Papers with Proposer

3 papers
UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision (2026.acl-long)

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Challenge: Unified Multimodal Models have achieved remarkable success in cross-modal comprehension, but a gap persists in their ability to translate internal knowledge into faithful and controllable synthesis.
Approach: They propose a self-improvement framework that partitions a single UMM into three collaborative roles: Proposer, Solver, and Judge.
Outcome: The proposed framework improves on TIIF, DPG, CompBench and UniCycle benchmarks.
Better LLM Reasoning via Dual-Play (2026.findings-acl)

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Challenge: Large Language Models (LLMs) have made remarkable progress through Reinforcement Learning with Verifiable Rewards (RLVR) however, external supervision remains a bottleneck for tasks and domains for which supervised data are scarce or non-existent.
Approach: They propose a novel dual-play framework that adversarially trains two models initialized from the same base model.
Outcome: The proposed framework improves the math reasoning performance of large language models.
MARCH: Multi-Agent Reinforced Check for Hallucination (2026.acl-long)

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Challenge: Existing methods to detect hallucinations suffer from inherent confirmation bias, where the verifier inadvertently reproduces the errors of the original generation.
Approach: They propose a framework that enforces rigorous factual alignment by leveraging deliberate *information asymmetry* by combining a pipeline of three specialized agents: a Solver, a Proposer, and a Checker.
Outcome: Extensive experiments across hallucination benchmarks demonstrate that MARCH substantially reduces hallucinism rates.

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