RL-trained search agents interleave retrieval with reasoning to answer knowledge-intensive questions, achieving strong end-to-end accuracy on multi-hop QA benchmarks. Yet end-to-end accuracy conflates two distinct abilities: searching for relevant documents and reading what they contain. Existing evaluations do not measure whether the agent reads the documents it retrieves. We design a controlled evaluation that force-injects gold-bearing documents into each agent’s retrieval pipeline, isolating reading from retrieval. Across five RL search agents on three multi-hop QA benchmarks, up to 46.2% of samples are answered incorrectly even when the gold answer is literally present in the retrieved documents—retrieval succeeds but reading fails. We term this phenomenon Retrieval Myopia (RM). The agent’s interaction format is itself a substantial cause: simply re-presenting the same documents to the same model in a plain QA format, with no weight updates, recovers up to 48.9% of RM cases. Guided by this diagnosis, we propose CWFT (Confidence-Weighted Format Transfer), a training-free pipeline that anchors the agent’s prediction against k confidence-weighted clean-format re-reads. CWFT consistently improves accuracy across all evaluated system–dataset configurations on three multi-hop QA benchmarks.

Vision language models (VLMs) demonstrate strong zero-shot performance, but often perpetuate social stereotypes in person-centric queries, yielding skewed demographic distributions. Current debiasing methods apply uniform bias corrections across all input queries regardless of their bias sensitivity, creating a fundamental fairness–utility trade-off. Strong debiasing distorts semantically meaningful information in bias-insensitive queries, while weak debiasing fails to mitigate stereotypes in bias-sensitive ones. This one-size-fits-all approach hampers simultaneously achieving high utility on bias-insensitive queries and fairness on bias-sensitive queries. We introduce Reward-Gated Test-Time Adaptation (RG-TTA), a reinforcement learning-based test-time adaptation framework that selectively applies debiasing based on input sensitivity. RG-TTA adaptively triggers fairness regularization based on the bias sensitivity of each input during test-time policy adaptation, while focusing exclusively on optimizing cross-modal alignment for bias-insensitive inputs. Experiments on fairness benchmarks (e.g., FairFace, UTKFace) demonstrate substantial bias reduction while simultaneously improving zero-shot utility, resolving the trade-off of uniform debiasing.