The paper introduces FlipDir, a training‑free inference‑time technique that mitigates answer flips in vision‑language models by steering hidden states along a low‑rank subspace derived from contrastive image pairs. It employs a margin‑based gate to attenuate steering only during uncertain decoding steps, thereby restoring original predictions while keeping stable ones unchanged. The authors also present VisFlip, a benchmark framework that evaluates models across nine dataset‑variation combinations in scientific reasoning, robot‑scene understanding, and medical VQA, showing that FlipDir consistently outperforms existing methods on recovery and preservation metrics.
arXiv:2608. 04496v1 Announce Type: cross Abstract: Visual inputs in vision-language models (VLMs) are often encoded into substantially longer token sequences than text, making visual tokens a major bottleneck for efficient inference.
VBVR-Pro is a closed‑loop testbed that enables native visual reasoning through generation, offering 300 procedurally generated tasks that scale training and allow strong transfer to external benchmarks. It supplies verifiable reward scorers based on deterministic, task‑specific rules, outperforming VLM‑as‑a‑judge approaches and providing reliable signals for reinforcement learning. The suite also facilitates controlled modality studies, revealing that video generation excels at persistent spatiotemporal tracking while interleaved generation offers a compute‑efficient alternative, and highlights the importance of vision‑native trajectories for reasoning.
The paper investigates how the order of generating explanations—whether a rationale is produced before or after the answer—affects vision‑language reasoning. By conducting controlled experiments on knowledge‑intensive QA, visual entailment, and compositional grounding tasks, the authors show that larger models are required for reliable rationale‑first generation, while answer‑first generation is less susceptible to format errors. The study concludes that explanation ordering, model scale, pre‑training knowledge, fine‑tuning, and task structure jointly influence prediction accuracy and reasoning faithfulness.
arXiv:2606. 00987v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown strong visual understanding and language-guided grounding abilities, yet their capacity for multi-temporal visual reasoning remains underexplored.
arXiv:2606. 20244v1 Announce Type: cross Abstract: Vision-language models (VLMs) often underperform on evidence intensive tasks because decisive visual evidence are small, localized, and easy to overlook, leading to failures in evidence readout even when high-level reasoning is intact.
The paper introduces a progressive training strategy for embodied vision‑language models aimed at reducing spatio‑temporal hallucinations. It first creates a Chain‑of‑Thought dataset that breaks complex reasoning into detailed spatiotemporal steps, then uses supervised pre‑training on this dataset followed by fine‑tuning with weakly‑labeled data. Experiments show the method improves backbone accuracy and narrows the forward‑backward performance gap from over 70% to 6.53%, indicating stronger dynamic reasoning and fewer temporal biases.
arXiv:2606. 07647v1 Announce Type: cross Abstract: Large vision language models (LVLMs) have made rapid advancements and are deployed across various applications, yet hallucinations remain a major challenge.
arXiv:2606. 11576v1 Announce Type: cross Abstract: Modern Vision-Language Models (VLMs) benefit from chain-of-thought prompting and test-time scaling, but these gains often come with prohibitive inference cost due to large visual contexts and long decoding chains.
arXiv:2607. 11436v1 Announce Type: new Abstract: Vision-language models increasingly succeed on multimodal reasoning benchmarks, yet their visual evidence often becomes unstable once it enters the language stack, weakening evidence-grounded reasoning.
arXiv:2609.13228v1 Announce Type: new Abstract: Vision Language Models (VLMs) should rely on visual evidence that directly determines the correct answer, but supervision for grounding visual reasonin...
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:2601. 02918v4 Announce Type: replace Abstract: Image Quality Assessment (IQA) is a long-standing problem in computer vision.