OmniVerifier-M1: Multimodal Meta-Verifier with Explicit Structured Recalibration
OmniVerifier-M1 is a generalist visual verifier trained using symbolic meta-verification rationales (e.g., bounding boxes) and decoupled reinforcement learning objectives for binary judgment versus meta-verification. The paper finds that symbolic verifier outputs outperform textual explanations as rationales, enabling rule-based RL rewards without auxiliary judge models, and that decoupling RL objectives substantially improves performance over joint optimization. The system further enables M1-TTS, a verifier-driven agentic generation pipeline supporting dynamic region-level self-correction in multimodal outputs.
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OmniAgent: POMDP-based active perception agent for long video understanding with test-time scaling
Researchers introduce OmniAgent, a multimodal agent that reformulates long video understanding as a POMDP-based iterative Observation-Thought-Action cycle, selectively distilling audio-visual cues into persistent textual memory rather than processing all frames uniformly. The system uses Agentic Supervised Fine-Tuning and a novel reinforcement learning method (TAURA) with turn-level entropy for credit assignment. OmniAgent demonstrates positive test-time scaling and achieves state-of-the-art open-source results across ten benchmarks, with its 7B model outperforming Qwen2.5-VL-72B on LVBench (50.5% vs. 47.3%).
VERITAS: Visual verification enables inference-time steering and autonomous improvement for robot policies
Researchers introduce VERITAS, a generator-verifier framework pairing a pre-trained generalist robot policy with a gradient-free visual verifier to steer actions at inference time without additional training. Verified rollouts are also used for offline self-improvement via fine-tuning, achieving performance gains comparable to expert demonstrations but without human intervention. The work demonstrates that inference-time verification is a scalable mechanism for autonomous policy improvement during deployment.
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.
Prover-Verifier Games improve legibility of language model outputs
OpenAI presents research on prover-verifier games as a mechanism to improve the legibility and verifiability of language model outputs. The approach frames output generation as a game between a prover (the model producing solutions) and a verifier (checking correctness), incentivizing clearer, more human-auditable reasoning. The work targets a core alignment challenge: ensuring AI-generated solutions are interpretable and trustworthy to both humans and automated systems.
Self-Trained Verification (STV) Unlocks Training- and Test-Time Self-Improvement for Reasoning Models
This paper introduces Self-Trained Verification (STV), a method that trains a verifier to imitate a more informed version of itself by leveraging reference solutions as supervision signal, addressing the core bottleneck in both test-time verification-refinement loops and self-training pipelines. At test time, STV roughly doubles accuracy on hard math and achieves a 14x lift on scientific reasoning tasks. At training time, the authors combine STV with RL in a procedure called Verifier-in-the-Loop (ViL) training, yielding a 33% further gain in pass@1 over an already RL-converged generator, with standalone pass@1 climbing 30% relative past standard RL convergence. The work argues that verification quality, not generation, is the primary bottleneck for scaling reasoning on hard problems.
Vision-OPD: On-Policy Self-Distillation for Fine-Grained Visual Understanding in MLLMs
Vision-OPD addresses a 'regional-to-global perception gap' in multimodal LLMs, where models answer fine-grained visual questions more accurately when given cropped evidence regions than full images. The method instantiates a crop-conditioned teacher and full-image-conditioned student from the same MLLM, minimizing token-level divergence along on-policy rollouts to transfer regional perception to the full-image policy. This self-distillation requires no external teacher models, ground-truth labels, reward verifiers, or inference-time tools. Benchmarks show competitive or superior performance against larger open-source, closed-source, and agentic 'Thinking-with-Images' models.
Benchmark for view-level visual evidence identification in multi-view MLLMs for autonomous driving
A new arXiv preprint introduces a multi-view visual question answering benchmark targeting evidence-source identification in autonomous driving scenarios. Given six synchronized NuScenes camera views and a question, models must identify which camera view supports the answer — not just produce a correct answer. The 122-pair benchmark spans causality, counterfactual reasoning, and intent prediction, and exposes grounding failures that answer-only evaluation misses. The work addresses a meaningful gap between answer accuracy and correct visual grounding in safety-critical multimodal systems.
MAST: Mechanism-guided selective unlearning for RLVR-trained reasoning models
Researchers introduce MAST (Mechanism-Aligned Selective Targeting), a method for selectively unlearning capabilities induced by reinforcement learning from verifiable rewards (RLVR) in language models while minimizing collateral damage to retained knowledge. The approach ranks attention-projection tensors by off-principal energy and gradient coupling to identify a targeted subset for update, rather than applying full-parameter gradient ascent. Evaluated on Qwen2.5-Math-1.5B and Qwen3-1.7B-Base, MAST achieves statistically significant forgetting on target MATH problems while preserving GSM8K performance, whereas full-parameter unlearning collapses retained capabilities. The method generalizes across seeds and unlearning objectives (NPO/SimNPO).