arXiv AI
Jun 11
ProcessThinker: Enhancing Multi-modal Large Language Models Reasoning via Rollout-based Process Reward
arXiv:2606. 11209v1 Announce Type: cross Abstract: Visual question answering increasingly requires multi-step reasoning.
Beyond Final Answers: CRYSTAL Benchmark for Transparent Multimodal Reasoning Evaluation
CRYSTAL is a diagnostic benchmark comprising 6,372 multimodal reasoning instances that assess models through verifiable intermediate steps. It introduces two metrics—Match F1 and Ordered Match F1—to evaluate step-level precision, recall, and order. The benchmark, built via a Delphi-inspired pipeline with four independent MLLMs, reveals systematic failures in current models, such as cherry‑picking and disordered reasoning, and proposes a Causal Process Reward and CPR‑Curriculum to improve reasoning performance.
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arXiv AI
Aug 11
StructReward: Efficient Structured Process Rewards for Self-Correcting Multimodal Reasoning
arXiv:2608. 08326v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as an effective approach for improving multimodal reasoning.
Hugging Face Trending Papers
Aug 8
StructReward: Efficient Structured Process Rewards for Self-Correcting Multimodal Reasoning
Reinforcement learning with verifiable rewards (RLVR) has emerged as an effective approach for improving multimodal reasoning. However, most existing methods evaluate an entire response using a binary reward based only on final-answer correctness, thereby discarding the supervision available in intermediate reasoning steps.
arXiv AI
Jul 1
Breaking Failure Cascades: Step-Aware Reinforcement Learning for Medical Multimodal Reasoning
arXiv:2606. 31825v1 Announce Type: cross Abstract: Recent multimodal large language models have shown great promise in clinical image reasoning, but existing post-training pipelines remain predominantly outcome-centric, relying on final answer correctness or sequence-level preferences.
arXiv Computer Vision
Sep 21
DRT: Dense Reasoning Trace for Efficient and Grounded Multimodal Reasoning
arXiv:2609.21675v1 Announce Type: new Abstract: Despite the remarkable progress in Multimodal Large Language Models (MLLMs), prevailing Chain-of-Thought (CoT) paradigms remain confined to the natural...
arXiv AI
2d ago
Selection-Based Structured Reasoning: Toward Efficient Multimodal Search Agents
arXiv:2610.01892v1 Announce Type: cross Abstract: Multimodal agents commonly generate free-form reasoning before each action. For small models, limited model capacity can result in lengthy reasoning...
arXiv AI
Jun 2
Process Reward Agents for Steering Knowledge-Intensive Reasoning
arXiv:2604. 09482v2 Announce Type: replace Abstract: Reasoning in knowledge-intensive domains remains challenging as intermediate steps are often not locally verifiable: unlike math or code, evaluating step correctness may require synthesizing clues across large external knowledge sources.
arXiv AI
Aug 12
VERDICT: Training-Free Step-Wise Verification of Multimodal Reasoning via Disagreement-Aware Consensus
arXiv:2608. 10665v1 Announce Type: new Abstract: Multimodal large language models often generate reasoning chains containing subtle errors that lead to incorrect answers.
arXiv AI
Jun 12
PaLMR: Towards Faithful Visual Reasoning via Multimodal Process Alignment
arXiv:2603. 06652v2 Announce Type: replace-cross Abstract: Reinforcement learning has recently improved the reasoning ability of Large Language Models and Multimodal LLMs, yet prevailing reward designs emphasise final-answer correctness and consequently tolerate process hallucinations--cases where models reach the right answer while misperceiving visual evidence.
arXiv AI
6d ago
Can Linguistic Reasoning Vectors Enhance Multimodal Reasoning Ability?
The paper introduces LIFT, a lightweight vector‑intervention technique that transfers reasoning capability from a base large language model (LLM) to a vision‑language model (VLM) without retraining the VLM backbone. LIFT defines Reasoning Vectors as differences in hidden states between a reasoning path with an explicit trace and a solver path without it, and injects these vectors into the VLM’s language‑side activations. Experiments on two VLMs across six reasoning benchmarks show that vectors derived from the base LLM consistently outperform those derived from the aligned VLM, indicating that the base LLM is a more effective source for recovering degraded reasoning. "whyItMatters":"The study demonstrates that a simple, frozen‑backbone intervention can partially restore reasoning abilities in multimodal models, highlighting the value of leveraging the original language model’s reasoning power."
arXiv AI
Jun 8
CORE: Contrastive Reflection Enables Rapid Improvements in Reasoning
arXiv:2605. 28742v2 Announce Type: replace Abstract: Language models can use verifiable rewards to improve at a wide variety of reasoning tasks.
arXiv AI
Jul 28
Offline-Online Curriculum RL for Multimodal Reasoning
arXiv:2607. 23700v1 Announce Type: new Abstract: Multimodal large language models exhibit capabilities on reasoning tasks, yet often produce flawed intermediate steps while yielding correct final answers.