5arXiv cs.CL (Computation and Language)·
Test-time scaling for small VLMs on multilingual visual MCQ: conditions matter more than methods
A new arXiv paper examines whether test-time scaling (TTS) transfers to small open vision-language models using EXAMS-V, a multilingual visual multiple-choice benchmark. The study compares self-consistency, describe-then-reason with PRM-guided beam search, and post-hoc selectors across Qwen2.5-VL-7B-Instruct and Qwen3.5-4B. Key findings: prompt parseability and decoding budget (token limit) dominate gains, while elaborate search/verification methods like PRM-guided beam search underperform plain majority vote at 8x the cost. The best configuration achieves 84.1% on ImageCLEF 2026 test split, ranking first on the Visual MCQ leaderboard.
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Training framework reduces calibration error 60%+ in Medical VQA multimodal LLMs
A new arXiv preprint proposes a finetuning framework to improve verbalized uncertainty calibration in multimodal LLMs applied to Medical Visual Question Answering. The composite loss function combines Brier-style calibration, anchor regularization, contrastive image-text alignment, and KL-based stabilization, evaluated on MedGemma 4B IT and Qwen2-VL 7B Instruct across three medical VQA benchmarks. The method reduces calibration error by 60% or more and improves discrimination by 26% or more while preserving predictive accuracy, outperforming prompting-, sampling-, and training-based baselines.
ParaEval framework reduces MCQA benchmark sensitivity to answer phrasing
A new arXiv preprint identifies a systematic flaw in multiple-choice QA benchmarks: log-likelihood scoring conflates surface-form familiarity with actual capability, producing false performance gaps exceeding 2 points between models trained on identical knowledge. The authors propose ParaEval, which queries models with multiple paraphrases per answer option and scores on the most favorable phrasing, reducing the false gap to below 1 point. The effect is confirmed on frontier 70B and 120B open-source models, suggesting widespread benchmark inflation in standard MCQA evaluations.
Self-improving VLMs can silently regress when verifier quality is task-mismatched
A new arXiv paper demonstrates that verifier-driven self-DPO, a common recipe for self-improving visual-language models, can silently degrade student model performance when the verifier's task-rubric accuracy is insufficient for the target task. Experiments on Qwen-3-VL-2B and Qwen-2.5-VL-3B across MathVista, MMMU, and BLINK show regressions of 3.4–10.9 percentage points below frozen baselines, with the counterintuitive finding that more accurate-but-still-wrong verifiers cause larger regressions than near-random ones. The authors provide a mechanistic explanation via a variance theorem for progress-gated replay and offer operational guidance: measure target-task rubric accuracy before running any verifier-driven loop and rank verifiers by task-specific quality rather than parameter count.
Towards Reliable Multilingual LLMs-as-a-Judge: An Empirical Study
This paper systematically investigates strategies for extending LLM-based automatic evaluation (LLMs-as-a-Judge) to multilingual settings, covering high-, mid-, and low-resource languages (English, Spanish, Basque). The authors compare instruction translation, monolingual vs. multilingual supervision, and model size, finding that fine-tuned smaller models can match proprietary models when in-domain data is available, while zero-shot larger models are preferable out-of-domain. Two meta-evaluation datasets are extended to Spanish and Basque, and all data and code are publicly released.
Act2Answer: Benchmarking commonsense and world knowledge retention in Vision-Language-Action models
Researchers introduce Act2Answer, a protocol for evaluating how much commonsense and factual knowledge VLA models retain after fine-tuning on robotics data. The approach converts knowledge benchmark questions into tabletop object-placement episodes, yielding action-grounded success rates that reduce confounds from low-level control failures. A large-scale study of 7 VLA models and 9 VLM baselines finds that VLAs retain solid performance on simple concepts but show larger gaps on richer semantic categories compared to their source VLMs, and that VQA co-training is associated with better knowledge retention.
Self-Guided Test-Time Training improves long-context LLM utilization by up to 15%
Researchers propose Self-Guided Test-Time Training (S-TTT), a method that addresses the degradation in accuracy LLMs exhibit on long inputs by having the model first identify relevant evidence spans before applying test-time training only to those spans. A preliminary study on LongBench-v2 shows that TTT on randomly sampled spans hurts performance while TTT on oracle spans substantially helps, motivating the self-guided selection approach. Evaluated on LongBench-v2 and LongBench-Pro with Qwen3-4B-Thinking-2507 and Llama-3.1-8B-Instruct, S-TTT achieves up to 15% relative accuracy improvement. The method offers a practical path to better long-context utilization without the prohibitive cost of full-context adaptation.
ZPPO: Teacher-in-prompt training method outperforms distillation and GRPO for small vision-language models
Researchers introduce Zone of Proximal Policy Optimization (ZPPO), a training method inspired by Vygotsky's zone of proximal development that embeds teacher guidance in prompts rather than policy gradients or logit imitation. On hard questions where student rollouts fail, ZPPO constructs Binary Candidate-included Questions (BCQ) and Negative Candidate-included Questions (NCQ) to help the student discriminate correct from incorrect responses, with a replay buffer that recirculates hard questions until mastered. Evaluated on the Qwen3 family (0.8B–9B) with a 27B teacher across a 31-benchmark suite covering VLM, LLM, and video tasks, ZPPO outperforms both distillation and GRPO baselines, with the largest gains at the smallest model scale. The method addresses a known failure mode of RL training where zero-reward rollouts produce no gradient signal.
Systematic evaluation of multi-personality conditioning and dynamic switching in vision-language models
This paper introduces explicit personality conditioning for multimodal large language models (MLLMs) and proposes an evaluation framework covering single-personality induction, multi-personality composition, and dynamic personality switching. Experiments reveal that personality induction improves image captioning but degrades performance on precise reasoning tasks like VQA. The authors find balancing and residual effects during multi-trait composition and switching, and show that existing prompt-based personality induction methods transfer poorly to multimodal settings.