Rendering document text as images allows vision-language models to encode documents as visual tokens, which can reduce input sequence length compared with text input. This reduction in input length is particularly useful for reranking, where each query involves scoring multiple candidate documents and token savings app...
Seongtae Hong, Youngjoon Jang, Jungseob Lee et al.· 0 citations
Fine-tuning adapts aligned large language models (LLMs) to downstream tasks, but a few dozen harmful examples can remove their refusal of harmful requests. Prior work localizes safety-related behavior to specific layers, directions, and tokens, suggesting targets for protection. We test whether successful localization...
Jungseob Lee, D. Lee, Sugyeong Eo et al.· 1 citation
PriceCheck is introduced, which builds a compact family of decision rules from label-free checks such as re-solving a problem, and shows that choosing how checks are combined and stopped matters alongside how well a verifier ranks answers.
D. Lee, Jungseob Lee, Chanjun Park et al.· 2 citations
A byte crossover is derived, the context length at which the two savings are equal, together with ideal speedup bounds for each branch and for their composition, from model dimensions and keep ratios alone and is derived at https://github.com/js-lee-AI/ByteCross.
Jungseob Lee, Seungyoon Lee, Seongtae Hong et al.· 1 citation
AgentHop, a diagnostic benchmark of 1,011 multiple-choice questions paired with a controlled seven-tool sandbox under fixed token, turn, and tool-call constraints, and the full benchmark set and the harness are released to support diagnostic agent benchmarking.
Chanhee Park, Jeongho Yoon, Sun Han et al.· 0 citations
Client-Resolved Generation (CRG), a genera- tion interface that separates server-side generation from the lexical realization of input-derived content, is introduced, which provides a practical interface for privacy-sensitive cloud LLMs by reducing plaintext exposure across both input and output pathways while preserv-...
Jeongho Yoon, Chanhee Park, Yong-Chan Chun et al.· 0 citations
Nameless tokenization is proposed, which leaves the control entries with a reserved identifier and no surface string, so the content encoder cannot emit one and message content reaches the model unaltered.
This claim for multi-turn, tool-calling agents, where it now matters most, is tested for post-training quantization to 4-bit weights and diagnostics, the per-channel error rate and success under a shrinking budget come from logs benchmarks already collect.
DART is introduced, a training-free routing framework that samples two cheap no-think drafts, accepts direct answering when the drafts agree, and predicts a thinking budget from draft entropy when they disagree, and preserves or improves always-thinking accuracy in most settings while reducing thinking-token use.
Jungseob Lee, Seongtae Hong, Seungjun Lee et al.· arXiv.org· 1 citation
On HealMed, performance declined most in low-resource languages, although the size of the gap varied markedly across languages and models, whereas many open-source and medically specialized models showed larger and less consistent gaps.
Yingjian Chen, Fan Gao, Sherry T. Tong et al.· 0 citations
Funnel of Thoughts (FoT) is introduced, an inference-time method that preserves the full 32-trajectory voted accuracy while halving its attention FLOPs, a 28.8% reduction in full-model inference cost.
Chanhee Park, Sun Han, Jeongho Yoon et al.· 0 citations
This paper proposes Cross-lingual Ranking Preference Optimization~ (CRPO), a novel framework that leverages robust preference knowledge from English to facilitate preference alignment in the target language, thereby enhancing language adaptation and output quality.
Seungyoon Lee, Minhyuk Kim, Jungseob Lee et al.· 0 citations
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