TELOS: FROM SEMANTIC ACCESS TO REALIZATION VIA EXPRESSION MEMORY AND SUFFIX CONTRAST

Radiology report generation requires accurate expression of clinical concepts, including radiographic findings, anatomical sites, negations, and diagnostic uncertainties. While vision–language models (VLMs) generate fluent narratives, overall language fluency does not guarantee the reliability of specialized terminology, particularly when clinical expressions span multiple subword tokens. Standard fine-tuning updates continuous parameters but leaves domain concepts implicit, forcing the decoder to coordinate multi-token realization through unguided subword steps. We investigate whether specialized terminology can be better supported without altering native autoregressive decoding by coupling addressable expression memory with targeted suffix supervision. To this end, we introduce TELOS (Term-level Expression Latent memory with Onset-to-Suffix contrast, an expression-level adaptation framework motivated by classical teleology (τ ´ϵλoς, realization of whole concepts from initial potential). TELOS supports native subword decoding of a frozen VLM through explicit memory and targeted supervision. It maintains an addressable Clinical Term Dictionary indexed by complete expressions. At sparse decoder depths, a Bilateral Evidence Router (BER) combines contextual language cues with visual re-attention to regulate weighted memory access, using an explicit abstention state to suppress current memory reads during non-specialized phrasing. Causal depthwise writeback propagates expression-relevant support across neighboring steps to assist multi-subword realization. To support local realization decisions, Onset-to-Suffix Contrast (OSC) provides targeted supervision at suffix positions against high-scoring non-reference competitors. The resulting interface leaves the pretrained backbone, tokenizer, and vocabulary unchanged. Extensive evaluations on MIMIC-CXR across LLaVA-1.5, Qwen2.5-VL, and LLaVA-Rad demonstrate consistent improvements in report quality and terminology realization: on LLaVA-1.5, TELOS improves RadGraph-F1 from 0.158 to 0.240 and BLEU-4 from 0.080 to 0.158 over decoder LoRA, requiring only 25% trainable parameters compared with matched LoRA.