires a key prerequisite: the model must already be able to generate high-utility reasoning paths with non-negligible probability. |
| Approach: | They propose a framework that uses answer-conditioned reasoning as a variational surrogate for question-only reasoning. |
| Outcome: | Experiments on 11 benchmarks and 3 models show that RAVR reduces hesitation, strengthens conclusion consolidation, and promotes problem-specific strategies in reasoning. |
PolitNuggets: Benchmarking Agentic Discovery of Long-Tail Political Facts (2026.acl-long)
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| Challenge: | Large Reasoning Models (LRMs) are embedded in agentic frameworks and are under-evaluated. |
| Approach: | They propose a multilingual benchmark for agentic information synthesis using PolitNuggets . they standardize evaluation with an optimized Supervisor–Searcher multi-agent system . |
| Outcome: | The proposed model can discover and synthesize "long-tail" facts from dispersed sources. |